Comparison of Value-at-Risk models: the MCS package

TL;DR

This paper evaluates and compares various VaR forecasting models using Hansen et al.'s Model Confidence Set procedure, highlighting the superior performance of non-linear volatility models post-2008 financial crisis, and introduces the R package MCS.

Contribution

It introduces the R package MCS for model comparison and applies Hansen's procedure to evaluate VaR models across different regions after the financial crisis.

Findings

Non-linear volatility models outperform others post-2008 in Europe.

The MCS package effectively identifies superior VaR models.

European countries benefit from advanced non-linear models.

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. (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.