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AuthorAbuzayed B.
AuthorAl-Fayoumi N.
AuthorCharfeddine L.
Available date2019-10-06T09:38:33Z
Publication Date2018
Publication NameApplied Economics
AbstractThis 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. 2017 Informa UK Limited, trading as Taylor & Francis Group.
Subjectlong memory
SubjectSector analysis
Subjecttrue versus spurious
Subjectvolatility modelling
TitleLong range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting
Issue Number23
Volume Number50

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