ICA-based High Frequency VaR for Risk Management

Independent Component Analysis (ICA, see Comon, 1994 and Hyvärinen et al., 2001) is more appropriate when non-linearity and non-normality are at stake, as mentioned by Back and Weigend (1997) in a financial context. Using high-frequency data on the French Stock Market, we evaluate this technique when generating scenarii for accurate Value-atRisk computations, reducing by this mean the effective dimensionality of the scenario specification problem in several cases, without leaving aside some main characteristics of the dataset. Various methods for specifying stress scenarii are discussed, compared to other published ones and classical tests of rejection are presented (Christoffersen and Pelletier, 2003).