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

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  • Bana Abuzayed
  • Nedal Al-Fayoumi
  • Lanouar Charfeddine

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.

Suggested Citation

  • Bana Abuzayed & Nedal Al-Fayoumi & Lanouar Charfeddine, 2018. "Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting," Applied Economics, Taylor & Francis Journals, vol. 50(23), pages 2569-2599, May.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:23:p:2569-2599
    DOI: 10.1080/00036846.2017.1403559
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    Cited by:

    1. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Roman Kaminskiy & Nataliya Shakhovska & Jana Kajanová & Yurii Kryvenchuk, 2021. "Method of Distinguishing Styles by Fractal and Statistical Indicators of the Text as a Sequence of the Number of Letters in Its Words," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    3. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    4. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    5. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    6. Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    7. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Charfeddine, Lanouar & Maouchi, Youcef, 2019. "Are shocks on the returns and volatility of cryptocurrencies really persistent?," Finance Research Letters, Elsevier, vol. 28(C), pages 423-430.
    9. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.

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