Risk management in general and credit risk analysis in particular has been the focus of extensive research in the past several years. Credit risk is the dominant source of risk for banks and the subject of strict regulatory oversight and policy debate (BIS (2001a,b)). Most recently, the proposal by the Bank for International Settlements (BIS) to reform the regulation of bank capital for credit risk (known as the New Basel Accord, or BIS 2) has sparked an intense debate in the literature (inter alia, Jones and Mingo (1998), Altman, Bharath and Saunders (2002)). One strand of this debate centers on the effect of business cycles and especially of severe economic downturns on bank risk and value-at-risk capital requirements (Carpenter, Whitesell and Zakrajšek (2001), Carey (2002), Allen and Saunders (2002)). However, this debate has been taking place largely without the benefit of an explicit model linking the loss distribution of a bank’s credit portfolio to the evolution of macroeconomic factors at national and global levels. Given the increasing interdependencies in the global economy, risk managers of commercial or central banks alike may well be interested in questions like “What would be the impact on the credit loss distribution of a given bank (or banks) in a given region if there were large unfavorable shocks to equity prices, GDP or interest rates in that or other regions?” Such questions can be answered with our model.
Our aim is to develop a conditional modeling framework for credit risk analysis which establishes an explicit linkage between a portfolio of credit assets and the underlying international macroeconomic system. The model is able to distinguish between default (and loss) due to systematic versus idiosyncratic (or firm specific) shocks, providing an explicit channel for and model of default correlation. This enables us to conduct policy analysis on the effect of changes in macroeconomic risk factors on credit risk. Our approach is thus a step towards joint consideration of market and credit risk.
Credit risk modeling can be broken down along several dimensions. One split is between asset-or firm-based versus portfolio approaches. Broadly, there are two important variables describing asset/firm level credit risk: the probability of default (PD) and the loss given default (LGD). Most of the work on PD and LGD has been done without explicit conditioning on business cycle variables; exceptions include Carey (1998), Frye (2000) and Altman, Brady, Resti and Sironi (2002).
These studies find, perhaps not surprisingly, that losses are indeed worse in recessions. Tapping into information contained in equity returns (as opposed to credit spreads from debt instruments), Vassalou and Xing (2002) show that default risk varies with the business cycle. Allen and Saunders (2002) survey academic and practitioner models of credit risk with a specific focus on the treatment of systematic or cyclical effects. They find that although many models consider the correlation between default (PD) and systematic (e.g. macroeconomic) factors, few extend this dependence to LGD.
Most credit portfolio models link the portfolio loss distribution to states of the world which provides the channel for default correlations. However, with only one exception this linkage is to a single, unobserved systematic risk factor. That is the case for adaptations of the options based approach à la Merton (1974) found in credit portfolio models such as Gupton, Finger and Bhatia’s (1997) Credit Metrics, KMV’s Portfolio Manager, as well as in the actuarial approach employed by CSFB’s Credit Risk (Credit Suisse First Boston (1997)) where the key risk driver is the variable mean default rate in the economy. Wilson’s (1997a,b) model (Credit Portfolio View) is an exception. He allows for the macroeconomic variables to influence a firm’s probability of default using a pooled logit specification. However, because the defaults are grouped, typically by industry, and modeled at the (single country) national level, any firm-specific heterogeneity is lost in the estimation.
Business cycle fluctuations can have a major impact on credit portfolio loss distributions. Carey (2002), using re-sampling techniques, shows that mean losses during a recession such as 1990/91 in the U.S. are about the same as losses in the 0.5% tail during an expansion. Bangia et al. (2002), using a regime switching approach, find that capital held by banks over a one-year horizon needs to be 25-30% higher in a recession that in an expansion.
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Macroeconomic Dynamics and Credit Risk: A Global Perspective
