多时间尺度下行业间系统性金融风险溢出及拓扑结构分析

doi:10.16381/j.cnki.issn1003-207x.2020.0324

摘要/Abstract

摘要： 系统性金融风险频发，其表现出的风险溢出效应受到国内外学者广泛关注。通过极大重叠离散小波变换和溢出指数方法，从静态和动态视角定量研究不同时间尺度和阶段下我国市场行业间系统性金融风险溢出特性，并构建多时间尺度和不同风险阶段下风险溢出网络，分析其拓扑结构演化规律。实证结果发现：行业间总体风险溢出水平较高，市场整体风险联动性强；原始尺度下国防军工、农林牧渔、有色金属行业为主要的风险溢出行业，资本市场及相关行业、化工及机电、部分服务业、医药生物行业始终是主要的风险接受行业；不同时间尺度下行业间风险溢出特性会发生明显的改变，且随着时间尺度的增加，整体风险溢出特性开始减弱；内生危机时行业间风险溢出水平要高于外生危机；同一社区下的行业之间会优先发生风险的传递。所得结果可为监管者和投资者提供一定的决策参考。

关键词: 小波变换；行业指数；波动溢出；网络拓扑分析；复杂网络

Abstract: In recent years, systemic financial risks frequently break out, which causes great attention from scholars at home and abroad. At present, most scholars are concerned with the spillover effects between two sectors from a static perspective. To uncover the spillover effect among sectors in China, the industry indexes of Shenwan Index are selected, which includes 28 sectors as the research object. Firstly, from static and dynamic perspectives, the characteristics of systemic financial risk spillover effect of economic sectors in different time scales and time periods are studied by using the maximum overlap discrete wavelet transform method and the spillover index method. Then, the networks of risk spillover effect in different time scales and time periods are constructed to analyze its topology evolution laws. Experimental results are as follows: the level of risk spillover of economic sectors is relatively high, and the connectedness of the overall market is strong; the main risk spillover sectors include the national defense industry, agriculture, forestry, animal husbandry, fishery, and non-ferrous metals at the original scale; the main risk acceptance sectors involve capital markets, chemical, electromechanical, service industries, and biopharmaceutical industry; the characteristics of risk spillover effect of economic sectors vary significantly under different time scales, and the risk spillover effect tends to be weak with the increase in time scales; the risk spillover effect of endogenous crisis is higher than that of exogenous crisis; the risk spillover effect favors for the sectors in the same community. Our conclusions provide theoretical supports for decision-makers. Regulators should pay more attention to the main risk spillover sectors under different time scales and protect the main risk acceptance sectors to make sure they run smoothly. For investors, it is necessary to recognize the risk spillover characteristics of industries in different time scales, evaluate the impact of the main risk spillover industries, and make rational investments.

Key words: wavelet transform； industry indexes； volatility spillover； topology analysis； complex network

中图分类号:

F830.91

刘超, 郭亚东. 多时间尺度下行业间系统性金融风险溢出及拓扑结构分析[J]. 中国管理科学, 2022, 30(10): 46-59.

LIU Chao, GUO Ya-dong. Systemic Financial Risk Spillover and Its Topology Analysis of Sector Indexes in China under a Multi-Scale View[J]. Chinese Journal of Management Science, 2022, 30(10): 46-59.

参考文献

［1］张晓朴. 系统性金融风险研究：演进、成因与监管［J］. 国际金融研究, 2010, 25(7): 58-67.Zhang Xiaopu. A study on systemic financial risk: Evolution, causes and supervision［J］. Studies of International Finance,2010,25(7):58-67.
［2］姜永宏,穆金旗,聂禾.国际石油价格与中国行业股市的风险溢出效应研究［J］.经济与管理评论,2019,35(5):99-112.Jiang Yonghong, Mu Jinqi， Nie He. A study of the risk spillover effect of the international oil price and Chinese industry stock market［J］. Review of Economy and Management,2019,35(5):99-112.
［3］佟家栋,谢丹阳,包群,等.“逆全球化”与实体经济转型升级笔谈［J］. 中国工业经济, 2017, 34(6): 5-59.Tong Jiadong, Xie Danyang, Bao Qun, et al. The talks on paper of the deglobalization and transformation and upgrading of real economy［J］. China Industrial Economics, 2017, 34(6): 5-59.
［4］何青,钱宗鑫,刘伟. 中国系统性金融风险的度量—基于实体经济的视角［J］. 金融研究, 2018, 454(4): 53-70.He Qing, Qian Zongxin, Liu Wei. Systemic financial risk and real economic activity in China［J］. Journal of Financial Research, 2018, 454(4): 53-70.
［5］ Nazlioglu S, Soytas U, Gupta R. Oil prices and financial stress: A volatility spillover analysis［J］. Energy Policy, 2015, 82(7): 278-288.
［6］ 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.
［7］ Liu Xueyong, An Haizhong, Li Huajiao, et al. Features of spillover networks in international financial markets: Evidence from the G20 countries［J］. Physica A: Statistical Mechanics and its Applications, 2017, 479(15): 265-278.
［8］ Reboredo J C, Ugolini A. Systemic Risk in European Sovereign Debt Markets: A CoVaR-Copula Approach［J］. Journal of International Money and Finance, 2015, 51: 214-244.
［9］ Zhang Xiwen, Jiang Hui. Application of Copula function in financial risk analysis［J］. Computers & Electrical Engineering, 2019,77: 376-388.
［10］ Yu J, Meyer R. Multivariate stochastic volatility models: Bayesian estimation and model comparison［J］. Econometric Reviews, 2006, 25(2-3): 361-384.
［11］ Balcilar M, Ozdemir Z A. The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters［J］. Physica A: Statistical Mechanics and its Applications, 2019, 534(22): 1-14.
［12］熊正德,韩丽君.金融市场间波动溢出效应研究—GC-MSV模型及其应用［J］. 中国管理科学, 2013, 21(2): 32-41.Xiong Zhengde, Han Lijun. An empirical study on the volatility spillover effect between financial markets—GC-MSV model and application［J］. Chinese Journal of Management Science,2013,21(2): 32-41.
［13］何德旭, 苗文龙. 国际金融市场波动溢出效应与动态相关性［J］. 数量经济技术经济研究,2015,32(11):23-40.He Dexu, Miao Wenlong. Spillovers effect and dynamic correlation among international financial market［J］. The Journal of Quantitative & Technical Economics,2015,32(11):23-40.
［14］陈暮紫,赵婷婷,刘承林,等.跨部门金融机构系统重要性和共振效应的动态演化研究——基于中国A股市场的实证［J］.中国管理科学,2020,28(4):36-47.Chen Muzi, Zhao Tingting, Liu Chenglin, et al. Dynamic evolution study on inter-sector financial institution systemic importance and resonance effects［J］. Chinese Journal of Management Science,2020,28(4):36-47.
［15］胡毅,李瑞,张希,等.基于分位数回归的系统性风险和经济增长关系研究［J］.管理评论,2019,31(12):3-14.Hu Yi， Li Rui， Zhang Xi, et al. A study of the relationship between systemic risk and economic growth based on quantile regression［J］. Management Review，2019,31(12):3-14.
［16］刘超,李元睿,姜超,等.中国证券公司系统性风险测度及演化特征研究—来自20家上市证券公司的数据［J］.中国管理科学,2019,27(5):11-22.Liu Chao, Li Yuanrui, Jiangchao, et al. Research on systematic risk measurement and evolution characteristics of China’s securities companies——empirical data from 20 listed securities companies［J］. Chinese Journal of Management Science,2019,27(5):11-22.
［17］ Diebold F X， Yilmaz K. Better to give than to receive: Predictive directional measurement of volatility spillovers［J］. International Journal of Forecasting, 2012, 28(1): 57-66.
［18］ Diebold F X, Yilmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms［J］. Journal of Econometrics, 2014, 182(1): 119-134.
［19］ Kang W, Ratti R A. Oil shocks, policy uncertainty and stock returns in China［J］. Economics of Transition, 2015, 23(4): 657-676.
［20］ Roy R P, Roy S S. Financial contagion and volatility spillover: An exploration into Indian commodity derivative market［J］. Economic Modelling, 2017, 67: 368-380.
［21］ Tiwari A K, Cunado J, Gupta R, et al. Volatility spillovers across global asset classes: Evidence from time and frequency domains［J］. The Quarterly Review of Economics and Finance, 2018, 70(4): 194-202
［22］尹力博,吴优.离岸人民币区域影响力研究—基于信息溢出的视角［J］. 金融研究,2017, 40(8): 1-18.Yin Libo, Wu You. The research of offshore RMB’s regional influence: An perspective based on information spillover［J］. Journal of Financial Research,2017,40(8): 1-18.
［23］王奇珍,王玉东.国际油价、美国经济不确定性和中国股市的波动溢出效应研究［J］. 中国管理科学, 2018, 26(11): 50-61.Wang Qizhen, Wang Yudong. International transmission of volatility among crude oil price, economic uncertainty and the stock market［J］. Chinese Journal of Management Science,2018,26(11):50-61.
［24］ Wang Xunxiao, Wu Chongfeng. Asymmetric volatility spillovers between crude oil and international financial markets［J］. Energy Economics, 2018, 74(6): 592-604.
［25］刘超,徐君慧,周文文.中国金融市场的风险溢出效应研究——基于溢出指数和复杂网络方法［J］. 系统工程理论与实践, 2017, 37(4): 831-842.Liu Chao, Xu Junhui, Zhou Wenwen. Study on risk spillover effect of financial markets in China based on methods of spillover index and complex network［J］. Systems Engineering-Theory & Practice,2017,37(4):831-842.
［26］马旭平,王军,孙晓蕾,等.主权风险溢出网络动态特征研究:以“一带一路”国家为例［J］.系统工程理论与实践,2019,39(6):1363-1372.Ma Xuping， Wang Jun， Sun Xiaolei， et al. A study on the dynamics of sovereign risk spillover networks:Evidence from the countries along the Belt and Road［J］. Systems Engineering-Theory & Practice,2019,39(6):1363-1372.
［27］杨子晖,周颖刚. 全球系统性金融风险溢出与外部冲击［J］. 中国社会科学, 2018, 12: 69-90.Yang Zihui, Zhou Yinggang. Global systemic financial risk spillovers and their external shocks［J］. Social Sciences in China,2018,12:69-90.
［28］杨子晖,陈雨恬,张平淼.重大突发公共事件下的宏观经济冲击、金融风险传导与治理应对［J］.管理世界,2020,36(5):13-35+7.Yang Zihui, Chen Yutian, Zhang Pingmiao. Macroeconomic shock, financial risk transmission and governance response to major public emergencies［J］. Journal of Management World,2020,36(5):13-35+7.
［29］ Baruník J, Kocˇenda E, Vácha L. Gold, oil, and stocks: Dynamic correlations［J］. International Review of Economics & Finance, 2016, 42(2): 186-201.
［30］ Madaleno M, Pinho C. Internastional stock market indices comovements: A new look［J］. International Journal of Finance & Economics, 2012, 17(1): 89-102.
［31］ Jena S K, Tiwari A K, Roubaud D. Comovements of gold futures markets and the spot market: A wavelet analysis［J］. Finance Research Letters, 2018, 24(1): 19-24.
［32］ Tweneboah G, Alagidede P. Interdependence structure of precious metal prices: A multi-scale perspective［J］. Resources Policy, 2018, 59: 427-434.
［33］熊正德,文慧,熊一鹏. 我国外汇市场与股票市场间波动溢出效应实证研究—基于小波多分辨的多元BEKK-GARCH(1,1)模型分析［J］. 中国管理科学,2015, 23(4): 30-38.Xiong Zhengde, Wen Hui, Xiong Yipeng. Empirical research on spillover effect between foreign exchange market and stock market by wavelet multi-resolution analysis and multivariate BEKK-GARCH(1,1) model［J］. Chinese Journal of Management Science,2015,23(4): 30-38.
［34］朱鹏飞,唐勇,洪小梅,等.P2P网贷利率存在波动溢出吗？—基于时-频域溢出指数的实证研究［J］.中国管理科学, 2021,29(4): 82-92.Zhu Pengfei， Tangyong， Hong Xiaomei, et al. Does the p2p lending interest rate have volatility spillovers?—an empirical study based on time-frequency domains spillover index［J］. Chinese Journal of Management Science,2021,29(4): 82-92.
［35］ Dajcman S. Interdependence between some major European stock markets-A wavelet lead/lag analysis［J］. Prague economic papers, 2013, 22(1): 28-49.
［36］刘向丽,王旭朋. 基于小波分析的股指期货高频预测研究［J］. 系统工程理论与实践, 2015, 35(6): 1425-1432.Liu Xiangli, Wang Xupeng. Research on high frequency data forecasting of stock index futures market based on wavelet analysis［J］. Systems Engineering-Theory & Practice, 2015, 35(6): 1425-1432.
［37］黄文彬,严佳佳,邓婷婷. 欧美股票市场对我国股票市场的传染效应研究—基于次贷与欧债危机背景［J］. 系统工程理论与实践, 2017, 37(8): 1982-1991.Huang Wenbin, Yan Jiajia, Deng Tingting. An empirical study of contagion effect from American and European stock markets to Chinese stock markets based on the subprime crisis and the European［J］. Systems Engineering-Theory & Practice,2017,37(8):1982-1991.
［38］张于喆,周振,石昱馨. 军民融合产业发展形势和政策建议［J］. 宏观经济管理, 2018, 34(9): 37-42+54.Zhang Yuzhe, Zhou Zhen, Shi Yuxin. The situation of the development of the integration of the military and civilian sectors and policy suggestions［J］. Macroeconomic Management, 2018,34(9):37-42+54.

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