Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches
Langnan Chen and
International Review of Economics & Finance, 2017, vol. 49, issue C, 276-291
In order to reduce the uncertainty associated with a single predictor model, we incorporate the bagging and combination approaches into a HAR model with the lags of realized volatility and other potential predictors to forecast the realized volatility of agricultural commodity futures in China. We evaluate the performances of the two approaches by employing the mean square forecast error (MSFE) loss function, the modified DM test and the model confidence set (MCS) test at the multiple horizons over the three out-of-sample periods. We find that the realized forecasts from the HAR model with bagging and principal component (PC) combination approaches produce the lowest MSFE at relatively longer forecast horizons. We also find that the simple average of the forecasts from the HAR models with bagging and PC combination methods leads to a further reduction in MSFE, suggesting that they are the effective methods to forecast the realized volatility of agricultural commodity futures in China.
Keywords: Realized volatility; Forecast; Agricultural commodity futures; Bagging approach; Combination approaches (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:49:y:2017:i:c:p:276-291
Access Statistics for this article
International Review of Economics & Finance is currently edited by H. Beladi and C. Chen
More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().