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A simplified approach to modeling the co‐movement of asset returns

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  • Richard D. F. Harris
  • Evarist Stoja
  • Jon Tucker

Abstract

The authors propose a simplified multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model (the S‐GARCH model), which involves the estimation of only univariate GARCH models, both for the individual return series and for the sum and difference of each pair of series. The covariance between each pair of return series is then imputed from these variance estimates. The proposed model is considerably easier to estimate than existing multivariate GARCH models and does not suffer from the convergence problems that characterize many of these models. Moreover, the model can be easily extended to include more complex dynamics or alternative forms of the GARCH specification. The S‐GARCH model is used to estimate the minimum‐variance hedge ratio for the FTSE (Financial Times and the London Stock Exchange) 100 Index portfolio, hedged using index futures, and compared to four of the most widely used multivariate GARCH models. Using both statistical and economic evaluation criteria, it was found that the S‐GARCH model performs at least as well as the other models that were considered, and in some cases it was better. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:575–598, 2007

Suggested Citation

  • Richard D. F. Harris & Evarist Stoja & Jon Tucker, 2007. "A simplified approach to modeling the co‐movement of asset returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(6), pages 575-598, June.
  • Handle: RePEc:wly:jfutmk:v:27:y:2007:i:6:p:575-598
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    Cited by:

    1. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    2. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Working Papers in Economics 14/04, University of Canterbury, Department of Economics and Finance.
    3. Allen, David E. & McAleer, Michael & Powell, Robert J. & Singh, Abhay K., 2017. "Volatility Spillovers from Australia's major trading partners across the GFC," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 159-175.
    4. Allen, David E. & Amram, Ron & McAleer, Michael, 2013. "Volatility spillovers from the Chinese stock market to economic neighbours," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 238-257.
    5. David E Allen & Michael McAleer & Robert J Powell & Abhay Kumar Singh, 2012. "Volatility spillovers from the US to Australia and China across the GFC," KIER Working Papers 838, Kyoto University, Institute of Economic Research.
    6. Akram Hasanov & Ahmad Baharumshah, 2014. "Exchange-Rate Risk and Exports," Problems of Economic Transition, Taylor & Francis Journals, vol. 57(1), pages 80-101.
    7. Richard D. F. Harris & Jian Shen & Evarist Stoja, 2010. "The Limits to Minimum-Variance Hedging," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(5-6), pages 737-761.
    8. Farhat Iqbal, 2013. "Robust estimation of the simplified multivariate GARCH model," Empirical Economics, Springer, vol. 44(3), pages 1353-1372, June.
    9. Polanski, Arnold & Stoja, Evarist, 2014. "Co-dependence of extreme events in high frequency FX returns," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 164-178.
    10. Polanski, Arnold & Stoja, Evarist & Zhang, Ren, 2013. "Multidimensional risk and risk dependence," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3286-3294.

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