Value-at-risk under market shifts through highly flexible models

Managing market risk under unknown future shocks is a critical issue for policymakers, investors, and professional risk managers. Despite important developments in market risk modeling and forecasting over recent years, market participants are still skeptical about the ability of existing econometric designs to accurately predict potential losses, particularly in the presence of hidden structural changes. In this paper, we introduce Markov-switching APARCH models under the skewed generalized t and the generalized hyperbolic distributions to fully capture the fuzzy dynamics and stylized features of financial market returns and to generate value-at-risk (VaR) forecasts. Our empirical analysis of six major stock market indexes shows the superiority of the proposed models in detecting and forecasting unobservable shocks on market volatility, and in calculating daily capital charges based on VaR forecasts.