Should investors include commodities in their portfolios after all? New evidence

Daskalaki, Charoula; Skiadopoulos, George

doi:10.1016/j.jbankfin.2011.02.022

Cited by 313 publications

(223 citation statements)

References 63 publications

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“…Moreover, the risk factors that drive the dynamics of commodity returns may differ from those that affect stock and bond returns (e.g., Domanski and Heath, 2007;Dwyer et al, 2011). Empirical studies such as Arouri et al (2011), Daskalaki and Skiadopoulos (2011), and Narayan et al (2013) provide evidence of valuable diversification benefits from adding commodity assets into portfolios of traditional assets (bonds and stocks).…”

Section: Introductionmentioning

confidence: 99%

“…Tang and Xiong (2012) find increasing correlation between non-energy commodity futures and oil futures returns, which implies that prices of individual commodities are no longer driven by their own supply and demand conditions. the inclusion of commodity futures into stock portfolios have been found to decrease, particularly during crisis periods where they are needed (Silvennoinen and Thorp, 2010;Daskalaki and Skiadopoulos, 2011). Large fluctuations in commodity prices over recent years have also been causes for concern among governments, policymakers and traders 3 , which have significant impacts (either positive or negative) on equity markets (e.g., Baur and McDermott, 2010;Narayan and Sharma, 2011;Filis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets

Bekiros

Nguyen

Sandoval

et al. 2017

European Journal of Operational Research

131

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This paper investigates the dynamic causal linkages among U.S. equity and commodity futures markets via the utilization of complex network theory. We make use of rolling estimations of extended matrices and time-varying network topologies to reveal the temporal dimension of correlation and entropy relationships. A simulation analysis using randomized time series is also implemented to assess the impact of de-noising on the data dependence structure. We mainly show evidence of emphasized disparity of correlation and entropy-based centrality measurements for all markets between pre-and post-crisis periods. Our results enable the robust mapping of network influences and contagion effects whilst incorporating agent expectations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets

Bekiros

Nguyen

Sandoval

et al. 2017

European Journal of Operational Research

131

View full text Add to dashboard Cite

show abstract

“…1 This widespread interest in commodity investments is partly associated with the view of commodities as a good diversification tool, since their correlations with stocks and bonds have been low or negative (Gorton and Rouwenhorst, 2006;. Recently, Daskalaki and Skiadopoulos (2011) point out that these diversification benefits are preserved only during the recent commodity price boom (2003)(2004)(2005)(2006)(2007)(2008), but in their study vanish in an out-of-sample context. It is also a common belief that commodities provide a good hedge against inflation (Bodie, 1983;Edwards and Park, 1996).…”

Section: Introductionmentioning

confidence: 99%

Futures Basis, Inventory and Commodity Price Volatility: An Empirical Analysis

et al. 2012

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We employ a large dataset of physical inventory data on 21 different commodities for the period 1993-2011 to empirically analyze the behaviour of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we explore the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom period.JEL classification: C22, C58, G00, G13

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“…Here, we examine the effect of rebalancing using an optimization technique. Employing five individual futures contracts, Daskalaki and Skiadopoulos [2011] find no out-of-sample benefits from adding one individual commodity futures contract to the equity assets. Overall, these and similar studies that employ only a few futures contracts ignore the potential benefit resulting from low correlations between individual futures.…”

Section: Endnotesmentioning

confidence: 95%