Comovement in the commodity futures markets: An analysis of the energy, grains, and livestock sectors

Adhikari, Ramesh; Putnam, Kyle J.

doi:10.1016/j.jcomm.2019.04.002

Cited by 27 publications

(15 citation statements)

References 34 publications

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“…The underlying commodities we use in this study are diverse. Such diversity tends to manifest itself in smaller return correlations between constituents of different sectors rather than members categorized within the same sector (Adhikari and Putnam 2020). This heterogeneity can be viewed through the lens of distributional returns.…”

Section: Sectoral Risk and Returns Analysismentioning

confidence: 99%

“…We focus on robust optimization constructed on the traditional mean-variance and conditional value-at-risk measures of risk to create optimally weighted futures portfolios for five diverse commodity sectors-foods and fibers, grains and oilseeds, livestock, energy, and precious metals. The motivation for investment among commodity sectors, as opposed to individual commodity futures, is due to a preference for diversification among heterogenous assets (Adhikari and Putnam 2020). The simulated data are generated using a normal distribution with a mean and covariance matrix of prior returns from either a 12-, 15-, or 18-month lookback period.…”

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confidence: 99%

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Robust Optimization-Based Commodity Portfolio Performance

Adhikari

Putnam

Panta

2020

IJFS

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This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and conditional value-at-risk optimization techniques are used to construct the robust commodity futures portfolios. This paper documents the benefits of applying a sophisticated, robust optimization technique to construct commodity futures portfolios. We find that a 12-month lookback period contains the most useful information in constructing optimization-based portfolios, and a 1-month holding period yields the highest returns among all the holding periods examined in the paper. We also find that an optimized conditional value-at-risk portfolio using a 12-month lookback period outperforms an optimized mean-variance portfolio using the same lookback period. Our findings highlight the advantages of using robust optimization for portfolio formation in the presence of return uncertainty in the commodity futures markets. The results also highlight the practical importance of choosing the appropriate lookback and holding period when using robust optimization in the commodity portfolio formation process.

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Section: Sectoral Risk and Returns Analysismentioning

confidence: 99%

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confidence: 99%

Robust Optimization-Based Commodity Portfolio Performance

Adhikari

Putnam

Panta

2020

IJFS

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“…Janzen et al (2018) examine linkages between cotton price volatility and speculation in other financial assets and report limited evidence of financial speculation leading to volatility in cotton prices. Adhikari and Putnam (2020); Dahl et al (2020) examine co-movement across the energy and agriculture commodities and document spillovers from oil to cotton prices. In the Indian context, Shrinivas and Gómez (2016) study price transmission across international prices (U.S.) and Indian domestic prices and find evidence of integration between Indian and international cotton markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Price Transmission in Cotton Futures Market: Evidence from Three Countries

Singh¹,

Soni²

2021

JRFM

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This study examines the price transmission between cotton prices in U.S., Indian, and Chinese futures markets. We focus on studying the long-run price movements using cointegration and alternate causality tests. The empirical results indicate the following: (a) the U.S. cotton futures market continues to be the most dominant market, and it leads price changes in India and China; (b) the cotton prices in India also impacts the cotton prices in China as we report a unidirectional relationship flowing from India to China; (c) there is duality of direction of price transmission for U.S. and Chinese commodity markets as we document bi-directional causality between U.S. to Chinese cotton futures for the entire period and uni-directional causality from U.S. to Chinese markets for the two sub-periods; (d) the long-term relationship between the three markets has seen a significant shift as documented by the absence of cointegration which may be due to changes in government policy, especially in India and China specifically after 2014. Overall, results provide support for further reforms especially for Indian and Chinese commodity exchanges so that they can play a vital role in the price discovery process especially for commodities that are largely produced or consumed in these economies.

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“…We find that a simple one‐factor model (Delle Chiaie et al, 2017) provides a better fit to the comovement of commodity returns, casting doubt on the excess comovement documented in the literature. Adhikari and Putnam (2020) use a copula approach to study the comovements of commodity markets and find that the excess comovement within sectors is stronger than across.…”

Section: Introductionmentioning

confidence: 99%

The dynamics of commodity return comovements

Prokopczuk

Simen

Wichmann

2021

Journal of Futures Markets

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We compare factor models with respect to their ability to explain commodity futures return comovements. A simple one‐factor model based on the first principal component extracted from a panel of commodity returns outperforms a macroeconomic model, and explains most of the realized comovements. We find that intersectoral correlations display more time variations than intrasectoral correlations. Dissecting the evidence further, we find that comovements are driven by the variation of the factor as opposed to exposure to it. Our results cast doubt on the persistence of the effects of financialization and emphasize the importance of the dynamics of the factor variance.

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Comovement in the commodity futures markets: An analysis of the energy, grains, and livestock sectors

Cited by 27 publications

References 34 publications

Robust Optimization-Based Commodity Portfolio Performance

Robust Optimization-Based Commodity Portfolio Performance

Price Transmission in Cotton Futures Market: Evidence from Three Countries

The dynamics of commodity return comovements

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