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Ying, Jiang, Shamin, Ahmed and Xiaoquan, Liu (2016) Volatility forecasting in the Chinese commodity futures market with intraday data. Review of Quantitative Finance and Accounting . ISSN 1573-7179

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Abstract

Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.

Item Type:	Article
Additional Information:	The final publication is available at Springer via http://dx.doi.org/10.1007/s11156-016-0570-4
Keywords:	Out-of-sample predictability; Long memory time series; Futures market regulation; Realized volatility; Econometric models
Schools/Departments:	University of Nottingham Ningbo China > Faculty of Business > Nottingham University Business School China University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
Identification Number:	https://doi.org/10.1007/s11156-016-0570-4
Depositing User:	Fuller, Stella
Date Deposited:	21 Jun 2016 13:48
Last Modified:	15 Nov 2017 01:43
URI:	https://eprints.nottingham.ac.uk/id/eprint/34280

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