Journal of Computational Finance

Computing tails of compound distributions using direct numerical integration

Xiaolin Luo, Pavel V. Shevchenko


An efficient adaptive direct numerical integration (DNI) algorithm is developed for computing high quantiles and conditional Value at Risk (VaR) of compound distributions using characteristic functions. A key innovation of the numerical scheme is an effective tail integration approximation that reduces the truncation errors significantly with little extra effort. High precision results of the 0.999 quantile and conditional VaR were obtained for compound losses with heavy tails and a very wide range of loss frequencies using the DNI, fast Fourier transform (FFT) and Monte Carlo methods. These results, particularly relevant to operational risk modeling, can serve as benchmarks for comparing different numerical methods. We found that the adaptive DNI can achieve high accuracy with relatively coarse grids. It is much faster than Monte Carlo and competitive with FFT in computing high quantiles and conditional VaR of compound distributions in the case of moderate to high frequencies and heavy tails.

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