TY - JOUR
T1 - Commodities returns’ volatility in financialization era
AU - Handika, Rangga
AU - Putra, Iswahyudi Sondi
N1 - Publisher Copyright:
© 2017, © Emerald Publishing Limited.
PY - 2017
Y1 - 2017
N2 - Purpose: This 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/approach: This paper uses the VaR back-testing approach at six different commodities, seven different volatility models and five different time horizons. Findings: This 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/value: This 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.
AB - Purpose: This 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/approach: This paper uses the VaR back-testing approach at six different commodities, seven different volatility models and five different time horizons. Findings: This 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/value: This 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.
KW - Back-testing
KW - Commodity markets
KW - Investments performance
KW - Value-at-Risk
UR - http://www.scopus.com/inward/record.url?scp=85027876212&partnerID=8YFLogxK
U2 - 10.1108/SEF-10-2015-0254
DO - 10.1108/SEF-10-2015-0254
M3 - Article
AN - SCOPUS:85027876212
SN - 1086-7376
VL - 34
SP - 344
EP - 362
JO - Studies in Economics and Finance
JF - Studies in Economics and Finance
IS - 3
ER -