A generalized Fourier transform approach to risk measures

We introduce the formalism of generalized Fourier transforms in the context of risk management. We develop a general framework in which to efficiently compute the most popular risk measures, value-at-risk and expected shortfall (also known as conditional value-at-risk). The only ingredient required by our approach is the knowledge of the characteristic function describing the financial data in use. This allows us to extend risk analysis to those non-Gaussian models defined in the Fourier space, such as Lévy noise driven processes and stochastic volatility models. We test our analytical results on data sets coming from various financial indexes, finding that our predictions outperform those provided by the standard log-normal dynamics and are in remarkable agreement with those of the benchmark historical approach.