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Kamakura Corporation Kamakura Risk Manager: Training Guide VERSION JULY, 2012 © 2012. The Kamakura Corporation. All rights reserved. This document contains confidential and proprietary information and trade secrets belonging to The Kamakura Corporation. Microsoft and Windows are registered trademarks of Microsoft Corporation. SOFTWARE SUPPORT Department Contact Telephone Email Client Support Ardi Tavakol 1-310-442-5819 atavakol@ AndrewCowton61-(0)3-9563-6082acowton@ Jinyoung Lee 82 (10) 3641 0135 jylee@ JohnLee1-845-592-0844jlee@ Toshio Murate 81 (90) 8033 6755 tmurate@ MarkSlattery1-708-975-0510mslattery@ Xiaoming Wang 86 (10) 8520 0455 xwang@ Li Li 86 (21) 6103 7052 lli@ Development Support Kenji Imai 1-808-791-9888 kimai@ General Inquiry 1-808-791-9888 support@ Website  Make c odierips stmentadjused ban othe yield as of valuation  tcnsuioD eht erutuf hsacswo lf morf ehtda tnemtsuj erad esab no eht For ch eaduct roPID, sers un caselect rom fthree different nting coucas method(ACCOUNTING MEHOTD inthe ral Geneab) Tto ne irmdeteif/how KRM s calculateum premiand nts:discou 01 = dleH ot ytirutaM 02 = g TradinSrities ecu 03 = e Availablfor S aleThe ce choi ofunting AccoMethod will e etermindw hoKRM will culate calthe g owinfoll svaluein the NI put out tables:- kooBlacBna eAB_KBL_PP( )dlei f- I tseretnIemocne/ epsxnI(NTI_NOCEM )dleif- Uezrinla deia Gn /L sso noI tnvenmtseU(NRELAI_ MCleif)d - dleutmucacAUaee zrildn niaG /L sso noIsevn tntemhciw hffa stce ehtEUQITY tces noi fo ehtab aelcn ethseU(NRELAE_QT )dleif Accounting Method KRM Process TgnidraStirsuecie RK Mi llwce reltaalcu ehtram tke eulav foeht seitiruces ta hcae emit petsa dnroce rdzeirlnua de sesgnsiol/as noI stvnnemtseU(NRELAI_ MCeifl.)d T latomocni e= INTI_NOCEM + Ae rofSela RK Mi llwc eerltaalcu ehtram tkelav eu foeht seitiruces ta ehca emit petsa dnrocer d duemtuaclcaaeu zrildnagsni ses/sol noIvn tneemtsU(NRELAE_TQ .)dleif .-oM-aturit y roF siht gncitnuocA,dohteM RK M :lliw Use par d ank boos cebalanfrom the rtfolio potable to evired eht evitceffe dleiy ot ytirutamY( )MT dna eht detaicossa tnuocsid/ Y MT si dleh tnatsnoc revo eht efil fo  ezitromA eht tnuocsid/mimerp revo eht efil foht einstrument r) nea(non-lind a zegnirecothe evant rel tnuoma ni hcaeg nnituoccA doirePs lnuociltaaC neercSP retemarasetoN ano iltgfta resitsu nrentm hewrewr odfraasre tera sauyul ltac clduea sdaeb no(dnxIe etar +.g)rnai M roFsi ht wene om,htd MRKseg riont ehdxn yn aMuoitistni ntc thamnf iogtlasasst en adtisil nsc uhcs ae,yeht erateyarvl ieret nulat oh tel vle foth ei ltgfanoret an ads o ehtyl novne altersi seusi hrtehwtye hhv aea ib faeotlrpranmi g .daesrpH c,ne ehtvkeae-e rnbniste treta re adtaca neb rpenrt iedetin owtyasw : )a( roFfx diecs-saw ho,lf tisi yruoxif deencrinfai gtare .vlele (b) For floating cashsw o,lf- tisi yruoeg rtatfearncgini g rnaimt o yruovne alterltgfano tano ilgFta rescre euititrsaue M taNxet Rste As esmuh taa llfn iotglata reimsuresntt n lliw tearmu ta triehx etnrut asme taeht xen t teserc evtsreipetsr et ad ceenlRoi TROPablcnsae LGicilnetioRao eclneioRLG abalcsne ot adtaoeld adn iott eh b otlanre GeeLgdre . Balance Tpyes Y uo nacc yfiepsRK M tahw epyt fo secnalaB ot edunlciin s its ectionjpro(NE TINCOME tab of the R unID nr)sece Current roPtfloi oOnly: Iude nclonly ractual tConcords re ce an(balet shesize lwile declinover time)  New Business and Rollveor: Include al ntractuCo+ Rollover of uring matctions satran+ Nw es rdreco New Business Only No(Rolleovr) :I edulcn lautcartnoC +N we sdrocer gnirutam(s noitcnasart ton d)crepal loRlevor Onl yN( oeN wuBsiness) :I edulcna lutcartnoC +R revollo fog maturinnst ransactioce alan(b etshe ezis lliw niamer tnatsnoc revo ). NI Output Tables KRM’s net come inon ulatisimprocess ll wipopulate a variet y fo llA tuptuos elbats eu a nommocnaming ention:conv NIRP* :D tcudorp level snoitcejorp NIUC* :T tuCe levl nscoitejorp NINXT* : noitcasnarT level scnoitejorp re whe* ies fcispethe type of ce baland ane incom s:ectionproj RES : tnerruCoiloftrop .e(.g, NIRESRP )D RLL :R revollo (g., NIRLLRP )D NBS: New s ssineubnts ucoac(., NINBSPR D) ALL: Current + Rover oll+ Nw ebuss sineunts acco.(eg., NIALLPRD) The first 2 ers lettof the ming naion conventotes denwer heths tresulare ased bon valuation or ased TP-byield sc:uerv NI: future rest intee comind asebon the g ricinpyield rve cu TP: future st interee incombased on TP rates nd/or aTP rds forwa(as d ecifiesp ybthe Net Income Method )iocntseleKRM Training Guide i tflneoadC &riartpoerPy Forecasting  5-4 1 Other NI put outtable s udeinclNIRESULT: uctProdlevel ry summaof balancesheet and come inent statems figureused for cing alanautobs sepurpoN :seto revenehWNI sissecorp,de RK M lliw syawla etalupopNIRESUL T  All ry summaoutputs will e bquoted n ie thbase ncycurre deifitnedi ni ehtR nuI DLG(V_BLAS .)elbat RK M lliw osla evas ngierof egnahcxe etara atd evitaler( ot ehta esb,ycnerruc s a fo eht atad etad ni ehtR nuI )D ciatedassowith the s ncierrecun i thery mmasuuotput s;tablethis ion tanformican be found n i ethNXTRXF_ . Repricing Gap ehT dleifRERPIEC PAG ni llaNI tuptuo selbatstnreepers the icing rrepap gfor a cific speccounting ad: eriop Rc igrnpie PAG rof dexiFR eta= clnaipriPsch a wolf Rng riciepP GAfor FloatingRate = Og standinutce balanat the next ce repri . Average Run-Off KRM’s rket mavaluation ss ceproves eridan ge averalife measure for 2 Go to Calculationtab S tcele ehteme rtaPnpyssalC enifeD eht etnoM olraCma rsarpete -TYEP =I lvaretn -S elpmahta Ps= # fo etnoM olraCtioitsanre . Exercise: Initiate Option-adjusted Valuation tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele ehtRun I D uoydetrc ail rrae ni ehtin iganrt rofVanuolit asenlo oC -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) I f,rasseceny u etadpht edorP tcuS teI D I f,rasseceny uetadpht e oiloftroPTelba S tceleSTRAT RPOECSSIN G morfRPOECSS unem otit ientia eht nouiltaave csosrp O ecnssecorpi gn sahmoc,ldetpe tcelesERROR LO G morfRPOECSS em un ot eiverwht e gsasseemrpcudo de by ehtssecorp R eivewt ehs esrtlu aiv ehtR setrop roEcxle . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  : g parinComKRs M’ tenrepaympresults h s(ca )swolf otE adesb-lecxs noitaluclac rof tnereffid  : aring pComRKs M’NI sh cas wflo(after ment) paypreto valent quieMV ns rur font differeayment Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. KRM Training Guide i tflneoadC &riartpoerPy Prepayment Option  11-02 21. PRODUCTS WITH ITEDNREMTANI EMATURITIES . ATUOB NNO-MTARUGNI PRCUDOTS RKc d-M sreffo a mret desab-erutcurts noitaulav fo ytirutam-non stisoped dnad tierc drac :snaol ehT hsacw olf maerts sah na etinifedni ytirutam The rtfolio ponces experieual continuation fluctin s nceabals aa sult reof ity optionalin the products. In this ctionsewe will focus on the s stepd rerequito t seup and ess cproNaturity n-mos accountKRM Training Guide i tflneoadC &riartpoerPy Products with nIdeterminate Maturities  1-12 . TREMGOLONIY . Data IDs To se ue thKRc M-dule,mod you willd neeto e sucal storihidata to erate gene thd quirere onssirereg :sretemarap  Balance History DI: cal riHistos ceanbalfor nt differetypes of aturity  rPoduct Rate Histroy DI :c lairotsiHc turdop setartnereffid sepyt fo  Market aRte Histroy DI: cal storiHiarket mrates ies rhistod seu ot etaulave eht ytivitisnes dna tnemevom niuct prods ceanbaland rates relative to the ral . Regression Data Set ID AR noisserge ataDS teI Dnstseerper a n collectioof ce Balanand Rate Data IDs hat tKRM will use to e teragenon ssireregrs rametepad seufor valuing non-maturity . Regression Statistics Whilethe on ssigrereines routin RK Mn case ue thRn oisserge ataDS teI sD ot evired eht yrasseecn,sretemarap r sesuu dlohss yawla yfirev eht stluser yber wgnieiv ehtn esehTs scititats lliwhelp ne etermidr whethehe tresults n cabe used nithe va So em senuiledig htiwa sdrger ot noissreger ygolonimretd seu niRK :M  S-TITATSIT :C sihTr seusaem ehtc enacifingis foa sion sregreefficient coin plaining xe ethr behavior ovariation in e thosit depances balor A value ualeq to or er highthan 2 s gestsugthat thevariable s i a  R-SUQARED: This ress umeaw hoch muof the tvariaion in the time s riesea datcan be ailned expby the on e Thser cloto 1, he tbetter. r Fo,elpmaxe a eulav setacidni taht09 % fo ehtvariation in e thsit depoances bals iptured caby the es variablin e thon  UDRIBNSTAW-ON :s ihT eulavw sohs rehtehw erehts irial seatcioornr elin the on ssigreres dualresi hcwhi si gnitceffa ehtn I fRK Mstceted rial secorrelation in the al sidurevalues of the ,noisserger ti lliw tpmorp eht resuot ust adjthe on regressifor .noitalerroc-otua In hcus ,sesac kcilc no ehtADJUST FOR UATO-CORRELATION buttonto runre N :etoIn RK,M eht elbactpecan -WatsoDurbine rangis n . . Regression Parameters for Balances KRM’s on ssireregs alysianwill rive deg followinparameters to efine dents movemin the sc ealanbof non- ytirutams tnu:ccao  aBlance Sensitiivty :S ytivitisne fos ecnalab ot eht level fo tekram (.e, etard eviecer yb eht knabn wheg investinthe s dceeprofrom ts sipodeor the rate paid by the ank bto fund e thdit cres).rdca  Long Run Balance : ehT "eroc"n oitrop fo eht latotscenaalb s deruaem sa a egatneecrp fo eht necalab tnuoma ni eht tsrif noitavresbo tniop fo eht ecnalabrotsihy data. Ne otthat this is not the e sampt ceconas eroc (.e, oitarfo secnalab taht lliw animer htiw eht knaB dna era evitisnesniot tsertni.)setar sihT ni tcafn stseerper eht detamitse ezis fo eht tisoped oiloftrop ta ytieutprep evitaler ot stin terruc (.e, gnikat otnin tuoccaw htorg dna eht lautneve gninettalf tuo fo ehtw  Balance Decay Rate :E laitnenopx etar fo yaced revo emit fo eroCsc eanalb rea denifed sa secnalab taht dnet otn iamern i ehtoilooftrp ssaeldrger fo eht level ehTe yacd etar lliwtypically havea value n weebet0 and 1. For e,lexamp a eulav 4 setacidni taht noitneter fo gnitsixe nstuocca si84 tnecrep rep,htnom ro tuoba16 tnperceof the s balancecay der pemonth. 2-12  Produtcs with nIdeterminate Maturities i tflneoadC &riartpoerPy KRM Training Guide  aBlance rGwto haRte :E laitnenopxe tar fow htorg revo emitfo s ihT srefer ot ehtinflow of w ness nebusior /ndar rolloveof g ehT htworg etar lliw evah a RK M lliw trevnoc esehtr sretemaap ot decuder mrofiuqevalent efficients cofor the wing folloeters mpara otsimulate ovement min rtfolio po s:cebalan Laeggd Balance :L go eulav fo ecnalabn i ehts  Constant: Base ceanbal aDta ioPnt : htworG dnert aMrket aRte :k rtaeM etar . Regression Parameters for Rates RK s’M noissergers isylana lliw evired gniwollofeetmarap sr ot enifed stnemevom ni eht tcudorp setar rof-non s eehTe sretamarp eraiuqer der rof eht noitaulav ssecorp niRK :cd-M  Rate Sensitivity :S ytivitisne fo eht tcudorp setar ot eht elevlfo tekramset (.e, etar d ceivereby the nk ban wheg investinthe s dceeprofrom ts sipodeor the rate paid by the ank bto fund e thdit cres).rdca  Fixed Csot: Fixed st A negative value is ble ssipo ni I f eht tseretni etar si,orez then this meter rapawould nt sereprethe st coof ng ciservithe  Reversino to Lon gRun Rate :E alitnenopx etar fo yaecd revoe mit foc tduorp setar ot eht nurg-onl ehT yaced etar lliw yllacipyt evah a eulav neewteb0 dna1.  Floor Rate : ehT nur-gnol RK M lliw trevnoc esehtr sretemaap ot decuder mrofiuqevalent efficients cofor the wing folloeters mpara otsimulate ovement min rtfolio po s:cebalan Laeggd Prudoct Rate : tcudorPe tar ni ehts  Constant: Base ceanbal aMrket aRte :k rtaeM etar . Regression IDs Y uo nac seu ehtR noisserge ataDS teID s ot etarenegdna save sionsregrerameters pathat are uired reqor fthe valuationof rity The sisreregon ers parametare d savein Rons ssireegIDs hat tare dengissa ta eht tcudorPI . Fees, Expenses, and Reserves n ehW gniulavn stuocca htiw etarnimetednis,eitirutam RKM will cus fon othe “total” cash flow ciated assoth wi eht ytirutam-non,oiloftrop :gdniulcni  rPcoessing epxEnses :s ihT stneserper eht elbairav,tsoc ten fo tahws i derutpac ni eht tcudorp,etar c detsasaio htiw gniniatniamseht escnal abeRP(OECSSE_ Xleif d ni oiloftrop.)elbat esehT ssnepexe esaercni htiw ytivitca non g nikcehCcausn tco arehet mo st lsytcose baeucsos oritepd ncasit,depo w,drawith r,nsfetra d an writeecks chon the ccount,a with chea ctionsatran sultingre in a ecnanetniam esnepxe yb S sgnivanstuocca have less viity actand r loweg toNe : ecnis gnrissecoPEesnepx s si tunpi sa a tnecrep fo,secnalab ehtc detalulacnepxe es lliw esaercni ni noitroporp ot eht ecnalab no ,yllacipyT ti lliw ton tsoct eh knab erom ot ssecorp a1$,000 n tuocca naht a1$00,000 I f eht secnalab egnar ylediwh nitiw ac tdurop,oiloftrop siht dluoc tluserin e widng rangig nssicerop It may be rableprefeto p rougthe nts coucaby ecalanbsize, and n ruarate seps essionrregd  Reserve Reuqirements: his Ts presentrethe ntage perceof cebalans that do t noearn any st interedue toatory reguls mentquirere(CUR _PMTfield in o ortfolipt).elba ,ylelarneG erehts i na esrevni pnihsoitaleren betweReserve Rments reequind ae thvalue of a epodsit r Higheserve rements quirerece reduKRM Training Guide i tflneoadC &riartpoerPy Products with nIdeterminate Maturities  3-12 eht eulav fo a tisoped oiloftrops euaceb tiuecders the age ercentpof w locost sits epodthat n cae binvested ned).a(lo Rrves seeust mbe eld hin quid liortfolios pthat can be wndraon d emandnd as thur welothe total return on ents investmed fundwith  Expected sLos :s ihTr stnesepers essoln o tiderc,sdrac ten fo tahwd yaerla ebn detuocca rof ni eht tiderccard ct produrates. You can estimate an regressiond ad ciateassovalue for ch eacredit ass The total olio fportvalue is the sum of therate . Balance Size and Precision The on calculatitime for aturity on-mns ccountandspede ry vech muon the nce balaze siof he The ce toleranr fothe cal rinumeevaluation of ns equatiosi tes sa an egatecrep fo eht tisoped koob :eulav  If the ce tolerans d anthe sit podece balans i$10, this s % fothe sit depovalue d an sonce rgeconveis fairly fast nce sithe nce ratoles  If the ce tolerans d nance balas i1,037,25,4099, then nce ergeconvis ow slce since rantoles ivery .thgit Please note hat tthe ion valuats sultreare pletely comsce; alablas aresult, you can apply the ook -bum-topremiratio rived deg usina ce nbalaof 100 to e etermindthe um premiunt amofor any ze 4-12  Produtcs with nIdeterminate Maturities i tflneoadC &riartpoerPy KRM Training Guide . DTA ESABADGISE N– SETUP roF siht,ssecorp RK M lliwer ecneref eht KRM Training Guide i tflneoadC &riartpoerPy Products with nIdeterminate Maturities  5-12 . EREXCSESI: SET PU DNAPRCO SSENNO-MTARUTI YACCUNTOS . Exercise: Add Historical Data tSep KRM Menu rcSeen Actions1 ataD / tcudorPN tatMi-rnouyFrom EDIT nu,me select ADD HISTORY SET I DSpp utro rotsiHy I tupn2 ra hrcetcaI D dnacseodi tprni rofa leacBnRiseserog nS te I tupn2 rah cretcaI D dnacsedoi tprni rofd otrcPuR etaRrge neoissSte S tceleNEW rofcnalaB e otsirHyna dn itup2 retcarahcI Da dn noirtcsped S tceleNEW rof tcudorPR eta rotsiHyna dp ntiu2 retcarahcI D dnanoiirt csped S tceleNEW rof tekraMR eta rotsiHyna dp ntiu2 retcarahcI D dnanoiirt csped E retn atadcqneeruf y rof eht lacirohtsiata d I tupn eht etadrgna e trats(dna dnetad )se rof ehtcirot sliahdata kcilC noCREATE REOCRSD ot evas sihtputes 6-12  Produtcs with nIdeterminate Maturities i tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions2 ataD / tcudorPN tatMi-rnouyS tcele eht tsiHroyI sDoy uah dtaerc dea dn retne ehttsihro Spp utro rotsiHy toNes: - tekraM setar rofsoped :sti siht essterpern eht etarviecerde by eht knab rofvniit sgne ehtcorpeeds morf esehttialiilb - tekraM setar rof tidercsdrac: sihttneserper s eht'knas bufidn both cases, technicallythis should eb eht I n ecitcarp1-,htnom tnom-3,h ro htnom-6r seta erar evylhhg iycdletrr ao htiwht e trohs etarna d teyrpdiv oeteb ret stif ni ehtis Save the newinformation The gwinfollooffers some es guidelinin ating cre ricalhisto datasets to e bd seuor f onssireregs analysiof on-n ytirutam stnu:ccao Kamakuras endcommre aminimum of 05 cal rihistobseos noitavr stonip ot yletaruccar eutpac eht snrettapin the data. While r wefens servatioobdo not ecen ylirass etadilavni eht,stluser yeht yams euac tnemerusaem rorre dnad ael ot tnacifingisni rocemically onoable sonreaunon KRM Training Guide i tflneoadC &riartpoerPy Products with nIdeterminate Maturities  7-12  Ron ssireegs quationere afitted st againcal historidat ab yereht gniylpmi taht ralimis sanrettp fo roivahebwill r occuin the future. us,Th g ectinselthe e riatropappdata set is cial cruto the s: analysi  If g underlyindata s udeexclew nss sinebu(. the ata dis rom fa fixed up gro fonts accouhout rougth ethcal historie times),seri then the snoisserger dnau tptuo lliw tcelferthe decay and st interee ratr behavioof existing unts accoonly. Conv,ylesre fi eht aatd sedulcnig nitsixes sneisub sulp wenn tuocca ytivitca neht eht stuptuo lliw tcelferimisa rl  For sechiranf cingpriosit (deps umremipn oions) uisitcqaoth bisting xend aew nss sinebu ouldshbe ded,inclu nce sithe d uireacqentity will be a going cocern nand will rate genew nes accountmilar sito st  If chbranr oank bns sitiocquiaare ed dcluin in thea,dat thenthe onssigrereations equ will notfit the atad sa llewc enis eht atad lliw wohsp smuj hguorht emit eud ot  Lk oofor ers outliin the a datset. r Foample,ex if uoy dah a emit-eno hcnarb,esuahcrp uoy yam tnawot aezilmron ruoy atad yb gnippirtss iht ytivitca morfs ncebalad ans ratefrom the e timof sepurchaon.  ataDu dlohsn tseerper tcudorp cgniirp roivaheb tahtsi deetcpxe ot eunitnocn i I f ehtnoitutits nihas recently d ngechang cipri,ycantlsignifi then history is not a good indicator of future nce baland arate .ytivitca O enn oituloss i ot teg a evitatneserperpmasle of data om rf ar,pee r oe ragaveof a roupg of,sreep taht evah neeb gnciirp eromtsisnoc yltne htiw ruoy  roFc eanalb,noitamitse emosc srenoitirtap neibmoc ehtn secalab rof elpitlum sctnuoca otni enoa As a sult,re they estimate the t accounbalances ecay (dd angrowth) ased bon an gation reaggof all n transactioccount as ceanbal(., ude incl,gckinche money market, and k ssboopas savings accountin one data ).set The tage advanof this method is at thany noiseor erratic ehavior bof n a lindividuant’s accoua dates seriwill e bed averagut oin the aggregation; the ck rawbads ithat the unique r behavioof individual ccounts awill be . Exercise: Run Regressions tSep KRM Menu rcSeen Actions1 lasny AsiTslooN ttaiMr-nuoy oG otcnala BeRrgoe issnebat Rosrignses e epTyi n eht newRrog iesnseema n S tcele DDAorfm EIDT unem ot ddA a newis senerorgmane S tcele eht ecnalaB ataDS teID. Y uo nacilc kc noEIDT TAD A ot eiverwht elab aecn atad otb e desui n ehtgoei rssren kcilC noht eCOUPMTE RERGESSION nottub ot nur ehtissenerorg Save the newinformation 2 oG otR etaR noisserge bat epTyi n eht newRrog iesnseema n S tcele DDAorfm EIDT unem ot ddA a newis senerorgmane S tcele ehtRta e ataDS Y uo nac kcilc noEIDT TAD A ot eiverw eht dtocrup etar atad ot ebsu de ni ehtneoerir sgs kcilC noht eCOUPMTE RERGESSION nottub ot nur ehtissenerorg Save the newinformation 8-12  Produtcs with nIdeterminate Maturities i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Add a Record to the Database tSep KRM Menu rcSeen Actions1 Data / Portfolio by tiderC CdraLo snaS tcele albat e ot evas enwrc sedroot Itnemurtsn &Nrutamt-n ioystisopeD S tcele DDANEW REOCR D morfEIDT unem dna lofolwht e DDAi drazw ota ddT noitcasnraI,D S ecurityID, and Product ID. toNe: T o viewexisting data, select alb aetna d esu ehtlorlcs snottub<(< ro>>) ot og ehte ntx oscder ro pmuj ot ehtebigin ngnor dne fo vitanretlAle,y uoy nac tecsela cificeps naosinctartI D morf eht -nporoddw S tcele ehttep y foiruces ty(deposits or credit cards) I tupn ehtc enerassyrpd otcurahcre tsccaitsilab,(ecna ,etar r evreseuqere,msetrin ssecorp gniexp,esne .cte.:) - RK Mli lwsidal yp ehtervela tn eaclabnadn etar rof aevi gn droceri n ehtilo fotrop aetlb tahtoy ua siThli lwlcndiu e hsac detaler-olfw sdleif hcus saCroe aBlances, eRserev eRuqirements, dnarPcoessin gepxEnses. - Y uo nacv oedirrena y foht - T eh ezis fo eht eaclabni llw ltceryid tceffa ehtissegcnor pdeep s I tupn rehtoere ltnavI sD ot eb desui nVRA, tiderc erpuseoxlucoliatca n dnait:egronp S tceleSVAE morf ehtSRCEEN unem KRM Training Guide i tflneoadC &riartpoerPy Products with nIdeterminate Maturities  9-12 1- 120  rPodutcs wit hnIdeterminate Maturities i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Update Product Set ID for Valuation tSep KRM Menu rcSeen Actions1 S pute /cudorP st &Product ID Go to the CLCATULAIONtabspuor G rFo hcaedo rtPcuI D deggatt o mt-inrountay o:istcansnart S teOCCAUNTIN GEMTOH D= N no tirutaMytcudorP S tceleR etaReisser gnoID S tcelelacBnae Rss enrogeiID I tupn ehtRERGESSION RPEICSION ara pretme sa( % foalaB)ecn S tceleSVAE morfSRCEEN unem . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  dc-Regressions : ng ariCompKRM’s d erivedon ssireregrs rametepato Eased  dc-asChlfo : ng mpariCoKRs ’Msh caw flond arate forecasts to Ebased Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. KRM Training Guide i tflneoadC &riartpoerPy Products with nIdeterminate Maturities  11- 12 22. TRANSITION MTARXI . ATUOB TRTANSIION MATRIX sition Trantrix maallows RKM rss euto rate rponcoieir thrnal intecredit g ratinstem syr fors,rrowebo ng udinclins assumptioabout oan lloss ns orovisipnd aults,defa into thens ectioprojuced prodby the net encomin I n siht noitces ew lliw sucof no eht lareneg putes rof eht KRM Training Guide i tflneoadC &riartpoerPy Transition Matrix  1-22 . TREMGOLONIY ehT noitisnarT xirtaM secudortni ehtwollof gni nstemele ot ten emocni n:scoitejorp tiderC :, A,AA A,A,A B,BB..., tluafeD ro ,tnerruC 03, 06, 09,.., tluafeD bilities Probathat a loan lwiltransition mfroone rati gn ot :rehtona ., ytilibaborp taht eht naol gnitar lliwsecg nha morf A>- BBB ro A>- AA cy quenDelinrates to be d argechen wh apayment is entelinqud Loan ss los isionvproto be allocated sed baon the n loarating  R yrevoce setar rofn ya secnalab taht ni tcaf tluafed Oal rdinngs nkiraof sk rihout wita on valuatirk; woframeand  Relative ngs nkiraof sk rihin wita full (or artial) pation tA eht tcudorPI D,level resu nachco eso morf owt sepyt fo tiderC :sledoMd tierCR ksie ldoM dnan onitisarT :xirtaM Credit Risk Mode lsU(erd-eifned )Transition Matri xAvailable for VAR and c hasticstoNI Available for c nistidetermiand c astichstoNI sModule escpsreo scseroesp tluafeDsr etemaraP detamsiet ybRK MsintiarT no xirtaM sid edivorp yb ytilibaborP detareneG tluafed tneve setanimret htoB tluafeD dna nyceuqnileD tceffa the curity send a1(cipal*Prin-recovery A e chang mfroCurrent to 30D s stopa monthly sh Ca wFlo )etarsi .tnemyap nehW ti semoc ot,tluafed san iRC  A egnahc nie tluafd ytilibaborp seodmodel, (1-recovery rate) s ipaid out. tonc eftfa  R modna esab dleiy sevruc tekraMV eula  nge Chaad spresed ban othe culationCald culatecalfault Yield rves cuand Chava-Jarrow Yield rves cuand s gechanin rating d sebaon Rkis ctorFant endeindep svariable noitisnartr xitamRcovery eRate stant Concovery re rateRcoe yrev etar denifed yb tcudorP &S ytiruceI DLoan Loss noisivorP si deificeps yb tcudorPI D dna noisivorP gnitar/etats ni % fo eht . Rating Definitions Rs atingare efined din s termof the s statuof nt rrecupayments:  erehT lliw eb enog nitar rof snaol taht eran terruc no rieht tnemyap There will be one g ratinfor loans re whethe wer borrohas efaulteddon its nt payme There can be oneor remos ratingr fofferent dileves l fo,ycneuqniled hcae detaitnereffid yb eht ghtnel fo emit taht ehtn terruc tnemyap si tsap s’ti eud etadU sres nac etaerc larevesRitang IDs, ach eof ch whican have different ation bincomof Rating IDs arec deifieps ta eht tcudorPI . . Delinquent Cash Flows For any cstion antrawith a uentdelinqrating, the cash flows will be usted adjg rdinaccothe lengthof cy uendelinqed assignto the rating. If a loan es chreama ytirut htiw a tneuqniled,gnitar eht ytirutam lliw eb 2-22  Transition Matrix i tflneoadC &riartpoerPy KRM Training Guide :dednetxe ytiarutM etaD + htgnel fo emit detaicossa htiw ehtR (.g, fiR gnita B = htnom-3,tneuqniled neht eht uytirtam etad lliw eb dednetxe yb 3.)shtnom s A a,tluser eht tekram eulav fo eht noitcasrnat lliw ebaffected nce siKRM will scount dithe justad de hsac wolf ot eht . Delinquen cyPenalt yI f a naol si,tneuqniled uoy nacc yfieps eht ytlanep tahtuld shoe bssed asseto/paid by the Thepenalty rate (sepafrom the al normn upocoon the ) loancan be ed efind as: etulosbA % = ycneuqnileD etar x tneuqniled tnemyap tnuoma  R evitale % = ycneuqnileD etar x nopuoc no eht naol x tneuqniled tnemyap tnuoma Absolute nge chain rate = cy ennqu(Delirate + n ocoupon the ) loanx delinquent payment unt amocy quenDelins types/ratee racified sper foeach . Transition Matrix The n nsitiotramatrix cts reflethe empirical y abilitrobpof making a sition tranfrom one g ratinor ncyeelinqudstate to er anothover a cified esptime riod pe(., e onmonth). s hiTs udeinclent provemimor rationdeterioas llews an gniaimer ni eht  stic DeterminiNI: the s loan’Rating will nge chad sebaone e thnsition trawith he tst hehigprobability. For ,elpmaxe fi a s’naol gnitars i A dna txen doirep stiar gnit nac evropmi ot AA2(0 %,)ytilibaborp n iamer ta A05( %,)ytilibaborp pord ot BB03( %,)ytilibaborp nehtt eh s’naol gnitar lliwn iamer ta A ni eht  Sstic tochaNI: Rs ating willnge chad sebaon a rageneted m ndorar numbefor a uniform Withn teiciffus etnoM olraC,snoiutalmis rof a nevignuoccA gnit,doireP eht regaeva rebmun fon secerrucco lla( )snoitalumis rof hcae fo eht 3sop elbis gnitar setats lliwa etmixorppathe probabilities ied fspeciin the sition Trantrix MaUsers cancreate several sitionTranaMtrix IDs, ch eaof ch whin ca havedi tnereff nointisart seitilibaborp rof( ehts eam ro tnereffidR gnitaI)sD. ehT nsoniatriT xirtaMI Dsi spc deifie ta eht tcudorPI . ePriod Observation Period in nsition raTrix Mat= ency frequch whig . Loan Loss Provision For ch eal leveof Rg,atin you can y ecifspthe unt amoof Lss oss ionroviPat thould she bd ddeato the oanloss tA eht dne foa hcen gnituocca,doirep RKM will ated lcucalthe uire reqs onrovisips a(Loan Loss n Provisio% x Ong utstandiEnding pal ciPrin)ceBalan. E :elpmax fiL oss onisivorP = %5 neht  Acct riod pe= 001, cebalan= 100, loss n rovisiop= 5 Acct riod pe= 002, cebalan= 80, loss n rovisiop= 4 Acct riod pe= 003, cebalan= 60, loss n rovisiop= 3 Users can create ral seveLoss n sioProviIDs, ch ea fowhich n caave hdifferent on provisis ssumptiona r(fothe emasr o tnereffidR gnitaI.)sD ehTL ssosivorP noiI D si deificeps ta eht tcudorPI . Recover yRate Rcovery ee rats cateindie th ercentagpeof valuethat ca n eb derevocer retfa a latoT eulav ssol lliw lauqeV eula *1( –R yrevoceR.)eta Users can create several Rcovery eRtea IDs, ch eaof ch whican have tndifferery TheR ycreeovR etaI Dsi ciefisp de ta eht tcudorPI KRM Training Guide i tflneoadC &riartpoerPy Transition Matrix  3-22 . DTA ESABADGISE N- SETUP rFo sihtsecorp,s RK Mli lwcneref er ehtnil lgofowlbatse 4-22  Transition Matrix i tflneoadC &riartpoerPy KRM Training Guide . EREXCSESI: SETUP RFO TRTISNA NOIMATRIX For net me coins nectioprojthat deinclutransition tramix ons,mptiassu sers ull wineed to ne defial interncredit ,sgnitar noitisnart,seitilibaborp iled ycneuqn dna yrevoceretar,s dna naol esaelP wollof ehts stepd outlinew beloto allthe necesry sas tnponecomuired reqr fothe n sitiotransted djumatrix-aet n . Exercise: Setup Rating Definitions tSep KRM Menu rcSeen Actions1 S pute /tiderC Rn itgaosniitfnieDS tcele DDAorfm EIDT unem ot dda a enw derocna d retne ehtvele rtnamronfoniit a epTyi n ehtRta gniID n ifeeD ehten wRTAIN,G n iduglcni aiirtcsnepod Sficep yehh trew ro ton sihtR ganit sicostsaai de htiw au adtfletneve I etacidnht eledqni tneu mretof r Neto - tnerruCRitan g sahaPst uDe Tenro =0 ti hwTerm = Current - tluafeDRitan g seod ntovah ena ycep scifi sgnittes n ifeeD ehtp eTy foile DqnneucyetPl anyRta e ot ebesud I tupn eht enDeiulcqnyReta Save the newinformation Close the screen KRM Training Guide i tflneoadC &riartpoerPy Transition Matrix  5-22 . Exercise: Setup Transition Probabilities tSep KRM Menu rcSeen Actions1 S pute /tiderC T noitisnra xirtaMS tceleRCETAE TAMRI X morfE tid unemot etaerc enwTiti snnora retfA noitelpmocfo ehti:draz w S tcele aR gnitaI D morf eht tsil xob Sficep y ehtOoi tnavbreso irdeP SVAE eht enwI noitamrofn Click on INITILAIZE TRAGET MATRI Xto te acrethe table I tupn ehtisnartt noiolear ip:tbsbi ekaM erustaht eht noitisnart sebiobtraiplrof hcae orwa dd pu ot100 kcilC noSVAE TAMRI X ot evas eht noitisnartseitilibaborp Close the screen 6-22  Transition Matrix i tflneoadC &riartpoerPy KRM Training Guide .: eStup oLan oLss rPoivsions tSep KRM Menu rcSeen Actions1 S pute /tiderC Lao nL ssoS tcele DDAorfm EIDT unem ot etaerc enwL ssoisi vnoorPI D oibs-invyorPRtnaig epTyi n eht noriPsviID S tcele aRitagn I D dnaic oestsaaRnitag I tupn ehtivo rnPosi %.e(.g, 10 %ni tup sa10 ) Save the newinformation Close the screen KRM Training Guide i tflneoadC &riartpoerPy Transition Matrix  7-22 . Exercise: Setup Recover yRates tSep KRM Menu rcSeen Actions1 S pute /tiderC R vocreeyReta kcilC noA DD notbtu ot dda a enw orw ot ehtelb attoNe :Y uoac n ddac esrdro ot nait sginexSET I D ro dda a enwSET ID epTyi n ehtcreervoy rates byS ecurityID. N :eto ehtS utcieryI sDificee pds erehhsu odl evahrroipcd sngen sehctam ni ehtOPRT .elbat I f on hctam si,dnuof RK Mi llw emussar evocery etar= 0 % 5 S eva eht enwnoitamrofni Close the screen 8-22  Transition Matrix i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Update Portfolio Table tSep KRM Menu rcSeen Actions1 T ehn igollofw sdleifi n ehtOPRT elbat tsum evahavu lsepdsneoi rgroc oth taw uoyha veif idceeps ni ehtRTAIN G dnaREOCVERY b:aste/snleercs -SUCRITYI_: DS tiruceyI D rof(re vocerytar )e -RTAIN GRC(EIDT :)batR gnita rofled nieuqcnyutatse KRM Training Guide i tflneoadC &riartpoerPy Transition Matrix  9-22 . Exercise: Update Product Set ID tSep KRM Menu rcSeen Actions1 S pute /cudorP st & tcudorPID S tcele ehtorP tcudS teI D spuor G Go to the RCIDETtab I n ehtRCEIDT RIS KOMEDL ,noitces tcelesOMEDL TYEP = Tnoitisnra xirtaM rFo hcaed otrcPuI,D tceles eht etpaaiorppT noitisnra,xirtaM L ssooisi,vnorP dnaR vocereyReta I sD S tceleSVAE morfSRCEEN unem . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  Tnarsition Mtairx #1. :slx lareneGr weivevo fo tuptuo rofrimetedstiic nnd asticc hastotion sirant matrixulrtes.  Tnarsition Matirx 2#.xls: Validation of puts outg udinncl(is)rating r foc nistidetermiand c astichsto nsitiotran  Tnarsition Mtairx iwth : neral Gew rvieoveof outputfor n transitiomatrix en whthere re Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. 1- 220  Transition Matri xi tflneoadC &riartpoerPy KRM Training Guide 23. CRTIDE RISK ANALYSIS . ATUOB CREDIT RKSI dit CreRsk isr ovidepthe s meanfor g swerinanthe nuestioq : tahw si ehtc detepxe ssol fo eaulv fir eehts i a evitisop ytilibaborp fo tluafed yb eht ytrapretnuoc ni a ?noitcasnart In this ctionsewe will focus on the ral genesetup for Credit Rsk KRM Training Guide i tflneoadC &riartpoerPy Credit Risk nAalysis  1-3 2 . TREMGOLONIY The ngfollowiterms re ad ceroduintin this . Credit Risk Model Parameters g nidnepeDn o eht tiderCR ksie ldoM uoye,tcles RK M lliws eu tekram atad ot evired eno ro erom fo ehtg niwollof sretemarapRC(RAPM MA )elbat taht lliw eb deilppa ot eht oiloftropd tierc ksir :sisylana L0ADBMA : tleuafd ytisnetni detaluclac( rof lla )sledom  L1ADBMA : elbacilppa otJ2worra sledom EDL :AT tnatsnoc yrevocer etar GAMMA: liquidity premium, used so alin Jw1arro nd a2 s modelen (whd bonsc epriare d).seu  LA :AHPn aem noisrever fo mreTSr eutcurtedoMl ble plica(apto st-rate intereent penddeult) defa SI :AMG trohs etar ytilitalov fo mreTS erutcurtel Modpplicable (ato -rate terestinent penddeult) . Reference (Counterpart) yName The key link en betwethe derived creditrisk eters rampaand a rtfolio poof risky s mentinstruis the arty nterpcouname, also referred to as the REFERENCE me nan (ithe dit cresk ris) delmo roISSUER name (in the POR T.)selbat  KRM ates culcald ans savecredit sk rirs rametepar foeach unterparty co ng Duridit crerisk s,sianaly KRM willreview aech ctionsatranand k loofor credit sk rieters paramciated assoth withe party rcounteinfoion rmatfor the . Risk-free & Risk yYield Curves All credit sk rianalyses will re requirs euto setup 2 types of yield rves:cu RISRF-KEE N eto taht uoy nac ylno evah1 ree risk-fd lyiecurve r fony aYield e  RISYK dleiy c:evur roF yna nevigcn,eyrrcu lsareevclss sea fosik ry dleiyse vrcuc an eb desicfpei otcca tonu rof tnereffidslevel foksir .i(.e, tidecrs )daper evitaler ot eht . Recover yRate Rcovery ee rats cateindie th ercentagpeof valuethat ca n eb derevocer retfa a latoT eulav ssol lliw lauqeV eula *1( –R yrevoceR.)eta . ROC Accura cyRatio The R rceiveeOg peratincs risticteChara(ROC) rvecuwas yallriginodeveloped n ir ordeto re asumethe nal gsito noise ratio in radio The ROC curve s hae becomngly increasiular pops aa sure meaof model ecnamrofrepn i sdleif gnignar morf enicidem I t si yllacipyt desu otr eusaem ehtc enamrofrep fo a ledom taht sid esu ot tciderpc hihw fo owt setats lliwcucor ck (sir onot sick, ulted defaor ton,detluaefd .cte.) The ROC cy ccuraaratio is derived s aows: lfol c etalulaC ehtc laiteroeht tluafed ytilibaborp roft eh eritne esrevinu fos einapmoc ni ac lairotsiha atdbase that s udeinclboth aultdefed d anulted n-defanoes compani mroF lla elbissop sriap fo seinapmoc hcusthat the ir pas deinclue ondefaulted ” “companyand one dnon-defaultepany”.“com For e: exampl 2-32  Credit Risk nAaslyis i tflneoadC &riartpoerPy KRM Training Guide o One r paid ulcobe the r,cembeDe 2001 defaulted boservation for Enron nd athe Oer,ctob 1987 noitavresboo rf elarneG,srotoM hcihw did ton tluafed ni o Another air pd coulude incldefaulted Eron,n r ecembDe2001, d anefaulted on-dnEnron, N rebmevo2001, dna  I f eht tluafed ytilibaborp ygolnhcet yltcerrocetars the defaulted company s aore msky,ri award e on tniop ot  I f eht tluafed ytilibaborp ygolonhceter stlus ni a,eit evig flah a tniop I f eht tluafed ytilibaborpygolonhcet si,tcerrocni evig orez stniop Add up all thepoints for all of the pairs, nd adivide by the er numb ofpairs The s sultreare intuitive and r cleafor del mo gs:kinran S eroc fo100 :% tcefrep ledom taht sknar yreveis geln eno fo eht gnitluafeds einapmoc sa erom yksir naht yreveu gnitlafed-non ynapmoc S eroc fo05 % ro :ssel e ldom ton This a score can e beved achi ybflipping a in co S erocn eewteb091-00 :% ledoM si ylemertxe doog S erocn eewteb8009- :% ledoM si yrev doog Ect. A rative compastudy of the ROC cy ccuraARatio for l alpublicly ed trads epanicomin the . s wshothe evitciderp seitilibapac fosiht .oitar ehT hparg woleb swohs,taht ve ne evif sraey roirp ot,tluafed ehtRO C ycarucca oitar si llew evoba eht05 % level rof eht tluafed ytilibaborp seires neewteb11-369899 gnisu eht-JChavaw arromodel. e ThROC racy accuratio ris sedily steaas default s ecomrs ecloand RCO uccAra ycRatios rfom 06Montsh Prior to Deafutl to Mont ho fDeafutlK jPcD-aJrro wCahav Reduecd oFrm 1eYar eDafult Proabibiltyofr l lAiLsted S. uccAra ycRatio in Month o feDfault is rPior to eDfault KRM Training Guide i tflneoadC &riartpoerPy Credit Risk nAalysis  3-3 2RCO Accuracy Ratio evlewT shtnomo rirp ot,tluafed eht ycarucca oitarsi 87..%45 owT sraeyroip r ot,tluafed eht evitciderp ycarucca oitar ,%79 c hihwn saem eht tluafedrp seitilibabo rof ehte sniapmoc taht dluow yletamitlu tluafed erewd yaerla rehgih %79 fo lla gniteluafd-nons einapmoc ni lla tuP rehtona,yaw g nitluafeds einapmoc evah tluafedborps abilitier highethan 06% of all s paniecom54 nths mo yehT evah tluafedb seitilibaorp rehgih naht07 % fo lla seinapmoc22 shtnom roirp yehT evah tluafed seitilibaborp rehgih naht80 % fo llaocs einapm10 shtnom yB eht htnom fo,tluafed ,niaga yeht evah tluafed seitilibaborp rehgih %7 fo lla gnitlueafd-nons einapmoc revo lla esehT stluser nac ebd seu yltcerid ybanif licnaitutitsni sno ot teem ehtn steumeriqer rof ehtI lanretnR sgnitaBased ach proApin the Nw epital Cas Accordsepropod by the l BaseCommittee on ng . Credit-adjusted VAR KRM’s sditt-eadd juCreV-Rue-atalk sialysis ans decluinthe g nfollowis step(for ery veMonte Carlo rio): nasce  etareneG modnar srebmun rof  c etalulaC ehts  rate Genea random er umbnbetween 0 and 1  c etalulaCe miT ot :tluafeD modnar(nlmber) nu/ da0lamb sured ea(min years) o I f emiT ot tluafeD <VRA gnidloHd,oireP neht reeht si a tluafed tneve dna eht eulavs i detsujda saV( eula *R yrevoceR )eta ehT ecnereffid neewteb eht esab eulav dna eht htn tsrow eulavs iVRA. n = er numb fos arioscen* ( 1- ce enconfidlevel ) . Credit-adjusted Projections S citsahcot ten emocniumis noital stluser lliw osla teg detsujda rofyna tluafed ytilibaborp,snoitamitse gnisu a ralimis enituor ot taht deilppa rofs detujda-tiderCVRA. In this ,seca if the nting accoumethod s rerequi rketma eulav stnemtsujda ta hcae gnitnuocca,doirep RK M lliwacate cullthe usted credit-adj arketmvalues and also drocer eht detcepxe sessol ta hcae 4-32  Credit Risk nAaslyis i tflneoadC &riartpoerPy KRM Training Guide . DTA ESABADGISE N- STE PU& OUTUTP rFo sihtsecorp,s RK Mli lwcnerefe re ehtllofni gowlba tse ralimis( otamnr lo etnoMraC olVRA ) KRM Training Guide i tflneoadC &riartpoerPy Credit Risk nAalysis  5-3 2 . EREXCSESI: SETUP RFO CRTIDE RKSI PROCSSINGE roF tiderCR ksis isylana sresu lliw deen ot enifed tluafed seitilibaborpna d snoitalerroco rf eht tnavelers eitranpretuoc ni s eheTn soitpmussa nac eb sdeu ni nnoitcujnoc htiwg nitsixe putes rofVRA and/or NI to rate rponcoicredit sk rinto ithe Please w follothe s stepoutlined w beloto e b eablto eviredd tierC scitylana rof ruoyw . Exercise: Setup Risk-free and Risk yYield Curves Setup 2 yield s:curve1. R eerksif- evrcu htiwYIEL DUCRVE RTTAIUBET =Ri ks reeF evruC eton( taht uoy nacn ylo evah1 ree risk-f yieldcurve for ny aation combinof iYel dCurev Class dnaCurrency )2. Rksi yY dlei evruC htiwYIEL DUCRVE UPROPSE <> Rksi eerFtoNe : ehtR eerf-ksi dleiy evruc lliw eb desu ni retalSetc snoi nehw gnivired tidercir ks sretemarap desab tiderc sevitavired rof yksir . Exercise: Define Counterparties in Master Reference Table All er issumes na/ rtiesr pauntecontered ein the PORT e,tabl will d neeto be identified in KRM’s rsteamce referene tabl tSep KRM Menu rcSeen Actions1 S pute /tiderC Re recfneNasemS tcele DDANEW REOCR D morfEIDT unem ot retne enwREFERENEC emana dndosi tenpicr+ itain doldaitamo ronfni S tceleSVAE morf ehtSRCEEN unem 6-32  Credit Risk nAaslyis i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Input Counterparties in the Portfolio tSep KRM Menu rcSeen Actions1 Data / Portfolio bye Fxdi &n igtaFolS tcele na oiloftroP elbatItnemurtsn Rate Instruments Go to teh CRTIDEtab roF eht tnaveler sdrocer tupni eht naISSUER ,eman ser pgenrietn eht rettrnaupocyni eht evitcepserlnosihdg S tceleSVAE morf ehtSRCEEN unem KRM Training Guide i tflneoadC &riartpoerPy Credit Risk nAalysis  7-3 2 . Exercise: Input Default Probabilities For ch eaer ssuiin the PORT table, you will need to specify the ault This measure can be put in yllaunamr o deevird aivRK s’ tSep KRM Menu rcSeen Actions1 ataD / tekraM tiderCR ksiS tcele DDACR MSET I Dm orfEIDT unem dna retn2 rehtccarasretemara Pitniason egd S tcele ehtRfcener eeN ema,dna rof eht edderis,sdeta ne ret ehtu atfledmarape treRAP(TMAER = Ladb0ma) S tceleSVAE morf ehtSRCEEN unem . Exercise: Input Default Correlations Y uo lliw deen ot yficeps woh eht tluafed seitilibaborp eraocated rrelin a on sihT esicrexelliw a eterc a elpmis (.e, stnatnoc noitalerroc fo tluafed gnoma lla )seitranpretuoctSep KRM Menu rcSeen snoitc A1 ataD / tekraM tluafeD oG ot ehtSILPME OCRRELITAON batsnoitalerroC S tcele DDANEW REOCR D morfEIDT unem S tcele ehtarpp iertpoaRC MS teI D,dna rof ehtsed deritad,se retne ehtri sdeedir tonacolreleavu S tceleSVAE morf ehtSRCEEN unem 8-32  Credit Risk nAaslyis i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Setup Recover yRates tSep KRM Menu rcSeen Actions1 S pute /tiderC R vocereyReta S tcele DDANEW REOCR D morfEIDT unem epTyi n eht cerrevoyr seta bySe tirucyI Dc( gnidnopserro otS tiruceyI sD ni eht oiloftrop.)elbat A ngissseht e ot aR vocereySET ID toNe : rof aisabc ,nur o uy nac levaeOCST TYEP llun rokam e S tceleSVAE morf ehtSRCEEN unem . Exercise: Setup Loss Provision ID tSep KRM Menu rcSeen Actions1 S pute /tiderC L ssosi vnooriP –S tcele DDANEW REOCR D morfEIDT unemDefault epTyi n ehtLo ssois invorPI ;D rof hcae level fo tluafedrp,bloibtaiy ni fed eht %fo i sinvoorptaht luo hds eb tesdi sea S tceleSVAE morf ehtSRCEEN unem KRM Training Guide i tflneoadC &riartpoerPy Credit Risk nAalysis  9-3 2 . Exercise: Update Product Set ID tSep KRM Menu rcSeen Actions1 S pute /cudorP st & tcudorPID S tcele ehtorP tcudS teI D spuor G Go to the RCIDETtab S tceleOBN DOMEDL TYEP =N o tiderCRsi k siht( ylno seilppa rofn nrie“ o”olpfed tlualiat)vanuo rFo hcaedo rtPcuI,D tceles eht eptaaoirpp tiderc ksirrap :asretme - Creidt iRsk oMedl = utfl-aedsbD ea- Credit Parameter Set ID = Select CRM Set ID - soLs rPivosino DI : enifeDcrgeapt nee ssolropi sinvo rof tnereffid slevel fouafeldt - Recovery Rate ID : ehtL DGo itpnmussa1(rr-evoc ey)etar - eRcvoer yCsot : tsoCtaicossa de htiw ynar cerevoy stroffe-tsopu atfled- sopxEure :E DAluocil tasacnes abd( noba lecan ro)timil - iRsk Weithg Medol :S tceleedoM lpet y- iRsk eWithg DI :I fR ksithgWie ledoM= Szdindarta,de neht stcelethe Risk Weight I D- sopxEure Class : enifed ehtt eyp foerorrobs wht niiw ehtgvi neProduct ID S tceleSVAE morf ehtSRCEEN unem . Exercise: Setup Risk Factor ID S pute aR ksir otcaFI D ot ebd esu rof eht tiderCVRA The risk rs factoll wie includLbdo0am to rate rpoincoariability vin fault deties biliprobass Ne to taht eht ksir srotcaf ni siht esicrexe lliw ton edulcni tseretni setarr o sA aersul,ts het s tsulreulds how hosd itre-cnonVRA=0 while detsujda-tidercVRA lliwlfer tce ksir eud You will also need to d advolatilities and s ationcorrelfor the sk rictors RISKCFAT table RISKCFAT tableSourec Table NameCTAEGORY1 CTAEGORY2 8 = Ladb0ma R eercenfeNa emRCRAPMMA 1- 320  Credit Risk nAalsyis i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Setup for Credit VAR Anaslyis tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele ehtRun I D uoydetr cail rraee ni ehtin iganrt roftnoM elra Cosenlo oCVAR sisanaly -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) In the CALCULATION PARMEATER ,noitces tceles ehtRCEIDT OMEDL I D rofua fteDlareneGtnoi tSep KRM Menu rcSeen Actions2 U depta llaer etlnav setad rof eht etriCdR ksi:atad - tnerruCetaD : etaD foecorpgniss - Voit anulaa D:et etaD ot eb desu roflaivtnauo - tekraM :etaD etaD ot ebesu d ot rehtagram tkeatad S tcele ehtOPRT elbatna dPcudro tS te tahtul csneidoctnutra preyna duaf etdlamnrooiftn i n ifeeD ehtle rtenavVRA na dOMNTE RACLO RAPEMATERS S tceleSVAE morfSRCEEN unem . Exercise: Setup Accounting Period S pute na gnitnuoccAd oirePS teI D htiw eht tsrif etadg ondinspcorreto the datefor the CreditRsk itere rampaestimation KRM Training Guide i tflneoadC &riartpoerPy Credit Risk nAalysis  1-312 . Exercise: Setup for Stochastic Forecast Anaslyis tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele ehtRun I D uoydetr ca tiderCVRA nalsaisy senlo oC -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) S teLACUCLATION TYEP =rep-itoliuM dnitsgacer oF S teRIS KEMSAURE =Ssahcottic Net Income Simulation tSep KRM Menu rcSeen Actions2 S teOFRESAC TRAPEMATER S teLABNAEC TYEP = tnerruC oiloftroPO yln tSep KRM Menu rcSeen Actions 4 S tcele ehtcciAt ngunore PdoiS teI D S tceleSVAE morf ehtSRCEEN unem . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  erCdit RAV. :slx rnosiapmoC foRK s’Ms detujda-dtiercVAR ns culatiocalto Ed Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. 1- 322  Credit Risk nAalsyis i tflneoadC &riartpoerPy KRM Training Guide .42CR PARAMETERS - DTLUAFE SWAPS . ATUOB CREDIT RKSI M LEDOPARAMTEER CRBILATANOI You can se umarket ation informon ult defapswas ot evired eht tidercsirk In this n ctiose we willfocus on settingup, ng,ssicerop andreviewing s outputfor this meter rapa KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -eDafutl wSaps  1-42 . TREMGOLONIY The ngfollowiterms re ad ceroduintin this . Default Swaps For rge las artieunterpcowho ave htively catraded default s,swap you n case urket mate ran informatioto etamitse eht dtierc ksir ledomr :etemarap Characteristics : roF hcae,ytrapretnuoc uoy lliw deen oted enif eht larenegs erutaef fo eht tleuafdp aws tnemyap(,ycneuqerf ,laurcca .cte.)  Histroical aRtes: r Fo cheaarty,rpcounte and for ch eaof the seussi yb tahta,ytrpretnuoc uoy lliw deenc lairotsihs etar/(.e, c lairotsiha atd.)setad . Default Swap Tpyes KRM n cark wowith the g owinfolltypes fodefault ps:swa Digital : roF hcus,sevitaveird eht lluf lanoiton si diap ta emit fo tluafed Non-digital: ry recoveis sed baon nce rerefed bonsticcteriarachs, yrevocerseta,r cte.. tluafedo-srti-Ft roBasket lt defaus swap aregital non-dicredit derives vatire wheayoff p dependon default of underlyingcree . Model Assumption You can ct selefrom the g followinptions assumto stimate ethe it credrisk del mo eters:mpara OCNSNAT TEDUAFL TINETNSIYT = eht tleuafdsnetni ytiL( adbma0) si tnednepedni fo stneomevm nis ertetni setar INETRESR-TETA EDEPNEDN TEDUAFL TINETNSIYT = tluafeD seitilibaborp(Lbda am0 &Lambda 1) lliw yrav gnidnepedn o etar sretemarap- sEtimate ST MaPrameters = Scify pent constas valuefor e th termcture strudel mors eterampaha (alpand ) gmasi - Use Stored TSM Parameters = Sct elea TSM Set ID ch whies cludincal rihistor metearaps valuea (alphand gma) siased bn os rationcalibusing al cstorihiyield . Model vs. Market Rates oT evired eht smrettiderc/erutcurts sk riparameters, KRM will culate cal sti nwo scetar/seirp rof eht tluafeds swap lde(MoRates) and are comp mtheto the Market It will then e solvfor the it credrisk ters erampathat will minimize the red ua-sqsumce rendiffen weebetthe model nd amarket 2-42  CR aPrameters -eDfautl wSaps i tflneoadC &riartpoerPy KRM Training Guide . DTA ESABADGISE N- STE PU& OUTUTP roF siht,ssecorp RK M lliwer ecneref eht gniwoollf selbat KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -eDafutl wSaps  3-42 . EREXCSESI: SET PU DNACRBILATA EPARAMTEERS For Credit Rsk is nalysiars usewill ed neto te calibrathe modelto market rks enchmabnd aestimate the sretemarap eriuqer rof esaelP wollof ehtpets sd euniltow oleb fi uoy nalp ot seu tluafed spaws otrate calibthe dit CreRisk . Exercise: Create New Session ID tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderCDefault Sap wS tcele DDAorfm EIDT unem ot etaerc ane wS noisseI D R ksimaraP reteRseta E noitamits2 Default SapwGo to hCaratcersitscitabRseta I tupnegare nl tludafea spw scitsirhectcararof hcae prueottcrany(eRference aNme )i n ehtftropoilo toNe : eht recfneer senmaen iddfereh ehlsu odvah eoi pdgsnnerroc sehctam ni ehtISSUER dleifof eht SVAE ehtrofni noitma 4-42  CR aPrameters -eDfautl wSaps i tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions3a Default SapwGo to Swap RatestabRseta I tupncirotsi hlauaf etdla pswa rset rof hcae ptroectanuyi(lcnidu gndifferent tenors) SVAE ehtrofni noitma KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -eDafutl wSaps  5-42 tSep KRM Menu rcSeen Actionsb3 rFoatdi- gnloinua ftelda,spsw kcilc noREFERENEC OBNDS S tcele DDAorfm EIDT unem ot retne ehtatpairpro emr onfonita rofcnere feerobdn se taidcossai htw ehtua fteldap ws S tceleSVAE morfSRCEEN unem tSep KRM Menu rcSeen Actions4 Default SapwGo to rPoecsstabRseta kcilC noht eREFERENEC NEMAS nottub ot tceles eht seipotrcetnau ot ebes ud ni ehtita T ehsil t fotrapret nsueoci sie sadb no ehto itnamriofn deipfscii n ehtHCRATCAERISTISC bat 6-42  CR aPrameters -eDfautl wSaps i tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions5 Default SapwGo to rPoecsstabRseta S tcele ehtdeta egnar otb esu de nimitosi eTh setadi llw ebsab deo n ehtcir oltasihtad a ptniu ni ehtSa pwR S tcele ehtarpp iertpoad olMeo i:tpmnussA - rFo llavelera tn,sretemarap pniu t ehti tlinaima rraepte u)lsa(ve dnaci dentiah hrteew eht u)lsa(ve sih taw uoyo dluwkil eRK Mot tratsti hwI(NITILA ) ro dneti hwF(INLA ) niacrbila gnit )rs(reate IfgnitcelesInterest-rate Dependent eDfault nItensit y+ sUe Stroe dTSM Parameters, tceles ehtST MS teI D ot eb desui n ehtcrboi ltna ssecorp SVAE ehtS noisseI D KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -eDafutl wSaps  7-42 tSep KRM Menu rcSeen Actions6 Default SapwS tceleRPOECSS morfEIDT unem otentiait i ehttemarap reoitamnitseRseta kcilC noERROR nottub ot iverewma/ sssergoerretaler de ot ehtoi tamnitsecorp sse esolCKR M neercs retemaraP gnittif:ssecorp - RK Mi llwsu e ehtrrucCne ye tsaadicos htiw ehta spwetar s+ eht eerf-ksir galfi nRKYMIC D ot teg eht iertpaoarpiy dlec evruI Do y(u nac olny ehvano e eerf-ksirY CI D rof aevi )cy - ev idreDtekram a psw setarra e devas ot ehtSDWO_PUT elbata dnmoca pdre ot ehtkram te setard idveorp by - T eh deviredretm erutcurtsna d tiderc ksirapar sretem eras deva ot ehtCRRAMP . Exercise: Update Product Set ID tSep KRM Menu rcSeen Actions1 S pute /cudorP st &Product ID Go to rCedtitabspuor G rFo ehtavelernt tcudorPI,sD tceles ehtarpp iertpoaRCEIDT RIS KOMEDL RAPAMETER SE TI D SVAE ehtrofni noitma 8-42  CR aPrameters -eDfautl wSaps i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Update Run ID and Initiate Credit Risk Anaslyis tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele eht etriCdR ksiRu nI Do uyrc detae reielrai n ehtiartni ngd etrC(isenlo oCVRA ro/dnaSt citsahcoNI) -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) S tcele ehtarppi reptoadorP tcuS teI D S tceleSTRAT RPOECSSIN G morfRPOECSS unem otit ientia ehtsseco rp O ecnssecorpi gn sahmoc,ldetpe tcelesERROR LO G morfRPOECSS em un ot eiverwht e gsasseemrpcudo de by ehtssecorp R eivewt ehs esrtlu aiv ehtR setrop roEcxle . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  :n soirapmoC foRK s’M daetsmite tidercsk rirs parameteg usindefault s swapto Ea Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -eDafutl wSaps  9-42 .52CR PARAMETERS - RISKY BONDS . ATUOB CREDIT RKSI M LEDOPARAMTEER CRBILATANOI You can se umarket ation informon orate corp(risky) s ondbto e derivthe CR In this n ctiose we willfocus on settingup, ng,ssicerop andreviewing s outputfor this meter rapa KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Risk yBonds  1-5 2 . TREMGOLONIY The ngfollowiterms re ad ceroduintin this . Corporate (Risk) yBonds For e rglasr tienterpacouwho have ely activd traderrpocoate s,ondb you can use market e cpri ationinformto estimate e thdit cresk ridel Thend bostics ctreriachacan be eled odm intoKRM’s PORT sbleta ekil yna rehto  The ary primg modelinents uiremreqfor the risky ds: bonThe s ondbn canot e havany lity optiona For of the issues by rterpacount y uoy lliw deenc lairotsih naelcr . Model Assumption You can ct selefrom the g followinptions assumto stimate ethe it credrisk del mo eters:mpara OCNSNAT TEDUAFL TINETNSIYT = eht tleuafdsnetni ytiL( adbma0) si tnednepedni fo stneomevm nis ertetni setar INETRESR-TETA EDEPNEDN TEDUAFL TINETNSIYT = tluafeD seitilibaborp(Lbda am0 &Lambda 1) lliw yrav gnidnepedn o etar sretemarap- sEtimate ST MaPrameters = Scify pent constas valuefor e th termcture strudel mors eterampaha (alpand ) gmasi - Use Stored TSM Parameters = Sct elea TSM Set ID ch whies cludincal rihistor metearaps valuea (alphand gma) siased bn os rationcalibusing al cstorihiyield scurve RECOVERY RATE: You can y ecifspa stantconreco eryve ratfor all sr ntieerpaoucr odefine ia (v eryovrece) tablednitf fer eryovreces ratr foea chissue of sky ris (.e, ybUCSI )P ybhcae .ytraprectnou . Model vs. Market Rates oT evired eht smret tiderc/erutcurts ksir,seretmarap RK M lliw etaluclac sti nwo secirp rof eht yksir sdnob ledoM(R )seta dna erapmoc meht ot eht It will then solve r fothe dit cresk rirameters pathat willminimize the red -squasumce differenn weebetthe del moand rket marates. 2-52  CR Parameters - Risk yBonds i tflneoadC &riartpoerPy KRM Training Guide . DTA ESABADGISE N- STE PU& OUTUTP roF siht,ssecorp RK M lliwer ecneref eht gniwoollf selbat KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Risk yBonds  3-5 2 . EREXCSESI: SET PU DNACRBILATA EPARAMTEERS For Credit Rsk is nalysiars usewill ed neto te calibrathe modelto market rks enchmabnd aestimate the eters mpararequire for themodel. Please w follothe steps d outlinew beloif you plan to seucorporate ) sky(ris bondto brate calithe dit CreRisk . Exercise: Define Counterparties in Master Reference Table llAr eussi seman /s eitrapretnuocd reetne ni ehtOPR T,elbat lliw deen ot eb denifitedi niRK s’M retsamce referene tabl tSep KRM Menu rcSeen Actions1 S pute /tiderC R eercenfeNasem kcilC noA DD ot retne enwREFERENEC enmana drcosietdnpi SVAE eht enw noitamrofni 4-52  CR Parameters - Risk yBonds i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Setup and Input Risk yBond Portfolio tSep KRM Menu rcSeen Actions1 ataD / oiloftroP dnoBlofotrioP S tcele albat e ot evas enwrc sedroot Spp utro S tcele DDANEW REOCR D morfEIDT unem I tupn ehtcen erassyrpdo N :eto rFo llab sdnoi n sihtilo,fotrop RK Mi llwss aemuht elacbna eUC(RAB_KB_L )= 100 S tceleSVAE morfSRCEEN unem KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Risk yBonds  5-5 2 . Exercise: Input Risk yBond Prices tSep KRM Menu rcSeen Actions1 ataD / tekraMYlei sd & dnoBGo to the noBd rPicetabsecirP tnioP rosruci n ehthaser pedtse aerana dodlbu e kcilc noht e thgir esuomnobtt u Itupn:atad -T ehS tiruceyI sD dluohs evaherrocpsnoni dg sehctam ot ehtSr ucetiyIDs thini wthe riskybond PORT table -RK M lnoyr serqieu ehtlcae nsecirp T o eteled,atad hg itlhh llaht eor sw tahtuoy si hw otl eedte dna tecselEIDT \EDLETE ROWS. T ol eedte naiet sgixne idyl evrucI,D o uy tsum tsrif eteled llai Select FILE / CLOSE from menu Save the newinformation . Exercise: Create New Session ID tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderCR ksiyBn odirPsceS tcele DDAorfm EIDT unem ot etaerc ane wS noisseI D R ksimaraP reteE noitamits S tcele ehtdeta egnar otb esu de nimitosi eTh setadi llw ebsab deo n ehtcir oltasihtad aib aaevlal rof S tcele ledoMA noitpmuss- Cnostant eDfault nItensity = tnatsnocludaf tebtaibloirp y- nIterestr-ate eDepnednt eDfault nItensity = u atfled sbeioibrtapili llw rvyan inpdgde no trearpartmse S tcele ehtarpp iertpoad olMeo i:tpmnussA - rFo llavelera tn,sretemarap pniu t ehti tlinaima rraepte u)lsa(ve dnaci dentiahh treew eht u)lsa(ve sih taw uoyo dluwkil eRK Mot trats htiwI(NITILA ) ro dneti hwF(INLA ) niiiltaa crgbnht em)asr(arpete. 6-52  CR Parameters - Risk yBonds i tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions I fgnitcelesInterest-rate Dependent eDfault nItensit y+ sUe Stroe dTSM Parameters, tceles ehtST MS teI D ot eb desui n ehtcrboi ltnassecor p IfgnitceleseRcevor yaRte - sUe aDtaabse, RK Mi llwol ko ta ehtR vocreeyReta elbatna drae shc rof ehtr pieprtapoaul aevn gadiess ot S tcele ehtksir y dnobilofptoro KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Risk yBonds  7-5 2 tSep KRM Menu rcSeen Actions2 kcilC noht eSELETC ISSUER nottub otl etsce ehtretn usoecitrap ot ebes ud ni ehtoi T eh tsil foipt rrsetnuaeoc sie sabd no ehto itanmriofn depisfcii n eht arMetsRcnerefee .elbat SVAEV eht noitamrofni dnareturn to the main screen tSep KRM Menu rcSeen Actions 3 S tceleRPOECSS morfEIDT unem otentiait i retemarapintosieta m kcilC noERROR nottub ot iverewma/ sssergoerretaler de ot ehtoi tamnitsecorp sse esolCKR M neercs retemaraP gnittif:ssecorp - RK Mi llwsu e ehtrrcunCe ye tadsiacos htiw ehto bdnrspeci + eht eerf-ksir galf niRKYMCI D ot teg eht etaoirpppaiy dlec evruI Doy (u nac olny hevano e eerf-ksirY CI D rof aevi )cy - evi rdeDob dn psecriOM(EDLRPI )C eravas de ot ehtNBDDO_UT elbat dnapmeo rcad ot eht tekramb dnorp seciRM(TKRP_IEC )di vdoerp by - T eh deviredretm erutcurtsna d tiderc ksirapar sretem eras deva ot ehtCRRAMP 8-52  CR Parameters - Risk yBonds i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Update Product Set ID tSep KRM Menu rcSeen Actions1 S pute /cudorP st &Product ID Go to rCedtitabspuor G rFo ehtavelernt tcudorPI,sD tceles ehtarpp iertpoaRCEIDT RIS KOMEDL RAPAMETER SE TI D SVAE ehtrofni noitma . Exercise: Update Run ID and Initiate Credit Risk Anaslyis tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele eht etriCdR ksiRu nI Doy u detaerc reilerai n ehtianritng senlo oC -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) S tcele ehtarppi reptoadorP tcuS teI D S tceleSTRAT RPOECSSIN G morfRPOECSS unem otit ientia ehtsseco rp O ecnssecorpi gn sahmoc,ldetpe tcelesERROR LO G morfRPOECSS em un ot eiverwht e gsasseemrpcudo de by ehtssecorp R eivewt ehs esrtlu aiv ehtR setrop roEcxle . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  amraPRC-iRsykoBnd..xls :n soirapmoC foRK s’Ma detsmite tidercsk rirparsa meteg usine trarpoco(risky) s bondto Ed Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Risk yBonds  9-5 2 .62CR PARAMETERS - USER-DEFINED VARIABLES . ATUOB CREDIT RKSI M LEDOPARAMTEER CRBILATANOI Y uo nacs eu yna rebmun fos (.g, ynapmocifnancial atements,st c onomice,srotcaf .cte ) otevired ehtRC = In this n ctiose we willfocus on settingup, ng,ssicerop andreviewing s outputfor this meter rapa KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  1-62 . TREMGOLONIY The ngfollowiterms re ad ceroduintin this . Logistic Regression s Analyseg ardingre defaultbilities probahave only twoval :seu eht ynapmoc si rehtie yhtlaehr o sAa result, we can use a ry binalogistic n ressioregwhere the ent nddepevariable s ia dummy e variablot (nin default = 0, indefault = 1). The on ssireregn cadetermine the anatory explent) pendnde(is ablevarig L citsigon oisserger smrof aila ren noitanibmoc fo eht yrotanalpxeaves blriad ans sformtranthem o int seitilibaborp yb a The eral genform of a stic logi onssireregmodel is: 1(/p[nl ])p-= a+ XB+ e OR  1(/p[) ]p- = apxe Bpxe Xpxe epxe r :eehw  ln = eht larutan,mhtiragol logexp, erewhexp=…  p = eht ytilibaborp taht ehttneve Y, ,tluafed sruccoY(p1= )  )p-1(/p = the s "oddratio"  ln])p-1(/p[ = eht gol sddo,oitar ro "tigol"  llA rehto stnenopmoc fo eht ledom,XB( )eare the me saas a r alineon . Maximum Likelihood Estimation To culate calthe ts encoefficiof t eh citsigoln,oisserger ew esu eht mumixam doohilekils N lamroa reniln soissergerr yanidro(s tael serauqsamitsetion) mize minithe sumof red squas cesitandof thedata s point tothe on ssireregline. mum Maxiod holikelies ationtims maximizethe log ood kelihliwhich ects lref woh ylekil ti si taht ehtb devreso seulav fo ehtped tnedneb elairav yam eb detcridep morf eht devresbo suelavof the . Logistic Regression Statistics You uld showalys averify the sultres of a n regressios analysiby reviewg inthe on These scitsitats lliw pleh enimreted rehtehw eht stluser nac eb desu ni ehttaulav S emo senilediug htiws rdregato on ssigrereinology termsed un iKRM:  LIEKLIOHO DUFNITCON : ehToohilekil d noitcnufL( ) serusaem ehtorp ytilibab taht eht devresbo seulavof the dent depenmay bected predifrom the rvseobed s valueof the Ther highe theod likelihoction fune (valun etweeb0 and1), the r highe eht ytilibaborp fog nivresbo eht sp ni  LO GLIEKLIOHO :D ehT gol fo doohilekiln oitcnuf nacav yr morf0 ot sunim ytinifni ti( si evitagenu esacebthe log of any r numbeless than 1 is ative).neg The lo g doohilekil si detaluclac ylevitareti gnisu mumixam  LIEKLIOHO DRITAO : ehT doohilekil oitar tsettatsstic,i etimes somed callviance,de s iat mewhso likeng aricompe thsteda djuRd are-squof two ar s ihT oitars eruseamu snoitbirtnoc fo ae wnr The d hoolikeliratio test atistic stres compawo tstic logis: sionsrereg ehT esab citsigoln oissergerR(E0G ) A nd secostic login gressiore(RE1G) tthaadds bvariae l j ot eht tes fos elbairav ydaerla detneserper ni eht esab,noisserger RE0G. To culate caler whethble avarij s addry explanatower po ot ruo ytiliba ot nialpxe,tluafed ew :etaluclac 2-62  CR aPrameters -sUerd-eifned aVriabels i tflneoadC &riartpoerPy KRM Training Guide D = -2 [ ln d (likelihooof REG0) – ln hood keli(liREG1 with e variablj ) added] The c statistiD s haa -squarcehdi stdiution ribwith 1 e egredof dom freeif the new e variablis s uoucontin (likea srieseof ult defas) obabilitierp rok-1 sreedegof edomfre fithe ablevariis ordinal ke lia rating ycnega gnitar ro na roF,elpmaxe fi an internal ting rahas 10 es,categori ere thd woulbe nine sreedegof m edofren ig asurinmethe sticalstatisince nificagof ble varia jto thew ehTnull s sihypothethat the w neble varias hano ory anatexplwer pos id rejecteat me sotarget bability rop level (.g, yas ;)%5 fi,detcejer neht ti si dias taht eht wen elbairav j sdda ot ruo ytiliba ot  STANRDAD ERROR: Therd standaiation devfor the es etamit tneiciffeoc fo tnednepednib  WAL-STTAT: The Wald ratio in stic logisisgreres onis the ivalent equto the re t-scoed suin raline snoissergero t serusaemt eh lacitsitats ecnacifingis fo hcaelbairav s’e noitubirtnoc ot evitciderp ytilibapac(., Wald Sat t> 2 te candiical statistice cansignifiof the nt endeepindble variaj). ver,weHo the d Walstatistic as hbeen wn shoto be rate inaccuat time and so it uld shobe sed uin n binatiocomwith the doohilekil oitar tset ot enimreted ehts lacititats ecnacifingis fo yna nevig The ck laof cy ccuraas ia ctby-produ ofthe fact thatthe rs erro ina stic logion ssigrereare not RK M seirt ot tciderp a In the ase,datab if a co ynapm sah,deteluafd eht tnedneped elbairav si1. I f ehtn yapmoc sah tontluafed,de eht tnedneped elbairav I f ew etamitsethe default obability rps aa r numbep mfrothe stic logirregon,ssie ur or rroeterm will re ithebe 0= -p-p or 1-p. Er rros rmte havea omial For this ,snaoer eht dlaWs ettsi acuhm ss el ectcauarsett naht eht gol doohilekil oitar tset  IHCS-UQRAE :D ASsitiatc als ettsrevs uthe null s othesihypthat ach eable varihas o nnatory The are squ-chiution distribhas 1 egreedof freedom. The re asqu-Chivalue ld shoube ed rpacomwith the lacitirc eulav ni eht elbat ;woleb fi ehtrvesbo de eulav si ssel naht eht detcepxe eulav rof aevign ,ytilibaborp ew nactcejer eht lluns isehtopyh taht eht elbairav sah lacitirCV seula fo era-uqsihCree Degof ytilibaborPdom Free= 1 0. 0. :: Note that thebility probaated calcul mfrothe Chi-d uaresqis an n approximatiothat is meaningful onlyn ehw dectalulac morf a egralr laciotsih elpmas fo tnednepednib selairav  V-PLAUE : A lareneg eruseam fo encnacifigis rofb elairavj withs ardgreto thell nusis hypothehat tble variais not It s depictthe nce nfidecolevel at which ull nn cabe ctedrejeKRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  3-62 . User Variables er Whethr oot nthe ny compahas uity eqng,outstandi as gnols a uoy evahc sseca otc lairotsihu tlafed,atad uoy nac yficeps ynae rbmun fo lanretxes elbairav sarapt of a dit creng se Thers eued efindinputs can clude indata mfroloan ation cappliand cal locr dtie suaerubs a llews a tekram s Kamakura’nternal irch seareng usiccounting adata for . sp aniecomen webet163-19899 s hafound hatt eht gniwollof selbairav evorpmi tluafeD,ytilibatciderP sai detacidn yb riehtacitsitatsl gnificance si(a ue is derednsicostatistically nt): gnificasi tSatistiac laVriaelb iSgniifacnec Ecxses R nrute no nommoCSco tkcxEess eRturn = eht lhtnyom nrrute noht e mrif sunim lht nyoavul-eeighted wCRS PNYSE /Amex indexreturn N teI latoT/emcno ssAset - Log (R elativeSize) eRlative Siez = oitar fo latot mriftiu qeykram te 7ivied d byatot lNYSE dna-14AME Xequit yvlue aS kcotc eirPV ytilitoal foiverp suo s’htnom yliad 6 latoTL latoT/seitilibai . Macro Factor Variables Typical user-definedbles varia willprresent e calrihistoation informhat t willvary Within RKM, his t noitamrofni sniamer citats ssorcaid tnereff etnoM olraC ;soiranecs sa a,tluser eht tluafed ytilibaborp setamitsewill ain remconstant across all ontM e ,revewoH uoycan also roducint e orcam rotcafs elbairav taht yam ro yam ton eb ynapmocc (.g, level fo tseretni )setar tub od yrav T he tcdirek sri ereatrampon iatmiste scprseo ancde uncli shetes pyt ofes ablariv ot eervidhe te atriopap noisserger stneiciffeoc ot eb desu rof gnitamitse I f eseht oarcm rotcaf selbairav era denifeds a ksir srotcaf,neht ni eht Monterlo Caprocess, KR M willeneratg ee wn orcamr otcaf seulav rof hcae oiranecs ybereht gnitceffa ehtborp ytiliba fo tluafed ni . Reference Variables While acroMVsa riablell wihave ical rhisto rvedseobdata that ll wibe d user fothe ter ramepan estimatioscprseo, Rcere nfVesb lraia ntsreepc isattonfirmation dseufor tation Ees xamplde inclury stinduand/or hic . Functional Variable lanoitcnuFs elbairavr ea denifed sa ac noitnuf fo rehtos (.g, orcaM,rotcaF R) . Default Probabilities I n noitidda ot gnitamitse eht tiderc ksir ledomesretmarap, RK M lliw osla etareneg ehtc lariotsih ytilibrabop fo tluafed eulav( neewteb0 dna1) rof This s senptrere the ectedexpvalue or f the dentpendevariable in e thc 4-62  CR aPrameters -sUerd-eifned aVriabels i tflneoadC &riartpoerPy KRM Training Guide . DTA ESABADGISE N- STE PU& OUTUTP roF siht,ssecorp RK M lliwer ecneref eht gniwoollf selbat KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  5-62 . EREXCSESI: SET PU DNACRBILATA EPARAMTEERS For Credit Rsk is nalysiars usewill ed neto te calibrathe modelto market rks enchmabnd aestimate the eters mpararequire for themodel. Please w follothe sts eped outlinw beloif you plan to ply aped user-defin astad/elbairav ot etarbilac ehtd tierCR . Exercise: Create New Session ID tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderCUife d-nrdes kcilC noA DD ot etaerc ane wS Teh DDAidra zwi llwR ksimaraP reteVselbair a tpmorp uoy:ot E noitamits -I tupnirocist edpn rof ehtSisse noID -ni feDe2 rahca rectgisteadno n rof ehtRC M retemaraPS teI D desu( ot evast eh atfledu)sretempa ra TADES :StcelesUe aDtes in iHstroical aDtaabse. RPOECSS TYEP : enifed rehtehwRK M dluohsclac etalu - htoBisse rngoeric isftfneoc dnau datfleorpbia tbsilie ro -J tsu eht tluafedboripbaitsile. T sih semussa tahtiss enroger seticniffeoca heverdla aybe envir eddea dn os uoyi llwene d otficeps y eht sedta rof hhciwht e msarreatpeh dae NALAYSIS EPRIO :D siht dluohslf etrce ehtuqcenref y fosih lactiro atad uoyi llwb eni sgu rof ehtis E retn ehtSTRAT dnaEN D etad fo ehto tlsaichri data oteb desu OUTUPT:StceleedoMl aPrameters &Default rPaboiblities. T sih noitcelesi llwrp tpmoRK M ot ecudorp ntolno y eht teircdu atfledma,rraepte Ladbm0, tubsla o ehtredvi de tluadfeol ariyptbbi morf ehtiss enrogersa (.e, ehteped dntne)ealirabv SVAE ehtS noisseI D 6-62  CR aPrameters -sUerd-eifned aVriabels i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Define Counterpart yNames tSep KRM Menu rcSeen Actions1a S pute /tiderC Re recfneNasemS tcele DDANEW REOCR D morfEIDT unem ot retneen wREFERENEC emana dndosi tenpicr+ itain doldaitamo ronfni SVAE eht enw noitamrofni tSep KRM Menu rcSeen Actions1b lansAy siT sloo / tiderCUife d-nrdes kcilC noht eSELETC REEFRENEC NEMAS .nottub R ksimaraP reteVselbair aE noitamits S tcele ehtiotcra psreuetn otb e desui n eht N :eto rFotsigol ciisse rsgneort o ebes ud rofni vdirgederc ti tluafedm,asrraepte o uyi llwne ed ot etaerc aral egsrevu ien fon aspemioc tahtu lecdni hbtohet hlyana d T eh noeirsgseri llw ebi sgunitlpu melnpiendee dtn (.g, na pymoc )slaaincinf ot etamitse riehtlptxa enaroye ropw niuimtia slgn talfeduorp sbleibtaiped dnt(eneralvb a:ei0,t=lounadf-ne 1a=fe du)tl S tceleSVAE morf ehtSRCEEN unema dnCLOSE ehtneerc s KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  7-62 . Exercise: Define Independent Variables tSep KRM Menu rcSeen Actions1a lansAy siT sloo / tiderCUife d-nrdes kcilC noht eEIDT LAL USER EDIFNE DVRAILBAES nottub R ksimaraP reteVselbair aE noitamits kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn enw cisab:selbairav - aVriable name = 8 retca rahc eman rof ehtealir bav- Cloumn name = ngissA amulo cn ot T ehmulo cn eman siif idceeps san_RAVn hre wnn nacb eteebe nw01 a dn20, sa gnol sa ehtb mrune sah tonrlaa edyb neesu dei n T sih noitamrofni sir deriuqe ylno rofUSER VRAILBAES. - eDscritpion = L mnrof-gseodi tpinrc foaeilravb SVAE ehtrofni noitma Ns et:o -,nyltreruC eht ylnoREEFRENEC VRAILBAE dewolla ybRK M siINUDSRTY. Y uo nac enifed suoirav yrtsudnis edoc taht nace bd esu nihtiw a -UFNITCONLA VRAILBAES enifeds pihsnoitaler desab no orcaM arotcFr o/dnaRc ehTp ihsnoitaler sic deifiepss a a,alumrof ralimis niof tarmand cture struto the formula used in e thst intere etar roF :elpmaxe I FI(NUDSRTY2=,1,0 ) *S05P0 si eht lanoitcnuFV belaira The ula formpoints to a Rce eferenable riva(INDUSRTY) nd aa Macro ctor Favariable (S50P0)  I n siht,elpmaxe rof hcae reussi ni ehtetemarap r noaitmitse,ssecorp RK Mw lli enimreted stiI yrtsudn noitangisedR(EEFRENEC NEMA RAHCETCARISITSC cs.)neer I f ehtINUDSTRY ation signde= 2, KRM will se uthe SP500 Macro ctor Fae ariablvas na ry explanatoe variablin the on tSep KRM Menu rcSeen Actions1b lansAy siT sloo / tiderCUife d-nrdes kcilC no ehtNAHCEG SELETCE DVRAILBAES nottub R ksimaraP reteVselbair aE noitamits 8-62  CR aPrameters -sUerd-eifned aVriabels i tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions ah Cegncmul on retlif ot ohswLAL VRAILBAES S tceleirlabvaes ot eb desuof r ehtlansaysi SVAE ehtrofniitma no dna esolC eht neercs tSep KRM Menu rcSeen Actions T sih lliwlla mcyoaittua deptua eht niamerces n dna hsoweht detcelesselbairav KRM Training Guide i tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  9-62 . Exercise: Input Historical Data for User Variables tSep KRM Menu rcSeen Actions1A lansAy siT sloo / tiderCUife d-nrdes kcilC no ehtIHSOTRILAC TADSABAE nottubR ksimaraP reteVselbair aE noitamits S tcele DDAorfm EIDT unem siht(i llw triesna enw orwi neht )elbat E retn ehtrot sliahcifonitanmr,o bypruerotac,nty rof hcaeravlba ie ot ebe sud ni ehtma rraepte:noiittsaem - ataD etad= etad rof ehtrofnnoit ma- eRference name = counterpartyname - aBnkrutp = ficepsyhh,treew rof ehtigev nad,et eht rettnruaopcya swrknab tpu0(o=nt ,tpurknab 1=bur k)ntap- aVriable Columns = rofhcae dilavravba eil(nn = 01 uorhtg h20,) p ntiu ehtveler tnaitamrnofni rof eht nevigdeta SVAE ehtrofni noitma . Exercise: Input Historical Data for Macro Factor Variables tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderCUife d-nrdes kcilC noht eEIDT LAL USER EDFINE DVRAILBAES ottu bn dnaht eR ksimaraP reteVselbair a kcilC noht eRCAMO OTCAFR VRAILBAES batE noitamits kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn ehtrot sliahcifonitanmr o rof hcaercam o -T sih atadlu odhs ebtsnneotcsi ni( smret fo etad )egnarti hwcir oltasih atadi vdoerdp rofto rehirlab vaseni -T eh eulav niVRAILBAE uloc nm foRCAMO OTCAFR VRAILBAES neercs tsumam hct ehtVRAILBAE NEMA feden id niUSER EDFINE DVRAILBAE .neercs SVAE ehtrofni noitma 1- 620  CR aPrameters -Userd-eifned aVriabelsi tflneoadC &riartpoerPy KRM Training Guide . Exercise: Setup Reference Variables tSep KRM Menu rcSeen Actions1 S pute /tiderC R eercenfeNa emS tcele DDANE WREOCR D morfEID T scitrsaihr Cetca F mro DDAiz,draw tceles ecfneerr snema ot ebdaed dsu( eTCL eky ot,tceles esu=> ot gard ot thgir dnaRCETAE REOCRSD) rFo hcael advicner eeferman e tahti llwb edn uidlec niht e retemapraoi tamnitsecorp,sse tupni ehtREFERENEC VRAILBAE ot ebes ud dna ehtr peptapaoirmg etsneVLAUE cossai deta rof taht reussi / rFolpmaxe :e -REEFRENEC NEMA =lleD -RAHCTCAERISTISC R( ercefneViralba )e= Irtsudn y -VLAUE I(tsu dnrymges )tne= 1 = retupmoC(f rolpma xeeihts lu odc eb ehtSI C)edo c SVAE ehtrofni noitmaKRM Training Guidei tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  11-62 . Exercise: Update Session ID tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderCUife d-nrdesS tcele ehtSse nosiID R ksimaraP reteVselbair aE noitamits TADE SOUREC :Sficep y eht edtagn aer ot ebu dse nimiitt sneaoc o:rspse -S tceleUSE TADES IN IHSTORILAC TADSABAE toNe : fio uy eiercenxpeten xdedripssgenco emit eud ot eht ezis fo oryu rloatcsiihbsaatad,e uoy nacr censiaeht eee pds by letyapresaficepsiy gnut claa setadht taRK Muo hdslesu rof noitamitse ssecorp[KRM ill wnot have to find distinct dates from HZRD_HIS table]: *S tceleUSER EDIFNE DETAD RNAEG * kcilCo n ehtUSER EDIFNE DTADES snottub *I tupn eht tcaex detanarg e rof ehtSisse noI D uoy eraonikr gwhtiw N :eto RK Mil lw esurot sliahci atad otvired e ehtrgeori ssne esitcinffeoc dnaud T eh tiderCR ksir aspretemali lw nolyb e lduectlaac rof eht tsalsb onaovirte etadi neht etadgna reE(N D )etad -U etadp eht rspseco etaddna cne uyqerf fo ehtr oltascihdat a -U etadpupottu stluser ot eb ddoercpu byKR M S tcele ehtniendeep dtn bsaeilrav ot ebsu dei n ehto - oD ton tcelesINTERTPC tnatsnoc( )mret ecnis ti si motuayllacitad ddae otrge rnoeissitcnnou f - oD ton tceles0RKB_U Pih chw si ehte dntenpediralvba e SVAE ehtS noisseI D 1- 622  CR aPrameters -Userd-eifned aVriabelsi tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions2 S tceleRPOECSS morfEIDT unem otetnaii ti ehttemarap reoitanmitse kcilC noERROR nottub ot iverewma/ss esgreorretaler de ot ehtoi tamnitsecorp sse esolCKR M neercs retemaraP gnittifcos rspe:- Ron ssireegs coefficientd antest statistics for ch eaof cial finans uresmeaare en writtto RHZOD_UT - tluafeD seitilibaborpra e devas - Term cture struand credit sk rimeters arapare erivedd based on the on ssireregs nctionfuin RHZD_OUT. R stluser ea devas ot ehtRCRAPM . Exercise: Update Product Set ID tSep KRM Menu rcSeen Actions1 S pute /cudorP st &Product ID Go to rCedtitabspuor G rFo ehtavelernt tcudorPI,sD tceles ehtarpp iertpoaRCEIDT RIS KOMEDL RAPAMETER SE TI D SVAE ehtrofni noitmaKRM Training Guidei tflneoadC &riartpoerPy CR aPrameters -sUerd-eifned aVriabels  31-62 . Exercise: Update Run ID and Initiate Credit Risk Anaslyis tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele eht etriCdR ksiRu nI Doy u detaerc reilerai n ehtianritng senlo oC -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) S tcele ehtarppi reptoadorP tcuS teI D S tceleSTRAT RPOECSSIN G morfRPOECSS unem otit ientia ehtsseco rp O ecnssecorpi gn sahmoc,ldetpe tcelesERROR LO G morfRPOECSS em un ot eiverwht e gsasseemrpcudo de by ehtssecorp R eivewt ehs esrtlu aiv ehtR setrop roEcxle . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  aaPrmRC-sUeeDrifned..xls: n soariCompof KRM’s mated esticredit risk rs parameted sebaon dr-defineuses elbairav otE Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. 1- 624  CR aPrameters -Userd-eifned aVriabelsi tflneoadC &riartpoerPy KRM Training Guide .72CR PARAMETERS - MRET NOMLEDO . ATUOB CREDIT RKSI M LEDOPARAMTEER CRBILATA NOI You can se uhe tal traditionMerton delMoto derive the CR In this n ctiose we willfocus on settingup, ng,ssicerop andreviewing s outputfor this meter rapa KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  1-72 . TREMGOLONIY The ngfollowiterms re ad ceroduintin this . Securit yTpye - Equity ehTSEUCRIYT I D neercs niRK Mivorp sed a ytilicaf ot enifed tnereffidepyt s fo ytiruces,sdnob( ,sevitavired equity) by their market symbols d ana ptive descriname. en Whany of these scu ritieseare secreferen erehwesle ni eht,erawtfos RK M lliw yalpsid eht . Index Symbol & Name ehTINED XSYOBML I D neercs niRK M sedivorp a ytilicaf ot enifeduoiravs arket mces indiby their olsb symand aptive scridename. n Wheny a fothese ces ndiire as ncererefeere whseeln ithe ftware,so RKM will a ylpsid eht 2-72  CR Parameters - Merton Model i tflneoadC &riartpoerPy KRM Training Guide . Equit yOutstanding Shares The ber numof shares ng utstandiox the equity price= E ytiuq eulav rof hcaea ytrpretnuoc ta a . Debt Face Value tbeD ecaFVa eul nac ebd enifed ni yna yaw taht uoy meed tsom etairporppa rof Semo s exampleude: incltotal liabilities ok (boce),alanb only rm long-tebt,de 50% of rm short-tedebt + 50% of long- mret,tbed .cte . Beta Calculation  RK M setaclulac ateB sgniu eht gniwollof :alumrof  V( ytilitalo foE ytiuqR snrute rof mriF n revoelpmas riodpe/ Vol of rket MaIndex R urnetover plesam )doirep * noitalerroc fo snruter neeewtb mriF n dna atekrMI xedn revop elmas ). Default Probabilities I n noitidda ot gnitamitse eht tiderc ksir ledomesretmarap, RK M lliw osla etareneg ehtc lariotsih ytilibrabop fo tluafed eulav( neewteb0 dna1) rof This s senptrere the ectedexpvalue or f the dentpende elbairav ni ehtc . K-Value The stant conK s srenterepthe ratio of E detcepx tekraMR nrute otR ksireeF RK Msev los rof siht eulav hcus taht mus fonna lau tluafed seitilibaborp lliw lauqe eht lautca rebmun fo stluafedn i ehto rirp sihT sekam K tnsatnoc rof enor aey ta For e,exampl if the r numbeof es nicompais 100 / 98 at the beginning / end of the 1 year riod peely,ectivspre ehtc lautar eubmn fo stluafeds i2. u tB eseht owtes mpanico arenot ded ncluin i theefault dility brobapn culatiocal whichis e donst again89 esn't Doit undso? ngestra Were ignoit assuming e th mplesaes zis i ?bgiI f a raey snigeb htiw100 seinapmoc dna sdne htiwwto s,default hen,t r fo the12nth -mood erip ningeginbwith 100 es,panicom KRM s ngechaK so at ththe sum fothe u tlafed seitilibaborp ta eht gninnigeb fo eht raeys equal2. . Firm Volatilit yRK M setaluclac mrif ytilitalov gnisu eht ytiuqe tekramov ytilital dna eht kcalBSo ehT mrif ytilitalovs emoceb na tupni ot gniviredthe rty’s rpaunteco assetvalue. . Asset Value The equity arket mvalue lso (athe equityBlank Ses cholva) lueis afunction of the tessA )mrif( eulav dna O ecnRK M devired eht mriF,ytilitalov tisu e eht ytiuqe tekram eulav ot evired eht deilpmi KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  3-72 . DTA ESABADGISE N- STE PU& OUTUTP roF siht,ssecorp RK M lliwer ecneref eht gniwoollf selbat 4-72  CR Parameters - Merton Model i tflneoadC &riartpoerPy KRM Training Guide . EREXCSESI: SET PU DNACRBILATA EPARAMTEERS For Credit Rsk is nalysiars usewill ed neto te calibrathe modelto market rks enchmabnd aestimate the sretemarap eriuqer rof s eaelP wollof ehtpetss d eoutlinw beloif u yoplan tose uthe n Mertoodel M ot etarbilac ehtd tierCR . Exercise: Define Counterpart yNames llAr eussi seman /s eitrapretnuocd reetne ni ehtOPR T,elbat lliw deen ot eb denifitedi niRK s’M retsamce referene tabl tSep KRM Menu rcSeen Actions1 S pute /tiderC R eercenfeNasem kcilC noA DD ot retne enwREFERENEC enmana drcosietdnpi SVAE eht enwitamrofni no dnaLCOSE eht neercs KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  5-72 . Exercise: Define Debt Variable tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderCUife d-nrdes kcilC noht eEIDT LAL USER EDIFNE DVRAILBAES nottub R ksimaraP reteVselbair aE noitamits kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn eht enw:sealirabv - aVriable name uohs dl =EDECAFTB - Cloumn name = ngissA amulo cn ot T ehmulo cn eman siif idceeps san_RAVn hre wnn nacb eteebe nw01 a dn20, sa gnol sa eht brmuen sah tonrlaa edy bneesu dei n - eDscritpion = L mnrof-gseodi tpinrc foaeilravb SVAE ehtrofni noitma tSep KRM Menu rcSeen Actions2 lansAy siT sloo / tiderCUife d-nrdes kcilC no ehtIHSOTRILAC TADSABAE nottubR ksimaraP reteVselbair aE noitamits kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn ehtrot sliahcifonitanmr,o byprueotcran,ty rof eht tbed:aeilravb - aDta adte = etad rof ehtrofnnoiit ma- eRference name = counterpartyname - aBnkrutp = ficepsyhh,treew rof ehtigev nad,et eht rettnruaopcya swrknab tpu0(o=nt ,tpurknab 1=bur k)ntap SVAE ehtrofni noitma 6-72  CR Parameters - Merton Model i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Setup Equit yIssues tSep KRM Menu rcSeen Actions1 S pute /Eitiuqes &S tiruceyI DS tceleSECURITY TYPE = Euit yqtvariseD kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn ehtqetiu yob smly & seevditpirc nema rof hcae eutqiyieuss SVAE ehtrofni noitma KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  7-72 tSep KRM Menu rcSeen Actions2 kcilC noht eRAMEKT TA DAnot tub kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn ehtrot sliahcieti uqycirp :se -S ficepy atadeta d -S tcele eht iertppoarpercrnuc y -S tcele ehtpi areptorpaetiuq y morf eht tsilb xo -I tupn ehtcirp e rof ehtevi gnetad -I tupn eht tnerruc tekramteb atoNe : Wnehsu gni ehto trneMed olm otrb ieltaca ehtd etriCR ksi dloMem,asraepte RK Mi llwlaucclat eti uqeyb ate dna(hto reitats )scist sa fo ehtoi tamnitseetad SVAE ehtrofni noitma 8-72  CR Parameters - Merton Model i tflneoadC &riartpoerPy KRM Training Guide tSep KRM Menu rcSeen Actions 3S pute /Eitiuqes &Oni dnagtstuS tcele DDANE WREOCR D morf DDAne mutvariseD Ssera h E retn ehtmun reb fo serahs:dhle -S ficepy atadeta d - kcilC no ehtSEUCRITY NEMA knalbe;idfl sihti llw onept ehSEUCRITY I D neercs morf hcihwoy u nac tscele ehter iddseuqtei y -S ficepyht e renbmu forahses dleh ta ehtgi nev etad SVAE ehtrofni noitma KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  9-72 . Exercise: Setup Market Index tSep KRM Menu rcSeen Actions1 S pute /Eitiuqes &E/dnoBqti uyIned x kcilC noA DD siht(i llw triesna enw orwi neht )elbat tvariseD E retn ehtqtei uynied xbsmlo y dnarvcist epdieema n SVAE ehtrofni noitma tSep KRM Menu rcSeen Actions2 kcilC noht eRAMEKT TA DAnot tub kcilC noA DD siht(i llw triesna enw orwi neht )elbat E retn ehtrot sliahcieti uqycirp :se -S ficepy atadeta d -S tcele eht iertppoarpercrnuc y -S tcele eht ierptpaorapeti uqynied x morfht e tsilob x -I tupn ehtd niexlav eu rof ehtevi gnetad SVAE ehtrofni noitma 1- 720  CR aPrameters -Merton Mode li tflneoadC &riartpoerPy KRM Training Guide . Exercise: Create New Session ID tSep KRM Menu rcSeen Actions1 lansAy siT sloo / tiderC notreMdloMe kcilC noA DD ot etaerc ane wS noisseI D R ksimaraP reteE noitamits I tupn2( rahcca )sret a enwtaD aS teI D osla( derrefer ot saCREIDT RIS KOMEDL SE TI.)D S tcele eht-ks iererfile dyevru c RPOECSS TYEP : enifed rehtehwRK M dluohsclac etalu - htoBuaf etdlab oslreipibtiadn tluafedtnitisne seiRC(RAPM.)MA I n siht,esac ce psfiy ehtRPOECSS TADE etad( rofh hciw psrretaemai llwb etami t)sdena d ehtHORIZON etad emit(zi rnoohvo rehci hw biobrtapily fo ucrbtkpnayi llw ebet admitse nac( ebi n eht))erut uf -J tsu eht T sih semussa tahtmarsarpete evahedr alya enbee vdidre dnas o uoyli lw edne otfice pys eht sedta rof hhciwht e sreatreamP dnaDa at eraiavaaellb. n ifeeDmarape tsre rof ateBita:lnuoclac -S ficepyhre ewRK Muo hdsl teg :ateB -USE LACUCLTAE DEBT A – esu eht abtevir ededr ugni eht tnerructami tnsoeirpsseco -USE EB ATIN VOLO_UT – esu ehtteb aerlda ym idtestea bya nil raeecorspse -NUEBMR foYADS = # fo lacirotsih syad ot edulcni ni eht noitamitse ssecorpS(TRAT TADE –NUEBMR OF YADS = tsrif lacirotsih )et T sihegna r tsumop sdenrroc ot ehtrot sliahci setadavlalibae rof eht tiuqey serahsa dn -OHLIYAD I D= ficepsy ehtd ihlaoyvnocit nseno ot esuhe nwnis ugcirotsi hla atadf ro htoi tamnitsessecorp -UCRRENYC = cnerrucy rof eht tekramnied x -INED XSYBMOL = ehtti uqey tekramed nxit o ebesud KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  11-72 tSep KRM Menu rcSeen Actions tSep KRM Menu rcSeen Actions2 kcilC noht eREFERENEC NEMAS nottub ot tceles eht seiptorcetnau ot ebes ud ni ehtitam T eh tsil foa psreeittnruoc sie sabd no ehto itanmriofn depisfcii n eht arMetsRcnerefee .elbat SVAE ehtr onfoniitma dnaretrn uto the main screen 1- 722  CR aPrameters -Merton Mode li tflneoadC &riartpoerPy KRM Training Guide retemaraP gnittif:ssecorp - RK Mi llwsu e ehtr oltasciihdat a ot eviredeht is sneorger stneicoicffena d T ehtiderC R ksi smraertaeplil wln oy eb dleautcal rof ehtsalt resbovation date in the da etgn aerRP(OECSS TADE )- RK Mi llwla ucectlaV ittailloyna d ateBsab deo nt eh atadee nbwteRP(OECSS TADE -NUEBMR foYADS ) dnaSTRA TTADE. R stluse lliw eb devas otVOLO_UT. - u atfelD sbeoiirbtpailra edeva s - tiderC ksir sraeprtema eraredi dvena deva sd ot ehtRCAPMR tSep KRM Menu rcSeen Actions 3 kcilC noPROECSS ot etaitini ehtmarap rete noitamitse kcilC noERROR nottub ot iverewma/ sssergoerretaler de ot ehtoi tamnitsecorp sse kcilC noRESULTS nottub ot eiverw ptuouni E lecxli:fe - E lecx elifi lscednu are sseior tovip selbata dn sueqirelkni de ot selbati n ehtKR Mb aetsad - I n hcaegape fo ehtE lecxlif,e er hserf eht tpovi elbat otu petad ehtohskr teewhti w atad - S eva enwfniormation in Excel esolCKR M . Exercise: Update Product Set ID tSep KRM Menu rcSeen Actions1 S pute /cudorP st &Product ID Go to rCedtitabspuor G rFo ehtavelernt tcudorPI,sD tceles ehtarpp iertpoaRCEIDT RIS KOMEDL RAPAMETER SE TI D SVAE ehtrofni noitma KRM Training Guide i tflneoadC &riartpoerPy CR Parameters - Merton Model  31-72 . Exercise: Update Run ID and Initiate Credit Risk Anaslyis tSep KRM Menu rcSeen Actions1 oiloftroPnA lsaisyis sgencorP loCrtnS tcele eht etriCdR ksiRu nI Doy u detaerc reilerai n ehtianritng senlo oC -Y uo nacsla o etaerc a pocy fo eht tnerrucR nuI D kcilc(o nOCYP UCRRENT REOCR Dm) S tcele ehtarppi reptoadorP tcuS teI D S tceleSTRAT RPOECSSIN G morfRPOECSS unem otit ientia ehtsseco rp O ecnssecorpi gn sahmoc,ldetpe tcelesERROR LO G morfRPOECSS em un ot eiverwht e gsasseemrpcudo de by ehtssecorp R eivewt ehs esrtlu aiv ehtR setrop roEcxle . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  amraPRC-Metron..xls: son pariComof KRM’s ed estimatcredit sk rieters paramg usinthe rton Meel Modto E Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. 1- 724  CR aPrameters -Merton Mode li tflneoadC &riartpoerPy KRM Training Guide 28. BASEL II . ATUOB B LESA IIThe pending risk-adjusted capital requirements that have been proposed by the BIS Basel Committee (Basel II) will impose a more flexible and risk sensitive approach to determining bank capital that the current approach but will require banks to improve their credit and operational risk methodologies and solutions. KRM includes full capabilities to satisfy Basel II requirements. Basel II provides three alternative approaches to measuring credit risk: - Standardized Approach - Foundation IRB - Advanced IRB Kamakura’s solutions address all three approaches. In this section we will focus on the general setup and outputs for Basel II analysis. KRM Training Guide Confidential & Proprietary Basel II  1-8 2 . TREMGOLONIY The following terms are introduced in this section. . Standardized Approach to Credit Risk (SACR) The Standardized Approach to Credit Risk (SACR) is a modified version of the risk-weighted capital adequacy approach adopted by the BIS Committee in 1988. The SACR assigns a risk weight to each asset and off-balance-sheet position based on broad borrower categories and counterparty characteristics, credit ratings assessments by External Credit Rating Institutions (ECAIs) and instrument characteristics of portfolio positions held by a bank. The product of the risk weight and the exposure of each position are used to determine the risk-weighted capital requirement of a bank. The SACR also considers netting agreements, collateral pledges against various obligations, financial guarantees and credit derivatives, and various financial commitments in determining the capital requirement. . Internal Rating Approach (IRB) Basel II provides two capital adequacy approaches that are based on a bank’s own methodologies for determining credit risk: the Foundation IRB approach and the advanced IRB approach. Under both IRB approaches, the probability of default of a borrower is estimated by the bank using an appropriate counterparty rating and default probability modeling approach. The foundation IRB approach requires that loss given default be determined according to a loss rate for each asset category provided by the regulatory authority. The advanced IRB approach allows the bank to estimate the loss given default using an appropriate loss rating and recovery rate modeling approach. . ECAI Credit Ratings Credit ratings are used to describe the creditworthiness of an individual counterparty or counterparty obligation in terms of one of a discrete set of creditworthiness levels. The creditworthiness level incorporated in a credit rating is a qualitative description of the likelihood that the counterparty will default in the foreseeable future. Different methodologies exist for determining the appropriate credit rating for a counterparty, such as those employed by the global credit rating agencies such as Fitch Ratings, Moody’s Investors Service, and Standard and Poor’s Corporation. . Credit Exposures The risk-weighted capital calculations under the Basel II SACR are based on credit exposures related to the individual portfolio positions in a bank’s portfolio. These credit exposures arise from both on balance sheet portfolio positions and off balance sheet positions. Under SACR, exposures for contractual claims such as loans or bonds are defined as the principal balances of these instruments. Exposures for corporate equity securities and other similar instruments are defined in terms of the current market value of these instruments, . the product of the number of shares held times the current market price per share. Exposures for market-sensitive derivative instruments, such as interest rate and foreign exchange derivatives, are defined as the current replacement value of these instruments plus an add-on amount related to the notional amounts of these instruments. Finally, the exposures of credit-related off balance sheet instruments are defined in terms of their notional amounts. . Risk Weighted Assets The risk-weighted capital calculations under the Basel II SACR are based on credit exposures related to the individual portfolio positions in a bank’s portfolio. These credit exposures arise from both on balance sheet 2-82  Basel II Confidential & Proprietary KRM Training Guide portfolio positions and off balance sheet positions. The amount of regulatory capital required under the Basel II accord is determined by the amount of risk-weighted assets of a bank. The risk-weighted asset amount of an individual credit exposure is the product of the amount of the credit exposure, a risk weight appropriate to the exposure, and a credit conversion factor appropriate to the exposure. Under the Basel II SACR, the risk weights appropriate to each exposure depend upon (i) the type of counterparty and the appropriate composite credit rating of the counterparty associated with the exposure, (ii) the composite credit rating associated with the exposure, if any rating has been assigned to the exposure by an ECAI, or (iii) the type of portfolio instrument associated with the exposure. The risk weights applied to exposures by the SACR are set at 0%, 20%, 50%, 100% or 150% except for certain special cases. . Risk Weight Models KRM includes the following risk weight models for Basel’s calculations:  IRB Advanced Approach (ADV)  IRB Foundation Approach (FND)  Standardized Approach (STD) For the IRB approach, KRM offers exposure selections for the retail sub-classes:  CRP – use the sovereign, corporate, and bank risk-weighting function;  SME – use the SME risk-weighting function (sub-class: small- and medium-sized entities)  SVR – use the sovereign, corporate, and bank risk-weighting function;  BNK – use the sovereign, corporate, and bank risk-weighting function;  RES – use the retail residential real estate risk-weighting function;  QRR - use the qualifying retail revolving exposure risk-weighting function;  RTL – use the other retail exposure risk-weighting function KRM Training Guide Confidential & Proprietary Basel II  3-8 2 . DTA ESABADGISE N– SETUP For this process, KRM will reference the following tables . Basel II - IRB 4-82  Basel II Confidential & Proprietary KRM Training Guide . Basel II - Standardized KRM Training Guide Confidential & Proprietary Basel II  5-8 2 . EREXCSESI: SETUP RFO BL ESAII PRCOGNISSE For Basel II analysis users will need to define a list of reference names and their characteristics and their default probabilities (including potential recovery). Please follow the steps outlined below to provide KRM with the necessary information to derive the Basel II results. . Exercise: Estimate Probabilit yof Default For the IRB approach, use one of Credit Risk Parameter Estimation models in KRM to derive default probabilities for all issuers and counterparties in the portfolio (., lambda0 in CRMPARAM table). Alternatively, for KRIS subscribers, default parameters can be downloaded directly from KRIS. . Exercise: Define Recover yRates For the Advanced IRB approach, define LGDs. tSep KRM Menu rcSeen Actions1 Setup / Credit Recovery Rate Use EDIT Menu to add new records toNe: You can add records to an existing SET ID or add a new SET ID Type in the recovery rates by Security ID (corresponding to Security IDs in the portfolio table) Set Recovery Cost and Haircut Exposures = 0 (for this exercise) Save the new information . Exercise: Define Potential Exposure Amounts tSep KRM Menu rcSeen Actions1 Setup / Credit BIS Potential Select ADD from EDIT menu to add a new row to the table Exposure Weight Specify weights, by remaining term, that would be applied for potential exposure calculations (product codes in this screen link to PRODCREDIT field in the portfolio table) Save the new information 6-82  Basel II Confidential & Proprietary KRM Training Guide . Exercise: Define BIS Risk Weights tSep KRM Menu rcSeen Actions1 Setup / Credit BIS Risk Weight Select ADD from EDIT menu toNe: - Required only for Standardized Approach - For this exercise, assume that inputs are for the Banking book with no past dues Within a Risk Weight ID, define the range of Ratings and associated risk weights. For the Banking book, set Remaining Life = 999 Save the new information KRM Training Guide Confidential & Proprietary Basel II  7-8 2 . Exercise: Define Reference Name Rating tSep KRM Menu rcSeen Actions1 Setup / Credit Reference NamesSelect ADD NEW RECORD from EDIT menu to enter new REFERENCE name and description + additional information Specify rating(s) for each reference name. - You can save up to 3 different ratings; however, for Basel II calculations, KRM will reference RATING1. - These ratings should have corresponding entries in the Risk Weight ID to be used. toNe: - Required only for Standardized Approach - For this exercise, assume only modeling for domestic exposures The REFERNCE NAMEs in this table are linked to the ISSUER/COUNTPARTY fields in the portfolio table Select SAVE from the SCREEN menu 8-82  Basel II Confidential & Proprietary KRM Training Guide . Exercise: Define Industr yCategory tSep KRM Menu rcSeen Actions1 Setup / Credit Reference Name Select ADD from EDIT menu Characteristics toNe: Required only for Standardized Approach Define various Industry categories Save the new information . Exercise: Define Reference Name Characteristics & Industry tSep KRM Menu rcSeen Actions1 Setup / Credit Reference Name Select ADD from EDIT menu Characteristics toNe: Required only for Standardized Approach For each reference name, define a characteristic (., Industry) and specify a segmentation value within that characteristics (., Manufacturing) Save the new information . Exercise: Update Product Set ID KRM Training Guide Confidential & Proprietary Basel II  9-8 2 tSep KRM Menu rcSeen Actions1 Setup / Products & Product ID Select the Product Set ID Groups Go to the RCIDETtab Select BOND MODEL TYPE = No Credit Risk (this only applies for “inner loop” default valuation) For each Product ID, select CRM Set ID and Recovery Rate ID tSep KRM Menu rcSeen Actions2 Select the Credit-risk related elements - soLs rPivosino :DIDefine percentage loss provisions for different levels of default - Recovery Rate ID: the LGD assumption (1-recovery rate) - eRcvoer yCsot :Cost associated with any recovery efforts post-default - sopxEure :EAD calculations (based on balance or limit) - iRsk Weithg Medol :Select Model type Save the new information 1- 820  aBsel IIConfidential & Proprietary KRM Training Guide . Exercise: Update the Portfolio Table tSep KRM Menu rcSeen Actions1 Data / Portfolio by All Relevant Go to teh CRTIDEtabInstrument Products Update the following fields in the Portfolio table(s): - ISSUER field provides information about counterparties for bonds and loans (., AMRT_TYP < 700 and OO_TYPE <> 0) - COUNTPARTY field provides information about counterparties for OTC derivatives (., AMRT_TYP > 700) - CREDIT EXPOSURE field provides products codes associated with BIS Potential Exposure calculations - RISK WEIGHT ID: select Risk Weight ID (for Standardized Approach) - EXPOSURE CLASS: select Exposure Class (for IRB Approach) - PAST DUE: set PAST DUE = Non-Defaulted (IRB) and Current (Standardized) - SENIORITY: set = SENIOR - COLLATERAL & EXPOSURE: For this exercise set = Neither or Collateralized Exposure Save the new information KRM Training Guide Confidential & Proprietary Basel II  1-812 Exercise: Process Basel II Setup KRM will calculate Basel II results for basic Market Valuation analysis (Calculation Type = Market Valuation for Run ID). The following fields have been added to DMV_OUT table:  RW = Risk Weight ( = 50%)  LGD = Loss Given Default ( = 50%)  BRW = Benchmark Risk Weight ( = 50%)  PD = Probability of Default ( = 50%)  M = Maturity (in years)  B = Maturity Adjustment Factor  EAD = Exposure at Default  RWA = Risk Weighted Assets  MODEL o STD: Standardized method o IRB-FND: IRB Foundation approach o IRB-ADV: IRB Advanced Approach The calculations will vary depending on the MODEL selected and the instruments. An example of these calculations can be reviewed in the Excel test file Basel (\ KRM \ EXCEL TESTS directory). Approach Borrower Borrower Defaulted / Variable Variable Paragraph Calculation KRM Type 1 Type 2 Non-Defaulted Name IRB ANY ANY ANY Cumulative N foot 67 N (x) denotes the cumulative distribution distribution function for a function standard normal random variable (. the probability that a normal random variable with mean zero and variance of one is less than or equal to x). Excel: NORMSDIST IRB ANY ANY ANY Inverse G foot 67 G (z) denotes the inverse cumulative cumulative distribution function distribution for a standard normal random function variable (. the value of x such that N(x) = z). Excel: NORMSINV. IRB Corporate CORP Non-Defaulted Correlation R 272 R = × (1 - EXP (-50 × PD)) Exposure / (1 - EXP (-50)) + × [1 - (1 - EXP(-50 × PD))/(1 - EXP(-50))] IRB Corporate ANY Non-Defaulted Maturity b 272 b = ( - × ln Exposure Adjustment (PD))^2 IRB Corporate CORP Non-Defaulted Capital K 272 K = [LGD × N[(1 - R)^ × Exposure G(PD) + (R / (1 - R))^ × G()] - PD x LGD] x (1 - x b)^ -1 × (1 + (M - ) × b) IRB Corporate CORP Defaulted Capital K 272 The capital requirement (K) for Not Exposure a defaulted exposure is equal implemented to the greater of zero and the difference between its LGD (described in paragraph 468) and the bank’s best estimate of expected loss (described in paragraph 471). IRB Corporate SME Non-Defaulted Correlation R 273 R = × (1 - EXP (-50 × PD)) Not Exposure / (1 - EXP (-50)) + × [1 - (1 implemented - EXP(-50 × PD))/(1 - EXP(-50))] - × (1 - (S-5)/45) 1- 822  aBsel IIConfidential & Proprietary KRM Training Guide Approach Borrower Borrower Defaulted / Variable Variable Paragraph Calculation KRM Type 1 Type 2 Non-Defaulted Name IRB Corporate SME Non-Defaulted Annual Sales S 273 S is expressed as total annual Not Exposure sales in millions of euros with implemented values of S falling in the range of equal to or less than €50 million or greater than or equal to €5 million. Reported sales of less than €5 million will be treated as if they were equivalent to €5 million for the purposes of the firm-size adjustment for SME borrowers. IRB Corporate CORP Non-Defaulted Probability of PD 285 For corporate and bank Exposure Default exposures, the PD is the greater of the one-year PD associated with the internal borrower grade to which that exposure is assigned, or %. IRB Corporate SVRN Non-Defaulted Probability of PD 285 For sovereign exposures, the Exposure Default PD is the one-year PD associated with the internal borrower grade to which that exposure is assigned. IRB Corporate ANY Defaulted Probability of PD 285 The PD of borrowers assigned Not Exposure Default to a default grade(s), implemented consistent with the reference definition of default, is 100%. IRB Corporate ANY ANY Exposure atEAD 317 Exposure measurement for FX, Default interest rate, equity, credit, and commodity-related of exposure for these instruments under the IRB approach will be calculated as per the rules for the calculation of credit equivalent amounts, . based on the replacement cost plus potential future exposure add-ons across the different product types and maturity bands. IRB Corporate ANY ANY Exposure atEAD 310 Exposure measurement for off-Not Default balance sheet items (with the implemented exception of FX and interest rate, equity, and commodity-related derivatives) IRB Corporate ANY ANY Exposure atEAD 309 Exposure measurement for on- Default balance sheet items FND Corporate ANY Non-Defaulted Effective M 318 For banks using the foundation Exposure Maturity approach for corporate exposures, effective maturity (M) will be years except for repo-style transactions where the effective maturity will be 6 months. KRM Training Guide Confidential & Proprietary Basel II  1-832 Approach Borrower Borrower Defaulted / Variable Variable Paragraph Calculation KRM Type 1 Type 2 Non-Defaulted Name ADV Corporate ANY Non-Defaulted Effective M 320 Except as noted in paragraph Exposure Maturity 321, M is defined as the greater of one year and the remaining effective maturity in years as defined below. In all cases, M will be no greater than 5 years. For an instrument subject to a determined cash flow schedule, effective maturity M is defined as: Effective Maturity (M) = ∑ t * CFt / ∑ CFt where CFt denotes the cash flows (principal, interest payments and fees) contractually payable by the borrower in period t. IRB Retail RES Non-Defaulted Correlation R 328 R = Exposure IRB Retail RES Non-Defaulted Capital K 328 K = LGD × N[(1 - R)^ × Exposure G(PD) + (R / (1 - R))^ × G()] - PD x LGD IRB Retail REV Non-Defaulted Correlation R 329 R = Exposure IRB Retail REV Non-Defaulted Capital K 329 K = LGD × N[(1 - R)^ × Exposure G(PD) + (R / (1 - R))^ × G()] - PD x LGD IRB Retail RTL Non-Defaulted Correlation R 330 R = × (1 - EXP(-35 × PD)) Exposure / (1 - EXP(-35)) + × [1 - (1 - EXP(-35 × PD))/(1 - EXP(-35))] IRB Retail RTL Non-Defaulted Capital K 330 K = LGD × N[(1 - R)^ × Exposure G(PD) + (R / (1 - R))^ × G()] - PD x LGD IRB ANY ANY Non-Defaulted Risk-Weighted RWA 330 RWA = K x x EAD Exposure Assets IRB Retail ANY Defaulted Capital K 328 The capital requirement (K) for Not Exposure a defaulted exposure is equal implemented to the greater of zero and the difference between its LGD (described in paragraph 468) and the bank’s best estimate of expected loss (described in paragraph 471). IRB Corporate SVRN Non-Defaulted Correlation R 272 R = × (1 - EXP (-50 × PD)) Exposure / (1 - EXP (-50)) + × [1 - (1 - EXP(-50 × PD))/(1 - EXP(-50))] IRB Retail ANY Non-Defaulted Probability of PD 331 PD for retail exposures is the Exposure Default greater of the one-year PD associated with the internal borrower grade to which the pool of retail exposures is assigned or %. 1- 824  aBsel IIConfidential & Proprietary KRM Training Guide . Excel-Based Examples & Tests The following Excel files compare results from KRM and Excel-equivalent calculations.  Basel : Verification of Basel II outputs You can find these files in the directory \ KRM \ EXCEL TESTS. KRM Training Guide Confidential & Proprietary Basel II  1-852 29. PROT OILOFSUPPORT . SROPPUTGNI IROFNMTA NOIROF PROT OILOFTABLE The rtfolio potable s eprovida ch ri arrayof elements to model a variety of ucts prodnd In somes,seca ver,weho KRM lwilre requitional ddiaatiinformon from g pportinsues tablto yerlroppmodel cific spe s:erutaef Risk s weightfor credit re osuexpions tcalcula Sred tructush caflows that can ot nbe d modele gnisu eno foRK s’M dradnatsn oitazitroma sdohtem a Bermudons optiwith ecified spstrike se exerciates d S paw eef stnemyapIn this ctionsewe will focus on the s stepd rerequito t seup each of these KRM Training Guide i tflneoadC &riartpoerPy Portfolio uSpport  1-92 . EREXCSESI: SETUP RFO SROPPUTGNI PROT OILOFIROFNMTANOI . Exercise: Input BIS Credit Risk Weights tSep KRM Menu rcSeen Actions1 S pute /tiderC IBS Eso pexruS tcele DDAorfm EIDT unem ot ddA a thgiW e I tupn eht etapioarppocruPd T ehRPOUDTC OCED luo hds hctam ehtpniut s ni ehtRPORCDEIDT dleif fo eht oiloftroplbate rFo hcaedor Ptcu,edoC ndi feet eh istehgw ot ebpal pdeiot ehtoi tnpo eulav rof tnereffid gniniamersmret Save the newinformation 2-92  Portfoilo Support i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Setup Structured Cash Flows tSep KRM Menu rcSeen Actions1 ataD / oiloftroPS derutcurtsaC htoNe :T sih sicilppaba elONLY :fiSpp utroFsl ow - T noitcasnraoi tanzitromAp eTy= telluB - I n lareneG bat foeFx id &Flni tgaoR eta rtPcudoee,rncs uoyvah e detcelessUe Aribtrar ymArotiaztino Table S tcele DDAorfm EIDT unem ot ddA a I tupntazit rnoomAiI D rofcih hw hsac solfwi llw T ehOMARTI_1_D dleifi n ehtlbat eRAOMABRT tsum hctamSUCRITYI_D dleifi n ehttrop oiflobaetl n ifeeD gnimita dnvin tonncoe roficanp ilrpselcanb a I tupncnir pplia leacbna &e stsadaico etarrof hcaetade Save the newinformation KRM Training Guide i tflneoadC &riartpoerPy Portfolio uSpport  3-92 . Exercise: Setup Bermuda Option Strike Dates tSep KRM Menu rcSeen Actions1 ataD / oiloftroP adumreBO noitptoNe :T sih sicilppaba elONLY :fiSpp utroStrike Dates - T ereh si na eddeembd roo -nnpooit ehtnuldnr ieygructes iy - I n ehtO noitp noitces fot eh tcudorPeerncs o uy evahs deeltcesUe Strike Talbe S tcele DDAorfm EIDT unem ot ddA a I tupnStir uceyI D rofh hciweht sekirtsi llwb T ehSUCRTYI_ D dleifi n ehtlba teOTPS_TR K tsumam hct stupni otni eht emas dleif ni ehtilof toropatelb I tupn ehtlevre tnasetad I tupnO noitpts ekir setar tafid tfneresicreeex setad Save the newinformation 4-92  Portfoilo Support i tflneoadC &riartpoerPy KRM Training Guide . Exercise: Input Fees tSep KRM Menu rcSeen Actions1 ataD / oiloftroPF eeSerutc urttoNe :T sih noitamrofni sieriuq derONLY rof sdnoBa dnS spaw :fiSpp utro - I nOITPON /EFES bat foOBN D dnaSAW P neercs uoyh eva detcelesInclude Fees. tSep KRM Menu rcSeen Actions ataD / oiloftroP FeeSeru tcurt kcilC noA DD notbtu ot ddA a Spp utro I tupnSti ruceyI D rofh hciweef i llw ebppeaild. T ehSUCRYTI_ D dleif ni ehtlba teFEES_TRU tsum hctam sptniuni ot eht emaslei fd ni ehtilo fotroplbate n ifeeD gnimita dnvni otcnoe rofaeptmyn I tupn Feeotmnau Save the newinformation KRM Training Guide i tflneoadC &riartpoerPy Portfolio uSpport  5-92 . Excel-Based Examples & Tests The ngfollowiExcel files mpare cosults refr moRK M dnaE  : gnirapmoCRK s’Mu tptuo hsac(w )solf ot eht hsac wolfsr eutcurts denifed ni ehtsetup.  : g parinComKRs M’NI cash flows for structd reus noitcasnart ot tnelaviuqeVM .snur Y uo nac dnif eseht selif ni ehtr yotcerid \RK M \EECXL ETSST. 6-92  Portfoilo Support i tflneoadC &riartpoerPy KRM Training Guide 30. PRODUCT COVERAGE . ATDDIIONAL PRCUDOT CREVOGAE The ses exerciin this ng trainiguide hav e desu dexif etar stnemurtsni ottsulli etar eht suoirav serutaef wever,Ho youcan el moda variety of instnts rumed anderivatives in the system: gnitaolF etarsecit siureh tiw tnereffid zniotiratoma sepyt nialP dna xelpmoc sevitaviredI n siht noitces ew lliw sucof no yek stnemele deit ot gniledom emos fo eseht stcudorp KRM Training Guide laitnedifnoC &yrateirporP rPodutc Coevrage  1-03 . FTAOLNGI RTA EINSTRUMENTS Floating rate s curitiese areed modelilar simto e thfixed etar stnemurtsni htiw eno yek :ecnereffid eht tseretnirate s ngechaover the erm tof the nt rumestinsebad on ents ovemm inan terest nirate To del mo gnitaolf etar,seitiruces esu ehtOBNSD dnaSSPAW 30-2  rPodutc Coevrage laitnedifnoC &yrateirporP KRM Training Guide . Terminology . Interest Rate Reset For ch eaating florate ction,nsatra RKM will use he t gniwollof enituor ot enimreted eht etairporppaetar en betwethe valuation e datand the t nexreset date:  I f eht tnerruCR etaUC(RRG_SR_ Tn i oiloftrop)elbat ><0, RK M lliw ton esu ehtS daerpRAM(IGN n i oiloftrop) ;elbat eht tnerruCR eta si demsusa ot eb etairporppa nopuocs a fo eht tsal  I f ehtN txeR etaN(RG_TXSR_ T ni oiloftroplbat )e ><0, RK M lliw esu ti ta eht txen teser etad Thereafter, tes raat all future reset dates = forward es rat+ S dreap If the CurrentRate = 0, RKM will rst fiook lfor cstorihi la xedni setarI(NR_TETA lbat )e sa fo eht tsal teserdate and dd athe S . Interest Rate Lags After ngreadithe rd corerom fthe olio portftable, RK M lliw etaluclac eht teser setad desab no eht teser ycneuqerf dna tA yna nevig teser,etad RK M lliws eu eht gal noiotamrfni ni eht oilooftrp elbatR(SL_T,GA e denifd ni )syad ot enrimeted nehw eht xedni etar lliw eb tes rof eht txen I f eht teser gal 5= dna eht tser etad = raM13, RK M lliwc etalulac eht wen xedni etar desab no noitamrofni sa foa rM2 6 xedni().etad . Interest Rate Cap/Floor Y uo nace lodm2 tnereffids noitpo nihtiw gnitaolf etar :stnemurtsni c PeriodiFloor: Cap/the ximum maor mum iminrate se reacse/decreainn withia yment pa dperio L emitefi :roolF/paC eht mumixam ro muminim etar doewlla tuoghuorht eht mret fo eht tnemurtsni . mAortiaztion llA gnitaolf etar snaol lliw yllacitamotua evah evitagenman oitrazito fi ehtn terruc tnemyapUC(R TMP_ ro/dnaCUR2_PMT fields) s doeot ncover the otal taccrued nteist rer and/oal cipprinyment pat amoundue in a ngive I n hcus,sesac RK M lliw dda ehtc tiifed ot eht KRM Training Guide laitnedifnoC &yrateirporP rPodutc Coevrage  3-03 . SECURTIIZED ATESSS Sd ritizeecussets a[., d zeCollateralige MortgaOs gationiblO(CMs)], aree d/incomnbo estmentinvs clevehi ylnommoc desu ni . htiWOMC,s laretalloc morf segagtromNF(,AM LHF CM roNG )AM sid cealpotni a evitcetorpr tsut dengised ot niatniam ehtaretallocl and nsure erepayment of CMO rity omrFthe st,tru eral sevdifferent sses clad (callees) chtran fos bondwith s variourities matu andupon cos rate reaissued. Istors nveace plheir ts assetwithin sthe es chetranng oosichg onmaa riety vaof s uponco S ecni eht gniylrednu segagtrom era deruces yb,laretalloc OMC s reffo ssel tnesmetvniks ri evitaler ot rehto rdFor . anBks wh itlioso pofrth atidnec luch suzrieidt uecs,setsa ss acceo ta 3 party cashflow rprovideis the first step for eling modthem in Kamakuracu ynlterr nac ssecca noitamrofni derdivop ybI xetn otvalue os portfoliof ritized RK s’MS dezitiruce tessA tcrudoP neercs swolla uoyo ter eveirt evitpircsed noitamrofni rof Fields wn shoin gray ent spreren informatiod provideby Intex. 30-4  rPodutc Coevrage laitnedifnoC &yrateirporP KRM Training Guide N :seto ehT "etaD-fo-sA" si na tuptuo RK M lliw wohs eht etad fo tsom yltnecer elbaliava atad sA a,tluser siht noitamrofni lliw ylno ebs an terrucs a eht tsomn terrucI xetn atad rof eht laedss gardle(re ofthe date d associatewith the portfolio ble).ta  Users n cadd aor e movres tranchefrom the oT dda a,laed uoy tsum edivorp aUCSI,P n transactioID, nt curreface value danoriginal cefa value g ndinsporre(coto a valid deal in the Intex s).rielibra ese Tht srenerepthe minimum informationsary snecer foKRM to ntify idethe transaction s a anI xetn laed dna rofI xetn ot ssecorp I f ehtd lae si ton ni ehtI xetn,esabatad RK M lliw nrutern aerror  Both al originface d wneond arrent cuface d wneo arer You can ulatec calnal origiface d wneo yb eht oitar tnerruccafe /  ehT neercs lliw yalpsid eht ledoms deeps erehw(pa )elbacilp rof1, ,3 ,6 12 shtnom  I f ereht si a tnaemyp yaled nehtN txe yaP etaD lliw ton eb eht emas saE dn  The index ction funs han ebebased on e th"slope" ndaant"."const KRM Training Guide laitnedifnoC &yrateirporP rPodutc Coevrage  5-03 . INTERTSE RTA ESSPAW An st intererate swap is a ctualracontment agreeenb etwe twoes rtircopuanteng reeiagto make odic erippayments to each other r foan d reeageriod pof time based on up anotional unt amoof oT ledom tseretni etar,spaws esu ehtOBNSD dnaSWSPA 30-6  rPodutc Coevrage laitnedifnoC &yrateirporP KRM Training Guide . Terminology Y uo nac ledom eht dexifr o/dna gnitaolf edis fo ahcewsp a tsuj sa uoy dluow a lacipyt dnob htiw A wefs ecnaunc cifieps otRK M evah neeb . Tpye: Canecalelb wSaps You can el odm2 types of able celcans swapin KR M Y ruo ytrapretnuoc sah eht thgir ot etanimret nag  Y ruo evah eht thgir ot dnetxe nag . Tpye RK Msref fo eht gniwollofroma noitazitsepyt rofs paws:  uBllet : nialP allinav paws mArotiizng: Use the d cturestrucash flow e tablR(ABAMOR )To t yficeps eht yltcaxe lapicnirp nwodyapule schedor /danown step-d-up/stepfeaturs efor e onor both s sideof the  l-XFinked: nto Qas swapere whthe pay r oceive re desican b ked linto FX rates. .: nIterest vs. Pamyent Calculation RK M nac ylppa2 tnereffid yadiloHI sD nihtiw a nevig :paws Calculation :S cyefpi yadiloHI D rofs ertetni etar sncoitcaallu Payment :S cyefpi yadiloHI D yadiloHID rof tnemyap etad snoitavired KRM Training Guide laitnedifnoC &yrateirporP rPodutc Coevrage  7-03 . INTERTSE RTA EOPTIONS Interest rate s optioncan be deledmon ius variorPct odus screending pendeon t eh epyt fo noitpo uoy erag kiwnorwith. . Caps & Floors Interest rate s capcrt otepyou st against ntereis raterig nis yb gnitimil eht mumixam tseretni etar uoy evahot pay ut (byou can still ke tae advantag offalling st reintera.)set I tseretn etar sorolfc tretop uoys tniaga tseretnirates g clinindeby limiting the m minimust ntereirate you can receive (but you n cast lli ekat egatnavda fo gnisirs )ar esehTs noitpo nac eb edeldom ni eht PAC &LFOORS 30-8  rPodutc Coevrage laitnedifnoC &yrateirporP KRM Training Guide . Terminology . A Cap n ca bea ntee guarafor one rticular padate, wn knosa a An interest rate cap resernetps a es rise . cCapsolF/ors KRM n cadel mothe ng followiexotic s/floors:cap iB(nar )yiDigtal: This type of interest rate r p/floocaprovs edi eht reyub htiw a dexif tuoyap eliforpe ssldrager fo woh raf ni eht yenom eht  KncokO-ut :sih T epyt fos ertetni etar roolfc/pasc uder eht muimerp fo a olamrnI tseretn roolf/pac yb gnitanimret rokncoking-out the cap/floor if a defined er arribe (thock knin level) s  KncoknI- :s ihT epyt fo tseertni etar roolf/pacu secder ehtu mrimep fo ao lamrnI tseretn roolf/pac yb gnitavitcar okncokin-ign he tr cap/flooif a d definederlying nu(e..g, L,)robi e schaer eht debircserp-kconKO . About wSaptions Y uo nac yfirev seulav rof a pnoitaws yb gniulav aoc noitanibm fo a allinavp aws dna a elbalecnac :paws  S noitpaw thgir( ot yapexif )d =Y uoevah a thgir ot lecnac paws -V allina paws Swaption ht (rigto receive xedi) f= Va anillsw pa –r ytnapretuoC sah a thgir otc lenac paws KRM s treatns swaptios aptions on ods bonwith ke stri= 100 dna a eulav fo rap rof eht gnitaolf sA a,tluser nehw gniledomSs,noitpaw RK M seodo tnu eriqeryna noitamrofni tuoba ehtI xednI D fo eht gniylrednuswap. . oValtiilties ehT mret ytilitalov si desu ybRK M ni ecnerefer ot lareves laicnanif :smret Black rPice olVatility : eht ytilitalov foitpo noc seirp sarrefer de ni ehtS-kcalB selohc noitaulav alumrof loVatilit yfo teh Sroht aRte : eht ytilitalov ro( dradnatsved )noitai fo eht trohs etarsa derrefer ot ni eht mrete  iRsk aFctro olVatility : eht etnoM olraC ytilitalov ro( dradnatsetaivdion) ed ciatsosa withthe sk rirs factoto eb desu niVRA . Option Volatilit ySet ID roF soilooftrp taht deulcni enor oo erm tseretnietar and r asimilunter-coover-the soptions,(cap floors, s,noitpaws dnobs,noitpo XF,)snoitpo rs usecan oose chn valuatios model tthaare nt stseiconwith ard standk rtaemsc (.g, calkB Sclohse, laimoniB,eert )ta I f uoy tceles eht kcalBSo selhc noitaulav,dohtem neht uoy lliw deen ot enifed ehtav suoir yripxe,setad ,sronet etc. for chwhiprice volatilities will e KRM captsr eu siht noitamrofni nihtiw naOn oitpV ytilitaloS teID. When sing socerpch suan option, KRM will: enimreteD eht noitpo epyt rof eht noitcasnart ni ehto oiloftrp elbat enimreteD eht yripxe etad dna rehtoatlered ation informarding regthe n sactiotran U esO noitpV ytilitaolS teID otretedmine the e triaappropoption tilities volaY uo nac sseccas iht neercsRAMEK T ATAD \OITPONS \O CTOITPON ILPMIE DVOLITALIYT KRM Training Guide laitnedifnoC &yrateirporP rPodutc Coevrage  9-03 0 30-1  Product Coevrage laitnedifnoC &yrateirporP KRM Training Guide . MRO ERREFECNESE This ction ses providean overview on someditiadonal s ctrodupcovered by KRM d anements elthat youuld shobe are waof. You should so ale suthe g followinsc ereferenfor more rmation infoon se thed anall other s uctprodthat can e bed modelin KRM:  : sihTE lecx elif serdivop deliated snoitpirecsd fo lla oilofotrp s ihT elif si dedulcni ni llaRK M snoitallatsni sa trapfo eht \RK M \SABATADE SRTUUTCRE .yrotcerid  HELP menu: The KRM Help menu (HELP TOPICS and RELESAE NOETS ) sedulcni deliated noitamrofni dnap gnipam senilediug rof lla stcudorpn ylterruc detroppus KRM Training Guide laitnedifnoC &yrateirporP rPodutc Coevrage  11-03 31. SUMMARY OF OTUTUPS . OTUTUP SCELETNOI KRM ces produa wide d andeep e ngraof s outputfor its varis oTretede minthe s sultrethat KRM ould shrate genefor a ss,cepro it offers a rs-eudefined ction seless ceprorebyw heyou can ose chohe tcific spes etablyou sh wiKRM to This n ctioseleis d seban oOUUTPT IDs d kelinto eht neercsOUUPT TLBATES O(UUPTLBAT_TES.) roFeach Output ID, you can e cludinone r omore es tabl tahtu dlohs ebd etalupop rof KRM Training Guide laitnedifnoC &yrateirporP uSmmar yo fuOtputs  1-13 1) Click on OUUPTT button inthe PROCESSING CONTROL enscre 2 )Sc elet DDAOUUPT TI D morf ehtEID T unem3) After inputting new O utputID in the ADD zard,wi ose cho“Select table name frmo list” )4I n ehtSELE TCLBATE ,neercs esoohc ehtNALAYSIS YTEP taht lliw eb denifed rof ehtR )5S tcele yna lanoitidda snoitaluclac tahtiw llbe ded incluas rt paof the Run ID setup. 31-2  uSmmar yo fuOtputs laitnedifnoC &yrateirporP KRM Training Guide )6 ehT tfel edis fo eht neercs lliw tsil lla tuptuo selbatht ta era tnaveler ot eht epyt fos isylana detacidni nis step(2) and (3). ck Clin othe tables u yowish for KRM to d populateand k ccli onthe ==> button to drag mtheover to the rightside. 7) Once you ve hacted selethe d siredetput outabl,se kcilcn o ehtRCEETA ROSW nottub ot etadpu ehtOUUTPT_ID. 8 )I n ehtRPOECSSIN GOCNRTOL esrc,ne sc eelt eht wenO KRM Training Guide laitnedifnoC &yrateirporP uSmmar yo fuOtputs  3-13 . Special Cases Please ote nthe ng followial specis secaregarding RKM output tables: ehTEDIATL LEVEL dereffo niOUUPT TLBATE scr neesi detasacosi ylno htiw eht gniwollofsel b:at LIATEDL_LEVE1= LIATEDL_LEVE2= 1(2()) HTAP_CM ehcnarT ylno ehcnarT + looP TUO_ADP esaB esaC ylno esaB esaC + lla soiranecS 1() roF :002=PYT_TRMA - leveL 1 = tuptuo rof OMC ehcnart dna OMC-non .snoitcasnart - leveL 2 = edulcni tuptuo rof OMC .sloop roF dexif dna gnitaolf etar ,snaol HTAP_CM lliw edulcni stuptuofi - noitaulaV dohtem = etnoM olraC - naoLedulc nsi stnemyaperp 1=TPOFOEPYT( dna deknil ot trnPemyape ssalC ta tcudorP)level 2() roF IN ,sessecorp MRK sesut eh gniwollof cigol ni gnitalupop:HTAP_CM fI citsinimreteD IN htiw elgniS tekraM etaD HTAP_CM ton detalupop eslE fI citsinimreteD IN htiw ELPITLUM tekraM ,etaD desab-emiT ssertS ,tseT ro citsahcotSIN fI noitaulaVteM doh = etnoM olraC )tnemyaperp( HTAP_CM detalupop YLNO rof eht esabesac eslE fItaunloaiV dohteM = laimonirTecitt aL HTAP_CMtondetalupop eslE HTAP_CM si detalupop rof llaoirane css 31-4  uSmmar yo fuOtputs laitnedifnoC &yrateirporP KRM Training Guide  For c astichstoMonte o Carlns simulatiothat are se tupas d ributedistscses,epro the g followincial spe snoitidnoc :ylppaiDstriubted Monte Carol aVR RVRT UO_n iT TUPUO DI detceleS toNt deceleS TUO_SVC TUO_SVC syawla detalupop TUO_SVC detalupop ylno fi neve( fi tone ldestc ni eht detceles ni eht tuptuO DI tuptuO )DI INCRT OU_in T OUTPU ID detceleS toNt deceleS TUO_VMV TUO_VMV syawla detalupop TUO_VMV detalupop ylno fi neve( fi tone ldestc ni eht detceles ni eht tuptuO DI tuptuO )DI Distributed Stochastic NI nI TUPTUO ,DI RVR TUO_ si detceles and TUC****IN …si detceleS toNt deceleS TUO_SVC TUO_SVC detalupop ylno fi TUO_SVC syawla detalupop detceles ni eht tuptuO DI neve( fi tone ldestc ni eht tuptuO )DI nI TUPTUO ,DI INCRT OU_is leected s and XT****INN …si detceleS toNt deceleS TUO_VMV TUO_VMV detalupop ylno fi TUO_VMV syawla detalupop detceles ni eht tuptuO DI neve( fi tone ldestc ni eht tuptuO )DI KRM Training Guide laitnedifnoC &yrateirporP uSmmar yo fuOtputs  5-13 . Process Tpyes and Outputs ehT tsil fo selbat deyalpsid ni ehtSELE TCLBATES csreen s i sedbaon n informatioin the OUUPTT_SELECT .elbat E hcau tptuo elbat sid ekram rof eht tnavelerKRM s essecprothat it is d ciateassowith:  RPIRAMY S :nocielte niamccal noitalu sepyt dereffo  SEOCNRADY S :noitcele ni noitidda ot tuptuoselbat available r fothe PRIMARY ction,sele ditionaladoutputs may be available nding depe nothe s setupefined dfor the Run ID o Ie ncluds Cutor g dginHefor V saluationo Ie ncluds,Cut ging Hedr or Transfe gcinprifor sts caForeo Ie ncludRver ollora nd/oNw eessB usinfor s castreFo I n eht gniwollof :elbat E hca nmuloc ni ehtRPIRAMY noitces stsil eht tuptuos elbat taht era elbaliava rof ehtc evitepser niamn culatiocaltypes.  Each ncolumin the SECONDARY ction ses listdiad lanoit tuptuo selbat taht tnpemelpus eno fo ehtRPIRAMY Example 1 : eht nmulocVM : DDAUCST setacidni taht ni noitidda ot tuptuos elbatc deifieps rof astdna lmron (.e, 1 n muloc niRPIRAMY )noitcesr o desserts (.e, 2 column niPRIMARY ction),se KR M canso alulate pop thee tablCU_OTUT if the n valuatiocress ops cludein a Example 2 :In noitidda ot ehtUCO_TU T morfE1elpmax, if thevaluation s sproceso ales cludni age HedID, then KRM can also eopulatpthe table HDG_OUT. 31-6  uSmmar yo fuOtputs laitnedifnoC &yrateirporP KRM Training Guide yramirPSeolitce ndn oycreaSSelc enotiOutput Table ACCR_INT X X X X X LOOP_OMC X X X KCOLB_TOMC X X X TUO_TOMC X X X CUT_OUT X X CVH_OUT X CVS_OUT X X TUO_TLFD X X TUO_PFD X X TUO_VMD X X X X X X TR_TSACEROF X X TUO_PTF X GL_RECON X X HDG_EFF_RATIO X HDG_OUT X X HDG_REGRESS X HDG_RESULT X INCR_OUT X X X HTAP_CM X X X X X DRPLLAIN X X NINBSCUT X DRPSBNIN X NINBSTXN X TUCLLRIN X NIRLLPRD X NXTLLRIN X TLUSER_LAB ER X NIRESCUT X DRPSERIN X X NIRESTXN X X NOINTOUT X X PDA_OUT X X X X X X X X KRM Training Guide laitnedifnoC &yrateirporP uSmmar yo fuOtputs  7-13oitau lnaV)VM( oita unlaV )VM(- ssertSTtse RAV - lacirotsiHRAV - etnoM olraCVAR – Matrix Facer ots )IN( -aBcis F tsacero )IN( -tS citsahco kaerBtsoC OAS refsnarTi rgPnic(F )PTMV: Add Cuts :VM ddAgdeHni gNI: Add Cuts :IN ddAgnigdeH :IN ddAFPT IN +F :PT ddAuC stNI: Add Rollover/NewBus (RN) NI + RN: Add Cuts IN +R :N ddAF PTNI + RN + FTP: Add Cuts yramirPSeolitce ndn oycreaSSelc enotiOutput Table ETAD_FR X X X TUO_VFR X X X SC NROUTC X SPREAD X TUO_CTS X TUO_XTS X TUO_XTMT X X DRPLLAPT X TPNBSCUT XTPNBSPRD X TPNBSTXN X TPRESCUT X TPRESPRD X TPRESTXN X TUCLLRPT X DRPLLRPT X NXTLLRPT X TUO_PFV X X TUO_VMV X X VRR_OUT X X X X TUO_TRV X X 31-8  uSmmar yo fuOtputs laitnedifnoC &yrateirporP KRM Training Guide oitau lnaV)VM( oita unlaV )VM(- ssertSTtse RAV - lacirotsiHRAV - etnoM olraCVAR – Matrix Facer ots )IN( -aBcis F tsacero )IN( -tS citsahco kaerBtsoC OAS refsnarTi rgPnic(F )PTMV: Add Cuts :VM ddAgdeHni gNI: Add Cuts :IN ddAgnigdeH :IN ddAFPT IN +F :PT ddAuC stNI: Add Rollover/NewBus (RN) NI + RN: Add Cuts IN +R :N ddAF PTNI + RN + FTP: Add Cuts . OTUTUP TSELBA Listed w elobis full set and ption descrir foall KRM put out tables: elbaTN emari spctioDneLABA1PMT yraropmeTlbat e rof cgnnailabotuARCCAI_N T deurccA tseretni ta ssceorp etadBMH_OU Tark mBenchcal storihi svalueBMR_OUTarmkBenchskriBMS_OU Tark mBenchrity secuvalue after scalingCMOO_POL d Detailepool n informatiofor d ritizecuseassets CMO_BLTOCK d Detaileche tranock blrmation infofor zed curitisets sesaCMO_OTU Td Detaileche tranon rmatiinfofor zed ritisecusets saRCRAPM MA laniF tiderC ksir ledom sretemarapRCRAPM2MA I laitin tiderC ksir ledom sretemarap yb ycnerrucCU_OTU TCut Nr(s) beum ciatedsosawith ction saTranID in PORT tableCVOH_U TCut Vs aluefor cal riHistoVAR VCSO_U T tuCVseu la rofM etno olraC & xirtaMVRA NBDO_DU T deviredsc eirp morf tiderCrisk model meter arapon istimateng usisky ribonds DFL_OTU TEstimated e tim(from ss creopate) dto a default event O_PFDU T serutuF ceirP morf a tekram noitaulavr ssecopVMDO_U Tn,terruC r,laciotsih dna sserts detsetcs noitanartV seulaSDO_PWU T deviredsc eirp morf tiderCksir ledom retemaarp noistamite gnisu dtierc sevitaviredERBCHMS TEr rrolog for Analytical s ltoo(smoot,gnih sikr rcoatfc /rolrov,noitamsiet )ERRSMLBT E rorr gol rofr yramip snoitaluclac tekram(,eulav VRA, .cte )OFRESACR_T T detsaceroFI xedn dna ycnerruCE egnahcxR setaO_PTFU TrefsnarTsetarLGR_EOCN Rn coitailinoce fo lareneg regdel otOPR T elbat secnalabE_GDH_RFFITAO R stluse fo egdeh ssenevitceffeHDG_OU TValuation danst caForesults reof g nhedgiand Basel II ting collateral/netR_GDHERGESS egdeHEvitceff sseneR noissergeE asetmitsRHDG_ESUL Tge Hedanice Complfor Rsk iR ctioneduNHVO_U T deviredsc eirp morf dnoBS gnihtoom naoitsmite gnisu yksir sdnobO_FPHU T lacirotsiHV ytiliatlo sevruCzard Ha lemodn regressios coefficient mfroCrdit erisk model eter rampan atioestimbased on HRZOD_U Td renifed-esu selbairav devireD ytilibaborp fo tluafed dnaa drdnats srorre morfd tierC ksir ledome retmarapn aoitmitseHZRRD_P Bbased on efined r-dseu sablevari drazaH ledomn oisserger scitsitats morfrCdit erisk odel meter rampaation estimbased on RZHSTD_ Td renifed-esu selbairavINRCO_U TI latnemercn noitubirtnoc fo hcae noitcasnart ot latotVRA HTAP_CM deliateD noitamrofni gnidrager hsac,swolf ,stnermyapep ,snecalab id tnuocs,srotcaf .cte MRNT_OU TK-value from dit Crerisk odel meter rampaation estimbased on the Merton delmoNIALLPRD ct-level Produs ctionprojee,ncom(i balance, sch a,wolf cte. ) rof eht latot oiloftrop NINBSCU TCut-level s ectionrojpe,mco(in cebalan, sch a,wolf .cte ) rof wensse nisubytic vaitNINBSPRD ct-level Produs ctionprojee,ncom(i balance, sch a,wolf cte. ) rof wensseunsbi yti cvaitNINSBNXT elev-loncistrTaannso icprtoeje,mc(oni aalbce,n sh caflow, etc.) for new ss neusibactivity NIRESCU TCut-level s ectionrojpe,mco(in balance, sh caflow, etc.) for the rrent cu npositioKRM Training Guide laitnedifnoC &yrateirporP uSmmar yo fuOtputs  9-13 elbaTN emari spctioDneNIRESPRD ct-level Produs ctionprojee,ncom(i balance, sch a,wolf cte. ) rof eht tnerruc nsooiptiNIRESNXT elev-loncistrTaannso icprtoeje,mc(oni abce,lan sh caflow, etc.) for the nt curre positionThis table res stothe s cebalanas well s aintere ts emocni U yletamitl siht si desuin The s ncebalaare in the base .ycnerruc esehT slatot era detcartxe morfNIRESUL TNIRESRPN/DINSBRPN/DIRLLPR Dselb at erehw eht siw c-eudotrpslat ot fosuoi ravsepy t fosh calances flows/bas i storedNIRLLCU TCut-level s ectionrojpe,mco(in balance, sch a,wolf .cte ) rof revollor cyativitNIRLLPRD ct-level Produs ctionprojee,ncom(i balance, sch a,wolf cte. ) rof revollor ytciavitNIRLLNXT elev-loncistrTaannso icprtoeje,mc(oni cnal,aeb sch a,wolf cte. ) rof revollor yctaivitsihT elbatssero t ehtscna labe rof ehtudorpcst htiwYT_TCCAEP nons ertetnicn ieom U yletamitl siht sid esu Thes cebalan arein the base NOINTOU TEach ion nsacttras ntributecoto -interest nonin emoc ot eht dnetxe fo eht % deificeps rof sticu dtorp ni ehtNOINIT MC elbatD_OPAU Tel ev-ilcotnsTaransh caws olf byh ascolwf deatRELABR_ESUL TN wesssueBRn/i reovlloyti vitcAs adbe nosno itidnoC sdecipefi niRELAB spuet selbatRFV_OU TValues rated enegby Monte o CarlVAR for all sk rictor fatypes pt ceexs FutureROC cy raaccuratio from Credit sk riel dmopareter maestimation sed abn oefined user-dROC_OU T svariableSCNROU CTC-uelte vsul tsrerofm k ertmaaluve ss rest sstetSO_CTU T level-tuC dirg tnioptisnes ytivi yek( etar noituards )siylanaSTX_OU Tevel -lctionsaTrangrid tnpois ytivitisne yek( etar noitaruds )siylanaSNTWO_U T deviredsc eirp morfeTrm Scture truon stimatieng usion opti scepriTMTX_OU TdD etailes tsulrefor nTraon sitiMatrix ss,cepro g ncludiinrating ges,chan s,ncieenqudeli etc. ct-level Produs ctipornojee,ncom(i ce,balan value, etc.) d baseon r transfes ratefor w neNPTSBRP Dsessn uibyt ivciat elev-loncistrTaannso icprtoeje,mc(oni caen,alb ,value etc.) based on sfer trantes rafor w neNPTSBNXT sessn uibyt ivciatct-level Produs ctionprojee,ncom(i ce,balan value, etc.) d baseon r transfes ratefor the currentRPTESRP D npositio elev-loncistrTaannso icprtoeje,mc(oni caen,alb ,value etc.) based on sfer trantes rafor the RPTESNXT ntrceur oniptosict-level Produs ctionprojee,ncom(i ce,balan value, etc.) d baseon r transfes ratefor over rollTPRLLRPD yticvait elev-loncistrTaannso icprtoeje,mc(oni caen,alb ,value etc.) based on sfer trantes rafor r overollRPTLLNXT yticvaitTSMPAR AMEstimated ha alpand sigmafor term cture strus odelmVFP_OU Ts Future cePriution Distrib-Matrix and onte Mrlo CaVRA VMV_OU Tction saTrans aluevfor every VAR ario scenor sk rictor faBeta, volatility, ation correlfrom Credit sk riodel mrameter paation stimesed ban othe Merton VOL_OU T modelVRRO_U TV eula taR ksi ta tuc levelVRT_OU TValue at Risk for each ctionsatranYRP_CO B aevitlumuC noitubirtsid fo orezs dleiy rof suoirav dleiy evruco srnet YIEL DS dehtoom dleiy sevruc 0 31-1  uSmmar yo fuOtputs laitnedifnoC &yrateirporP KRM Training Guide