Entropy measure of credit risk in highly correlated markets

Sylvia Gottschalk

Physica A: Statistical Mechanics and its Applications, 2017, vol. 478, issue C, 11-19

Abstract: We compare the single and multi-factor structural models of corporate default by calculating the Jeffreys–Kullback–Leibler divergence between their predicted default probabilities when asset correlations are either high or low. Single-factor structural models assume that the stochastic process driving the value of a firm is independent of that of other companies. A multi-factor structural model, on the contrary, is built on the assumption that a single firm’s value follows a stochastic process correlated with that of other companies. Our main results show that the divergence between the two models increases in highly correlated, volatile, and large markets, but that it is closer to zero in small markets, when asset correlations are low and firms are highly leveraged. These findings suggest that during periods of financial instability, when asset volatility and correlations increase, one of the models misreports actual default risk.

Keywords: Structural models of default risk; Single and multi-factor models; Asset correlation; Entropy; Jeffreys–Kullback–Leibler divergence; Random correlation matrices; Financial regulation (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:478:y:2017:i:c:p:11-19

DOI: 10.1016/j.physa.2017.02.083

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