Abstract
In this paper we consider Fourier transform techniques to efficiently compute the Value-at-Risk and the Conditional Value-at-Risk of an arbitrary loss random variable, characterized by having a computable generalized characteristic function. We exploit the property of these risk measures of being the solution of an elementary optimization problem of convex type in one dimension. An application to univariate loss models driven by Lévy or stochastic volatility risk factors dynamic is finally reported.
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Ramponi, A. On a Transform Method for the Efficient Computation of Conditional V@R (and V@R) with Application to Loss Models with Jumps and Stochastic Volatility. Methodol Comput Appl Probab 18, 575–596 (2016). https://doi.org/10.1007/s11009-015-9446-7
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DOI: https://doi.org/10.1007/s11009-015-9446-7