资源科学 ›› 2017, Vol. 39 ›› Issue (6): 1202-1211.doi: 10.18402/resci.2017.06.19
收稿日期:
2016-04-09
修回日期:
2017-04-13
出版日期:
2017-06-20
发布日期:
2017-06-20
作者简介:
作者简介:郭名媛,女,天津人,博士,副教授,主要研究方向为低碳经济,金融计量学。E-mail:
基金资助:
Received:
2016-04-09
Revised:
2017-04-13
Online:
2017-06-20
Published:
2017-06-20
摘要:
21世纪初期,原油价格经历了暴涨暴跌的巨幅波动,中国原油消费量同时逐年上涨,国际原油价格波动对中国经济发展的影响成为了关注热点。基础工业是发展工业、尤其是重工业的物质基础,对国民经济发展起着举足轻重的作用。中国的六大基础工业的发展与原油价格息息相关,研究原油价格对中国基础工业的影响十分必要。本文采用CARR模型和CCF检验法,研究了2005年1月4日至2014年7月31日期间油价冲击与中国六大基础工业的信息溢出效应。实证结果表明:①中国电力及公用事业、钢铁、机械、基础化工、煤炭、石油石化六大基础工业均显著地受到原油价格波动的影响;②中国电力及公用事业、钢铁、机械、基础化工与原油价格存在双向均值溢出效应和方差溢出效应;③对于煤炭行业,存在由原油市场到煤炭行业的单向均值溢出效应和单向方差溢出效应;④对于石油石化行业,存在由原油市场到石油石化行业的单向均值溢出效应和双向方差溢出效应;⑤从10个滞后阶数的显著性水平分析,中国基础工业与原油市场之间的信息溢出效应并不是十分稳定。
郭名媛, 王娜. 基于CARR模型的油价冲击与中国基础工业信息溢出效应研究[J]. 资源科学, 2017, 39(6): 1202-1211.
Mingyuan GUO, Na WANG. Information spillover effects of crude oil price shocks on Chinese basic industries according to CARR modeling[J]. Resources Science, 2017, 39(6): 1202-1211.
表1
BRENT原油价格与六大行业板块指数极差序列的描述性统计结果"
BRENT | EP | ST | MA | FC | CO | PP | |
---|---|---|---|---|---|---|---|
均值 | 2.637 3 | 2.910 7 | 3.162 2 | 3.491 3 | 3.406 0 | 3.674 1 | 3.052 9 |
中位数 | 2.367 7 | 2.587 7 | 2.650 1 | 3.349 5 | 3.227 9 | 3.236 7 | 2.773 4 |
最大值 | 14.117 3 | 13.775 1 | 12.729 8 | 12.380 3 | 12.074 8 | 14.088 7 | 12.816 5 |
最小值 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 |
标准差 | 1.528 9 | 1.744 6 | 1.941 7 | 1.861 3 | 1.860 5 | 2.006 4 | 1.738 1 |
偏度 | 1.935 5 | 1.364 3 | 1.235 1 | 0.822 5 | 0.854 1 | 1.076 2 | 1.134 1 |
峰度 | 10.408 8 | 5.900 2 | 4.690 4 | 3.898 9 | 3.932 9 | 4.297 6 | 4.775 4 |
J-B统计量 | 6 681.714 0 | 1 516.279 0 | 856.760 0 | 336.023 2 | 362.245 4 | 604.035 1 | 793.373 3 |
P值 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 | 0.000 0 |
表2
BRENT原油价格和六大行业板块指数极差序列的平稳性检验结果"
有常数项 | 有常数和趋势项 | 无常数和趋势项 | ||||
---|---|---|---|---|---|---|
ADF统计量 | P值 | ADF统计量 | P值 | ADF统计量 | P值 | |
BRENT | -9.136 6 | 0.000 0 | -9.629 6 | 0.000 0 | -2.376 3 | 0.016 9 |
EP | -4.223 6 | 0.000 6 | -5.491 2 | 0.000 0 | -2.040 2 | 0.039 7 |
ST | -4.189 6 | 0.000 7 | -5.216 7 | 0.000 1 | -1.979 9 | 0.045 7 |
MA | -4.168 3 | 0.000 8 | -6.209 5 | 0.000 0 | -1.815 9 | 0.066 1 |
FC | -4.145 3 | 0.000 8 | -6.114 5 | 0.000 0 | -1.843 4 | 0.062 2 |
CO | -4.911 2 | 0.000 0 | -6.105 9 | 0.000 0 | -1.995 4 | 0.044 1 |
PP | -3.729 8 | 0.003 8 | -7.322 6 | 0.000 0 | -1.691 1 | 0.086 0 |
表3
CARR最优模型估计结果"
最优模型 | 对数似然值 | |||||||
---|---|---|---|---|---|---|---|---|
BRENT | GCARR(1,2) | 0.051 4 | 0.248 7 | 0.255 1 | 0.478 2 | 0.682 8 | 13.280 9 | -3 091.109 8 |
(-0.001 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | |||
EP | WCARR(1,2) | 0.350 6 | 0.405 4 | 0.201 8 | 0.256 7 | 2.724 4 | 69.304 8 | -3 153.890 0 |
(0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | |||
ST | WCARR(1,2) | 0.299 1 | 0.339 4 | 0.207 3 | 0.347 5 | 2.565 7 | -3 474.610 3 | |
(0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | ||||
MA | GCARR(1,1) | 0.025 0 | 0.201 0 | 0.792 5 | 0.574 1 | 26.163 6 | -3 307.751 4 | |
(-0.008 6) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | ||||
FC | GCARR(1,2) | 0.027 0 | 0.279 6 | 0.452 8 | 0.260 8 | 0.358 2 | -3 190.082 2 | |
(-0.021 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | ||||
CO | GCARR(1,2) | 0.043 3 | 0.201 8 | 0.514 4 | 0.272 8 | 0.460 7 | 31.904 1 | -3 715.699 2 |
(-0.002 1) | (0.000 0) | (0.000 0) | (-0.000 2) | (0.000 0) | (0.000 0) | |||
PP | WCARR(1,2) | 0.399 9 | 0.415 5 | 0.271 4 | 0.169 4 | 2.637 1 | -3 355.018 1 | |
(0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) | (0.000 0) |
表4
GARCH最优模型估计结果"
最优模型 | 对数似然值 | |||||||
---|---|---|---|---|---|---|---|---|
BRENT | EGARCH(1,1)-t | -0.068 4 | 0.095 1 | -0.036 4 | 0.995 5 | -4 436.440 0 | ||
(0.000 0) | (0.000 0) | (0.000 2) | (0.000 0) | |||||
CO | GARCH(1,1)-N | 0.040 5 | 0.035 1 | 0.958 3 | -5 169.080 0 | |||
(0.001 7) | (0.000 0) | (0.000 0) | ||||||
EP | GARCH(1,1)-t | 0.025 9 | 0.060 6 | 0.931 8 | -4 229.190 0 | |||
(0.010 9) | (0.000 0) | (0.000 0) | ||||||
FC | GARCH(2,2)-t | 0.144 6 | 0.074 5 | 0.086 5 | -0.077 2 | 0.883 3 | -4 580.280 0 | |
(0.001 5) | (0.000 0) | (0.000 0) | (0.000 9) | (0.000 0) | ||||
MA | GARCH(1,1)-t | 0.041 0 | 0.046 2 | 0.944 5 | -4 658.160 0 | |||
(0.018 6) | (0.000 0) | (0.000 0) | ||||||
PP | GARCH(1,1)-t | 0.030 1 | 0.048 8 | 0.943 4 | -4 377.100 0 | |||
(0.014 5) | (0.000 0) | (0.000 0) | ||||||
ST | TARCH(1,1)-N | 0.014 9 | 0.037 7 | -0.014 1 | 0.966 0 | -4 633.750 0 | ||
(0.000 2) | (0.000 0) | (0.003 4) | (0.000 0) |
表5
BRENT原油价格与六大行业板块指数的均值、方差-Granger因果关系检验结果"
均值-因果检验 | 方差-因果检验 | 均值-因果检验 | 方差-因果检验 | |||||
---|---|---|---|---|---|---|---|---|
k | EP→BRENT | BRENT→EP | EP→BRENT | BRENT→EP | ST→BRENT | BRENT→ST | ST→BRENT | BRENT→ST |
0 | 1.905 8* | 1.905 8* | 1.422 2 | 1.422 2 | 0.938 6 | 0.938 6 | 1.048 7 | 1.048 7 |
1 | 0.656 0 | 2.581 0*** | 0.440 5 | 2.844 4*** | 0.933 8 | 1.695 1* | 0.469 3 | 2.092 6** |
2 | 0.196 3 | -0.632 1 | 0.383 1 | -0.612 9 | 0.253 8 | -0.713 5 | 0.210 7 | -0.426 2 |
3 | 1.853 2* | 0.641 7 | 1.589 8 | 0.608 1 | 1.565 8 | 0.162 8 | 1.278 5 | 0.517 2 |
4 | 0.804 5 | -0.526 7 | 0.272 9 | -1.106 1 | 0.593 8 | -0.114 9 | 0.205 9 | -0.799 7 |
5 | 0.134 1 | 2.791 7*** | 0.330 4 | 2.533 1 | -0.857 1 | 1.968 1** | -0.814 0 | 1.987 2 |
6 | 1.642 5 | 0.301 7 | 0.680 0 | -0.162 8 | 2.006 4 | 0.507 6 | 2.145 3** | 0.541 1 |
7 | -0.134 1 | -1.034 3 | 0.186 8 | -0.014 4 | 0.244 2 | -1.771 8* | 0.713 5 | -1.877 1* |
8 | 1.585 0 | 0.569 8 | 2.475 7** | 0.225 1 | 1.992 0** | 0.354 4 | 2.830 0*** | 0.086 2 |
9 | 0.387 9 | 0.148 4 | 0.258 6 | -0.110 1 | -0.258 6 | -0.392 7 | -0.450 1 | -0.038 3 |
10 | 0.301 7 | -0.287 3 | -0.105 3 | -0.502 8 | 0.268 2 | 0.047 9 | -0.249 0 | -0.536 3 |
均值-因果检验 | 方差-因果检验 | 均值-因果检验 | 方差-因果检验 | |||||
k | MA→BRENT | BRENT→MA | MA→BRENT | BRENT→MA | FC→BRENT | BRENT→FC | FC→BRENT | BRENT→FC |
0 | 1.120 5 | 1.120 5 | 0.584 2 | 0.584 2 | 1.149 2 | 1.149 2 | 0.632 1 | 0.632 1 |
1 | 0.493 2 | 2.370 3** | 0.340 0 | 2.341 6** | 0.402 2 | 2.446 9*** | 0.014 4 | 2.509 2** |
2 | -0.478 9 | 0.972 1 | -0.258 6 | 0.981 6 | -0.335 2 | -0.502 8 | -0.249 0 | -0.431 0 |
3 | 2.260 2** | -0.536 3 | 1.834 0* | -0.569 8 | 2.700 7*** | 1.034 3 | 2.480 5** | 0.838 0 |
4 | 1.134 9 | 0.186 8 | 1.034 3 | -0.335 2 | 1.082 2 | -1.000 8 | 0.727 9 | -1.465 3 |
5 | -0.866 7 | 1.273 7 | 0.177 2 | 1.728 7* | -0.249 0 | 1.728 7* | -0.081 4 | 1.340 8 |
6 | 1.513 2 | -0.507 6 | 0.981 6 | -0.282 5 | 1.594 6 | -0.210 7 | 0.905 0 | -0.239 4 |
7 | -0.555 5 | -1.867 5* | -0.660 8 | -1.618 5 | 0.890 7 | -1.819 6* | 0.703 9 | -1.589 8 |
8 | 0.708 7 | -0.589 0 | 1.115 7 | -0.646 5 | 0.560 3 | 0.555 5 | 0.823 6 | 0.627 3 |
9 | 0.119 7 | 0.186 8 | -0.210 7 | 0.076 6 | 0.459 7 | -0.014 4 | 0.397 4 | -0.186 8 |
10 | -0.225 1 | -0.541 1 | -0.804 5 | -1.201 9 | -0.038 3 | -0.383 1 | -0.512 4 | -0.670 4 |
均值-因果检验 | 方差-因果检验 | 均值-因果检验 | 方差-因果检验 | |||||
k | CO→BRENT | BRENT→CO | CO→BRENT | BRENT→CO | PP→BRENT | BRENT→PP | PP→BRENT | BRENT→PP |
0 | 0.282 5 | 0.282 5 | 0.933 8 | 0.933 8 | 1.110 9 | 1.110 9 | 0.823 6 | 0.823 6 |
1 | 1.393 5 | 3.701 5*** | 1.221 1 | 6.828 4*** | 0.105 3 | 2.547 5** | -0.023 9 | 2.279 3** |
2 | 0.229 8 | 1.953 7* | -0.043 1 | 1.326 4 | 1.240 2 | -1.115 7 | 1.091 8 | -1.273 7 |
3 | 0.023 9 | 0.272 9 | -0.440 5 | 0.086 2 | 1.278 5 | 1.494 0 | 1.034 3 | 1.359 9 |
4 | 1.422 2 | -0.493 2 | 0.545 9 | -1.149 2 | 0.708 7 | 0.014 4 | 0.201 1 | -0.799 7 |
5 | -1.192 3 | 1.474 9 | -1.326 4 | 0.689 5 | -1.292 9 | 1.221 1 | -0.727 9 | 0.852 4 |
6 | 1.149 2 | 0.545 9 | 1.359 9 | 0.340 0 | 1.613 7 | 0.383 1 | 1.134 9 | -0.062 3 |
7 | 0.502 8 | -0.205 9 | 0.234 6 | -0.038 3 | 0.962 5 | -0.751 8 | 2.178 8** | -0.775 7 |
8 | -0.110 1 | 0.287 3 | 0.062 3 | 0.703 9 | 1.340 8 | -0.287 3 | 1.958 5* | -0.589 0 |
9 | -0.028 7 | 0.732 6 | -0.823 6 | 0.742 2 | 0.986 4 | 0.411 8 | 0.545 9 | -0.229 8 |
10 | -0.584 2 | -1.321 6 | -1.034 3 | -1.225 9 | -0.684 8 | -0.885 9 | -1.043 9 | -1.106 1 |
[1] | Faff R W,Brailsford T J.Oil price risk and the Australian stock market[J]. Journal of Energy Finance & Development,1999,4(1):69-87. |
[2] | Hammoudeh S,Dibooglu S,Aleisa E.Relationships among US oil prices and oil industry equity indices[J]. International Review of Economics & Finance,2004,13(4):427-453. |
[3] | El-Sharif I,Brown D,Burton B,et al.Evidence on the nature and extent of the relationship between oil prices and equity values in the UK[J]. Energy Economic,2005,27(6):819-830. |
[4] | Elyasiani E,Mansur I,Odusami B.Oil price shocks and industry stock returns[J]. Energy Economics,2011,33(5):966-974. |
[5] | Broadstock D C,Filis G.Oil price shocks and stock market returns:New evidence from the United States and China[J]. Journal of International Financial Markets,Institutions and Money,2014,33:417-433. |
[6] | Cong R G,Wei Y M,Jiao J L.Relationships between oil price shocks and stock market:An empirical analysis from China[J]. Energy Policy,2008,36(9):3544-3553. |
[7] | Nguyen C C,Bhatti M I.Copula model dependency between oil prices and stock markets:Evidence from China and Vietnam[J]. Journal of International Financial Markets,Institutions and Money,2012,22(4):758-773. |
[8] | 金洪飞,金荦. 原油价格与股票市场的溢出效应-基于中美数据的比较分析[J]. 金融研究,2008,29(2):83-97. |
[Jin H F,Jin L.Spillover effects of crude oil price and stock market:A com-parative analysis based on China and America data[J]. Journal of Financial Research,2008,29(2):83-97.] | |
[9] | 杨熹. 中美股票市场与原油价格的溢出效应研究[J]. 时代金融,2011,32(7):78-78. |
[Yang X.Study on spillover effects of stock market and crude oil price in China and America[J]. Times Finance,2011,32(7):78-78.] | |
[10] | 安瑶,谢龄洪. 原油价格对中国股市走势影响的实证研究[J]. 现代经济信息,2011,26(2):1-1. |
[An Y,Xie L H.Empirical study on the impact of oil price on China's stock market[J]. Modern Economic Information,2011,26(2):1-1.] | |
[11] | 郭国峰,郑召锋. 国际能源价格波动对中国股市的影响-基于计量模型的实证检验[J]. 中国工业经济,2011,29(6):26-35. |
[Guo Y F,Zheng Z F.Effect of Chinese stock market to the volatility of international energy-based on empirical econometric model[J]. China Industrial Economics,2011,29(6):26-35.] | |
[12] | 刘亮亮. 国际石油市场与中国股市间的波动率溢出分析-基于CCF检验法[D]. 杭州:浙江工商大学,2012. |
[Liu L L.Volatility Spillovers between Global Oil Prices and Stock Market Returns in China-Based on the CCF Test Method[D]. Hangzhou:Zhe-jiang Gongshang University,2012.] | |
[13] | 金洪飞,金荦. 国际原油价格对中国股票市场的影响-基于行业数据的经验分析[J]. 金融研究,2010,31(2):173-187. |
[Jin H F,Jin L.The impact of international crude oil price on China's stock market:An empirical analysis based on industry data[J]. Journal of Financial Research,2010,31(2):173-187.] | |
[14] | 温彬,刘淳,金洪飞. 宏观经济因素对中国行业股票收益率的影响[J]. 财贸经济,2011,32(6):51-59. |
[Wen B,Liu C,Jin H F.The impact of macroeconomic factors on China's industry stock returns[J]. Finance & Trade Economics,2011,32(6):51-59.] | |
[15] | 李红霞,傅强. 能源价格冲击、宏观经济因素与行业股价决定-来自中国上市公司28个行业板块的经验证据[J]. 山西财经大学学报,2011,33(6):11-19. |
[Li H X,Fu Q.Energy price shocks,macroeconomic factors and determination of industry stock returns-empirical evidence from listed companies in Chinese stock market[J]. Journal of Shanxi University of Finance and Economics,2011,33(6):11-19.] | |
[16] | Li S F,Zhu H M,Yu K M.Oil prices and stock market in China:A sector analysis using panel cointegration with multiple breaks[J]. Energy Economics,2012,34(6):1951-1958. |
[17] | Zhang C G,Chen X Q.The impact of global oil price shocks on China’s bulk commodity markets and fundamental industries[J]. Energy Policy,2014,66:32-41. |
[18] | Wang X,Zhang C G.The impacts of global oil price shocks on China's fundamental industries[J]. Energy Policy,2014,68:394-402. |
[19] | Chou Y T.Forecasting financial volatilities with extreme values:The Conditional Autoregressive Range (CARR)model[J]. Journal of Money,Credit,and Banking,2005,37(3):561-582. |
[20] | Cheung Y W,Ng L K.A causality-in- variance test and its appli-cation to financial market prices[J]. Journal of Econometrics,1996,72(1-2):33-48. |
[21] | 程细玉,夏天. 金融市场波动性CARR 类模型与GARCH 类模型的比较研究[J]. 数学的实践与认识,2009,39(13):12-18. |
[Cheng X Y,Xia T.The comparative study between the CARR model and the GARCH race models on the volatility of the financial market[J]. Mathematics in Practice and Theory,2009,39(13):12-18.] | |
[22] | 张苏林. 我国黄金现货波动率预测能力分析-基于GARCH模型与CARR模型的比较[J]. 金融理论与实践,2011,33(8):47-50. |
[Zhang S L.Analysis on the forecasting ability of China's gold spot volatility-based on the comparison of GARCH model and CARR model[J]. Financial Theory & Practice,2011,33(8):47-50.] | |
[23] | Nakajima T,Hamori S.Testing causal relationships between wholesale electricity prices and primary energy prices[J]. Energy Policy,2013,62(11):869-877. |
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[14] | 郑德凤, 王燕燕, 曹永强, 王燕慧, 郝帅, 吕乐婷. 基于生态系统服务的生态福祉分类与时空格局——以中国地级及以上城市为例[J]. 资源科学, 2020, 42(6): 1110-1122. |
[15] | 王洁, 张继良. 住房空置率对房价的影响——基于35个重点城市的面板数据[J]. 资源科学, 2020, 42(6): 1135-1147. |
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