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

Fengping Tian, Ke Yang and Langnan Chen

International Journal of Forecasting, 2017, vol. 33, issue 1, 132-152

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.

Keywords: Realized volatility; Forecast; HAR model; Time-varying sparsity; Agricultural commodity futures (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (48)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:1:p:132-152

DOI: 10.1016/j.ijforecast.2016.08.002

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