pricing credit thousands names continue discussion tuesday conference call discussed ben issues thoughts complete approach starting point discussion main task build pricing system many names two components 1 price single name 1 1 price liquid single name 1 2 price illiquid single name 2 efficiently apply methodology multiple names approach would take 1 1 define small set liquid names b apply different models say six models ben mentioned names c include market prices names sit traders get trader intuition liquid name price note spectrum prices obtained b c e try determine attributes names may explain dispersion trader prices across models f quantify attributes possible g try different set liquid names repeat process see decisions last round still make sense approach 1 2 may define small set illiquid names b apply different models names c sit traders get trader intuition illiquid name price note spectrum prices obtained b try determine attributes names may explain dispersion trader prices across models e check similar attributes identified liquid names f define master set liquid names g look relationships analyzing cross section data attributes prices illiquid names liquid names mapping defined illiquid name set liquid names attributes mapping entered table pricing automated names theory success depend success round table sessions approaches 1 1 1 2 building new fundamental model always worthwhile task get going approaches immediately parallel developing new models may build new models added suite existing models believe ever single model answer questions names rather refine mappings relative choices among models time would mean continuing round table sessions traders limited data makes calibration hard would continually ask question calibrate throughout discussions 1 1 1 2 may help guide us new models vasant benjamin parsons 06 19 2000 04 11 ect london credit trading cc vince j kaminski hou ect ect vasant shanbhogue hou ect ect amitava dhar corp enron enron steven leppard lon ect ect grant masson hou ect ect dale surbey lon ect ect david wall risk mgmt lon ect ect jitendra j patel lon ect ect oliver gaylard lon ect ect subject pricing credit thousands names challenge next months build automated system provide differential pricing thousands credits 5 000 year end credits illiquid terms market price information making challenge harder end result important terms competitive pricing advantage need overall strategy plan achieve quantitative perspective currently several models credit pricing either use development fmc model default probability approach using bloomberg fair market par yield curves probabilities generated risky libor default bankruptcy swap prices computed using expectation methodology fmc model credit spread approach using fmcs directly taking libor credit spread tenor adjusting basis compounding differences bond model fmc approach taking fmcs benchmark curves model regresses input bonds specific name two best fitting benchmarks result zero yield curve shape fmcs level tweaked specific issuer prices generated using spread probability approaches testing bond model spline approach taking bonds specific issuer model fits exponential cubic spline zero coupon price curve builds zero yield curve testing market prices certain liquid names sectors ratings cds market prices used recovery event discount used get bankruptcy swap prices kmv using expected default frequencies edfs kmv model database build model price default swaps making appropriate risk adjustments kmv installed model worked next models returns price credit default bankruptcy accuracy price depends many factors liquidity regulatory differences bond cds markets recovery assumptions risk premia capital charges etc aim accurately price many liquid names possible based upon models use prices alongside financial information backbone full automated pricing system inputs proposed pricing system specific name model market prices issuers alongside name specific soft data credit reports financial statements credit liquid enough price generated information otherwise credit mapped onto subset liquid credits financial information historical price movements providing mapping weights model price periodically adjusted align market trader prices adjustment feed back weighting mapping composition loose terms could think system price illiquid credit weighted average liquid market prices bonds equities default swaps weightings calibrated using credit analysis financial ratios etc key steps implementing system establishing exactly want predict price rating probability score need clean market history calibrate really ratings need develop mapping rating score price getting cleaning historical financial credit data required calibrate model building mechanics model ie calibration methodology neural nets fuzzy logic seem obvious candidates exact methods software packages use determining automated methodology mapping names limited information model getting true market price order feed back error present price exists credits allocating resources development mckinsey claimed system would take 6 10 man months develop ideas comments requested need develop strategy asap model description fairly vague yet knowledge needed fill specific details help especially required continue move internet speed regards ben