 Forecasting stock volatility using pseudo-out-of-sample informationXiaodan Li, Xue Gong, Futing Ge and Jingjing Huang International Review of Economics & Finance, 2024, vol. 90, issue C, 123-135 Abstract: This paper proposes a novel forecast combination method and investigates the impact of technical indicators on volatility prediction in the Chinese stock market. Firstly, our analysis reveals that technical indicators based on good and bad volatility have a stronger explanatory power on stock volatility, exhibiting an evident asymmetric effect. In addition, we introduce two new categories of technical indicators based on price skewness risk and kurtosis risk, which exhibit more robust and significant predictive power on volatility than existing indicators. Finally, we propose a new forecast combination method that employs adjusted out-of-sample R2 as a weight, which outperforms a series of existing forecasting models. These findings are robustly confirmed in multiple robust tests, demonstrating the efficacy of our proposed approach. Keywords: Realized volatility; Technical indicator; High-frequency data; Forecast combination; Out-of-sample R2 weighted (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:90:y:2024:i:c:p:123-135 DOI: 10.1016/j.iref.2023.11.014 Access Statistics for this article International Review of Economics & Finance is currently edited by H. Beladi and C. Chen More articles in International Review of Economics & Finance from Elsevier |
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