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

Mauro Bernardi () and Leopoldo Catania ()
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Mauro Bernardi: University of Padua

Computational Statistics, 2016, vol. 31, issue 2, No 10, 579-608

Abstract: 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.

Keywords: Hypothesis testing; Model Confidence Set; Value-at-Risk; VaR combination; ARCH; GAS; CAViaR models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (22)

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DOI: 10.1007/s00180-016-0646-6

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