Choosing Factors and Complexity in Credit Risk Models
Summary
The document discusses moving from a single-factor credit risk model, in which one latent factor represents the economy, to models with multiple risk drivers. Suggested factors depend on the portfolio and available data. For corporate portfolios, sector, rating, and maturity are mentioned; for asset-backed or consumer exposures, credit scores, local employment, house prices, and loan-to-value ratios may help explain borrowers’ ability to pay.
The proposed benefit of adding factors is to explain more variation and improve confidence in predictions, but the discussion does not claim that expected loss, unexpected loss, or economic capital will necessarily rise or fall. Instead, it emphasizes fitting the model to the exposure and evidence, avoiding factors chosen only because they are correlated, and favoring parsimony. Model selection and calibration depend on historical data; information criteria such as AIC, BIC, and DIC are offered as tools for comparing single- and multifactor specifications. A t-Student multifactor copula and Monte Carlo framework are cited as one example, not as a universal choice.
Key ideas
- Factor selection should reflect the portfolio type and the data available.
- Corporate credit models commonly consider sector, rating, and maturity exposures.
- Adding factors aims to explain more variation, but does not guarantee lower or higher risk estimates.
- Use parsimony and avoid adding variables solely because they are correlated.
- Historical data and model selection criteria can inform whether a multifactor model is justified.
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Full text
# Multi Factor Credit Risk Models # Multi Factor Credit Risk Models I am working in the area of building credit risk models. Upto this point, the model I have been focused on using the Asymptotic Single Factor Model, more popularly known as Vasicek Single Factor Model. The single factor being representative of the state of the economy. Now I want to evaluate a more elaborate multiple factor model as used in KMV etc. I am looking specifically for guidance on the following - Are their any fundamental papers/references on multi factor credit risk models (A very popular one seems to be one by Michael Pykhtin available here And also one by algorithmics available here.) - What should the multiple factors be - geographical location/industry/sector etc.? - What would be the expected benefit of multiple factors over a single factor model? Would the Expected Loss, Unexpected Loss and ECAP be expected to be lower/higher compared to a single factor model? Looking for academicians, industry practitioners, modeling experts for any guidance. Thanks in advance ## Answer by jeff m (score 5, accepted) https://quant.stackexchange.com/a/4341 Most of the credit risk models are some derivative of survival models. Cox Proportional Hazard is one of the early and more popular models, Kaplan-Meier and Logrank tests are others you may have heard of. There are a few ways to go from here. The simplest is to model the sample as binomial with one population as current and the other as in default. A more complex method would be along the lines of an actuarial approach that takes levels of ability to pay into account. A good analogy is levels of sickness to predict potential insurance payouts. As for what factors used to model, I can't make any suggestions unless I know exactly what asset it is your modeling and what kind of data you have access to - consumer loans is a rather broad description. I'm assuming you're dealing with some type of ABS. If so, then ultimately all you care about is ability to make coupon payments. Credit scores, MSA employment, HPI, LTV, etc. could be of interest - but without more info I can't help much past that. The goal of a multiple factor model is similar to a single factor, you're just trying to build more confidence in your prediction by explaining more of the variance. Strive for parsimony and don't blindly build on correlations. ## Answer by Patrick (score 1) https://quant.stackexchange.com/a/4365 For corporate credit portfolios, sector, rating, and maturity are the usual suspects that go into the credit portion of risk model structure, which usually also have interest rates and liquidity pieces. This book is slightly outdated but will give you a good general introduction. ## Answer by Brian Adkins (score 0) https://quant.stackexchange.com/a/7908 This presentation may be of interest to you... https://support.precisionlender.com/entries/22591983-How-does-the-math-work- Full disclosure: this is my employer, but I think it's relevant to the discussion ## Answer by Gerard Torrent (score 0) https://quant.stackexchange.com/a/10456 I suggest you to take a look to the CCruncher project, an open-source project for credit risk modeling. It is a framework consisting of two elements: a technical document (introducing the t-Student multi-factor copula model, parameters estimation, etc.), and a software program that implements the Monte Carlo procedure. Currently, CCruncher considers that factors are industry sectors. It allows the risk disaggregation by region, location, types of obligors, etc. The discussion 'single factor vs multi-factor' can be responded in the parameter calibration stage and depends on the available historical data and AIC/BIC/DIC values.
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