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Volatility Forecasting in the Hang Seng Index using the GARCH Approach

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  • Wei Liu
  • Bruce Morley

Abstract

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market. Copyright Springer Science+Business Media, LLC. 2009

Suggested Citation

  • Wei Liu & Bruce Morley, 2009. "Volatility Forecasting in the Hang Seng Index using the GARCH Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 16(1), pages 51-63, March.
    • Handle: RePEc:kap:apfinm:v:16:y:2009:i:1:p:51-63
    DOI: 10.1007/s10690-009-9086-4
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    2. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    3. Guidi, Francesco, 2010. "Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models," MPRA Paper 19851, University Library of Munich, Germany.

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