Vector error correction heterogeneous autoregressive forecast model of realized volatility and implied volatility

This is the authors' abstract. We don't add key points for this paper.

A vector error correction model is proposed for forecasting realized volatility which takes advantage of the cointegration relation between realized volatility and implied volatility. The model is constructed by adding a cointegration error term to a vector-and-unit-root version of the heterogeneous autoregressive (HAR) model of Corsi (2009 Corsi, F. 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7 (2):174–96. [Google Scholar]). The proposed model is easier to implement, extend, and interpret than fractional cointegration models. A Monte Carlo simulation and real data analysis reveal advantages of the proposed model over other existing models of Corsi (2009 Corsi, F. 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7 (2):174–96. [Google Scholar]), Busch Christensen and Nielsen (2011 Busch, T., B. J. Christensen, and M. Nielsen. 2011. The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics 160 (1):48–57. [Google Scholar]), Cho and Shin (2016 Cho, S. J. and D. W. Shin. 2016. An integrated heteroscedastic autoregressive model for forecasting long-memory volatilities. Journal of the Korean Statistical Society, 45:371–380. [Google Scholar]), and Bollerslev Patton, and Quaedvlieg (2016 Bollerslev, T., A. J. Patton, and R. Quaedvlieg. 2016. Exploiting the errors:A simple approach for improved volatility forecasting. Journal of Econometrics 192:1-18. [Google Scholar]).