Forecasting Volatility with Time-Varying Coefficient Regressions
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-01
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC).
The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models.
By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods.
Portfolio exercises also demonstrate the superiority of time-varying coefficient models.
American Psychological Association (APA)
Zhu, Qifeng& You, Miman& Wu, Shan. 2020. Forecasting Volatility with Time-Varying Coefficient Regressions. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1152960
Modern Language Association (MLA)
Zhu, Qifeng…[et al.]. Forecasting Volatility with Time-Varying Coefficient Regressions. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1152960
American Medical Association (AMA)
Zhu, Qifeng& You, Miman& Wu, Shan. Forecasting Volatility with Time-Varying Coefficient Regressions. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1152960
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1152960