Systemic Risk in Energy Derivative Markets: A Graph-Theory Analysis

This article uses graph theory to provide novel evidence regarding market integration, a favorable condition for systemic risk to appear in. Relying on daily futures returns covering a 12-year period, we examine cross- and inter-market linkages, both within the commodity complex and between commodities and other financial assets. In such a high dimensional analysis, graph theory enables us to understand the dynamic behavior of our price system. We show that energy markets - as a whole - stand at the heart of this system. We also establish that crude oil is itself at the center of the energy complex. Further, we provide evidence that commodity markets have become more integrated over time.

[1]  Liberalizing European Energy Markets , 2008 .

[2]  J. Gower Some distance properties of latent root and vector methods used in multivariate analysis , 1966 .

[3]  K Kaski,et al.  Time-dependent cross-correlations between different stock returns: a directed network of influence. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  B. Metz IPCC special report on carbon dioxide capture and storage , 2005 .

[5]  Interest rate differentials, market integration, and the efficiency of commodity futures markets , 1999 .

[6]  Michael S. Haigh,et al.  Commodities and Equities: Ever a “Market of One”? , 2009, The Journal of Alternative Investments.

[7]  Rodney Garratt,et al.  Which bank is the "central" bank? , 2010 .

[8]  R. Mantegna Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[9]  H. Stoll,et al.  Commodity Index Investing and Commodity Futures Prices , 2009 .

[10]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[11]  S. Lumpkin,et al.  The Treasury Yield Curve as a Cointegrated System , 1992, Journal of Financial and Quantitative Analysis.

[12]  Aie,et al.  Energy Technology Perspectives 2012 , 2006 .

[13]  Edward S. Rubin,et al.  Comparative assessments of fossil fuel power plants with CO2 capture and storage , 2005 .

[14]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[15]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[16]  K. Kaski,et al.  Dynamics of market correlations: taxonomy and portfolio analysis. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  G. Caldarelli,et al.  Networks of equities in financial markets , 2004 .

[18]  Eduardo S. Schwartz The stochastic behavior of commodity prices: Implications for valuation and hedging , 1997 .

[19]  Tess Dance,et al.  Mapping geological storage prospectivity of CO2 for the world's sedimentary basins and regional source to sink matching , 2005 .

[20]  K. Kaski,et al.  Dynamic asset trees and Black Monday , 2002, cond-mat/0212037.

[21]  Amos Maritan,et al.  Size and form in efficient transportation networks , 1999, Nature.

[22]  Janusz A. Holyst,et al.  Correlations in commodity markets , 2008, 0803.3884.

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

[24]  Aie,et al.  CO2 Capture and Storage: A Key Carbon Abatement Option , 2008 .

[25]  D. Bessler,et al.  Causality And Price Discovery: An Application Of Directed Acyclic Graphs , 2004 .