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Haar wavelets-based approach for quantifying credit portfolio losses

Josep J. Masdemont and Luis Ortiz-Gracia

Quantitative Finance, 2014, vol. 14, issue 9, 1587-1595

Abstract: This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing the estimation of the VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.

Date: 2014
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Handle: RePEc:taf:quantf:v:14:y:2014:i:9:p:1587-1595