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Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?

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  • I‐Ming Jiang
  • Jui‐Cheng Hung
  • Chuan‐San Wang

Abstract

This study investigates the volatility forecasting abilities of return‐based and range‐based estimators for two stock indices and two individual stocks in the U.S. stock market. The forecasting performances are evaluated by two robust statistical loss functions, and further by financial applications in risk management and option pricing. Consistent with previous studies, the range‐based volatility forecasts outperform in terms of statistical evaluation, value‐at‐risk calculation, and option pricing. However, return‐based volatility forecasts prove superior in the evaluation of market risk capital requirements. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:1077–1094, 2014

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  • 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.
  • Handle: RePEc:wly:jfutmk:v:34:y:2014:i:11:p:1077-1094
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    2. Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023. "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, vol. 55(PB).
    3. Jui‐Cheng Hung & Hung‐Chun Liu & J. Jimmy Yang, 2023. "Does the tail risk index matter in forecasting downside risk?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3451-3466, July.
    4. 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.
    5. Degiannakis, Stavros & Filis, George, 2022. "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, vol. 123(C).

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