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Estimation and forecasting of stock volatility with range‐based estimators

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  • Joshy Jacob
  • Vipul

Abstract

This paper examines the estimation and forecasting performance of range‐based volatility estimators for stocks, with two‐scales realized volatility as the benchmark. There is evidence that the daily range‐based estimators provide an efficient and low‐bias alternative to the return‐based estimators. These are not downwardly biased in the presence of negative autocorrelation and low liquidity, as generally suspected. The drift is a major cause of the poor performance of Parkinson's estimator. The forecasts of volatility with these estimators are about as efficient as those with the benchmark itself but are more biased. The forecasts based on realized range are only marginally better on the criterion of bias and are about as efficient. Considering their simplicity and lower data requirement, the daily range‐based estimators appear to be more desirable. These results are particularly relevant for the option valuation and the risk management of derivative markets. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:561–581, 2008

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  • Joshy Jacob & Vipul, 2008. "Estimation and forecasting of stock volatility with range‐based estimators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(6), pages 561-581, June.
    • Handle: RePEc:wly:jfutmk:v:28:y:2008:i:6:p:561-581
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    Cited by:

    1. Chien, Cheng-Yi & Lee, Hsiu-Chuan & Tai, Shih-Wen & Liao, Tzu-Hsiang, 2013. "Information, hedging demand, and institutional investors: Evidence from the Taiwan Futures Exchange," Journal of Multinational Financial Management, Elsevier, vol. 23(5), pages 394-414.
    2. Sutton, Maxwell & Vasnev, Andrey L. & Gerlach, Richard, 2019. "Mixed interval realized variance: A robust estimator of stock price volatility," Econometrics and Statistics, Elsevier, vol. 11(C), pages 43-62.
    3. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    4. Sharma, Prateek & Vipul,, 2016. "Forecasting stock market volatility using Realized GARCH model: International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 222-230.
    5. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    6. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    7. Eduardo Rossi & Paolo Santucci de Magistris, 2013. "A No‐Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 77-102, January.
    8. Juan M. Londono, 2011. "The variance risk premium around the world," International Finance Discussion Papers 1035, Board of Governors of the Federal Reserve System (U.S.).
    9. Neda Todorova, 2012. "Volatility estimators based on daily price ranges versus the realized range," Applied Financial Economics, Taylor & Francis Journals, vol. 22(3), pages 215-229, February.
    10. I‐Ming Jiang & Jui‐Cheng Hung & Chuan‐San Wang, 2014. "Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(11), pages 1077-1094, November.
    11. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "A No Arbitrage Fractional Cointegration Analysis Of The Range Based Volatility," CREATES Research Papers 2009-31, Department of Economics and Business Economics, Aarhus University.
    12. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    13. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    14. Bley, Jorg & Saad, Mohsen, 2011. "The effect of financial liberalization on stock-return volatility in GCC markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(5), pages 662-685.
    15. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    16. Todorova, Neda & Souček, Michael, 2014. "The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range," Economic Modelling, Elsevier, vol. 36(C), pages 332-340.

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