Volatility forecasting in the Chinese commodity futures market with intraday data

Ying Jiang, Shamim Ahmed, Xiaoquan Liu

Research output: Journal PublicationArticlepeer-review

12 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1123-1173
Number of pages51
JournalReview of Quantitative Finance and Accounting
Volume48
Issue number4
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Econometric models
  • Futures market regulation
  • Long memory time series
  • Out-of-sample predictability
  • Realized volatility

ASJC Scopus subject areas

  • Accounting
  • Business, Management and Accounting (all)
  • Finance

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