Faculty of Economics and Business Causal dynamics within the food-versus-fuel nexus: parametric and nonparametric causality testing in US spot markets Thesis presentation Sebastian Fischer May 22nd, 2013
Introduction Data Methodology Recent development Key Findings Motivation Literature Link FAO Food Price Indices 2000−2012 Food 200 100 300 Oils 200 100 500 Sugar 300 100 300 Cereals 200 100 2000 2002 2004 2006 2008 2010 2012 Year ◮ Upward trend in agricultural commodity prices since 2000 ◮ Biofuel mandates strengthen link between energy and commodity markets → exponential growth in biofuel production ◮ Financialization of agricultural commodities → own asset class ◮ Food price crisis between 2006-2008; price levels reissued in 2011 ◮ Impacts on terms of trade, real incomes and fiscal positions 2 / 12
Introduction Data Methodology Recent development Key Findings Motivation Literature Link ◮ Link between energy and agricultural commodity markets ◮ Structure of this (potential) link ◮ Changing dynamics after introducing policies in favour of biofuels ◮ Concrete policy advice with respect to biofuel legislation ◮ Existing literature focuses on weekly observations, Granger causality within the parametric (linear) framework and limited number of variables 3 / 12
Introduction Data Methodology Key Findings Literature Link ◮ Two samples on nominal spot price series of key agricultural commodities and liquid fuels: log-levels log ( p t ) and log-returns r t = log ( p t ) − log ( p t − 1 ) ◮ 01/2000-12/2012; n=3,268; P1 2000-2006, P2 2007-2012 corn, wheat, soybean, sugar, diesel, gasoline, crude oil ◮ 08/2007-12/2012; n=1,368; P1 2007-2009, P2 2010-2012 diesel and gasoline replaced by ethanol and biodiesel ◮ Similar characteristics as returns from financial markets: zero mean, skewed and excess kurtosis. log ( p t ) ∼ I ( 1 ) , r t ∼ I ( 0 ) ; high positive (linear) correlations among fuel and commodity series; significant correlations between energy and commodity price series, but smaller magnitude. 4 / 12
Introduction Data Methodology Key Findings Literature Link ◮ Parametric Granger causality testing based on Granger (1969) by means of VAR or VECM specification due to Engle and Granger (1987) ◮ Evidence for nonlinearity by using BDS test of Brock, Dechert and Scheinkman (1996) ◮ Nonparametric Granger causality testing due to Diks and Panchenko (2006): focus on residual series of conditional mean models ◮ Pairwise and full-variate implementation ◮ Stepwise procedure: Conditional mean model → linear Granger causality → residual based testing for nonlinear Granger causality uncovers strictly nonlinear relations → multivariate GARCH-filter → remaining Granger causality is not due to risk transmissions ◮ Conditional second moments captured by multivariate GARCH of Engle and Kroner (1995): full GARCH-BEKK(1,1) µ t = E ( r t | F t − 1 ) , ǫ t = r t − µ t = H 1 / 2 z t and t K K (1) � � t | F t − 1 ) = CC ′ + ′ H t = E ( ǫ t ǫ ′ A ′ B ′ k ǫ t − 1 ǫ t − 1 A k + k H t − 1 B k , k = 1 k = 1 5 / 12
Introduction Data Methodology Key Findings Literature Link ◮ Causality in its most general form can be written as ( Y t + 1 , · · · , Y t + k ) | ( F X , t , F Y , t ) ≁ ( Y t + 1 , · · · , Y t + k ) | F Y , t ) . (2) ◮ The approach of Granger (1969) amounts to Wald-test in p p � � X t = c 0 + α i X t − i + β i Y t − i + u 1 t i = 1 i = 1 p p � � Y t = c 1 + γ i X t − i + δ i Y t − i + u 2 t . i = 1 i = 1 ◮ Diks and Panchenko (2006) restate (2) in terms of conditional t , Y l y distributions with ( X , Y , Z ) = ( X l x t , Y t + 1 ) as f X , Y , Z ( x , y , z ) = f X , Y ( x , y ) · f Y , Z ( y , z ) (3) f Y ( y ) f Y ( y ) f Y ( y ) and derive test statistic T n , which is asymptotically distributed as standard normal: n � n − 1 ( � f X , Y , Z ( X i , Y i , Z i ) � f Y ( Y i ) − � f X , Y ( X i , Y i ) � T n ( ǫ n ) = n ( n − 2 ) × f Y , Z ( Y i , Z i )) i = 1 6 / 12
Introduction Data Causality Methodology Robustness Key Findings Conclusions Literature Link Full-variate Nonlinear Causality Sample 1 after GARCH-BEKK filtering 1 Diesel Gasoline WTI Corn Wheat Soy Sugar P1 Diesel - *** *** Gasoline - WTI *** * - Corn - ** Wheat ** - Soy * - Sugar * - P2 Diesel Gasoline WTI Corn Wheat Soy Sugar Diesel - ** ** * ** Gasoline - *** * * ** WTI * * - * Corn * ** ** - ** * Wheat * *** - ** Soy ** ** *** - Sugar *** ** ** ** - 1 ***, ** and * indicate rejection of the null hypothesis at the 1%, 5% and 10% level, respectively. If i denotes the row and j denotes the column, causality running from X to Y is represented by the ij-th element; element ji refers to causality running from Y to X. 7 / 12
Introduction Data Causality Methodology Robustness Key Findings Conclusions Literature Link Full-variate Nonlinear Causality Sample 2 after GARCH-BEKK filtering 1 Biodiesel Ethanol WTI Corn Wheat Soy Sugar P1 Biodiesel - *** * ** * Ethanol * - ** WTI * - *** Corn - * Wheat ** - Soy *** * - ** Sugar * * ** - P2 Biodiesel Ethanol WTI Corn Wheat Soy Sugar Biodiesel - Ethanol - WTI ** - Corn - *** * Wheat *** - * Soy - Sugar - 1 ***, ** and * indicate rejection of the null hypothesis at the 1%, 5% and 10% level, respectively. If i denotes the row and j denotes the column, causality running from X to Y is represented by the ij-th element; element ji refers to causality running from Y to X. 8 / 12
Introduction Data Causality Methodology Robustness Key Findings Conclusions Literature Link ◮ Various multivariate GARCH models have been used to capture conditional second moments: full and diagonal GARCH-BEKK, Dynamic Conditional Correlation-GARCH (DCC) due to Engle (2002) ◮ Impact of considering three different lag lengths: Akaike (1973) and Schwarz (1978) information criterion plus fixed lag length of l=5 due to data frequency ◮ Decreased data frequency: weekly data using 5-day averages ◮ Results indicate importance of taking full variance-covariance structure into account, but no clear-cut solution with respect to lag length selection ◮ Results based on weekly data show decreased absolute number of causal relations and average significance, but these should be interpreted with caution 9 / 12
Introduction Data Causality Methodology Robustness Key Findings Conclusions Literature Link ◮ Increased causal dynamics during recent years, although GARCH-BEKK filtering removes many of the causal relations ◮ Might indicate more integrated and efficient markets ◮ Results for sample 2 suggest that link between biofuel and agricultural commodity markets is weaker than suggested ◮ Policy advice: commodity price buffers might dampen the impacts, especially for developing countries; re-adjust policy frame to lower pressure on supply side ◮ Improve infrastructure on biofuel data and extend research on nonparametric causal relations 10 / 12
Introduction Data Methodology Key Findings Literature Link ◮ Stelios D. Bekiros and Cees G.H. Diks. The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality. Energy Economics , Vol. 5, 2008. Cees Diks and Valentyn Panchenko. A new statistic and practical guidelines for nonparametric granger causality testing. Journal of Economic Dynamics and Control , Vol. 30, 2006. Zibin Zhang, Luanne Lohr, Cesar Escalante, and Michael Wetzstein. Food versus fuel: What do prices tell us? Energy Policy , Vol. 38, 2010. David Zilberman, Gal Hochman, Deepak Rajagopal, Steve Sexton, and Govinda Timilsina. The impact of biofuels on commodity food prices: Assessment of findings. American Journal of Agricultural Economics , 2012. 11 / 12
Introduction Data Methodology Key Findings Literature Link Data, program code and slides will be made available at http://uva-thesis.de.vu/ . Thank you for your attention! 12 / 12
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