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Measuring implied volatility: Is an average better? Which average?

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  • Louis H. Ederington
  • Wei Guan

Abstract

Options researchers have argued that by averaging together implied standard deviations, or ISDs, calculated from several options with the same expiry but different strikes, the noise in individual ISDs can be reduced, yielding a better measure of the market's volatility expectation. Various options researchers have suggested different weighting schemes for calculating these averages. In the forecasting literature, econometricians have made the same argument but suggested quite different weighting schemes. Ignoring both literatures, commercial vendors calculate ISD averages using their own weightings. We compare the averages proposed in both the options and econometrics literatures and the averages used by major commercial vendors for the S&P 500 futures options market. Although some averages forecast better than others, we find that the question of the best weighting scheme is of secondary importance. More important is the fact that the ISDs are upward biased measures of expected volatility. Fortunately, this bias is stable over time, so past bias patterns can be used to obtain unbiased volatility forecasts. Once this is done, most ISD averages forecast better than time series and naive models, and the differences between the averages produced by the various proposed weighting schemes are small. © 2002 Wiley Publications, Inc. Jrl Fut Mark 22:811–837, 2002

Suggested Citation

  • Louis H. Ederington & Wei Guan, 2002. "Measuring implied volatility: Is an average better? Which average?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(9), pages 811-837, September.
  • Handle: RePEc:wly:jfutmk:v:22:y:2002:i:9:p:811-837
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    Cited by:

    1. Tsiaras, Leonidas, 2009. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," Finance Research Group Working Papers F-2009-02, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    2. Lai T. Hoang & Dirk G. Baur, 2020. "Forecasting bitcoin volatility: Evidence from the options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1584-1602, October.
    3. Birkelund, Ole Henrik & Haugom, Erik & Molnár, Peter & Opdal, Martin & Westgaard, Sjur, 2015. "A comparison of implied and realized volatility in the Nordic power forward market," Energy Economics, Elsevier, vol. 48(C), pages 288-294.
    4. Yang Liu & Zhenyu Shen, 2024. "PSAHARA Utility Family: Modeling Non-monotone Risk Aversion and Convex Compensation in Incomplete Markets," Papers 2406.00435, arXiv.org, revised Nov 2024.
    5. Michael McKenzie & Ólan T. Henry, 2012. "The determinants of short selling: evidence from the Hong Kong equity market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 52, pages 183-216, October.
    6. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    7. Rita Laura D’Ecclesia & Daniele Clementi, 2021. "Volatility in the stock market: ANN versus parametric models," Annals of Operations Research, Springer, vol. 299(1), pages 1101-1127, April.
    8. An N. Q. Cao & Michel A. Robe, 2022. "Market uncertainty and sentiment around USDA announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(2), pages 250-275, February.
    9. Suresh Govindaraj & Yubin Li & Chen Zhao, 2020. "The effect of option transaction costs on informed trading in the options market around earnings announcements," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 47(5-6), pages 615-644, May.
    10. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    11. Kumar, Pawan & Singh, Vipul Kumar, 2022. "Does crude oil fire the emerging markets currencies contagion spillover? A systemic perspective," Energy Economics, Elsevier, vol. 116(C).

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