Volatility forecasting in the Chinese commodity futures market with intraday data

Jiang, Ying, Ahmed, Shamim ORCID: 0000-0003-3712-5213 and Liu, Xiaoquan
(2017) Volatility forecasting in the Chinese commodity futures market with intraday data. Review of Quantitative Finance and Accounting, 48 (4). pp. 1123-1173.

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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
Uncontrolled Keywords: Out-of-sample predictability, Long memory time series, Futures market regulation, Realized volatility, Econometric models
Depositing User: Symplectic Admin
Date Deposited: 17 Jun 2019 08:56
Last Modified: 19 Jan 2023 00:40
DOI: 10.1007/s11156-016-0570-4
Open Access URL: https://link.springer.com/article/10.1007%2Fs11156...
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3045881