2015/02/16 by Mauro Bernardi, Bernardi, Mauro, Leopoldo Catania +1
Economics, Econometrics and Finance · Mathematics · #Computation (stat.CO) #Credit Risk and Financial Regulations #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Market Dynamics and Volatility #stat.CO
paper · pdf · doi:10.48550/arxiv.1502.04472
25 pages. arXiv admin note: substantial text overlap with arXiv:1410.8504
arxiv created 2015/02/16 · openalex publication_date 2015/02/16 · arxiv updated 2015/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. (2011). The direct VaR estimate provided by the Conditional Autoregressive Value--at--Risk (CAViaR) models of Eengle and Manganelli (2004) are compared to those obtained by the popular Autoregressive Conditional Heteroskedasticity (ARCH) models of Engle (1982) and to the recently introduced Generalised Autoregressive Score (GAS) models of Creal et al. (2013) and Harvey (2013). The Hansen's procedure consists on 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, after the Global Financial Crisis (GFC) of 2007-2008, highly non-linear volatility models deliver better VaR forecasts for the European countries as opposed to other regions. The R package MCS is introduced for performing the model comparisons whose main features are discussed throughout the paper.