Speculative activity and returns volatility of Chinese agricultural commodity futures

Bohl, Martin T.; Siklos, Pierre L.; Wellenreuther, Claudia

doi:10.1016/j.asieco.2017.12.003

Cited by 40 publications

(32 citation statements)

References 47 publications

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“…These studies conclude that prices for Chinese futures are connected to prices for US futures and indicate that the efficiency of Chinese futures markets is continually improving. More recently, results by Bohl et al (2017) also mainly imply that speculative activity, measured by open interest and trading volume data, positively influences returns volatility in Chinese agricultural commodity futures markets. One such study by Chan et al (2004) estimates the relationship between daily volatility and trading volume in Chinese futures markets for copper, mungbeans, soybeans, and wheat.…”

Section: Chinese Commodity Futures Marketsmentioning

confidence: 97%

“…Their findings indicate contemporaneous correlations and lead-lag relations between trading volume and returns when absolute returns are used. More recently, results by Bohl et al (2017) also mainly imply that speculative activity, measured by open interest and trading volume data, positively influences returns volatility in Chinese agricultural commodity futures markets. Except for the last three studies mentioned, which indicate that (speculative) trading volume has a destabilizing effect on prices, there is only anecdotal evidence suggesting that futures markets in China are dominated by speculators.…”

Section: Chinese Commodity Futures Marketsmentioning

confidence: 97%

“…The CFFEX was only (2013). As Bohl et al (2017) did not utilize a time-varying approach, this paper can be seen as an extension to their study. Further, results by Brunetti, Büyükşahin, and Harris (2016) indicate that changes in the net positions of hedge funds in crude oil, natural gas, and corn decrease volatility and therefore stabilize prices.…”

Section: Chinese Commodity Futures Marketsmentioning

confidence: 99%

“…Their results are inconclusive but indicate that the speculation ratio positively influences volatility for most of the commodities examined. As Bohl et al (2017) did not utilize a time-varying approach, this paper can be seen as an extension to their study. established in 2006 and is China's exchange for financial derivatives.…”

Section: Chinese Commodity Futures Marketsmentioning

confidence: 99%

See 3 more Smart Citations

Speculation and volatility—A time‐varying approach applied on Chinese commodity futures markets

Wellenreuther

Voelzke

2018

Journal of Futures Markets

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Experts have long discussed and empirically investigated whether speculative activity increases volatility on commodity futures markets. Little empirical research, however, analyzes the role of speculators on commodity futures markets in China. Using time‐varying vector autoregression models with stochastic volatility, this paper investigates for four heavily traded metal and agricultural contracts, how the relationship between returns volatility and speculation evolves over time. Our findings indicate that speculative activity has little to no impact on volatility. On the contrary, for all commodities examined, returns volatility seems to amplify speculation.

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Section: Chinese Commodity Futures Marketsmentioning

confidence: 97%

Section: Chinese Commodity Futures Marketsmentioning

confidence: 97%

Section: Chinese Commodity Futures Marketsmentioning

confidence: 99%

Section: Chinese Commodity Futures Marketsmentioning

confidence: 99%

See 2 more Smart Citations

Speculation and volatility—A time‐varying approach applied on Chinese commodity futures markets

Wellenreuther

Voelzke

2018

Journal of Futures Markets

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“…Bohl, Siklos, and Wellenreuther () examined the speculative activity in a number of Chinese agricultural commodity futures, including cotton. They note that “the speculation ratio of cotton futures appears to be most volatile as indicated by its high standard deviation” (p. 78).…”

mentioning

confidence: 99%

The trilogy of China cotton markets: The lead–lag relationship among spot, forward, and futures markets

Demir

Martell

Wang³

2018

Journal of Futures Markets

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China is a leading participant in the world cotton market. China’s distinctive regulatory structure and procedures and business environment provide an opportunity to explore some unique market dynamics. This study investigates the interrelationship among the spot, futures, and forward cotton markets in China over a period of a major policy change: A temporary State reserve program for cotton that was established in 2011 and ended in 2014. This government intervention significantly distorted the way farmers, manufacturers, and speculators interacted and was not sustainable. Overall, our results support futures market’s dominant role in the price discovery process.

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Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

Fang

Guan

et al. 2020

Journal of Forecasting

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Improving the prediction accuracy of agricultural product futures prices is important for the investors, agricultural producers and policy makers. This is to evade the risks and enable the government departments to formulate appropriate agricultural regulations and policies. This study employs Ensemble Empirical Mode Decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model were then compared with the benchmark individual models, SVM, NN, and ARIMA. Our main interest in this study is on the short-term forecasting, and thus we only consider 1-day and 3-days forecast horizons. The results indicated that the prediction performance of EEMD combined model is better than that of individual models, especially for the 3-days forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods to forecast high-frequency volatile components. However, there is no obvious difference between individual models in predicting the low-frequency components.

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Speculative activity and returns volatility of Chinese agricultural commodity futures

Cited by 40 publications

References 47 publications

Speculation and volatility—A time‐varying approach applied on Chinese commodity futures markets

Speculation and volatility—A time‐varying approach applied on Chinese commodity futures markets

The trilogy of China cotton markets: The lead–lag relationship among spot, forward, and futures markets

Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

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