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The benefit of modeling jumps in realized volatility for risk prediction: Evidence from Chinese mainland stocks

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  • Liao, Yin

Abstract

Recent literature has focused on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility models can largely improve VaR prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.

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  • Liao, Yin, 2013. "The benefit of modeling jumps in realized volatility for risk prediction: Evidence from Chinese mainland stocks," Pacific-Basin Finance Journal, Elsevier, vol. 23(C), pages 25-48.
    • Handle: RePEc:eee:pacfin:v:23:y:2013:i:c:p:25-48
    DOI: 10.1016/j.pacfin.2013.01.002
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    Cited by:

    1. Doureige J. Jurdi, 2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    2. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    3. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    4. Rangan Gupta & Chi Keng Marco Lau & Ruipeng Liu & Hardik A. Marfatia, 2019. "Price jumps in developed stock markets: the role of monetary policy committee meetings," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 298-312, April.
    5. Vortelinos, Dimitrios I., 2015. "Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets," Review of Financial Economics, Elsevier, vol. 27(C), pages 58-67.
    6. Dimitrios I. Vortelinos, 2015. "Out‐of‐sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini‐futures markets," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 58-67, November.
    7. Li, Jie & Li, Guangzhong & Zhou, Yinggang, 2015. "Do securitized real estate markets jump? International evidence," Pacific-Basin Finance Journal, Elsevier, vol. 31(C), pages 13-35.
    8. Chan, Kam Fong & Powell, John G. & Treepongkaruna, Sirimon, 2014. "Currency jumps and crises: Do developed and emerging market currencies jump together?," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 132-157.
    9. Wang, Hao & Yue, Mengqi & Zhao, Hua, 2015. "Cojumps in China's spot and stock index futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 35(PB), pages 541-557.
    10. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2016. "Emerging trends in Asia-Pacific finance research: A review of recent influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 66-76.
    11. Cong-Duc Tran & Minh-Tuan Phung & Fu-Ju Yang & Yi-Hsien Wang, 2020. "The Role of Gender Diversity in Downside Risk: Empirical Evidence from Vietnamese Listed Firms," Mathematics, MDPI, vol. 8(6), pages 1-22, June.
    12. Wang, Li-Hsun & Lin, Chu-Hsiung & Fung, Hung-Gay & Chen, Hsien-Ming, 2015. "Governance mechanisms and downside risk," Pacific-Basin Finance Journal, Elsevier, vol. 35(PB), pages 485-498.

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    More about this item

    Keywords

    Value at risk (VaR); Realized volatility; Jumps;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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