Credit risk: Taking fluctuating asset correlations into account

TL;DR

This paper introduces an ensemble approach to model fluctuating asset correlations in credit risk, improving the empirical fit of asset value distributions and enhancing loss distribution estimates.

Contribution

It proposes a novel ensemble method that accounts for fluctuating asset correlations with only two parameters, maintaining analytical tractability and better matching empirical data.

Findings

The model accurately describes empirical asset value distributions.

Fluctuating correlations significantly impact credit loss estimates.

Monte Carlo simulations validate the approach's effectiveness.

Abstract

In structural credit risk models, default events and the ensuing losses are both derived from the asset values at maturity. Hence it is of utmost importance to choose a distribution for these asset values which is in accordance with empirical data. At the same time, it is desirable to still preserve some analytical tractability. We achieve both goals by putting forward an ensemble approach for the asset correlations. Consistently with the data, we view them as fluctuating quantities, for which we may choose the average correlation as homogeneous. Thereby we can reduce the number of parameters to two, the average correlation between assets and the strength of the fluctuations around this average value. Yet, the resulting asset value distribution describes the empirical data well. This allows us to derive the distribution of credit portfolio losses. With Monte-Carlo simulations for the Value at Risk and Expected Tail Loss we validate the assumptions of our approach and demonstrate the necessity of taking fluctuating correlations into account.