A generalised latent Poisson factor modelling approach for default correlations in credit portfolios

Mohamed Saidane
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Mohamed Saidane: Associate Professor, College of Business and Economics, Qassim University, Kingdom, Saudi Arabia

Journal of Risk Management in Financial Institutions, 2023, vol. 17, issue 1, 89-105

Abstract: Default risk is one of the major concerns for lending institutions and banking regulators. This paper focuses on the analysis of default data, using a new approach based on generalised latent Poisson factor models. In this case, the correlation structure of the default events is driven by a small number of common latent factors. Conditional to these factors, the defaults become independent and each default sequence is fitted to a generalised linear model with Poisson response and log-link function. This model provides a flexible framework for the computation of the value-at-risk and the expected shortfall of a credit portfolio. The practical implementation of the proposed local Fisher scoring estimation algorithm is illustrated by a Monte Carlo simulation study. Then, a real scenario, with default data taken from a large database provided by Standard & Poor's, is used to analyse the empirical behaviours of the different risk measures. The achieved results show promising performance.

Keywords: default correlation; factor analysis; generalised linear models; expectation-maximisation algorithm; credit value-at-risk; expected shortfall (search for similar items in EconPapers)
JEL-codes: E5 G2 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aza:rmfi00:y:2023:v:17:i:1:p:89-105

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