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Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions

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  • I.-Yuan Chuang
  • Jin-Ray Lu
  • Pei-Hsuan Lee

Abstract

This article analyses the volatility forecasting performance of the GARCH models based on various distributional assumptions in the context of stock market indices and exchange rate returns. Using rollover methods to construct the out-of-the-sample volatility forecasts, this study shows that the GARCH model combined with the logistic distribution, the scaled student's t distribution and the Riskmetrics model are preferable both in stock markets and foreign exchange markets. The exponential power and the mixture of two normal distributions are, however, less recommended. Furthermore, a complex distribution does not always outperform a simpler one, although the exact ranking depends on the application of underlying assets and the performance statistics being used.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:apfiec:v:17:y:2007:i:13:p:1051-1060
    DOI: 10.1080/09603100600771000
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    4. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
    5. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy (IfW Kiel).
    6. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy (IfW Kiel).
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    9. N’dri Konan Léon, 2015. "Forecasting Stock Return Volatility: Evidence from the West African Regional Stock Market," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 5(6), pages 1-2.
    10. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
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