Template-type: ReDIF-Paper 1.0
Author-Name: Chia-Lin Chang
Author-Email: changchialin@nchu.edu.tw
Author-Person: pch286 
Author-Workplace-Name: Department of Applied Economics, Department of Finance, National Chung Hsing University, Taiwan
Author-Name: Yiying Li
Author-Workplace-Name: Department of Quantitative Finance National Tsing Hua University, Taiwan
Author-Name: Michael McAleer
Author-Person: pmc90 
Author-Workplace-Name: Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute,
	The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of
	Economic Research, Kyoto University. 
Title: Volatility Spillovers Between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice
Abstract: Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. 
	In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock 
	in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as 
	oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate 
	models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and 
	empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in 
	bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not 
	surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. 
	Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating 
	multivariate conditional volatility models, specifically the BEKK and DCC models. A serious technical deficiency is that 
	the Quasi-Maximum Likelihood Estimates (QMLE) of a full BEKK matrix, which is typically estimated in examining volatility 
	spillover effects, has no asymptotic properties, except by assumption, so that no statistical test of volatility spillovers 
	is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well 
	known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the 
	parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. 
	The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and 
	agricultural markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers 
	might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are 
	given, and the different models used in empirical applications are evaluated in terms of the new definitions and 
	statistical criteria.
Classification-JEL: C22, C32, C58, G32, O13, Q42.
Keywords: Energy markets, Agricultural markets, Volatility and covolatility spillovers, Univariate and multivariate conditional 
	volatility models, BEKK, DCC, Definitions of spillovers.

Note: For financial support, the first author wishes to thank the National Science Council, Taiwan, and the third author wishes to 
	acknowledge the Australian Research Council and the National Science Council, Taiwan.
Length: 32 pages 
Creation-Date: 2015-06  
Number: 2015-08 
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1508.txt
File-URL: https://eprints.ucm.es/id/eprint/31201/1/1508.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1508