Abstract
This paper describes a model for calculating the expected losses as well as economic capital required to support an arbitrary portfolio of credit exposures. It does so by explicitly modelling both the marginal and absolute conditional loss distributions for any arbitrary portfolio of credit exposures; these portfolio loss distributions can be made conditional on the current state of the economy given the counterparty’s country, industry and rating. The conditioning relationships between the probability of a credit event (e.g. credit rating migrations or defaults) and the current state of the economic cycle are based on empirical regularities observed in historical data. This model differs from other credit portfolio models in several important aspects:
-
First, it models the actual, discrete loss distribution, dependent upon the number and size of credits, as opposed to using a normal distribution or meanvariance approximations; this allows the model to explicitly tabulate a ‘large exposure premium’ in terms of risk adjusted capital for less diversified portfolios.
-
Second, the losses (or gains) are measured on a marked-to-market basis for credit exposures which cannot be liquidated (e.g. most loans or OTC trading exposure lines) as well as those which can be liquidated prior to the maximum maturity of the exposure; these loss distributions can therefore be tabulated for any time horizon, including one which coincides with an organisations planning and budgeting process
-
Third, the tabulated loss distributions are conditional on the current state of the economy rather than being based on the unconditional or 20 year averages which do not reflect the portfolio’s true current risk.
-
Finally, a multi-factor model of systematic default risk, as opposed to a single factor model based on asset volatilities and CAPM or public rating histories, is explicitly estimated based on empirically observed ‘regional-and sectoral-betas’, allowing the model to mimic the actual default correlations between industries and regions at the transaction as well as portfolio level.2
Thomas C. Wilson is a Partner in the Zürich office of McKinsey & Company and specialises in serving financial institutions in the areas of trading and credit risk management strategies, organisation and risk measurement. Please direct any comments to Thomas C. Wilson, McKinsey & Company
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Altmann, Edward, 1983, Aggregate Influences on Business Failure Rates, in Corporate Financial Distress: A Complete Guide to Predicting, Avoiding and Dealing with Bankruptcy, John Wiley & Sons
DPG or Derivatives Policy Group, 1995, Voluntary Oversight of OTC Derivatives
Black, F. and M. Scholes, 1973, The Pricing of Options and Corporate Liabilities, Journal of Political Economy, May–June
Cumming, C., and K. Saini, 1981, The Macroeconomic Determinants of Corporate Bankruptcies in Japan and the UK., Federal Reserve Bank of New York, Research Paper, December
Gollinger, T. L., and J. B. Morgan, 1993, Calculation of an Efficient Frontier for a Commercial Loan Portfolio, Journal of Portfolio Management, Winter, Volume 19, Number 2
G30 or Group of Thirty, 1993, Derivatives: Practices and Principles, Global Derivatives Study Group, Washington DC, July
Hull, J. and A. White, 1991, The Impact of Default Risk on Option Prices, Working Paper, University of Toronto
Jarrow, R. A. and S. M. Turnbull, 1995, Pricing Options on Derivative Securities Subject to Credit Risk, Journal of Finance 50(1)
Kealhofer, Stephen, 1995a, Managing Default Risk in Portfolios of Derivatives, in Derivative Credit Risk: Advances in Measurement and Management, Risk Publications, London, 1995
Kealhofer, Stephen, 1995b, Portfolio Management of Default Risk, proprietary documentation, KMV Corporation, San Francisco
Langstaff, F. and E. Schwartz, 1992, Valuing Risky Debt: A New Approach, Working Paper, UCLA
Lawrence, D. 1995, Aggregating Credit Exposures: The Simulation Approach, in Derivative Credit Risk: Advances in Measurement and Management, Risk Publications, London
Moody’s Investor Service, 1994, Corporate Bond Defaults and Default Rates, 1970-1993, Moody’s Investor Service
Noto, N. A. and D. Zimmerman, 1981, A Comparison of Failure and Liability Trends and Implications for Business Assistance, Report No. 81-36E, Congressional Research Service, January
Rowe, D., 1995, Aggregating Credit Exposures: The Primary Risk Source Approach, in Derivative Credit Risk: Advances in Measurement and Management, Risk Publications, London
Thomas, L. C., Crook, J. N., and D. B. Edelman, 1992, Credit Scoring and Credit Control, Clarendon Press, Oxford
Wilson, T., 1996, Calculating Risk Capital, in Handbook of Risk Management edited by C. Alexander, J. Wiley & Sons
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wilson, T.C. (1998). Measuring and Managing Credit Portfolio Risk. In: Bol, G., Nakhaeizadeh, G., Vollmer, KH. (eds) Risk Measurement, Econometrics and Neural Networks. Contributions to Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-58272-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-58272-1_14
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1152-0
Online ISBN: 978-3-642-58272-1
eBook Packages: Springer Book Archive