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Macroeconomic Dynamics and Credit Risk: A Global Perspective

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  • M. Hashem Pesaran
  • Til Schuermann
  • Björn-Jakob Treutler
  • Scott M. Weiner
  • April

Abstract

The aim of this paper is to develop a framework for modeling conditional loss distributions through the introduction of risk factor dynamics. Asset value changes of a credit portfolio are linked to a dynamic global macroeconometric model, allowing macro effects to be isolated from idiosyncratic shocks from the perspective of default (and hence loss). Default probabilities are driven primarily by how firms are tied to business cycles, both domestic and foreign, and how business cycles are linked across countries. The model is able to control for firm-specific heterogeneity in an explicitly interdependent global context, as well as to generate multi-period forecasts of the entire loss distribution, conditional on specific macroeconomic scenarios. The approach can be used, for example, to compute the effects of a hypothetical negative equity price shock in South East Asia on the loss distribution of a credit portfolio with global exposures over one or more quarters. Our conditional modeling framework is thus a step towards joint consideration of market and credit risk. The approach has several other features of particular relevance for risk managers, such as the exploration of scale and symmetry of shocks, and the effect of non-normality on credit risk. We show that the effects of such shocks on losses are asymmetric and non-proportional, reflecting the highly non-linear nature of the credit risk model. Non-normal innovations such as Student t generate expected and unexpected losses which increase the fatter the tails of the innovations.

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Bibliographic Info

Paper provided by Wharton School Center for Financial Institutions, University of Pennsylvania in its series Center for Financial Institutions Working Papers with number 03-13.

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Handle: RePEc:wop:pennin:03-13

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Keywords: Risk management; economic interlinkages; loss forecasting; default correlation;

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