国际有色金属价格与美元指数的非线性关系
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摘要
国际有色金属价格与美元指数是研究宏观经济形势的重要参考指标,两者之间的联动效应是宏观经济中的一个重要现象。利用金融市场中的高频数据,通过构建DCC-GARCH模型计算了美元指数与5种有色金属价格的动态相关系数,并且将动态相关系数序列进行经验模态分解。对美元指数与有色金属价格进行Granger因果关系检验。结果显示:美元指数与国际有色金属价格之间存在显著的相关性;美元指数与铜、铅、锌、镍4种有色金属价格之间存在显著的非线性Granger因果关系,与铝价格之间存在非对称的Granger因果关系。依据实证研究结果,得出以下结论:美元指数与有色金属价格之间并不一定是负相关的关系;美元指数对有色金属价格的影响具有局部性;交易量与相关系数的大小及其波动性呈正向关系;有色金属价格与美元指数之间的相关性与经济周期有较强的同步性;美元指数与铜、铅、锌、镍4种有色金属价格之间存在着非线性的双向影响传导机制;美元指数与铝价之间的影响是非对称的关系,美元指数对铝价的影响为线性传导机制,铝价对美元指数的影响为非线性传导机制。
International non-ferrous metals prices and U.S. dollar index are important reference indicators for studying the macroeconomic situation, and the linkage effect between them is an important phenomenon in macro-economy. In this paper, using high-frequency data in the financial market, the dynamic correlation coefficient between the dollar index and five kinds of non-ferrous metal prices were calculated by constructing a DCC-GARCH model. And an empirical mode decomposition on the dynamic correlation coefficient was made. Finally, the Granger causality test was carried out about the dollar index and the prices of non-ferrous metals. The results show that there was a significant correlation between the dollar index and the international non-ferrous metal prices; there was a significant nonlinear Granger causality relationship between the dollar index and copper, lead, zinc, nickel;there existed an asymmetric causal relationship between aluminum price and Granger. According to the results of the empirical research, this paper draws the following conclusions: there is not necessarily a negative correlation between the dollar index and non-ferrous metal prices; the dollar index has a local influence on non-ferrous metal prices; there is a positive relationship between the size of the trading volume, volatility and the correlation coefficient; correlation has strong synchronism with the economic cycle;there is a nonlinear two-way transmission mechanism between the dollar index and copper, lead, zinc, and nickel; the influence of the dollar index on aluminum price is asymmetric. The influence of the US dollar index on aluminum price is linear transmission mechanism, and the influence of aluminum price on the dollar index is nonlinear conduction mechanism.
International non-ferrous metals prices and U.S. dollar index are important reference indicators for studying the macroeconomic situation, and the linkage effect between them is an important phenomenon in macro-economy. In this paper, using high-frequency data in the financial market, the dynamic correlation coefficient between the dollar index and five kinds of non-ferrous metal prices were calculated by constructing a DCC-GARCH model. And an empirical mode decomposition on the dynamic correlation coefficient was made. Finally, the Granger causality test was carried out about the dollar index and the prices of non-ferrous metals. The results show that there was a significant correlation between the dollar index and the international non-ferrous metal prices; there was a significant nonlinear Granger causality relationship between the dollar index and copper, lead, zinc, nickel;there existed an asymmetric causal relationship between aluminum price and Granger. According to the results of the empirical research, this paper draws the following conclusions: there is not necessarily a negative correlation between the dollar index and non-ferrous metal prices; the dollar index has a local influence on non-ferrous metal prices; there is a positive relationship between the size of the trading volume, volatility and the correlation coefficient; correlation has strong synchronism with the economic cycle;there is a nonlinear two-way transmission mechanism between the dollar index and copper, lead, zinc, and nickel; the influence of the dollar index on aluminum price is asymmetric. The influence of the US dollar index on aluminum price is linear transmission mechanism, and the influence of aluminum price on the dollar index is nonlinear conduction mechanism.
引文
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[19]鄢红兵,成金华,朱永光.国际有色金属价格影响因素的非对称性研究[J].中国国土资源经济,2017(3):59-63.
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[22]朱学红,冯宇文,郭尧琦.金融因素对期铜价格波动影响的实证研究[J].中南大学学报(社会科学版),2015(2):124-129.
[23]朱学红,沈玉芳,邵留国.石油和汇率冲击下的中国金属价格波动行为[J].系统工程,2012(11):30-36.
[24]黄健柏,程慧,郭尧琦,等.美元、石油和金属价格——基于VAR模型的实证研究[J].经济经纬,2012(3):45-49.
[25]陈强,陈娟,陈道轮.美元指数的动态演化机理——基于扩散模型的实证分析[J].国际金融研究,2016(4):37-48.
[26]ENGLE R F.Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation[J].Econometrica:Journal of the Econometric Society,1982:987-1007.
[27]BOLLERSLEV T.Generalized autoregressive conditional heteroskedasticity[J].Journal of Econometrics,1986,31(3):307-327.
[28]BOLLERSLEV T.Modelling the coherence in short-run nominal exchange rates:a multivariate generalized ARCH model[J].The Review of Economics and Statistics,1990:498-505.
[29]ENGLE R.Dynamic conditional correlation:a simple class of multivariate generalized autoregressive conditional heteroskedasticity models[J].Journal of Business&Economic Statistics,2002,20(3):339-350.
[30]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[31]GRANGER C W J.Testing for causality:a personal viewpoint[J].Journal of Economic Dynamics and Control,1980,2:329-352.
[32]GRANGER C W J.Causality,cointegration,and control[J].Journal of Economic Dynamics and Control,1988,12(2):551-559.
[33]DIKS C,PANCHENKO V.A new statistic and practical guidelines for nonparametric Granger causality testing[J].Journal of Economic Dynamics and Control,2006,30(9-10):1647-1669.
(1)数据来自于LME。
[2]成金华,汪小英.工业化与矿产资源消耗:国际经验与中国政策调整[J].中国地质大学学报(社会科学版),2011(2):23-27.
[3]王泰强,候光明,李杰,等.中国金属期货市场价格发现功能研究——基于面板协整方法[J].北京理工大学学报(社会科学版),2011(5):44-47.
[4]SLADE M E.The two pricing systems for non-ferrous metals[J].Resources Policy,1989,15(3):209-220.
[5]黄婷,李景华.美元指数与大宗商品价格关系的实证分析[J].经济纵横,2015(8):116-120.
[6]朱学红,谌金宇,钟美瑞.国际有色金属价格向我国通胀传递的时空特征及影响因素研究[J].国际贸易问题,2016(7):130-140.
[7]AKRAM Q F.Commodity prices,interest rates and the dollar[J].Energy Economics,2009,31(6):838-851.
[8]SARI R,HAMMOUDEH S,SOYTAS U.Dynamics of oil price,precious metal prices,and exchange rate[J].Energy Economics,2010,32(2):351-362.
[9]ZHANG H J,DUFOUR J,GALBRAITH J W.Exchange rates and commodity prices:measuring causality at multiple horizons[J].Journal of Empirical Finance,2016,36:100-120.
[10]CHEN H,LIU L,WANG Y,et al.Oil price shocks and U.S.dollar exchange rates[J].Energy,2016,112:1036-1048.
[11]JAWADI F,LOUHICHI W,AMEUR H B,et al.On oil-US exchange rate volatility relationships:an intraday analysis[J].Economic Modelling,2016,59:329-334.
[12]COUDERT V,MIGNON V.Reassessing the empirical relationship between the oil price and the dollar[J].Energy Policy,2016,95:147-157.
[13]SUN X,LU X,YUE G,et al.Cross-correlations between the US monetary policy,US dollar index and crude oil market[J].Physica A:Statistical Mechanics and Its Applications,2017,467:326-344.
[14]TIWARI A K,ALBULESCU C T.Oil price and exchange rate in India:fresh evidence from continuous wavelet approach and asymmetric,multi-horizon Granger-causality tests[J].Applied Energy,2016,179:272-283.
[15]TODOROVA N.The course of realized volatility in the LME non-ferrous metal market[J].Economic Modelling,2015,51:1-12.
[16]TODOROVA N,WORTHINGTON A,SOU cˇEK M.Realized volatility spillovers in the non-ferrous metal futures market[J].Resources Policy,2014,39:21-31.
[17]YUE Y,LIU D,XU S.Price linkage between Chinese and international nonferrous metals commodity markets based on VARDCC-GARCH models[J].Transactions of Nonferrous Metals Society of China,2015,25(3):1020-1026.
[18]崔海蓉,何建敏,张京波.我国有色金属期货波动溢出效应研究——以SHFE的铜和铝为例[J].北京理工大学学报(社会科学版),2011(04):29-32.
[19]鄢红兵,成金华,朱永光.国际有色金属价格影响因素的非对称性研究[J].中国国土资源经济,2017(3):59-63.
[20]吴丹,胡振华.基于TVP-VAR模型的有色金属价格时变相关性研究[J].财经理论与实践,2017(3):64-70.
[21]王高尚.后危机时代矿产品价格趋势分析[J].地球学报,2010,31(5):629-634.
[22]朱学红,冯宇文,郭尧琦.金融因素对期铜价格波动影响的实证研究[J].中南大学学报(社会科学版),2015(2):124-129.
[23]朱学红,沈玉芳,邵留国.石油和汇率冲击下的中国金属价格波动行为[J].系统工程,2012(11):30-36.
[24]黄健柏,程慧,郭尧琦,等.美元、石油和金属价格——基于VAR模型的实证研究[J].经济经纬,2012(3):45-49.
[25]陈强,陈娟,陈道轮.美元指数的动态演化机理——基于扩散模型的实证分析[J].国际金融研究,2016(4):37-48.
[26]ENGLE R F.Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation[J].Econometrica:Journal of the Econometric Society,1982:987-1007.
[27]BOLLERSLEV T.Generalized autoregressive conditional heteroskedasticity[J].Journal of Econometrics,1986,31(3):307-327.
[28]BOLLERSLEV T.Modelling the coherence in short-run nominal exchange rates:a multivariate generalized ARCH model[J].The Review of Economics and Statistics,1990:498-505.
[29]ENGLE R.Dynamic conditional correlation:a simple class of multivariate generalized autoregressive conditional heteroskedasticity models[J].Journal of Business&Economic Statistics,2002,20(3):339-350.
[30]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[31]GRANGER C W J.Testing for causality:a personal viewpoint[J].Journal of Economic Dynamics and Control,1980,2:329-352.
[32]GRANGER C W J.Causality,cointegration,and control[J].Journal of Economic Dynamics and Control,1988,12(2):551-559.
[33]DIKS C,PANCHENKO V.A new statistic and practical guidelines for nonparametric Granger causality testing[J].Journal of Economic Dynamics and Control,2006,30(9-10):1647-1669.
(1)数据来自于LME。