Glasserman, P., P. Heidelberger, and P. Shahabuddin, 2002, Portfolio value-at-risk with heavytailed risk factors. Mathematical Finance, 12(3):239--269.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for can then be computed by inverting the Fourier transform numerically. Mina and Ulmer [42] consider the case where returns are normal. Due and Pan [15] explore this idea further, and they extend the model to a fat tailed mixture model and show how to include credit risk. Glasserman et al. [27] generalize the method to returns with a multivariate Student s T distribution. Monte Carlo methods. Monte Carlo methods are the most general of the approaches, few assumptions have to be made about the structure of the function for portfolio value or the returns model for the risk factors. ....

....easy to implement they are arguably the most commonly used method. The disadvantage of Monte Carlo methods is slow convergence. Therefore, much research in this area is devoted to developing ecient variance reduction techniques to improve the speed of convergence, see for example Glasserman et al. [26, 27]. To distinguish our method from other Fourier methods, we call it the fast convolution method. The fast convolution method is di erent from existing methods (see [42, 15, 27] in that it does not require explicit knowledge about the characteristic function. It therefore enjoys greater exibility ....

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P. Glasserman, P. Heidelberger, and P. Shahabuddin. Portfolio value-at-risk with heavytailed risk factors. Mathematical Finance, 12(3):239-269, July 2002.

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Glasserman, P., P. Heidelberger, and P. Shahabuddin, 2002, Portfolio value-at-risk with heavytailed risk factors. Mathematical Finance, 12(3):239--269.

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