Volatility Spillover Between Oil And Agricultural Commodity Markets

Abstract

Globally, instability in the energy market is quickly reflected in the food market. Therefore, there exists a direct relationship between oil and food. This study adopts variance in causality tests on daily data from 01 January 1986 to 7 June 2012 in order to identify the causality of the food price crisis. The data is divided into three sub-periods: the pre-crisis period (01 January 1986 to 31 December 2005), the crisis period (01 January 2006-31 December 2008) and the post crises period (01 January 2009 - 7 June 2012). See figure1 The first part of the causality in variance analysis adopts the newly developed Hafner and Herwartz (2006) approach. It uses both univariate GARCH and the multivariate GARCH-BEKK model introduced by Engle and Kroner (1995) to analyse volatility spillover effects of the food crisis on the selected agricultural commodity markets (wheat, corn, soybeans, and sugar). In the multivariate analysis volatility spillovers have been taken into account by including a dummy variable in the conditional variance specification. The results show that the persistence of the volatility doesn't change significantly during and following the food price crisis. The second part of the causality in variance analysis uses a Granger test to determine whether the oil market influences the agriculture market, or vice versa. The test results show that while there is no risk of transmission between oil and the selected agricultural commodity markets (wheat, corn, soybeans, and sugar) in the pre-crisis period, during the crises the agriculture market's volatility - with the exception of wheat - spills into the oil market. After the crises, only the soybean market spills over to the oil market, while there is no effect in the other markets. This paper aims to foster an awareness and understanding of national food security issues. This understanding builds a knowledge-based society and helps policymakers make strategic plans that can be tailored to the unique challenges and resources of the region. It promotes innovation, discovery and economic diversification by using the newly developed multivariate garch model thereby showing that the dynamics of volatility transmission change significantly before, during, and after a food price crisis. During the crisis, risk transmission emerged as another dimension of the dynamic interrelationships between energy and agricultural markets.