Entropy measure of credit risk in highly correlated markets

Gottschalk, Sylvia ORCID logoORCID: https://orcid.org/0000-0002-8629-7209 (2017) Entropy measure of credit risk in highly correlated markets. Physica A: Statistical Mechanics and its Applications, 478 . pp. 11-19. ISSN 0378-4371 [Article] (doi:10.1016/j.physa.2017.02.083)


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.

Item Type: Article
Research Areas: A. > Business School > Accounting and Finance
A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 21547
Useful Links:
Depositing User: Sylvia Gottschalk
Date Deposited: 22 Mar 2017 16:34
Last Modified: 15 Sep 2020 17:02
URI: https://eprints.mdx.ac.uk/id/eprint/21547

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