Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis

Du, Xiaodong; Yu, Cindy; Hayes, Dermot J.

doi:10.1016/j.eneco.2010.12.015

Cited by 428 publications

(236 citation statements)

References 46 publications

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“…The evidence on the effects of speculation is mixed. In some studies, speculation is shown to have a statistically significant effect on price and volatility in oil markets, particularly in the lead up to the historically high oil prices achieved pre-crisis (Sornette et al, 2009, Kaufmann and Ullman, 2009, Cifarelli and Paladino, 2010, Du et al, 2011. In contrast, Büyüksahin and Harris (2011) find no evidence that non-commercial positions, including hedge fund positions, have a causal effect on oil prices.…”

Section: Introductionmentioning

confidence: 57%

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Cummins

Dowling

Kearney

2016

International Review of Financial Analysis

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We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short-medium-and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.

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Section: Introductionmentioning

confidence: 57%

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Cummins

Dowling

Kearney

2016

International Review of Financial Analysis

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“…Scalping is known as an intraday activity, made up of instant transactions by traders which open and close contract positions within a very short period of time to realize profits (Working, 1967;Cornell, 1981;Peck, 1981;Du et al, 2011). Scalpers are typically intended as types of traders who dart in markets even hundreds of times a day to make profits: they " […] stand willingly to buy a tick below the last trade or sell a tick above it" (Cornell, 1981, p. 305) and, again, "scalpers trade price ticks, holding a position for a matter of moments anticipating the last price change will be followed by an opposite price move" (Roswell and Purcell, 1992, p. 206).…”

Section: )mentioning

confidence: 99%

“…Scalping is generally proxied as the ratio of volume to open interest (Peck, 1981;Leuthold, 1983;Streeter and Tomek, 1992;Du et al, 2011). 2 Short term speculators are more likely to close a contract within a day than hedgers, whose orders are generally held for more than one day.…”

Section: )mentioning

confidence: 99%

Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation

2013

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This paper evaluates how different types of speculation affect the volatility of commodities' futures prices. We adopt four indexes of speculation: Working's T, the market share of non-commercial traders, the percentage of net long speculators over total open interest in future markets, which proxy for long term speculation, and scalping, which proxies for short term speculation. We consider four energy commodities (light sweet crude oil, heating oil, gasoline and natural gas) and seven non-energy commodities (cocoa, coffee, corn, oats, soybean oil, soybeans and wheat) over the period 1986-2010 analyzed at weekly frequency. Using GARCH models we find that speculation significantly affects volatility of returns: short term speculation has a positive and significant impact on volatility, while long term speculation generally has a negative effect. The robustness exercise shows that: i) scalping is positive and significant also at higher and lower data frequencies; ii) results remain unchanged through different model specifications (GARCH-in-mean, EGARCH, and TARCH); iii) results are robust to different specifications of the mean equation.JEL Codes: C32; G13; Q11; Q43.

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“…Moreover, Kaltalioglu and Soutas [20] did not find any evidence of volatility spillover from crude oil to agricultural commodities. Cha and Bae [21] and Du et al [22] find significant evidence of volatility spillover from crude oil to agricultural commodities. Serra [23] finds significant evidence of volatility spillover between crude oil, bioethanol and sugar prices in Brazilian market.…”

Section: Literature Reviewmentioning

confidence: 99%

On Volatility Transmission from Crude Oil to Agricultural Commodities

Kumar¹

2017

TEL

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The paper examines volatility transmission from crude oil market to agricultural commodities like wheat, corn, cotton and soybeans. We find that the volatility transmission from crude oil to agricultural commodities exhibits sudden changes over a study period. We also examine whether the sudden changes in volatility influence the observed sudden changes in volatility transmission from crude oil to agricultural commodities. Our results indicate the observed sudden change in volatility transmission mechanism is not influenced by sudden changes in volatility series.

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Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis

Cited by 428 publications

References 46 publications

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation

On Volatility Transmission from Crude Oil to Agricultural Commodities

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