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Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

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  • Mohammad Najand

Abstract

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH‐class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH‐M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.

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  • Mohammad Najand, 2002. "Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models," The Financial Review, Eastern Finance Association, vol. 37(1), pages 93-104, February.
    • Handle: RePEc:bla:finrev:v:37:y:2002:i:1:p:93-104
    DOI: 10.1111/1540-6288.00006
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    Cited by:

    1. Ana Filipa Carvalho & Jose Sa da Costa & Jose Assis Lopes, 2006. "A systematic modelling strategy for futures markets volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 16(11), pages 819-833.
    2. Francesco Guidi, 2009. "Volatility and Long-Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK," The IUP Journal of Financial Economics, IUP Publications, vol. 0(2), pages 7-39, June.
    3. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    4. Stéphane Yen & Ming-Hsiang Chen, 2010. "Open interest, volume, and volatility: evidence from Taiwan futures markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 34(2), pages 113-141, April.
    5. Ezzat, Hassan, 2012. "The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt," MPRA Paper 50530, University Library of Munich, Germany.
    6. Čermák, M. & Malec, K. & Maitah, M., 2017. "Price Volatility Modelling – Wheat: GARCH Model Application," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(4).
    7. Ezzat, Hassan, 2012. "The Application of GARCH Methods in Modeling Volatility Using Sector Indices from the Egyptian Exchange," MPRA Paper 51584, University Library of Munich, Germany.
    8. Kam C. Chan & Louis T. W. Cheng & Peter P. Lung, 2005. "Asymmetric Volatility and Trading Activity in Index Futures Options," The Financial Review, Eastern Finance Association, vol. 40(3), pages 381-407, August.
    9. Ashish Kumar, 2015. "Impact of Currency Futures on Volatility in Exchange Rate," Paradigm, , vol. 19(1), pages 95-108, June.

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