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Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models

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  • Lee , Hojin

    (Myong Ji University)

Abstract

We investigate the asymmetry between positive and negative returns in their effect on conditional variance of the stock market index and incorporate the characteristics to form an out-of-sample volatility forecast. Contrary to prior evidence, however, the results in this paper suggest that no asymmetric GARCH model is superior to basic GARCH(1,1) model. It is our prior knowledge that, for equity returns, it is unlikely that positive and negative shocks have the same impact on the volatility. In order to reflect this intuition, we implement three diagnostic tests for volatility models: the Sign Bias Test, the Negative Size Bias Test, and the Positive Size Bias Test and the tests against the alternatives of QGARCH and GJR-GARCH. The asymmetry test results indicate that the sign and the size of the unexpected return shock do not influence current volatility differently which contradicts our presumption that there are asymmetric effects in the stock market volatility. This result is in line with various diagnostic tests which are designed to determine whether the GARCH(1,1) volatility estimates adequately represent the data. The diagnostic tests in section 2 indicate that the GARCH(1,1) model for weekly KOSPI returns is robust to the misspecification test. We also investigate two representative asymmetric GARCH models, QGARCH and GJR-GARCH model, for our out-of-sample forecasting performance. The out-of-sample forecasting ability test reveals that no single model is clearly outperforming. It is seen that the GJR-GARCH and QGARCH model give mixed results in forecasting ability on all four criteria across all forecast horizons considered. Also, the predictive accuracy test of Diebold and Mariano based on both absolute and squared prediction errors suggest that the forecasts from the linear and asymmetric GARCH models need not be significantly different from each other.

Suggested Citation

  • Lee , Hojin, 2009. "Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models," East Asian Economic Review, Korea Institute for International Economic Policy, vol. 13(2), pages 109-142, December.
  • Handle: RePEc:ris:eaerev:0117
    DOI: 10.11644/KIEP.JEAI.2009.13.2.203
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    Cited by:

    1. Neeti Mathur & Himanshu Mathur, 2020. "Application of GARCH Models For Volatility Modelling of Stock Market Returns: Evidences From BSE India," Proceedings of Business and Management Conferences 10112533, International Institute of Social and Economic Sciences.
    2. Desa Daba Fufa & Belianeh Legesse Zeleke, 2018. "Forecasting the Volatility of Ethiopian Birr/Euro Exchange Rate Using Garch-Type Models," Annals of Data Science, Springer, vol. 5(4), pages 529-547, December.

    More about this item

    Keywords

    Asymmetric GARCH Effect; Leverage Effect; KOSPI Return Volatility; EGARCH; GJR-GARCH; PGARCH; Sign Bias Test; Negative Size Bias Test; Positive Size Bias test; Out-of-Sample Forecasting;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F33 - International Economics - - International Finance - - - International Monetary Arrangements and Institutions

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