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Forecasting realized volatility in a changing world: A dynamic model averaging approach

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  • Wang, Yudong
  • Ma, Feng
  • Wei, Yu
  • Wu, Chongfeng
Abstract
In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.

Suggested Citation

  • Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    • Handle: RePEc:eee:jbfina:v:64:y:2016:i:c:p:136-149
    DOI: 10.1016/j.jbankfin.2015.12.010
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    More about this item

    Keywords

    S&P 500 index; Realized volatility; Dynamic model averaging; Time-varying parameters; Portfolio;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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