Speculators, Commodities and Cross-Market Linkages

Büyükşahin, Bahattin; Robe, Michel A.

doi:10.2139/ssrn.1707103

Cited by 164 publications

(179 citation statements)

References 52 publications

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“…This is in line with Turhan et al (2013) findings for exchange rates and oil prices. Although this seems to be contradictory to the existence of a contagion effect, 8 decoupling of commodity and equity markets during crisis is also documented by Büyükşahin et al (2010) and Büyükşahin and Robe (2014). The turmoil in the oil markets may have severed the link between oil price and FSI as investors try to adjust their expectations to increased uncertainty and change their positions.…”

Section: Variance Spillover Test Resultsmentioning

confidence: 97%

“…Büyükşahin and Harris (2011) also support this result. On the other hand, using trader-position level data for non-public traders, Büyükşahin and Robe (2014) show that the association between stock and investable commodity indexes is driven by hedge fund activity, but not other types of traders. They report that during financial turmoil the role of hedge fund activity is weakened.…”

Section: Introductionmentioning

confidence: 89%

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Oil prices and financial stress: A volatility spillover analysis

2015

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Volatility spillover between oil prices and financial stress index is examined. Analysis is conducted for sub-periods: pre-crisis, in-crisis, and post-crisis Oil prices spill on financial stress before the crisis, but spillover reversed after the crisis. Volatility transmission pattern has similar dynamics before and after the crisis. Implications for investors and policy makers are discussed. Keywords:Oil prices Financial stress index Causality Volatility spillover abstract This paper examines whether there is a volatility transmission between oil prices and financial stress by means of the volatility spillover test. We employ WTI crude oil prices and Cleveland financial stress index for the period 1991-2014 and divide the sample into pre-crisis, in-crisis, and post-crisis periods due to the downward trend in oil price in 2008. The volatility model estimations indicate that oil prices and financial stress index are dominated by longrun volatility. The volatility spillover causality test supports evidence on risk transfer from oil prices to financial stress before the crisis and from financial stress to oil prices after the crisis. The impulse response analysis shows that the volatility transmission pattern has similar dynamics before and after the crisis and is characterized by higher and long-lived effects during the crisis. Our results have implications for both policy makers and investors, and for future work.

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Section: Variance Spillover Test Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 89%

Oil prices and financial stress: A volatility spillover analysis

2015

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“…2 Büyüksahin and Robe (2011) show that the equity-commodity link did not increase until 2008, but increases significantly amid the most severe episode of the global financial crisis -2009. Büyükşahin and Robe (2014 reach similar conclusions based on a nonpublic dataset of trader positions in 17 U.S. commodity futures markets, two U.S. equity market indices (S&P's 500 and Dow Jones' Industrial Average Index), and the MSCI World equity market index.…”

Section: Introductionmentioning

confidence: 88%

“…Cheng and Xiong (2014) document that the financialization has substantially affected commodity markets through risk sharing and information discovery mechanisms. Several studies including, among others, Büyüksahin et al (2010), Büyüksahin and Robe (2011), Tang and Xiong (2012), and Büyükşahin and Robe (2014) uncover the increased correlation not only between commodity futures returns, but also between commodity futures and equity returns. 2 As a result of this stronger comovement, diversification benefits from 1 The modern portfolio theory suggests that investors can improve their risk-adjusted return performance by allocating resources to imperfectly correlated assets, which is the case of equities and commodities.…”

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

128

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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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“…As proposed by Büyükşahin and Robe (2010), we compute the market share of non-commercial traders as the average of the long and short positions of all non-commercial (or speculators) traders…”

Section: Data Descriptionmentioning

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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Speculators, Commodities and Cross-Market Linkages

Cited by 164 publications

References 52 publications

Oil prices and financial stress: A volatility spillover analysis

Oil prices and financial stress: A volatility spillover analysis

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

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

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