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Forecasting volatility

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  • Louis H. Ederington
  • Wei Guan

Abstract

The forecasting ability of the most popular volatility forecasting models is examined and an alternative model developed. Existing models are compared in terms of four attributes: (1) the relative weighting of recent versus older observations, (2) the estimation criterion, (3) the trade‐off in terms of out‐of‐sample forecasting error between simple and complex models, and (4) the emphasis placed on large shocks. As in previous studies, we find that financial markets have longer memories than reflected in GARCH(1,1) model estimates, but find this has little impact on outofsample forecasting ability. While more complex models which allow a more flexible weighting pattern than the exponential model forecast better on an in‐sample basis, due to the additional estimation error introduced by additional parameters, they forecast poorly out‐of‐sample. With the exception of GARCH models, we find that models based on absolute return deviations generally forecast volatility better than otherwise equivalent models based on squared return deviations. Among the most popular time series models, we find that GARCH(1,1) generally yields better forecasts than the historical standard deviation and exponentially weighted moving average models, though between GARCH and EGARCH there is no clear favorite. However, in terms of forecast accuracy, all are dominated by a new, simple, nonlinear least squares model, based on historical absolute return deviations, that we develop and test here. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:465–490, 2005

Suggested Citation

  • Louis H. Ederington & Wei Guan, 2005. "Forecasting volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(5), pages 465-490, May.
  • Handle: RePEc:wly:jfutmk:v:25:y:2005:i:5:p:465-490
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    Cited by:

    1. Burkhard Raunig, 2018. "Economic Policy Uncertainty and the Volatility of Sovereign CDS Spreads," Working Papers 219, Oesterreichische Nationalbank (Austrian Central Bank).
    2. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    3. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    4. I.-Yuan Chuang & Jin-Ray Lu & Pei-Hsuan Lee, 2007. "Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions," Applied Financial Economics, Taylor & Francis Journals, vol. 17(13), pages 1051-1060.
    5. Kian-Guan Lim & Christopher Ting, 2012. "The term structure of S&P 100 model-free volatilities," Quantitative Finance, Taylor & Francis Journals, vol. 13(7), pages 1041-1058, November.
    6. Roni Bhowmik & Wu Chao & Jewel Roy Kumar & Wang Shouyang, 2017. "A Study on the Volatility of the Bangladesh Stock Market — Based on GARCH Type Models," Journal of Systems Science and Information, De Gruyter, vol. 5(3), pages 193-215, June.
    7. Dian‐Xuan Kao & Wei‐Che Tsai & Yaw‐Huei Wang & Kuang‐Chieh Yen, 2018. "An analysis on the intraday trading activity of VIX derivatives," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 158-174, February.
    8. 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.
    9. Burkhard Raunig, 2021. "Economic Policy Uncertainty and Stock Market Volatility: A Causality Check (Burkhard Raunig)," Working Papers 234, Oesterreichische Nationalbank (Austrian Central Bank).
    10. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    11. Md. Zahangir Alam & Md. Noman Siddikee & Md. Masukujjaman, 2013. "Forecasting Volatility of Stock Indices with ARCH Model," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(2), pages 126-143, April.
    12. Burkhard Raunig, 2023. "Using causal graphs to test for the direction of instantaneous causality between economic policy uncertainty and stock market volatility," Empirical Economics, Springer, vol. 65(4), pages 1579-1598, October.
    13. Ahmet Duran & Michael Bommarito, 2011. "A profitable trading and risk management strategy despite transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 829-848.
    14. Hossain, Md. Jamal & Akter, Sadia & Ismail, Mohd Tahir, 2021. "Performance Analysis of GARCH Family Models in Three Time-frames," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 55(2), pages 15-28.
    15. Sanjay Sehgal & Vidisha Garg, 2016. "Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets," Vikalpa: The Journal for Decision Makers, , vol. 41(3), pages 234-246, September.

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