Comparison of Value-at-Risk models using the MCS approach

Abstract

This paper compares the Value-at-Risk (VaR) forecasts delivered by alternative model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (Econometrica 79(2):453---497, 2011). The direct VaR estimate provided by the Conditional Autoregressive Value-at-Risk (CAViaR) models of Engle and Manganelli (J Bus Econ Stat 22(4):367---381, 2004) are compared to those obtained by the popular Autoregressive Conditional Heteroskedasticity (ARCH) models of Engle (Econometrica 50(4):987---1007, 1982) and to the Generalised Autoregressive Score (GAS) models recently introduced by Creal et al. (J Appl Econom 28(5):777---795, 2013) and Harvey (Dynamic models for volatility and heavy tails: with applications to financial and economic time series. Cambridge University Press, Cambridge, 2013). The MCS procedure consists in a sequence of tests which permits to construct a set of "superior" models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain confidence level. Our empirical results, suggest that, during the European Sovereign Debt crisis of 2009---2010, highly non-linear volatility models deliver better VaR forecasts for the European countries as opposed to other regional indexes. Model comparisons have been performed using the $$\textsf {R}$$R package MCS developed by the authors and freely available at the CRAN website.