It has long been recognized that banking is a procyclical business. That is, banks tend to contract their lending activity when business turns down because of their concern about loan quality and repayment probability. This exacerbates the economic downturn as credit constrained businesses and individuals cut back on their real investment activity. In contrast, banks expand their lending activity during boom periods, thereby contributing to a possible overheating of the economy that may transform an economic expansion into an inflationary spiral.
The proliferation of credit risk measurement models in banking may accentuate the procyclical tendencies of banking, with potential macroeconomic consequences. That is, the models’ overly optimistic estimates of default risk during boom times reinforces the natural tendency of banks to overlend just at the point in the business cycle that the central bank prefers restraint.
Moreover, if credit risk models are unduly pessimistic during recessions, then even the most expansionary monetary policy may not encourage banks to lend to obligors that are perceived to be poor credit risks. Recent Basel Committee proposals to utilize credit risk models such as CreditMetrics as a basis for bank capital requirements may further accentuate the procyclical nature of banking unless the credit cycle and its effect on credit risk are appropriately recognized in the model structure. If banks are constrained by risk sensitive (as measured by internal models) capital allocations and regulatory requirements, they may be unable to lend during low points in the business cycle and overly encouraged to lend during boom periods.
This is because risk sensitive capital requirements (eg, RAROC-based) increase (decrease) when estimates of default risk increase (decrease). Thus, if credit risk models overstate (understate) default risk in bad (good) times, then internal bank capital requirements will be too high (low) in bad (good) times, thereby forcing capital-constrained banks to retrench on lending during recessions and expand lending during booms. As stated by Andrew Crockett, the General Manager of the BIS, in a lecture on February 13, 2001: “[U]nderlying risk builds up as expansion and leverage continues, while apparent risk declines, with the rise in collateral values….[r]isks and imbalances have actually accumulated in the later stages of an upswing, only to materialise in the ensuing recession.” Concern about the macroeconomic implications of the procyclical nature of risk sensitive bank capital regulations has contributed to some modifications to the Basel Committee proposals for the new Basel Capital Accord.
In this paper, we examine the treatment of cyclical factors in both academic and proprietary credit risk measurement models.6 In Section 2, we begin by discussing what is meant by procyclicality. We then divide our survey of credit risk measurement models into four sections. Section 3 surveys how various credit risk measurement models incorporate cyclical effects into the estimation of default probability (PD). In Section 4, we describe models that examine the recovery rate (or one minus the recovery rate, the loss given default LGD) as a function of macroeconomic factors. In Section 5, we examine the correlation between PD and LGD. The procyclical flight to quality and the impact of systematic factors on the exposure at default (EAD) are examined in Section 6 and the paper concludes in Section 7.
contents
1. Introduction
2. What is procyclicality?
3. Cyclical effects on the probability of default (PD)
- 3.1 Academic models
- 3.1.1 Structural models of cyclical effects on PD
3.1.2 Reduced form models of cyclical effects on PD
3.2 Proprietary models
3.3 The New Basel Capital Accord
4. Cyclical effects on loss given default (LGD)
- 4.1 Academic models
- 4.1.1 Structural models of cyclical effects on LGD
4.1.2 Reduced form models of cyclical effects on LGD
4.2 Proprietary models
5. The correlation between PD and LGD
- 5.1 Academic models
6. Cyclical effects on exposure at default (EAD)
- 6.1 Structural models of cyclical effects on EAD
6.2 Reduced form models of cyclical effects on EAD
6.3 Integrating credit risk and market risk
7. Conclusion
References
Conference programme: “Changes in risk through time: measurement and policy options”
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