Do we need the constant term in the heterogenous autoregressive model for forecasting realized volatilities?. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Song, Hyejin; Shin, Dong Wan; Yoo, Jae Keun
• Is Part Of: Communications in statistics. Volume 47:Issue 1(2018)
• Page Start: 63
• Page End: 73
• Publication Date: 2018-01-02
• Subjects: Bias -- HAR model -- Long-memory -- Realized volatility -- Volatility forecasting; 62M10 -- 62P05; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1249882
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: No-constant strategy is considered for the heterogenous autoregressive (HAR) model of Corsi, which is motivated by smaller biases of its estimated HAR coefficients than those of the constant HAR model. The no-constant model produces better forecasts than the constant model for four real datasets of the realized volatilities (RVs) of some major assets. Robustness of forecast improvement is verified for other functions of realized variance and log RV and for the extended datasets of all 20 RVs of Oxford-Man realized library. A Monte Carlo simulation also reveals improved forecasts for some historic HAR model estimated by Corsi.