Are range based models good enough? Evidence from seven stock markets

Abstract

We study the performance of range-based models over varying market conditions and compare their performance against a set of alterative risk measurement models, including the more widely used techniques in practice for measuring the Value-at-Risk (VaR) of seven financial market indices. In particular, we focus on model accuracy in estimated VaRs over quiet and volatile moments utilizing loss functions and likelihood ratio tests for coverage probability. The empirical estimates based on these two criteria find that the range based-model of Yang and Zhang (2000) shows some success in estimated VaR risk measure, especially during quiet periods, than is the case for the other range based models considered. Also, we find that the EWMA and RiskMetrics models have an inconsistent marginal edge over the widely used GARCH and historical simulation specifications and that there is validity in the use of the EWMA and RiskMetrics models over range-based approaches as both capture and thus provide more accurate estimated VaR risk measure of market risk.