Trading Time Seasonality in Commodity Futures: An Opportunity for Arbitrage in the Natural Gas and Crude Oil Markets?

Ewald, Christian‐Oliver; Haugom, Erik; Lien, Gudbrand; Størdal, Ståle; Wu, Yuexiang

doi:10.2139/ssrn.3792028

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…Seasonal risk premia in natural gas markets as found by Shao et al (2015) will then translate into risk premia in long-term electricity futures. Thus, our findings of trading time seasonality in electricity futures markets may then be closely related to the findings of Ewald et al (2022b) on trading time seasonality J o u r n a l P r e -p r o o f…”

Section: Trading Time Seasonality and Hedging Pressuresupporting

confidence: 76%

“…Our analysis in sections 4 and 5 demonstrates that this is indeed the case. Ewald et al (2022b) identify the presence of such trading time seasonality in the U.S. natural gas and crude oil markets and argue that it might be a violation of the no-arbitrage assumption. Although electricity has different attributes to natural gas and crude oil, including being non-storable, storability in production inputs (see e.g., Douglas and Popova, 2008;Bunn and Chen, 2013) may still cause trading time seasonality.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 97%

See 1 more Smart Citation

Trading time seasonality in electricity futures

Størdal

Ewald

Lien

et al. 2023

Journal of Commodity Markets

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Section: Trading Time Seasonality and Hedging Pressuresupporting

confidence: 76%

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 97%

Trading time seasonality in electricity futures

Størdal

Ewald

Lien

et al. 2023

Journal of Commodity Markets

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“…Trading in futures contracts should therefore provide investors with a risk premium as long as the future's returns are correlated with the market. This is the case for crude-oil for example, see Ewald et al (2021). For salmon futures contracts this is not clear.…”

Section: Introductionmentioning

confidence: 99%

Salmon futures and the Fish Pool market in the context of the CAPM and a three-factor model

Ewald

Haugom

Kanthan

et al. 2021

Aquaculture Economics & Management

Self Cite

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Futures on fresh farmed salmon traded at the Fish Pool market in Norway are analyzed in the context of the Capital Asset Pricing Model (CAPM) and a corresponding three-factor model where contracts are separated based on their maturities. Looking into 1 month; 6 months and 12 months contracts, we find that all alphas and most betas are statistically insignificant. We conclude that the CAPM equilibrium condition holds and that Salmon futures prices move largely uncorrelated with the market portfolio and therefore offer no systematic risk premium. The latter documents that Fish Pool futures should be considered as a pure hedging instrument rather than an investment asset.

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“…Eugene Fama, the architect of EMH, coauthored the seminal paper describing the mechanisms by which prices of commodity futures diverge from fundamentals: asymmetric information and inventory constraints (Fama & French, 1987;Kumar & Seppi, 1992). More recently, there has been growth in sophisticated statistical arbitrage and machine learning systems designed to capitalize on informational deficiencies in commodity markets (Ewald et al, 2021;Wang & Yu, 2004). While advanced algorithms trading small statistical discrepancies eliminate some mispricing, Shiller (2003) argues that informational and behavioral inefficiencies persist across a variety of assets.…”

Section: Literature Reviewmentioning

confidence: 99%

Consumer Wagering Biases Exploited By International Sportsbooks

Feiler, Stefan

2024

Preprint

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Market efficiency, a cornerstone of financial economics, asserts that asset prices fully reflect all available information, thereby facilitating accurate market signals and optimal resource allocation. While the Efficient Market Hypothesis (EMH) underpins numerous economic theories and models, anomalies challenge its assumptions. Behavioral finance posits that psychological biases can lead to market inefficiencies, as evidenced by systematic mispricing biases in various markets, including equities and sports betting. This paper delves into the profitability of sports betting markets, leveraging machine learning and logistic regression to analyze a large sports wagering dataset. The study reveals significant behavioral biases exploited by sportsbooks, shedding light on how advanced analytics can capitalize on market inefficiencies. Insights gleaned from this research not only deepen our understanding of sports wagering dynamics but also offer implications for broader financial markets and the potential transformative role of similar analytic methodologies for asset price discovery.

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Trading Time Seasonality in Commodity Futures: An Opportunity for Arbitrage in the Natural Gas and Crude Oil Markets?

Cited by 3 publications

References 34 publications

Trading time seasonality in electricity futures

Trading time seasonality in electricity futures

Salmon futures and the Fish Pool market in the context of the CAPM and a three-factor model

Consumer Wagering Biases Exploited By International Sportsbooks

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