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Modelling time-varying conditional correlations in the volatility of Tapis oil spot and forward returns


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  • Matteo Manera
  • Michael McAleer
  • Margherita Grasso


This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and one-month forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCC-MGARCH) model of Bollerslev (1990), Vector Autoregressive Moving Average-GARCH (VARMA-GARCH) model of Ling and McAleer (2003), VARMA-Asymmetric GARCH (VARMA-AGARCH) model of Hoti et al. (2002), and the Dynamic Conditional Correlation (DCC) model of Engle (2002). The dynamic correlations are extremely useful in determining whether the spot and forward returns are substitutes or complements, which can be used to hedge against contingencies. Both the univariate ARCH and GARCH estimates are significant for spot and forward returns, whereas the estimates of the asymmetric effect at the univariate level are not statistically significant for either spot or forward returns. Standard diagnostic tests show that the AR(1)-GARCH(1, 1) and AR(1)-GJR(1, 1) specifications are statistically adequate for both the conditional mean and the conditional variance. The multivariate estimates for the VAR(1)-GARCH(1, 1) and VAR(1)-AGARCH(1, 1) models show that the ARCH and GARCH effects for spot (forward) returns are significant in the conditional volatility model for spot (forward) returns. Moreover, there are significant interdependences in the conditional volatilities between the spot and forward markets. The multivariate asymmetric effects are significant for both spot and forward returns. Overall the multivariate VAR(1)-AGARCH(1, 1) dominates its symmetric counterpart. The calculated constant conditional correlations between the conditional volatilities of spot and forward returns using CCC-GARCH(1, 1), VAR(1)-GARCH(1, 1) and VAR(1)-AGARCH(1, 1) are very close to 0.93. Virtually identical results are obtained when the three constant conditional correlation models are extended to include two lags in both the ARCH and GARCH components. Finally, the estimates of the two DCC parameters are statistically significant, which makes it clear that the assumption of constant conditional correlation is not supported empirically. This is highlighted by the dynamic conditional correlations between spot and forward returns, for which its sample mean is virtually identical to the computed constant conditional correlation, regardless of whether a DCC-GARCH(1, 1) or a DCC-GARCH(2, 2) is used. For these models, the dynamic conditional correlations are in the range (0.417, 0.993) and (0.446, 0.993), signifying medium to extreme interdependence. Therefore, the dynamic volatilities in the returns in Tapis oil spot and forward markets are generally interdependent over time. These findings suggest that a sensible hedging strategy would consider spot and forward markets as being characterized by different degrees of substitutability.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 16 (2006)
Issue (Month): 7 ()
Pages: 525-533

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Handle: RePEc:taf:apfiec:v:16:y:2006:i:7:p:525-533

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Cited by:
  1. David E. Allen & Ron Amram & Michael McAleer, 2011. "Volatility Spillovers from the Chinese Stock Market to Economic Neighbours," Documentos del Instituto Complutense de Análisis Económico 2011-38, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
  2. Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2009. "Modelling Conditional Correlations for Risk Diversification in Crude Oil Markets," CARF F-Series CARF-F-162, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  3. Tansuchat, R. & Chang, C-L. & McAleer, M.J., 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," Econometric Institute Research Papers EI 2010-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. David E Allen & Michael McAleer & Robert J Powell & Abhay Kumar Singh, 2012. "Volatility spillovers from the US to Australia and China across the GFC," KIER Working Papers 838, Kyoto University, Institute of Economic Research.
  5. Hakim, A. & McAleer, M.J., 2009. "VaR Forecast and Dynamic Conditional Correlations for Spot and Futures Returns on Stocks and Bonds," Econometric Institute Research Papers EI 2009-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  6. Matteo Manera & Marcella Nicolini & Ilaria Vignati, 2012. "Returns in commodities futures markets and financial speculation: a multivariate GARCH approach," Quaderni di Dipartimento 170, University of Pavia, Department of Economics and Quantitative Methods.
  7. Vargas, Gregorio A., 2008. "What Drives the Dynamic Conditional Correlation of Foreign Exchange and Equity Returns?," MPRA Paper 7174, University Library of Munich, Germany.


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