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

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.

Automated summary

The score-driven QAR-EGARCH-M model with Meixner distribution improves prediction accuracy for GARCH, outperforming EGARCH-M and GARCH in forecasting volatility for DJIA, which is significant for options investors at Chicago exchanges.