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Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity

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  • Tian, Fengping
  • Yang, Ke
  • Chen, Langnan

Abstract

We develop a time-varying HAR model where both the predictors and the regression coefficients are allowed to change over time, and use it to forecast the realized volatility in the fast-growing agricultural commodity futures markets of China. The proposed model is constructed by incorporating all potential predictors in a time-varying HAR framework, and giving the independent normal-gamma autoregressive (NGAR) process priors to the regression coefficients. The out-of-sample forecast results show that the proposed HAR model with time-varying sparsity improves the forecast performances substantially relative to both the simple HAR model and more sophisticated HAR-type models in almost all cases. Finally, the forecast performance of the proposed model is robust to the alternative proxies of volatility.

Suggested Citation

  • Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
  • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:132-152
    DOI: 10.1016/j.ijforecast.2016.08.002
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