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

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.

KEYWORDS:

• Volatility forecasts
• risk premium
• Meixner distribution
• dynamic conditional score
• generalized autoregressive score

JEL CLASSIFICATION:

• C22
• C53

Acknowledgments

The authors would like to thank to Lorenzo Cristofaro, Matthew Copley, Demian Licht, and Jacob Rasmussen.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

Funding from Universidad Francisco Marroquín is gratefully acknowledged.