Volatility forecasting in the Chinese commodity futures market with intraday data

Ying Jiang (), Shamim Ahmed () and Xiaoquan Liu ()
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Ying Jiang: University of Nottingham Ningbo
Shamim Ahmed: University of Nottingham
Xiaoquan Liu: University of Nottingham Ningbo

Review of Quantitative Finance and Accounting, 2017, vol. 48, issue 4, No 10, 1123-1173

Abstract: 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.

Keywords: Out-of-sample predictability; Long memory time series; Futures market regulation; Realized volatility; Econometric models (search for similar items in EconPapers)
JEL-codes: C5 G12 G13 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11156-016-0570-4

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