Price volatility spillovers among agricultural commodity and crude oil markets: Evidence from the range-based estimator

Abstract

Th e paper examines the price volatility spillovers among the crude oil, soybeans, corn, wheat, and sugar futures markets over the period 1/1/2006-11/29/2013. We separately investigate the periods of the pre-crisis, the crisis, and the post-crisis in fi nancial markets. We use the Yang-Zhang estimators for the historical volatility and fi nd that there is a volatility sprawl from the crude oil to corn markets. Th ere is also bi-directional causality between the corn and soybeans markets. In addition, we observe signifi cant volatility spillovers from both the soybeans and the corn markets to the wheat markets. Th e results are also valid in a diff erent sub-period analysis.

What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?

“…When the nexus is reversed, we detect relatively high risk spillover effect from soybean to corn even in very low volatility conditions in corn market, amounting to 43%, and this influence gradually increases with the rise of volatility in corn market, reaching 88% and 73% when volatility is at its peak. These results are in line with the paper of Gozgor and Memis (2015) who reported strong bidirectional volatility transmission between the soybeans and corn markets. They explained that strong nexus between corn and soybean probably comes from the fact that both corn and soybean are used in the biofuel production.…”

Decoding the Australian electricity market: New evidence from three-regime hidden semi-Markov model

“…It is also important to note that the recent literature has extensively analysed the volatility spillovers across the energy markets. A strand of this literature investigates the volatility spillovers between energy (mainly oil) and stock markets (Arouri et al, 2012), while another strand focuses on the spillovers between energy and the commodity markets (mainly agricultural commodity and precious metals markets) (Du et al, 2011;Ewing and Malik, 2013;Gozgor and Memis, 2015). There is also a technical part of the literature that explicitly investigates whether the econometric methodologies that have been employed to analyse volatility spillovers are robust to the frequency of the data (Yarovaya et al, 2016), to the different time horizons (Gozgor et al, 2016) (e.g.…”

Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets

“…Gozgor and Memis (2015) also concludes that modeling leverage effect on the futures of oil, soybeans, corn, and wheat markets are crucial to understand the price volatility transmission mechanism among the crude oil and agricultural commodity markets. In light of these recent findings, the GJR-GARCH model specification in this paper is able to capture the long-memory and the leverage effects in the crude oil and agricultural commodity markets (Yarovaya et al, 2016).…”

“…These break dates are related to the boom-and-bust cycle in the commodity markets, and they are used in many empirical papers (e.g., Gozgor and Memis, 2015;Nazlioglu et al, 2013) The agricultural commodity markets (corn, sugar, soybeans and wheat) price and crude oil price data are based on the futures markets, and they are obtained from the data source by Bloomberg. The VIX data are also obtained from the Chicago Board Options Exchange (CBOE) and the equity market uncertainty (EMU) index data are obtained from Baker et al (2015) within the website (http://www.policyuncertainty.com/) of Scott R. Baker, Nick…”