January 27, 2023

Quantitative Finance Conference on Credit Risk
Friday November 4 & Saturday November 5, 2005

University of Western Ontario


Supported by
Tom Hurd, McMaster University
The Affine Markov Chain Model for Credit Risk
The AMC model of credit risk is a natural union of the structural and intensity based frameworks which combines credit ratings migration and default with stochastic interest rates and recovery rates. It has very nice properties: when applied to one or two firms, closed formulas can be derived for default probabilities, bond prices, and even credit default swaps, resulting in very efficient computations. After describing this new approach, I will move on to look at basket derivatives on large numbers of firms, specifically collateralized debt obligations (CDOs). The market for such products is now hot, but current CDO pricing technology does not rest on a coherent consistent model. I will describe how these products can be priced and hedged very flexibly and efficiently in the AMC modelling framework.

Joint work with Alexey Kuznetsov

Michael Gordy, US Federal Reserve
Model Foundations for the Treatment of CDOs in Basell II
In its proposal for a New Basel Accord, the Basel Committee on Bank Supervision (2004, Part 2.IV) offers two methodologies for assigning regulatory capital charges to securitization exposures under the Internal Ratings-Based (IRB) approach. The Ratings-Based Approach sets capital primarily as a function of an external rating, such as might be assigned by Moody's or S&P, and is to be employed whenever an external rating is available. As many securitization exposures are not externally rated, the alternative Supervisory Formula Approach (SFA) determines required capital as a function of the characteristics of the collateral pool and contractual properties of the tranche. The chapter sets forth the theoretical foundation for the SFA provided by the "uncertainty in loss prioritization" (ULP) model of Gordy and Jones (2003).

Greg M. Gupton, Sr. Director of Research, Moody’s KMV
Advancing Loss Given Default Prediction Models, Modeling framework, fitting, and validation
We describe LossCalc™ version 2: the Moody's KMV model to predict loss given default (LGD): the equivalent of (1 - recovery rate). LossCalc is a statistical model that applies multiple predictive factors at different information levels: collateral, instrument, firm, industry, country, and the macroeconomy to predict LGD. We find that Distance-to-Default measures (from the Moody’s KMV structual model of default likelihood) compiled at both the industry and firm levels are predictive of LGD. We find that recovery rates world-wide are predictable within a common statistical framework which suggests that estimates of economic firm value (which is then available to allocate to claimants according to each coutry’s bankruptcy laws) is a dominant step in LGD determination. LossCalc is built on a global dataset of 3,026 recovery observations for loans, bonds, and preferred stock from 1981-2004. This dataset includes 1,424 defaults of both public and private firms; both rated and unrate instruments; in all industries. We demonstrate out-of-sample and out-of-time LGD model validation. The model significantly improves on the use of historical recovery averages to predict LGD.

Niall Whelan, Scotia Capital
Quantifying Counterparty Credit Risk
Counterparty credit risk is extremely topical in light of the new Basel accords on capital adequacy. Minimising capital requirements associated with derivatives activities requires simulating counterparty portfolios. This can be very difficult since large financial institutions can have thousands of counterparties, collectively containing tens of thousands of transactions and involving dozens of distinct market risk factors. This is a large software engineering exercise. In this talk I will present ideas for making this manageable; they stress adaptability and flexibility while avoiding large-scale Monte-Carlo simulations. Time permitting, I will discuss other risk management applications, some of which involve the two-sided nature of credit risk.

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