Did index trader and swap dealer activity produce a bubble in the agricultural commodity market?

Soana, Maria Gaia; Verga, Giovanni; Volpi, Mattia

doi:10.5897/ajbm2019.8877

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…As a result, they find no cause and effect relationship, so that the Master's hypothesis can be rejected ultimately. Maria et al (2020) investigate the influence of commodity index trading activity on commodity futures prices using Granger causality tests during the period 2006 to 2017. They confirm the previous empirical studies that the spike in commodity prices in 2007 to 2008 and the period thereafter is not due to speculative behaviour of commodity index traders.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The empirical literature on speculation in commodity markets was initiated by the political and regulatory discussion of the Master's hypothesis (Masters and White, 2008) which accused the significantly increased inflows of commodity index funds for the increase in commodity prices. Concentrated on the commodity index trader's influence, the empirical literature unambiguously rejects the Master's hypothesis and shows that they do not have a significant impact on commodity prices, but provide liquidity to the financial commodity markets (see Brunetti & Reiffen, 2014; Irwin & Sanders, 2011, 2012; Maria et al, 2020; Palazzi et al, 2020; Sanders et al, 2010). The majority of the empirical literature on speculation in the commodity markets discusses the impact of financialisation as well as the role of speculators without considering a specific trader group.…”

Section: Introductionmentioning

confidence: 99%

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Long‐short speculator sentiment in agricultural commodity markets

Borgards

Czudaj

2022

Int. J Fin Econ

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This paper tests the hypothesis that long-short speculators are able to generate short-term investment returns based on their sentiment for twelve agricultural commodity futures. For this purpose, we dynamically model the equidirectional trading of long and short commodity futures of long-short speculators as a proxy for their market sentiment. We find evidence that the sentiment period returns are considerably positive and differ significantly from neutral sentiment periods for all commodities which underlines the sentiment's relevance. In line with the empirical literature, we can reject the argument of price manipulation as the price continues to develop into the direction of the sentiment period although long-short speculators trade non-directionally in the following. We rather indicate the existence of a short-term time-series momentum effect, which can be robustly identified without the requirement to define an external model parameter. From the superior sentiment-based momentum returns, we conclude that long-short speculators have valuable, exclusive information, which cannot be replicated by observing their trading activity with a time lag of eight trading days. We also find that a sentiment-based momentum strategy generates significantly higher returns than the long-short speculators have realized in the fifteen-year sample period which we attribute to the complexity of the long-short speculators' investment strategies.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long‐short speculator sentiment in agricultural commodity markets

Borgards

Czudaj

2022

Int. J Fin Econ

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“…Non-causality tests such as the Granger test can also be used to learn more about the connections between returns and speculation, especially in the oats market, which other authors, besides some exceptions [64,114], did not pay enough attention to. To get a better description of these interactions, however, one may employ the GARCH-M model for projecting risk premiums [96].…”

Section: Model Improvement and Future Research Recommendationsmentioning

confidence: 99%

“…Various methodological frameworks have been used to investigate the interplay between the financial markets, speculation in derivatives, and the agricultural commodities markets. This includes methods used for time-series analysis: the Johansen cointegration test [63], the Granger non-causality test [63,64], vector autoregression (VAR) models [65], nonparametric regression tests [65], the autoregressive distributed lag (ARDL) model [66], the generalized autoregressive conditional heteroskedasticity (GARCH) model [19,50], panel Granger non-causality analysis [22], the vector error correction model (VECM) [63,67], the dynamic conditional correlation (DCC) GARCH model [68,69], the stochastic volatility (SV) model [70,71], the standard heterogeneous autoregressive (HAR) model [72,73], the structural vector autoregressive (SVAR) model [1,34], the continuous Granger non-causality test [74], and quantile regression models [75,76].…”

Section: Introductionmentioning

confidence: 99%

Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security

Staugaitis

Vaznonis

2022

Agriculture

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Global commodity markets, due to major health crises, political tension, sanctions, growing demand, and other global supply and demand factors, are currently particularly unstable. In addition to the macro-environmental factors that drive the prices, agricultural and other commodity markets are becoming more susceptible to the continuously-growing speculation on major commodity exchanges. Therefore, the purpose of this study is to analyze the influence of financial speculation on agricultural and other commodity prices and return volatility. In our study, we use daily returns on wheat, soybean, corn, and oats futures from the Chicago Mercantile Exchange as well as two additional commodities (crude oil and gold) to compare the extent of this effect. To measure this impact, we, besides traditional tools for time-series analysis, apply the threshold autoregressive conditional heteroskedasticity (TGARCH) technique. We also provide a model using dummy variables for the season to determine whether or not financial speculation’s impact on return volatility differs among seasons, as seasonality plays an important role in return dynamics for agriculture. Our study’s findings show that financial speculation, except for the oats market, either has no impact or makes the underlying futures returns less volatile. Therefore, we draw the conclusion that either there is no relationship between the rise in short-run speculation and the volatility of agricultural commodity prices, or the link is at best questionable. Research results provide important implications for the sustainable development of commodity markets, as passive legislation measurers can be seen as more effective ones compared to more strict active ones in order to maintain these markets liquid and capable of distributing price risks for agricultural producers and manufacturers in a challenging economic and geopolitical environment.

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Did index trader and swap dealer activity produce a bubble in the agricultural commodity market?

Cited by 2 publications

References 26 publications

Long‐short speculator sentiment in agricultural commodity markets

Long‐short speculator sentiment in agricultural commodity markets

Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security

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