Forecasting realized variance using asymmetric HAR model with time-varying coefficients
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DOI: 10.1016/j.frl.2019.04.006
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Cited by:
- Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2022.
"A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data,"
International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 384-400, January.
- Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2019. "A Moving Average Heterogeneous Autoregressive Model for Forecasting the Realized Volatility of the US Stock Market: Evidence from Over a Century of Data," Working Papers 201978, University of Pretoria, Department of Economics.
- Feng, Lingbing & Qi, Jiajun & Lucey, Brian, 2024. "Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy," International Review of Financial Analysis, Elsevier, vol. 94(C).
- Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
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Keywords
Realized variance; Volatility forecasting; Semivariance; Time-varying coefficient; HAR;All these keywords.
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