Are there common factors in individual commodity futures returns?

Daskalaki, Charoula; Kostakis, Alexandros; Skiadopoulos, George

doi:10.1016/j.jbankfin.2013.11.034

Cited by 133 publications

(101 citation statements)

References 54 publications

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“…More generally, the commodity market appears to be segmented, rather than integrated, from other asset classes (Buyuksahin et al, 2010;Chong and Miffre, 2006;and Daskalaki et al, 2014). There is a reported small negative correlation between commodity returns against both equity and bond returns (Buyuksahin et al, 2010;Gorton and Rouwenhorst, 2006;and Greer, 2000).…”

Section: Introductionmentioning

confidence: 97%

“…A strand of research has found that the commodity market is heterogeneous (Erb and Harvey, 2006;Kat and Oomen, 2007;Daskalaki et al, 2014). Historically, commodity futures returns have been shown to be largely uncorrelated with one another (Erb and Harvey, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Historically, commodity futures returns have been shown to be largely uncorrelated with one another (Erb and Harvey, 2006). The heterogeneous structure of the commodity market makes it more difficult to identify systematic risk factors that may price common variation of commodity futures returns (Daskalaki et al, 2014). Furthermore, Skiadopoulos (2013) concludes that there are no common, or systematic, risk factors in commodity futures returns because, as an asset class, it is internally segmented.…”

Section: Introductionmentioning

confidence: 99%

“…Skiadopoulos (2013) argues that the commodity market is segmented from both equity and bond markets. Similarly, Daskalaki, et al, (2014) argue that the commodity market is segmented from the equity market. In addition, Chong and Miffre (2006) provide historical evidence that commodity and equity markets have become more segmented.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of different types of standard multifactor models applied to the commodity market can be found in Daskalaki et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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Commodity Market Heterogeneity and Cross-Market Integration

Kunkler¹

2017

AFL

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Abstract:We evaluate the recent levels of heterogeneity and cross-market integration for fluctuations in commodity futures returns for a post-financialcrisis data sample. We find that a single commodity-market risk factor explains 30.6% of the total variation in commodity futures returns. The commodity-market risk factor is significantly correlated with the dominant market-wide risk factors from other asset classes: +66.7% with a market risk factor for the US equity market; -74.2% with a US dollar risk factor for the FX market; and -27.8% with an interest-rate level risk factor for the US interest rate market. Thus, a part of the systematic variation in the commodity market is integrated with other asset classes.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Examples of different types of standard multifactor models applied to the commodity market can be found in Daskalaki et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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Commodity Market Heterogeneity and Cross-Market Integration

Kunkler¹

2017

AFL

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Modeling and forecasting commodity market volatility with long‐term economic and financial variables

Nguyen

Walther

2019

Journal of Forecasting

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This paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed GARCH-MIDAS approach which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for crude oil (WTI and Brent), gold, silver and platinum, our results show the necessity of disentangling the short-and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of the general real economic activity as well as the changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.

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Commodity Strategies Based on Momentum, Term Structure, and Idiosyncratic Volatility

Fuertes

Miffre

Fernández-Pérez

2014

Journal of Futures Markets

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This is the accepted version of the paper.This version of the publication may differ from the final published version. We show that simultaneously buying contracts with high past performance, high roll-yields and low idiosyncratic volatility, and shorting contracts with poor past performance, low rollyields and high idiosyncratic volatility yields a Sharpe ratio over the 1985 to 2011 period which is five times that of the S&P-GSCI. The triple-screen strategy dominates the doublescreen and individual strategies and this outcome cannot be attributed to overreaction, liquidity risk, transaction costs or the financialization of commodity futures markets. Permanent repository link

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Are there common factors in individual commodity futures returns?

Cited by 133 publications

References 54 publications

Commodity Market Heterogeneity and Cross-Market Integration

Commodity Market Heterogeneity and Cross-Market Integration

Modeling and forecasting commodity market volatility with long‐term economic and financial variables

Commodity Strategies Based on Momentum, Term Structure, and Idiosyncratic Volatility

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