Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting

Bana Abuzayed, Nedal Al-Fayoumi and Lanouar Charfeddine

Applied Economics, 2018, vol. 50, issue 23, 2569-2599

Abstract: This study evaluates the sector risk of the Qatar Stock Exchange (QSE), a recently upgraded emerging stock market, using value-at-risk models for the 7 January 2007–18 October 2015 period. After providing evidence for true long memory in volatility using the log-likelihood profile test of Qu and splitting the sample and dth differentiation tests of Shimotsu, we compare the FIGARCH, HYGARCH and FIAPARCH models under normal, Student-t and skewed-t innovation distributions based on in and out-of-sample VaR forecasts. The empirical results show that the skewed Student-t FIGARCH model generates the most accurate prediction of one-day-VaR forecasts. The policy implications for portfolio managers are also discussed.

Date: 2018
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DOI: 10.1080/00036846.2017.1403559

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