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Forecasting the Volatility of the Chinese Gold Market by ARCH Family Models and extension to Stable Models

Author

Listed:
  • Marie-Eliette Dury

    (UCA [2017-2020] - Université Clermont Auvergne [2017-2020])

  • Bing Xiao

    (CleRMa - Clermont Recherche Management - ESC Clermont-Ferrand - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA [2017-2020] - Université Clermont Auvergne [2017-2020])

Abstract

Gold plays an important role as a precious metal with portfolio diversification; also it is an underlying asset in which volatility is an important factor for pricing option. The aim of this paper is to examine which autoregressive conditional heteroscedasticity model has the best forecast accuracy applied to Chinese gold prices. It seems that the Student's t distribution characterizes better the heavy-tailed returns than the Gaussian distribution. Assets with higher kurtosis are better predicted by a GARCH model with Student's distribution while assets with lower kurtosis are better forecasted by using an EGARCH model. Moreover, stochastic models such as Stable processes appear as good candidates to take heavy-tailed data into account. The authors attempt to model and forecast the volatility of the gold prices at the Shanghai Gold Exchange (SGE) during 2002–2016, using various models from the ARCH family. The analysis covers from as in-sample and out-of-sample sets respectively. The results have been estimated with MAE, MAPE and RMSE as the measures of performance.

Suggested Citation

  • Marie-Eliette Dury & Bing Xiao, 2018. "Forecasting the Volatility of the Chinese Gold Market by ARCH Family Models and extension to Stable Models," Working Papers hal-01709321, HAL.
    • Handle: RePEc:hal:wpaper:hal-01709321
    Note: View the original document on HAL open archive server: https://hal.science/hal-01709321
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; Return; Volatility; Gold Market; ARCH; GARCH; GARCH-M; IGARCH; NGARCH; EGARCH; PARCH; NPARCH; TARCH; Student's t distribution; Symmetric Stable models; H-self-similar processes;
    All these keywords.

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