Variance-covariance-based risk allocation in credit portfolios

Mikhail Voropaev proposes high-precision analytical approximation for variance-covariance-based risk allocation in a portfolio of risky assets. A general case of a single-period multi-factor Merton-type model with stochastic recovery is considered. The accuracy and speed of the approximation are compared with and shown to be superior to those of Monte Carlo simulation

Merton-type models, also referred to as structural models, such as PortfolioManager (Kealhofer, 2001) and CreditMetrics (Gupton, Finger & Bhatia, 1997), have become the standard choice for financial institutions’ credit risk economic capital frameworks. In these models, default correlations between different borrowers are modelled using a set of common systematic risk factors associated with the state of the economy.

Computationally heavy Monte Carlo simulations are usually used for calculations

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Credit risk & modelling – Special report 2021

This Risk special report provides an insight on the challenges facing banks in measuring and mitigating credit risk in the current environment, and the strategies they are deploying to adapt to a more stringent regulatory approach.

The wild world of credit models

The Covid-19 pandemic has induced a kind of schizophrenia in loan-loss models. When the pandemic hit, banks overprovisioned for credit losses on the assumption that the economy would head south. But when government stimulus packages put wads of cash in…

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