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A new GARCH model with higher moments for stock return predictability

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  • Narayan, Paresh Kumar
  • Liu, Ruipeng
Abstract
The main purpose of the paper is to propose a new GARCH-SK predictive regression model that accommodates higher order moments (skewness and kurtosis) in testing the null hypothesis of no predictability. Using an extensive and well-known time-series dataset on stock returns and 19 predictors for the United States, we show that our proposed GARCH-SK model outperforms a model without these higher moments. The superior performance of our proposed model holds both statistically and economically and is robust to different data frequencies.

Suggested Citation

  • Narayan, Paresh Kumar & Liu, Ruipeng, 2018. "A new GARCH model with higher moments for stock return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 93-103.
    • Handle: RePEc:eee:intfin:v:56:y:2018:i:c:p:93-103
    DOI: 10.1016/j.intfin.2018.02.016
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    References listed on IDEAS

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    More about this item

    Keywords

    GARCH; Predictive regression; Higher order moments; Data frequencies;
    All these keywords.

    JEL classification:

    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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