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Risk correlation identification of futures market based on wavelet transform and quantile Granger causality test

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  • Zi Qian Wu

Abstract

Futures market is an important part of the financial market, with a high degree of liquidity and leverage effect. However, the futures market is also faced with various risk factors, such as price fluctuations, market shocks, supply and demand changes. In order to better determine the risk correlation between specific futures markets, this paper uses the wavelet transform—quantile Granger causality test method to identify the risk correlation of four major futures markets in the US futures market from the end of January 2009 to the end of March 2023, such as gold, crude oil, soybeans and natural gas. It provides a new perspective and method for the risk correlation identification of the futures market. The results show that futures contracts with different maturities and price fluctuations under different quantiles have a significant impact on risk correlation. Specifically, in 1-month and 6-month futures contracts, the strongest bidirectional risk correlation exists between gold and natural gas (T-statistics -15.94 and 10.92, respectively); In the 1-month futures contract, there is also a strong bidirectional risk association between crude oil and soybeans and natural gas (T-statistics are 6.87, 17.42, -2.05, 7.35, respectively), while in the 6-month futures contract, there is a bidirectional risk association between crude oil and soybeans (T-statistics are -2.49 and 18.374, respectively). However, natural gas has unidirectional risk association with crude oil and soybean (t statistics are 2.7 and -3.35, respectively); There is a bidirectional risk correlation between gold and soybean, that is, the risk correlation between gold and soybean increases with the increase of the degree of price fluctuation; There is a one-way risk association between gold and crude oil, soybean and gold, and crude oil and natural gas (the T-statistic is greater than the critical value of 1.96). In addition, there is a strong bidirectional or unidirectional risk association between all varieties at the 0.95 quantile. The research results of this paper have certain reference value for the supervision, investment and risk management of the futures market. This paper uses the wavelet transform and quantile Granger causality test method to identify the risk correlation of the futures market, providing a new perspective and method for the risk correlation identification of the futures market, and uses relatively new data to ensure the effectiveness of the empirical analysis. However, there are some limitations in this paper, such as the applicability of wavelet transform-quantile Granger causality test method. Future studies can further expand the sample range, compare the effects of different methods, and explore the risk transmission mechanism between different varieties.

Suggested Citation

  • Zi Qian Wu, 2023. "Risk correlation identification of futures market based on wavelet transform and quantile Granger causality test," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-30, November.
    • Handle: RePEc:plo:pone00:0294150
    DOI: 10.1371/journal.pone.0294150
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    1. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, June.
    2. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    4. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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