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Price volatility forecast for agricultural commodity futures: The role of high frequency data


Huang, W and Huang, Z and Matei, M and Wang, T, Price volatility forecast for agricultural commodity futures: The role of high frequency data, Romanian Journal of Economic Forecasting, 15, (4) pp. 83-103. ISSN 1582-6163 (2012) [Refereed Article]


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Copyright 2012 Romanian Journal of Economic Forecasting

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Realized measures of volatility based on high frequency data contain valuable information about the unobserved conditional volatility. In this paper, we use the Realized GARCH model developed by Hansen, Huang and Shek (2012) to estimate and forecast price volatility for four agricultural commodity futures. Empirical evidences, both in-sample and out-of-sample, show that the Realized GARCH model and its variants outperform the conventional volatility models that only use daily price data, such as GARCH and EGARCH. We also consider skewed student's t-distribution to account for the skewness and fat-tail in the agricultural futures prices. The empirical performances are relatively close for models using three different realized measures, as the measurement equation in the Realized GARCH model can adjust to the different realized measures to some extent.

Item Details

Item Type:Refereed Article
Keywords:high frequency data, fat-tail, skewness, realized volatility, agricultural futures
Research Division:Commerce, Management, Tourism and Services
Research Group:Banking, finance and investment
Research Field:Financial econometrics
Objective Division:Plant Production and Plant Primary Products
Objective Group:Other plant production and plant primary products
Objective Field:Other plant production and plant primary products not elsewhere classified
UTAS Author:Matei, M (Dr Marius Matei)
ID Code:85121
Year Published:2012
Web of Science® Times Cited:4
Deposited By:Research Division
Deposited On:2013-06-14
Last Modified:2017-09-18
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