Financial network structure and systemic risk

AbstractFor systemic risk, the impact of the financial network's characteristics remains imperfectly understood at best, even if the view that network structure is closely related to systemic risk has become a broad consensus. By choosing S&P 500 constituents as the research sample, we investigate the structural characteristics of the Engle-Granger networks and explore the impact of network centrality on one-quarter-ahead systemic risk. We find that a firm's network centrality is positively related to both dimensions of its systemic risk (i.e. the firm's vulnerability to, and contribution to, system-wide downturns). The results remain robust after we consider the potential endogeneity and various sensitivity checks. An examination of potential channels reveals that centrally located firms in the network have a high extent of co-movement with the market, and are likely to trigger systemic market failures caused by stock price crashes in clusters once they fall into a downturn. We further show that the positive relation between network centrality and future systemic risk is more salient for financial firms and more pronounced during recessions.Keywords: Systemic risknetwork centralityco-integrationEngle-Granger testJEL classifications: G1G3G18 AcknowledgementsThe authors are grateful to the editor and anonymous reviewers for their constructive comments, which led to a significant improvement of our original manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Notes1 The global financial crisis has revealed major deficiencies in Value-at-Risk (VaR), which has been criticized by many as incapable of capturing the systemic nature of risk since its focus is on an institution in isolation (Girardi and Ergün Citation2013).2 It is undeniable that the generalized variance decomposition method is more appealing in the high-frequency analysis of financial entities connectedness and the E-G method is suitable for studying non-stationary financial variables/time series, therefore, the two methods can be regarded as complementary rather than alternative.3 We define q as 5% and choose the S&P 500 index as proxy for the market index.4 The first network is established over the time period from Jan. 4, 2006 to Mar. 31, 2006. The second network is from Apr. 4, 2006 to Jun. 30, 2006. Similarly, the last network is from Oct. 8, 2020 to Dec. 31, 2020.5 The price series of the vast majority of stocks in our sample follow the I(1) process. This result is available upon request.6 For the network constructed in quarter t, blue represents stocks with market capitalization in quarter t rankings from 1 to 136 (large-size), red represents stocks ranked from 137 to 272 (medium-size), and yellow represents stocks ranked from 273 to 408 (small-size).7 The normalized number of edges is the fraction of all statistically significant edges (at the 1% level) between N nodes in all N(N−1) possible edges.8 Firms that are important hubs or intermediaries in the market.9 Financialsi is an indicator variable that takes on the value of 1 if the firm belongs to the financials, according to the Global Industry Classification Standard (GICS), and 0 otherwise.10 Untabulated results show that the equity multiplier is not significantly correlated with the two systemic risk measures (MES and ΔCoVaR) at the 10% significance level. Also, the equity multiplier has no significant effect on the systemic risk in models controlling for time effects and individual effects.11 We adopt the Under-Identification (Kleibergen-Paap rk LM statistic), Weak-Identification (Kleibergen-Paap rk Wald F statistic) and Overidentification (Hansen J) tests to evaluate whether our instrumental variables met validity requirements.Additional informationFundingThis work was supported by the National Natural Science Foundation of China (Nos. 72192800, 72101035, 71471020), the Science and Technology Innovation Program of Hunan Province (No. 2023RC1060), the Postgraduate Scientific Research Innovation Foundation of Hunan Province (No. CX20230925), and the Postgraduate Scientific Research Innovation Foundation of CSUST (No. CLSJCX22125).Notes on contributorsChuangxia HuangChuangxia Huang received the BS degree in Mathematics in 1999 from National University of Defense Technology, Changsha, China. From September 2002, he began to pursue his MS degree in Applied Mathematics at Hunan University, Changsha, China, and from April 2004, he pursued his PhD degree in Applied Mathematics in advance at Hunan University. He received the PhD degree in June 2006. He is currently a Professor of Changsha University of Science and Technology, Changsha, China. He is the author of more than 100 journal papers. His research interests are in the areas of complex network and financial risk management.Yanchen DengYanchen Deng was born in Hengyang, China, in 1999. He received the BS degree in financial management in 2021 from Hebei GEO University, Shijiazhuang, China. He is a MA student in applied statistics at Changsha University of Science and Technology, Changsha, China. His research interests include complex network, financial risk management and economic analysis and forecasting.Xiaoguang YangXiaoguang Yang received his BS degree in Applied Mathematics and his PhD degree in Computational Mathematics from Tsinghua University in 1986 and 1993 respectively. He is a Professor at Academy of Mathematics and Systems Science, Chinese Academy of Sciences and University of Chinese Academy of Sciences. He currently serves as the President of System Engineering Society of China. He has published more than 300 journal papers. His research interests include risk management, financial market, and game theory.Yaqian CaiYaqian Cai was born in Hunan, China, in 1998. She received the BS degree in information and computing sciences in 2021 from Changsha University of Science and Technology, Changsha, China. She is a MA student in statistics at Changsha University of Science and Technology, Changsha, China. Her research interests include complex network, financial risk management and economic analysis and forecasting.Xin YangXin Yang received the BS degree in Finance in 2011 from Central South University of Forestry and Technology, Changsha, China. From September 2011, he began to pursue his MS degree in School of Economics & Management at Changsha University of Science & Technology, Changsha, China. From September 2014, he pursued his PhD degree in Business School at Central South University and received the PhD degree in December 2017. He is currently a lecturer of Changsha University of Science and Technology, Changsha, China. His research interests are in the areas of complex network and financial risk management.