Commodities returns’ volatility in financialization era

Commodities returns’ volatility in financialization era PurposeThis paper aims to indirectly evaluate the accuracy of various volatility models using a value-at-risk (VaR) approach and to investigate the relationship between the accuracy of volatility modelling and investments performance in the financialized commodity markets.Design/methodology/approachThis paper uses the VaR back-testing approach at six different commodities, seven different volatility models and five different time horizons.FindingsThis paper finds that the moving average (MA) VaR model tends to be the best for oil, copper, wheat and corn (long horizon) whereas the exponential generalized autoregressive conditional heteroscedastic (E-GARCH) VaR model tends to be the best for gold, silver and corn (short horizon). Our findings indicate that MA volatility model should be used for oil, copper, wheat and corn (for longer time horizons) commodities whereas E-GARCH volatility model should be used for gold, silver and corn (for short time horizons) commodities. We also find that there is a positive relationship between an accurate VaR performance and commodity return. This indicates that a good job in modelling volatility will be rewarded by higher returns in financialized commodity markets.Originality/valueThis paper indirectly evaluates the accuracy of volatility model via VaR measure and investigates the relationship between the accuracy of volatility and investments performance in financialized commodity markets. This paper contributes to the literature by offering VaR approach in evaluating volatility model performance and reporting the importance of performing accurate volatility modelling in financialized commodity markets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Economics and Finance Emerald Publishing

Commodities returns’ volatility in financialization era

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1086-7376
DOI
10.1108/SEF-10-2015-0254
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to indirectly evaluate the accuracy of various volatility models using a value-at-risk (VaR) approach and to investigate the relationship between the accuracy of volatility modelling and investments performance in the financialized commodity markets.Design/methodology/approachThis paper uses the VaR back-testing approach at six different commodities, seven different volatility models and five different time horizons.FindingsThis paper finds that the moving average (MA) VaR model tends to be the best for oil, copper, wheat and corn (long horizon) whereas the exponential generalized autoregressive conditional heteroscedastic (E-GARCH) VaR model tends to be the best for gold, silver and corn (short horizon). Our findings indicate that MA volatility model should be used for oil, copper, wheat and corn (for longer time horizons) commodities whereas E-GARCH volatility model should be used for gold, silver and corn (for short time horizons) commodities. We also find that there is a positive relationship between an accurate VaR performance and commodity return. This indicates that a good job in modelling volatility will be rewarded by higher returns in financialized commodity markets.Originality/valueThis paper indirectly evaluates the accuracy of volatility model via VaR measure and investigates the relationship between the accuracy of volatility and investments performance in financialized commodity markets. This paper contributes to the literature by offering VaR approach in evaluating volatility model performance and reporting the importance of performing accurate volatility modelling in financialized commodity markets.

Journal

Studies in Economics and FinanceEmerald Publishing

Published: Aug 7, 2017

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