基于多层网络结构的行业间风险联动机制研究

doi:10.16381/j.cnki.issn1003-207x.2022.1546

Abstract

Abstract:

The report of the 20th National Congress of the Communist Party of China pointed out that “we should strengthen and improve modern financial supervision， strengthen the financial stability guarantee system， and bring all kinds of financial activities under supervision according to law”. In the time dimension， the accelerator effect between different industries forms a risk feedback mechanism； in the spatial dimension， different industries have amplified the systematic risk through the formation of complex financial associations through the role of the market. As a result， studying the risk contagion mechanism among industries is of great significance for preventing and resolving major systemic risks and maintaining financial stability.The CSI All Share Index is taken as object， DCC-t-Copula-CoVaR model is used to measure systematic risk spillovers and absorption levels as the explained variables， a multilayer network is constructed， key structural indicators are taken as the explanatory variables， and MIDAS method is used to study the risk linkage mechanism.Consequently， suggestions are put forward to prevent systemic risks among industries：The regulatory level should consider the time-varying characteristics of the risk network and describe the dynamic evolution mechanism of systematic risk. A macro prudential risk monitoring mechanism of “too relevant to fail” based on the perspective of multi-layer networks should also be designed. To prevent and defuse systemic risks， the Party's leadership over financial should be strengthened， pushing our economy towards high-quality development.A reference model and basis for establishing a cross industry risk linkage monitoring system is provided. Through effective monitoring of inter industry risk linkage， the ability to withstand extreme risk shocks is improved， so as to increase the resilience of the macro-economy， make progress while maintaining stability， and achieve high-quality development of the industry.It is found that from the perspective of risk transmission， multilayer network theory can better describe the complex correlation among industries. It is difficult for a single industry to cause a chain reaction，but risk resonance among industries will lead to major systemic risks. Industry risk network polycentric development and diversification of contagion paths evolve under the impact of major risk events. From the aspect of influencing factors： The key role of important industries in risk contagion is similar to the "systemically important role"，and the adjacent industries of important industries also deserve attention. The industries which have high relevance and close to the center in the network reflect the characteristics of both stable and fragile.

Key words: systemic risk, risk spillover, multilayer networks, MIDAS

CLC Number:

F832.5

Hong Shen, Chenyao Zhang, Xiaoxing Liu. Analysis of Risk Spillover Characteristics and Mechanism among Industries: Evidence from Multilayer Network[J]. Chinese Journal of Management Science, 2024, 32(12): 173-182.

Figures/Tables 11

References 35

1	Adrian T， Brunnermeier K. Covar［J］. American Economic Review， 2016（7）：1705-1741.
2	Acharya V V， Pedersen L H， Philippon T， et al. Measuring systemic risk［J］. The Review of Financial Studies， 2017，30（1）：2-47.
3	Dan G， Li Y， Lourie B， et al. Key performance indicators as supplements to earnings： Incremental informativeness， demand factors， measurement issues， and properties of their forecasts［J］. Review of Accounting Studies， 2019，24（1）：1147-1183.
4	Cheikh N B， Zaied Y B. Revisiting the pass-through of exchange rate in the transition economies： New evidence from new EU member states［J］. Journal of International Money and Finance， 2020，100：102093.
5	许启发，王侠英，蒋翠侠，等. 基于藤Copula-CAVia方法的股市风险溢出效应研究［J］.系统工程理论与实践，2018，38（11）：2738-2749.
	Xu Q F， Wang X Y， Jiang C X， et al. Investigating risk spillover effects among stock markets： A vine copula-CAViaR approach［J］. Systems Engineering - Theory & Practice， 2018，38（11）：2738-2749.
6	杨子晖，陈雨恬，陈里璇. 极端金融风险的有效测度与非线性传染［J］. 经济研究， 2019，54（5）：63-80.
	Yang Z H， Chen Y T， Chen L X. Effective measurement and nonlinear contagion of extreme financial risk［J］. Economic Research Journal， 2019，54（5）：63-80.
7	刘晓星，张旭，李守伟. 中国宏观经济韧性测度——基于系统性风险的视角［J］. 中国社会科学， 2021（1）：12-32.
	Liu X X， Zhang X， Li S W.Measurement of China’s macroeconomic resilience： A systemic risk perspective［J］.Social Sciences in China， 2021（1）：12-32.
8	Martinez J S， AlexandrovaK B， BravoB B， et al. An empirical study of the Mexican banking system's network and its implications for systemic risk［J］. Journal of Economic Dynamics & Control， 2014，40（3）：242-265.
9	Musmeci N， Nicosia V， Aste T， et al. The multiplex dependency structure of financial markets［J］.Lse Research Online Documents on Economics， 2016，2017（2）：1-13.
10	Baruník J， Koenda E， Vácha L. Asymmetric volatility connectedness on the forex market［J］. Journal of International Money and Finance， 2017，77：39-56.
11	Gross C， Siklos P. Analyzing credit risk transmission to the nonfinancial sector in Europe： A network approach［J］. Journal of Applied Econometrics， 2020，35（1）：61-81.
12	李守伟，文世航，王磊，等. 多层网络视角下金融机构关联性的演化特征研究［J］. 中国管理科学， 2020，12（28）：35-43.
	Li S W， Wen S H， Wang L， et al. Evolution characteristics of financial institutions’ interrelationships from the perspective of multilayer network［J］. Chinese Journal of Management Science， 2020，12（28）：35-43.
13	赵万里，范英，姬强，等.“一带一路”国家金融风险溢出研究——基于TENET网络方法［J］.系统工程理论与实践，2022，42（1）：24-36.
	Zhao W L， Fan Y， Ji Q， et al.Research on financial risk spillover of the countries along the Belt and Road：Based on TENETmethod［J］.Systems Engineering - Theory & Practice，2022，42（1）： 24-36.
14	Ahern K R， Harford J. The importance of industry links in merger waves［J］. The Journal of Finance， 2014，69（2）：527-576.
15	Acharya V V， Pedersen L H， Philippon T， et al. Measuring systemic risk［J］. The Review of Financial Studies， 2017，30（1）：2-47.
16	Nguyen L， Mateut S， Chevapatrakul T. Business-linkage volatility spillovers between US industries［J］. Journal of Banking &Finance， 2020，111（2）：1-26.
17	赵飞，陈彩虹. 技术关联、商业信用与风险跨行业传染［J］.现代财经（天津财经大学学报）， 2022，42（5）： 15-35.
	Zhao F， Chen C H. Technology association，trade credit and risk contagion cross industry［J］. Modern Finance and Economics （Journal of Tianjin University of Finance and Economics）， 2022，42（5）：15-35.
18	Ghysels E， Valkanov R. The MIDAS touch： Mixed data sampling regression models［J］. Cirano Working Papers， 2004，5（1）：512-517.
19	Owyang M T， Armesto M T， Engemann K M. Forecasting with mixed frequencies［J］. Federal Reserve Bank of St. Louis Review， 2010，92（6）：521-536.
20	Andreou E， Ghysels E， Kourtellos A. Should macroeconomic forecasters use daily financial data and how？［J］. Social Science Electronic Publishing， 2013，31（2）：240-251.
21	尚玉皇，郑挺国. 中国金融形势指数混频测度及其预警行为研究［J］. 金融研究， 2018（3）：21-35.
	Shang Y H， Zheng T G. The mixed frequency measuring of Chinese FCI and its risk warning behavior［J］. Journal of Financial Research， 2018（3）：21-35.
22	杨小玄，王一飞. 我国系统性风险度量指标构建及预警能力分析—基于混频数据动态因子模型［J］. 南方金融， 2019（6）：3-15.
	Yang X X， Wang Y F. Construction of China's systematic risk measurement indicators and analysis of early warning capability：Based on dynamic factor model of mixing data［J］. South China Finance， 2019（6）：3-15.
23	杨子晖. 金融市场与宏观经济的风险传染关系——基于混合频率的实证研究［J］. 中国社会科学， 2020 （12）：160-180.
	Yang Z H. The risk contagion relationship between the financial market and the macro economy： A mixed-frequency based empirical research［J］. Social Sciences in China， 2020（12）：160-180.
24	Jondeau E， Rockinger M. The Copula-GARCH model of conditional dependencies： An international stock market application［J］. Journal of International Money and Finance， 2006，25（5）：827-853.
25	Engle R F. Dynamic conditional correlation a simple class of multivariate GARCH models［J］. Journal of Business and Economic Statistics， 2002，20（3）：339-350.
26	吴金宴，王鹏. 哪些因素影响了股市风险传染？——来自行业数据的证据［J］.中国管理科学，2022，30 （8）：57-68.
	Wu J Y， Wang P. Factors affecting the risk contagion of the stock market： An evidence from industry-level data［J］.Chinese Journal of Management Science， 2022，30（8）：57-68.
27	Bonaccolto G， Caporin M， Panzica R. Estimation and model-based combination of causality networks among large US banks and insurance companies［J］. Journal of Empirical Finance，2019，54（C）：1-21.
28	Hmimida M， Kanawati R. Community detection in multiplex networks： A seed-centric approach［J］. Networks and Heterogeneous Media， 2015，10（1）：71-85.
29	Debarsy N， Dossougoin C， Ertur C， et al. Measuring sovereign risk spillovers and assessing the role of transmission channels： A spatial econometrics approach［J］. Journal of Economic Dynamics and Control，2018，87：21-45.
30	赵飞. 实体行业风险溢出机制与特征分析［J］.财经论丛，2021（9）：49-59.
	Zhao F. Analysis of risk spillover mechanism and characteristics among real industry［J］.Collected Essays on Finance and Economics，2021（9）：49-59.
31	苏帆. 系统性金融风险测度及其网络传染机制研究［D］.武汉：中南财经政法大学博士学位论文，2017.
	Su Fan. A research on measuring the systemic risk and its contagion in networks［D］.Wuhan： Zhongnan University of Economics and Law，2017.
32	Mantegna R N. Hierarchical structure in financial markets［J］. Computer Physics Communications， 1999，121（1）：153-156.
33	Rosen K H. 《离散数学及其应用（原书第7版）》［M］.北京：机械工业出版社， 2015.RosenK H. Discrete mathematics and its applications［M］. Beijing： China Machinery Industry Press， 2015.
34	Long H， Zhang J， Tang N. Does network topology influence systemic risk contribution？ A perspective from the industry indices in Chinese stock market［J］. PLOS one，2017，12（7）： 1-19.
35	李守伟，解一苇，杨坤，等. 商业银行多层网络结构对系统性风险影响研究［J］.东南大学学报（哲学社会科学版），2019，21（4）：77-84+147.
	Li S W， Xie Y W， Yang K， et al. Impact of multiplex network structures of banks on systemic risk［J］.Journal of Southeast University （Philosophy and Social Science），2019，21（4）：77-84+147.

方法	AIC	BIC	HQ	LL	R²	Adj R^2
Step	2555	2781	2644	-1220	0.526	0.446
U-MIDAS	2584	2698	2629	-1263	0.412	0.366
Almon	2582	2752	2650	-1248	0.454	0.387
Beta	2601	2771	2669	-1257	0.435	0.364

K	RMSE	K	RMSE	K	RMSE	K	RMSE
1	172.9044	4	88.8952	7	177.1481	10	186.7621
2	200.0264	5	72.5107	8	380.3202	11	187.6261
3	118.0944	6	192.8008	9	664.6316	12	110.3181

模型	(1)	(2)	(3)	(4)	(5)	(6)
DEG_L1	27.139^** (11.026)					12.901 (17.385)
BET_L1		-2.335 (2.247)				-3.721^* (2.149)
CLO_L1			26.839^** (12.310)			3.289 (18.404)
EIG_L1				17.684^* (9.104)		4.030 (10.644)
PAG_L1					81.299^** (38.565)	62.310 (41.913)
控制变量	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.4444	0.4272	0.4334	0.4568	0.4660	0.5120
Adj R-squared	0.3996	0.3809	0.3876	0.4129	0.4228	0.4415

模型	(1)	(2)	(3)	(4)	(5)	(6)
DEG_L1	28.121^** (11.043)					16.776 (17.397)
BET_L1		-4.614^** (2.232)				-5.793^*** (2.151)
CLO_L1			27.785^** (12.313)			3.406 (18.416)
EIG_L1				13.465 (9.171)		-0.453 (10.651)
PAG_L1					76.188^** (38.727)	65.495 (41.941)
控制变量	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.3789	0.3697	0.3680	0.3856	0.3997	0.4553
Adj R-squared	0.3287	0.3188	0.3170	0.3360	0.3512	0.3766

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Analysis of Risk Spillover Characteristics and Mechanism among Industries: Evidence from Multilayer Network

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