Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations

Abstract

In this paper, we investigate the adequacy of the fractionally integrated asymmetric power model to measure value at risk in the United Arab Emirates stock exchanges. Our empirical results show that the accuracy of the model is improved when value at risk is computed using innovations modeled as skewed Student-t distribution. Including a long memory in the conditional volatility process would also improve the results. We conclude that, the modeling of asymmetry, fat tails and long memory have potentially important implications for risk assessment, and hedging strategies in the UAE stock exchanges.

Measurement of extreme market risk: Insights from a comprehensive literature review

“…• A group of studies have performed comparative evaluation of the statistical accuracy of the VaR estimates from the student t-distribution with those from the generalized error distribution (GED), skewed(S) version of GED (S-GED), skewed t-distribution (STD), and the skewed generalized t (SGT) distribution. Notable studies include Angelidis et al ( 2004), Huang et al (2004), and Lin and Shen (2006), Maghyereh andAwartani (2012), andAssaf (2015). These studies find that while modeling the empirical returns distribution with the GED, S-GED, and the STD provide more accurate tail risk estimates over the t distribution, nonetheless the SGT distribution offers the most accurate results.…”