Empirical Evidence Linking Futures Price Movements of Biofuel Crops and Conventional Energy Fuel

This study proposes a dynamic vine copula based ARMAX-GARCH model to explore the dependence structures between energy futures and agricultural futures, and between corn future and soybean future conditional on energy futures etc. The more important thing is that we employ the empirical results of dynamic vine copulas to forecast the expected shortfall (ES) and the optimal portfolio weights (OPW) based on minimum ES and Monte Carlo simulation method results showed that the appropriate margins were skewed student t distribution for soybean future return, and student t distribution for crude oil, palm oil and corn future returns, and the time-varying copulas T copula, R-BB8(180\(^\circ \)), R-BB8(180\(^\circ \)), Gaussian copula, R-Joe(180\(^\circ \)) and T copula can preferably capture the dependences compared with static copulas in C-vine copula structure. Moreover, we found that the values of ES will converge to \(-0.0121, -0.0145\) and \(-0.0164\) at period t\(+\)1 under 5, 2 and 1 % level, respectively. Meanwhile, As long as we invest in strict accordance with the optimal portfolio weights, the ES will reduce 56, 54 and 53 % at 5, 2 and 1 % level, respectively.

[1]  Michael McAleer,et al.  Modelling Long Memory Volatility in Agricultural Commodity Futures Returns , 2009 .

[2]  R. Pindyck,et al.  The Excess Co-Movement of Commodity Prices , 1988 .

[3]  Agapi Somwaru,et al.  Dynamic Impacts of a Shock in Crude Oil Price on Agricultural Chemical and Fertilizer Prices , 1992 .

[4]  Hans Manner,et al.  A Survey on Time-Varying Copulas: Specification, Simulations, and Application , 2012 .

[5]  Roger M. Cooke,et al.  Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines , 2001, Annals of Mathematics and Artificial Intelligence.

[6]  Wo-Chiang Lee,et al.  Portfolio value at risk with Copula-ARMAX-GJR-GARCH model: Evidence from the gold and silver futures , 2011 .

[7]  Roger M. Cooke,et al.  Uncertainty Analysis with High Dimensional Dependence Modelling: Kurowicka/Uncertainty Analysis with High Dimensional Dependence Modelling , 2006 .

[8]  A. Frigessi,et al.  Pair-copula constructions of multiple dependence , 2009 .

[9]  Joe L. Outlaw,et al.  Examining the Evolving Correspondence Between Petroleum Prices and Agricultural Commodity Prices , 2007 .

[10]  Taufiq Choudhry,et al.  Short-run deviations and time-varying hedge ratios: Evidence from agricultural futures markets , 2009 .

[11]  M. Rockinger,et al.  The Copula-GARCH model of conditional dependencies: An international stock market application , 2006 .

[12]  Noel D. Uri,et al.  Crude oil price volatility and unemployment in the United States , 1996 .

[13]  Dominique Guegan,et al.  Pricing bivariate option under GARCH processes with time-varying copula , 2008 .

[14]  B. Hansen Autoregressive Conditional Density Estimation , 1994 .

[15]  T. Bedford,et al.  Vines: A new graphical model for dependent random variables , 2002 .

[16]  T. Palaskas,et al.  Is there excess co-movement of primary commodity prices? A co-integration test , 1991 .

[17]  Anastasios Malliaris,et al.  Linkages between agricultural commodity futures contracts , 1996 .

[18]  Yang Yin-sheng,et al.  Analysis of Price Trends of Crude Oil, Agricultural Commodities and Policy Choices of Biofuels in Developing Countries , 2012 .

[19]  H. Joe Families of $m$-variate distributions with given margins and $m(m-1)/2$ bivariate dependence parameters , 1996 .

[20]  L. Kilian The Economic Effects of Energy Price Shocks , 2007 .

[21]  Cindy L. Yu,et al.  Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis , 2011 .

[22]  Roger M. Cooke,et al.  Monte Carlo simulation of vine dependent random variables for applications in uncertainty analysis , 2001 .

[23]  Aristidis K. Nikoloulopoulos,et al.  Vine copulas with asymmetric tail dependence and applications to financial return data , 2012, Comput. Stat. Data Anal..

[24]  Songsak Sriboonchitta,et al.  Analysis of Volatility and Dependence between the Tourist Arrivals from China to Thailand and Singapore: A Copula-Based GARCH Approach , 2013 .

[25]  Aristidis K. Nikoloulopoulos,et al.  Tail dependence functions and vine copulas , 2010, J. Multivar. Anal..

[26]  U. Soytaş,et al.  World oil prices and agricultural commodity prices: Evidence from an emerging market , 2011 .

[27]  Ying Fan,et al.  How Does Oil Price Volatility Affect Non-Energy Commodity Markets? , 2012 .

[28]  Huimin Chung,et al.  The economic value of co-movement between oil price and exchange rate using copula-based GARCH models , 2011 .

[29]  Wing Lon Ng Modeling duration clusters with dynamic copulas , 2008 .

[30]  Ludger Rüschendorf,et al.  Distributions with fixed marginals and related topics , 1999 .

[31]  Andrew J. Patton Modelling Asymmetric Exchange Rate Dependence , 2006 .

[32]  Roger M. Cooke,et al.  Uncertainty Analysis with High Dimensional Dependence Modelling , 2006 .