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

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• 作者：Fengping Tian^aAuthor Vitae ; Ke Yang^bAuthor Vitae ; Langnan Chen^c ; ^{lnscln@mail.sysu.edu.cn}Author Vitae
• 关键词：Realized volatility ; Forecast ; HAR model ; Time-varying sparsity ; Agricultural commodity futures
• 刊名：International Journal of Forecasting
• 出版年：2017
• 出版时间：January-March 2017
• 年：2017
• 卷：33
• 期：1
• 页码：132-152
• 全文大小：2781 K
• 卷排序：33

文摘

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.