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Prediction accuracy of volatility using the score-driven Meixner distribution: an application to the Dow Jones

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  • Szabolcs Blazsek
  • Adrian Licht

Abstract

The score-driven QAR-EGARCH-M (quasi-autoregressive, exponential generalized autoregressive conditional heteroscedasticity-in-mean) model using the Meixner distribution is introduced to improve the prediction accuracy of GARCH. QAR-EGARCH-M extends the recent EGARCH-M model in a statistically innovative way because a new score-driven filter is included in the risk premium. Volatility forecasts of QAR-EGARCH-M, EGARCH-M, and GARCH, all with leverage effects, are compared for the Dow Jones Industrial Average (DJIA). QAR-EGARCH-M is superior to EGARCH-M and GARCH, which is relevant for DJIA options investors at Chicago Mercantile Exchange Globex and Chicago Board Options Exchange.

Suggested Citation

  • Szabolcs Blazsek & Adrian Licht, 2022. "Prediction accuracy of volatility using the score-driven Meixner distribution: an application to the Dow Jones," Applied Economics Letters, Taylor & Francis Journals, vol. 29(2), pages 111-117, January.
    • Handle: RePEc:taf:apeclt:v:29:y:2022:i:2:p:111-117
    DOI: 10.1080/13504851.2020.1859445
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    Cited by:

    1. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.

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