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Market interdependence among commodity prices based on information transmission on the Internet

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  • Ji, Qiang
  • Guo, Jian-Feng

Abstract

Human behaviour on the Internet has become a synchro-projection of real society. In this paper, we introduce the public concern derived from query volumes on the Web to empirically analyse the influence of information on commodity markets (e.g., crude oil, heating oil, corn and gold) using multivariate GARCH models based on dynamic conditional correlations. The analysis found that the changes of public concern on the Internet can well depict the changes of market prices, as the former has significant Granger causality effects on market prices. The findings indicate that the information of external shocks to commodity markets could be transmitted quickly, and commodity markets easily absorb the public concern of the information-sensitive traders. Finally, the conditional correlation among commodity prices varies dramatically over time.

Suggested Citation

  • Ji, Qiang & Guo, Jian-Feng, 2015. "Market interdependence among commodity prices based on information transmission on the Internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 426(C), pages 35-44.
    • Handle: RePEc:eee:phsmap:v:426:y:2015:i:c:p:35-44
    DOI: 10.1016/j.physa.2015.01.054
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    1. Pawe{l} Sieczka & Janusz A. Ho{l}yst, 2008. "Correlations in commodity markets," Papers 0803.3884, arXiv.org, revised Jan 2009.
    2. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    3. Tushar Rao & Saket Srivastava, 2012. "Modeling Movements in Oil, Gold, Forex and Market Indices using Search Volume Index and Twitter Sentiments," Papers 1212.1037, arXiv.org.
    4. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2013. "Conditional correlations and volatility spillovers between crude oil and stock index returns," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 116-138.
    5. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    6. Mensi, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong & Yoon, Seong-Min, 2014. "Dynamic spillovers among major energy and cereal commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 225-243.
    7. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    8. Liu, Li & Ma, Guofeng, 2014. "Cross-correlation between crude oil and refined product prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 284-293.
    9. Zhuang, Xiaoyang & Wei, Yu & Zhang, Bangzheng, 2014. "Multifractal detrended cross-correlation analysis of carbon and crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 113-125.
    10. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    11. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    12. Garcia, Philip & Irwin, Scott H. & Leuthold, Raymond M. & Yang, Li, 1997. "The value of public information in commodity futures markets," Journal of Economic Behavior & Organization, Elsevier, vol. 32(4), pages 559-570, April.
    13. Pal, Mayukha & Madhusudana Rao, P. & Manimaran, P., 2014. "Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 452-460.
    14. Liu, Ming-Lei & Ji, Qiang & Fan, Ying, 2013. "How does oil market uncertainty interact with other markets? An empirical analysis of implied volatility index," Energy, Elsevier, vol. 55(C), pages 860-868.
    15. Marzo, Massimiliano & Zagaglia, Paolo, 2008. "A note on the conditional correlation between energy prices: Evidence from future markets," Energy Economics, Elsevier, vol. 30(5), pages 2454-2458, September.
    16. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    17. Liu, Li, 2014. "Cross-correlations between crude oil and agricultural commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 293-302.
    18. Sieczka, Paweł & Hołyst, Janusz A., 2009. "Correlations in commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1621-1630.
    19. Bhar, Ramaprasad & Hamori, Shigeyuki, 2008. "Information content of commodity futures prices for monetary policy," Economic Modelling, Elsevier, vol. 25(2), pages 274-283, March.
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