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

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    Date
    2018
    Author
    Abuzayed B.
    Al-Fayoumi N.
    Charfeddine L.
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    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. 2017 Informa UK Limited, trading as Taylor & Francis Group.
    DOI/handle
    http://dx.doi.org/10.1080/00036846.2017.1403559
    http://hdl.handle.net/10576/12068
    Collections
    • Finance & Economics [‎436‎ items ]

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