Estimation of value-at-risk for exchange risk via kernel based nonlinear ensembled multi scale model

Abstract

Risk level in the exchange rate market is dynamically evolving with complicated structures. To further refine the analysis process and achieve more accurate measurement, this paper proposes a novel kernel based nonlinear ensembled multi scale Value at Risk methodology for evaluating the risk level in the exchange rate market. In the proposed algorithm, wavelet analysis is introduced to analyze the multi scale heterogeneous risk structures across different time scales. The Principle Component Analysis is used to extract principle components from the redundant forecast matrixes. Then the support vector regression technique is integrated into the modeling process to nonlinearly ensemble forecast matrixes and produce more stable and accurate results. Taking Euro market as a typical test case, empirical studies employing the proposed algorithm shows the superior performance than benchmark ARMA-GARCH and realized volatility based approaches. © 2008 Springer-Verlag Berlin Heidelberg.