Title: An equity-credit hybrid model for asset correlations

Authors: Fabio S. Dias

Addresses: Department of Statistics, University College London, UK

Abstract: Single factor Gaussian copula models are widely used to manage credit risk of loan portfolios, even driving how many large financial institutions are capitalised under Basel II/III. Under this formulation, the default correlation between two separate firms is directly explained by their asset correlation to a systematic factor, which can be estimated using either equity correlations or observed default rates, with the portfolio losses usually being simulated under a Gaussian copula model. Though it is widely accepted that the use of observed default rates or even equity returns to calibrate a single factor Gaussian copula model is likely to understate the tail risk. This paper proposes a Bayesian approach for a single factor Gaussian copula where the asset correlations are modelled using an inverse Wishart prior with the scale parameter calibrated to observed default rates and the degrees of freedom chosen using the in-sample continuous ranked probability score whilst the equity correlations are used to obtain the posterior distribution. The proposed hybrid model is shown to produce probabilistic forecasts of defaults with better out-of-sample performance than the standard single factor Gaussian copula even though it maintained low complexity and ease of implementation.

Keywords: asset correlations; credit risk management; structural credit model; factor copula models.

DOI: 10.1504/IJFERM.2020.107667

International Journal of Financial Engineering and Risk Management, 2020 Vol.3 No.3, pp.223 - 239

Accepted: 12 Jul 2018
Published online: 08 Jun 2020 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article