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Forecasting rate of return after extreme values when using AR-t-GARCH and QAR-Beta-t-EGARCH

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  • Blazsek, Szabolcs
  • Carrizo, Daniela
  • Eskildsen, Ricardo
  • Gonzalez, Humberto

Abstract

We compare the predictive performances of AR-t-GARCH and recent QAR-Beta-t-EGARCH models. We compare predictive performances for those days when an extreme value is observed, and also for the trading day after each day when an extreme value is observed. We use a historical dataset from the adjusted Dow Jones Industrial Average (DJIA) index. We assume that the forecast users of this study are DJIA options investors. We find that AR-t-GARCH dominates QAR-Beta-t-EGARCH on each day when an extreme value is observed, and QAR-Beta-t-EGARCH dominates AR-t-GARCH on the trading day after each day when an extreme value is observed.

Suggested Citation

  • Blazsek, Szabolcs & Carrizo, Daniela & Eskildsen, Ricardo & Gonzalez, Humberto, 2018. "Forecasting rate of return after extreme values when using AR-t-GARCH and QAR-Beta-t-EGARCH," Finance Research Letters, Elsevier, vol. 24(C), pages 193-198.
    • Handle: RePEc:eee:finlet:v:24:y:2018:i:c:p:193-198
    DOI: 10.1016/j.frl.2017.09.006
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    References listed on IDEAS

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    Cited by:

    1. Astrid Ayala & Szabolcs Blazsek, 2019. "Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(1), pages 65-92, March.
    2. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.

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    More about this item

    Keywords

    Dow Jones Industrial Average (DJIA); Beta-t-EGARCH; Extreme values;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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