Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S
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DOI: 10.1016/j.econmod.2020.06.004
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- Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
- Yue Zhang, 2021. "The COVID-19 Outbreak and Oil Stock Price Fluctuations - Evidence From China," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 2(3), pages 1-5.
- Yu, Xing & Li, Yanyan & Gong, Xue & Zhang, Nan, 2022. "Evaluating the performance of futures hedging using factors-driven realized volatility," International Review of Financial Analysis, Elsevier, vol. 84(C).
- Wu, Xinyu & Zhao, An & Wang, Yuyao & Han, Yang, 2024. "Forecasting Chinese stock market volatility with high-frequency intraday and current return information," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
- Jiang, Ping & Yang, Hufang & Li, Hongmin & Wang, Ying, 2021. "A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity," Energy, Elsevier, vol. 219(C).
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More about this item
Keywords
Realized-GARCH-Kernel-type models; Semiparametric kernel density estimator; Realized volatility;All these keywords.
JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
Statistics
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