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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