Factor-augmented HAR model improves realized volatility forecasting

ABSTRACT

This paper proposes a factor-augmented heterogeneous autoregressive (FAHAR) model for realized volatility. This model incorporates volatility information from other stock markets into several f actors, hence it is expected to improve forecasting. We also consider nonlinear modeling of the FAHAR based on the LSTM network in deep neural networks. Our empirical analysis shows that factor augmentation indeed improves forecasting for all the stock indices considered, implying the co-movement of world stock markets in the 2010s.

KEYWORDS:

• Realized volatility
• heterogeneous autoregressive (HAR) model
• deep learning
• LSTM network
• stock market linkage

JEL CLASSIFICATION:

• C22
• C32
• C45
• C53

Acknowledgments

We would like to thank an anonymous referee for providing many constructive comments and suggestions that helped us significantly improve the paper.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported in part by the National Research Foundation of Korea (NRF-2017R1A1A1A05000831, NRF-2019R1F1A1057104).