re : pricing credit on thousands of names we can continue the discussion in tuesday ' s conference call , but i discussed with ben about the issues below , and here are some thoughts . this is not a complete approach , but only a starting point for discussion . the main task is to build a pricing system for many names . this has two components - - - 1 ) how to price a single name ? 1 . 1 ) how to price a liquid single name ? 1 . 2 ) how to price an illiquid single name ? 2 ) how to efficiently apply the methodology to multiple names ? the approach i would take for 1 . 1 is a ) define a small set of liquid names b ) apply each of the different models we have , say , the six models ben has mentioned below , to these names c ) include market prices , if any , for these names d ) sit with traders , get trader ' s intuition on where each liquid name should price and note this on the spectrum of prices obtained in ( b ) and ( c ) e ) try to determine attributes of the names that may explain the dispersion of the trader prices across the models f ) quantify these attributes , if possible g ) try a different set of liquid names and repeat the process , and see if the decisions in the last round still make sense the approach for 1 . 2 may be a ) define a small set of illiquid names b ) apply each of the different models we have to these names c ) sit with traders , get trader ' s intuition on where each illiquid name should price and note this on the spectrum of prices obtained in ( b ) d ) try to determine attributes of the names that may explain the dispersion of the trader prices across the models e ) check if these are similar to the attributes identified for liquid names f ) define a master set of liquid names g ) look for relationships ( by analyzing cross - section of data ) between attributes or prices of illiquid names to those of liquid names once a mapping has been defined for an illiquid name to a set of liquid names and their attributes , then this mapping can be entered into a table , and the pricing can be automated for all names ( in theory ) ! the success will depend on the success of the round - table sessions for the approaches for 1 . 1 and 1 . 2 . building a new fundamental model is always a worthwhile task , but we can get going with the above approaches immediately in parallel with developing any new models that we may build . new models can be added to the suite of existing models . i do not believe there will ever be a single model that will answer all questions for all names , but rather we can refine the mappings and relative choices among models over time , which would mean continuing round - table sessions with traders . limited data makes calibration very hard , so i would continually ask the question " what do we calibrate ? " throughout the discussions for 1 . 1 and 1 . 2 , and this may help guide us to new models . vasant benjamin parsons 06 / 19 / 2000 04 : 11 am to : 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 a wall / risk mgmt / lon / ect @ ect , jitendra j patel / lon / ect @ ect , oliver gaylard / lon / ect @ ect subject : pricing credit on thousands of names all - our challenge for the next few months is to build an automated system to provide differential pricing on thousands of credits [ 5 , 000 by year - end ] . most of these credits will be illiquid in terms of market price information , making the challenge harder , and the end result more important in terms of competitive pricing advantage . what we need is an overall strategy for how we plan to achieve this from the quantitative perspective . currently we have several models for credit pricing either in use or under development : fmc model ( default probability approach ) . using bloomberg ' s fair market ( par yield ) curves , probabilities are generated from the risky - libor , then default / bankruptcy swap prices computed using expectation methodology . fmc model ( credit spread approach ) . using the fmcs , then directly taking the libor credit spread at each tenor , adjusting for basis and compounding differences . bond model ( fmc approach ) . taking the fmcs as benchmark curves , the model regresses the input bonds ( specific to a name ) on the two best fitting benchmarks . the result is a zero yield curve with the same shape as the fmcs , but with the level tweaked for the specific issuer . prices are then generated using both spread and probability approaches . under testing . bond model ( spline approach ) . taking only the bonds specific to an issuer , the model fits an exponential cubic spline to the zero - coupon price curve , then builds a zero yield curve from this . under testing . market prices . for certain liquid names , or sectors / ratings , cds market prices are used , then recovery and event discount used to get bankruptcy swap prices . kmv . using expected default frequencies ( edfs ) from the kmv model and database , we will build a model to price default swaps , making appropriate risk adjustments . kmv is being installed now , so model will be worked on next . each of these models returns a price ( credit default and bankruptcy ) , and the accuracy of the price depends on many factors - liquidity and regulatory differences between bond and cds markets , recovery assumptions , risk premia , capital charges , etc . the aim will be to accurately price as many liquid names as possible , based upon these models , then use these prices , alongside other financial information , as the backbone to a full automated pricing system . our inputs to the proposed pricing system for a specific name are model and market prices for all issuers , alongside name - specific ' soft ' data from credit reports and financial statements . if the credit is liquid enough , a price will be generated from their own information only . otherwise , the credit will be mapped onto a subset of liquid credits , with financial information and historical price movements providing the mapping and weights . the model price will then be periodically adjusted to align itself with market ( or trader ) prices , and this adjustment will feed back into the weighting and mapping composition . in loose terms , we could think of the system price for an illiquid credit as being a weighted average of liquid market prices ( bonds , equities , default swaps ) , where the weightings are calibrated using credit analysis , financial ratios , etc . the key steps to implementing such a system will be : establishing what exactly we want to ' predict ' - is it a price , a rating , a probability , or a score ? we will need a clean market history to calibrate to , which we only really have for ratings . we will then need to develop a mapping from rating / score to price . getting and cleaning the historical financial and credit data required to calibrate the model . building the mechanics of the model , ie , the calibration methodology . neural nets / fuzzy logic seem the obvious candidates , but which exact methods and software packages to use ? determining an automated methodology for mapping names with limited information into the model . getting the " true " market price , in order to feed back an error . at present such a price exists for very few credits . allocating resources to the development . mckinsey claimed such a system would take 6 - 10 man - months to develop . further ideas or comments are requested , as we need to develop our strategy asap . the model description above is fairly vague , as we don ' t yet have the knowledge needed to fill in the specific details . further help will be especially required on this if we are to continue to move at ' internet speed ' . regards ben