基于EMD-BEKK-GARCH模型的有色金属股票市场波动溢出研究

管理评论 ›› 2022, Vol. 34 ›› Issue (12): 39-48.

基于EMD-BEKK-GARCH模型的有色金属股票市场波动溢出研究

杨光^1,2, 张丁漩³, 魏云捷^1,4

1. 中国科学院数学与系统科学研究院, 北京 100190;
2. 中国科学院大学数学科学学院, 北京 100190;
3. 中国科学院大学经济与管理学院, 北京 100190;
4. 中国科学院预测科学研究中心, 北京 100190

收稿日期:2021-10-12 出版日期:2022-12-28 发布日期:2023-01-16
通讯作者: 魏云捷(通讯作者),中国科学院数学与系统科学研究院副研究员,博士。
作者简介:杨光,中国科学院数学与系统科学研究院博士研究生;张丁漩,中国科学院大学经济与管理学院博士研究生。
基金资助:
国家自然科学基金青年项目(71801213);国家自然科学基金面上项目(72171223);国家自然科学基金基础项目(71988101)。

An Empirical Study on Volatility Spillover of Non-ferrous Metal Stock Markets Based on EMD-BEKK-GARCH Model

Yang Guang^1,2, Zhang Dingxuan³, Wei YunJie^1,4

1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190;
2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100190;
3. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
4. Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190

Received:2021-10-12 Online:2022-12-28 Published:2023-01-16

摘要/Abstract

摘要： 2020年1月新型冠状病毒肺炎疫情爆发,随着防控政策加强与停工停产,全球范围内实体经济受到冲击,中国的经济与金融市场受到巨大影响。作为有色金属资源生产与消费大国,中国的铅、锌、铝、铜等有色金属的金融市场受到疫情冲击,出现较大程度的下滑。为了研究疫情期间中国有色金属的股票市场之间风险波动溢出情况,本文选取了铝、铜、铅锌对应的股票市场指数,将经验模态分解(EMD)与BEKK-GARCH模型结合,从不同周期的角度对该问题进行探讨。结果表明铝、铜与铅锌之间在长期、中期与短期存在着不同程度的波动溢出。对比未分解序列的波动溢出情况,EMD提供了不同周期的信息,这加强了我们对市场之间关系的研究。

关键词: 新冠肺炎疫情, 有色金属股票市场, 经验模态分解, BEKK-GARCH, 波动溢出

Abstract: The COVID-19 pneumonia epidemic broke out in January 2020. With the strengthening of the prevention and control policy and stop of production, the real economy in the worldwide was hit, and China’s economy and finance were greatly affected. As a major producer and consumer of non-ferrous metal, China’s financial market of non-ferrous metals such as lead, zinc, aluminum and copper has been impacted by the epidemic and declined to a large extent. To study the volatility spillover of China’ s non-ferrous metal stock markets during the epidemic from the perspective of different timescale, this paper selects the stock market indexes of aluminum, copper and lead-zinc, and combines empirical mode decomposition (EMD) with BEKK-GARCH model. The results show that there are different degrees of volatility spillovers among aluminum, copper and lead-zinc in the long-term, medium-term and short-term. Comparing the volatility spillovers of undivided series, EMD provides more information of different timescale, which strengthens our research on the relationship between these markets.

Key words: COVID-19 pneumonia epidemic, non-ferrous metal stock market, empirical mode decomposition, BEKK-GARCH, volatility spillover

杨光, 张丁漩, 魏云捷. 基于EMD-BEKK-GARCH模型的有色金属股票市场波动溢出研究[J]. 管理评论, 2022, 34(12): 39-48.

Yang Guang, Zhang Dingxuan, Wei YunJie. An Empirical Study on Volatility Spillover of Non-ferrous Metal Stock Markets Based on EMD-BEKK-GARCH Model[J]. Management Review, 2022, 34(12): 39-48.

参考文献

[1] 吴婷婷,朱昂昂. 新冠肺炎疫情对中国经济的影响及应对策略[J]. 南方金融, 2020,(5):3-11
[2] 陈赟,沈艳,王靖一. 重大突发公共卫生事件下的金融市场反应[J]. 金融研究, 2020,(6):20-39
[3] 方意,于渤,王炜. 新冠疫情影响下的中国金融市场风险度量与防控研究[J]. 中央财经大学学报, 2020,(8):116-128
[4] Kaplanski G., Levy H. Sentiment and Stock Prices:The Case of Aviation Disasters[J]. Journal of Financial Economics, 2010,95(2):174-201
[5] Corbet S., Hou Y. G., Hu Y., et al. Pandemic-related Financial Market Volatility Spillovers:Evidence from the Chinese COVID-19 Epicentre[J]. International Review of Economics & Finance, 2021,71:55-81
[6] 况秋华,刘春学,尹昊. 双循环背景下有色金属矿产品国际价格波动影响因素研究——以铜、锡为例[J]. 价格月刊, 2021, (9):1-9
[7] 黄鸿,蒋晓全. 我国有色金属期货价格与股票价格相关性研究[J]. 上海经济研究, 2010,(5):50-53
[8] 吴丹,胡振华. 基本有色金属期货市场价格波动实证分析[J]. 财经理论与实践, 2020,41(4):39-45
[9] 张国胜,刘晨,武晓婷. 我国商品期货市场套期保值效率评价与提升对策[J]. 中国流通经济, 2021,35(5):42-51
[10] 黄盈盈,贺美兰. 有色金属上市公司财务风险预警模型及应用——以J铜业公司为例[J]. 中国注册会计师, 2021,(6):102-105
[11] 蓝波,庄雷. 新冠肺炎疫情对金融市场冲击的影响研究[J]. 统计与决策, 2021,37(5):129-133
[12] 陶萍,刘先伟. 国外突发事件对国内股票市场的影响研究[J]. 预测, 2015,34(2):66-70
[13] Huang H. H., Chan M. L., Huang I. H., et al. Stock Price Volatility and Overreaction in a Political Crisis:The Effects of Corporate Governance and Performance[J]. Pacific-Basin Finance Journal, 2011,19(1):1-20
[14] Maillet B. B., Michel T. L. The Impact of the 9/11 Events on the American and French Stock Markets[J]. Review of International Economics, 2005,13(3):597-611
[15] 张一,刘艳辉,徐山鹰,等. SARS对中国股市冲击的实证分析[J]. 管理评论, 2003,15(5):3-7
[16] 张一,吴莹,汪寿阳,等. SARS对中国进出口贸易的影响分析[J]. 管理评论, 2003,15(4):23-27
[17] 杨秀刚,黄玉龙. 突发公共事件中金融市场价格与均衡价格的协整分析[J]. 经济论坛, 2011,(5):5-10
[18] 李晓林. 突发事件对金融市场的影响研究[D]. 电子科技大学博士学位论文, 2013
[19] 张栋凯,齐佳音. 基于微博的企业突发危机事件网络舆情的股价冲击效应[J]. 情报杂志, 2015,34(3):132-137
[20] Hamao Y., Masulis R. W., Ng V. Correlations in Price Changes and Volatility across International Stock Markets[J]. The Review of Financial Studies, 1990,3(2):281-307
[21] Kanas A. Volatility Spillovers across Equity Markets:European Evidence[J]. Applied Financial Economics, 1998, 8(3):245-256
[22] Miyakoshi T. Spillovers of Stock Return Volatility to Asian Equity Markets from Japan and the US[J]. Journal of International Financial Markets, Institutions and Money, 2003,13(4):383-399
[23] Baele L. Volatility Spillover Effects in European Equity Markets[J]. Journal of Financial and Quantitative Analysis, 2005,40(2):373-401
[24] Golosnoy V., Gribisch B., Liesenfeld R. Intra-daily Volatility Spillovers in International Stock Markets[J]. Journal of International Money and Finance, 2015,53:95-114
[25] 王鹰翔,张鲁欣. 基于向量GARCH模型的国际证券市场波动溢出研究[J]. 管理评论, 2011,23(6):49-53
[26] 韩立岩,郑葵方. 铜期货市场信息的国际传递[J]. 管理评论, 2008,20(1):9-16
[27] 陈潇,杨恩. 中美股市杠杆效应与波动溢出效应——基于GARCH模型的实证分析[J]. 财经科学, 2011,(4):17-24
[28] Baklaci H. F., Aydoğan B., Yelkenci T. Impact of Stock Market Trading on Currency Market Volatility Spillovers[J]. Research in International Business and Finance, 2020,52:101182
[29] 徐媛媛,王传美,李剑. 能源市场与玉米市场间价格溢出机制研究——基于三元VEC-BEKK-GARCH(1,1)模型[J]. 中国农业大学学报, 2018,23(5):168-177
[30] 郑燕,丁存振,马骥. 中国鸡蛋产业链不同市场环节价格传导效应分析[J]. 农林经济管理学报, 2018,17(6):727-737
[31] 危非. 中美股票市场相同资源行业间的溢出效应研究[D]. 江西财经大学硕士学位论文, 2017
[32] 田树喜,夏天洋,杨童舒. 中国A股市场三种股指期货价格先导及波动溢出效应的计量检验[J]. 东北大学学报(社会科学版), 2019,21(4):344-349
[33] 吕靖烨,郭泽,肖路. 国际油价与人民币汇率的非对称溢出效应研究——基于VEC-BEKK-GARCH模型[J]. 价格月刊, 2020,(1):23-29
[34] 韦星,宁薛平,李富有. 人民币无本金交割远期汇率与在岸人民币汇率关系的实证检验[J]. 管理评论, 2020,32(11):81-91
[35] 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 of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences, 1998,454(1971):903-995
[36] Zhang X., Lai K. K., Wang S. Y. A New Approach for Crude Oil Price Analysis Based on Empirical Mode Decomposition[J]. Energy Economics, 2008,30(3):905-918
[37] Zhu B., Ye S., Wang P., et al. A Novel Multiscale Nonlinear Ensemble Leaning Paradigm for Carbon Price Forecasting[J]. Energy Economics, 2018,70:143-157
[38] Wang B., Wang J. Energy Futures and Spots Prices Forecasting by Hybrid SW-GRU with EMD and Error Evaluation[J]. Energy Economics, 2020,90:104827
[39] 崔啸,郭琨,金圳妮,等. 基于TEI@I的中国利率多尺度波动特征研究[J]. 管理评论, 2020,32(7):111-122
[40] 汤铃,李建平,孙晓蕾,等. 基于模态分解的国家风险多尺度特征分析[J]. 管理评论, 2012,24(8):3-10
[41] 史美景,曹星婉. 基于EMD的时间序列不同频率波动及趋势研究[J]. 统计与决策, 2012,(16):27-30
[42] 阮连法,包洪洁. 基于经验模态分解的房价周期波动实证分析[J]. 中国管理科学, 2012,20(3):41-46
[43] 杨涛,魏云捷. 医药政策变动对二级市场影响的实证研究:基于集合经验模态分解[J]. 管理评论, 2021,33(3):66-74
[44] Dätig M., Schlurmann T. Performance and Limitations of the Hilbert-Huang Transformation (HHT) with an Application to Irregular Water Waves[J]. Ocean Engineering, 2004,31(14-15):1783-1834
[45] Engle R. F. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation[J]. Econometrica:Journal of the Econometric Society, 1981,50(4):987-1008
[46] Bollerslev T. Generalized Autoregressive Conditional Heteroskedasticity[J]. Journal of Econometrics, 1986,31(3):307-327
[47] Engle R. F., Kroner K. F. Multivariate Simultaneous Generalized ARCH[J]. Econometric Theory, 1995,11(1):122-150