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The accuracy of asymmetric GARCH model estimation

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  • Charles, Amélie
  • Darné, Olivier

Abstract

This paper reviews eight software packages when estimating asymmetric GARCH models (from their default option). We consider the numerical consistency of GJR-GARCH, TGARCH, EGARCH and APARCH estimations with Normal and Student distributions as well as out-of-sample forecasting accuracy, using the model confidence set procedure. We show that results are clearly software-dependent for both asymmetric volatility models, especially for the t-ratios. The out-of-sample forecast results show that the differences in estimating symmetric and asymmetric GARCH models imply slight differences in terms of forecast accuracy, not statistically significant, except in few cases from the QLIKE loss function. Further, the results indicate that the different specifications of the asymmetric GARCH-type models used by the different packages appear to have no significant effect on their forecast accuracy.

Suggested Citation

  • Charles, Amélie & Darné, Olivier, 2019. "The accuracy of asymmetric GARCH model estimation," International Economics, Elsevier, vol. 157(C), pages 179-202.
    • Handle: RePEc:eee:inteco:v:157:y:2019:i:c:p:179-202
    DOI: 10.1016/j.inteco.2018.11.001
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    Cited by:

    1. Neenu Chalissery & Suhaib Anagreh & Mohamed Nishad T. & Mosab I. Tabash, 2022. "Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis," JRFM, MDPI, vol. 15(9), pages 1-23, September.
    2. Abuzayed, Bana & Bouri, Elie & Al-Fayoumi, Nedal & Jalkh, Naji, 2021. "Systemic risk spillover across global and country stock markets during the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 180-197.
    3. Mukta Mani, 2025. "An Exploration of Contemporary Trends in Finance Research," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 12291-12316, September.
    4. Yuyun Hidayat & Titi Purwandari & Sukono & Igif Gimin Prihanto & Rizki Apriva Hidayana & Riza Andrian Ibrahim, 2023. "Mean-Value-at-Risk Portfolio Optimization Based on Risk Tolerance Preferences and Asymmetric Volatility," Mathematics, MDPI, vol. 11(23), pages 1-26, November.
    5. Paul R. Dewick, 2022. "On Financial Distributions Modelling Methods: Application on Regression Models for Time Series," JRFM, MDPI, vol. 15(10), pages 1-15, October.
    6. Pal, Debdatta, 2022. "Does hospitality industry stock volatility react asymmetrically to health and economic crises?," Economic Modelling, Elsevier, vol. 108(C).
    7. Michael Graham & Jussi Nikkinen & Jarkko Peltomäki, 2020. "Web-Based Investor Fear Gauge and Stock Market Volatility: An Emerging Market Perspective," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 127-153, August.
    8. Abuzayed, Bana & Al-Fayoumi, Nedal, 2021. "Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    9. Mateusz Tomal, 2021. "Modelling the Impact of Different COVID-19 Pandemic Waves on Real Estate Stock Returns and Their Volatility Using a GJR-GARCHX Approach: An International Perspective," JRFM, MDPI, vol. 14(8), pages 1-8, August.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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