Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory

Walid Chkili, Shawkat Hammoudeh and Duc Khuong Nguyen

Energy Economics, 2014, vol. 41, issue C, 1-18

Abstract: This paper explores the relevance of asymmetry and long memory in modeling and forecasting the conditional volatility and market risk of four widely traded commodities (crude oil, natural gas, gold, and silver). A broad set of the most popular linear and nonlinear GARCH-type models is used to investigate this relevancy. Our in-sample and out-of-sample results show that volatility of commodity returns can be better described by nonlinear volatility models accommodating the long memory and asymmetry features. In particular, the FIAPARCH model is found to be the best suited for estimating the VaR forecasts for both short and long trading positions. This model also gives for all four commodities the lowest number of violations under the Basel II Accord rule, given a risk exposure at the 99% confidence level. Several implications for commodity market risks, policy regulations and hedging strategies can be drawn from the obtained results.

Keywords: Commodity markets; GARCH models; Asymmetries; Long memory; Volatility forecasts (search for similar items in EconPapers)
JEL-codes: C22 G17 Q47 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (181)

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Related works:
Working Paper: Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014)
Working Paper: Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:41:y:2014:i:c:p:1-18

DOI: 10.1016/j.eneco.2013.10.011

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