Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

The investigation of the systemic importance of financial institutions (SIFIs) has become a hot topic in the field of financial risk management. By making full use of 5-min high-frequency data, and with the help of the method of entropy weight technique for order preference by similarities to ideal solution (TOPSIS), this paper builds jump volatility spillover network of China’s financial institutions to measure the SIFIs. We find that: (i) state-owned depositories and large insurers display SIFIs according to the score of entropy weight TOPSIS; (ii) total connectedness of financial institution networks reveal that Industrial Bank, Ping An Bank and Pacific Securities play an important role when financial market is under pressure, especially during the subprime crisis, the European sovereign debt crisis and China’s stock market disaster; (iii) an interesting finding shows that some small financial institutions are also SIFIs during the financial crisis and cannot be ignored.

[1]  Viral V. Acharya,et al.  CAUSES OF THE FINANCIAL CRISIS , 2009 .

[2]  Fenghua Wen,et al.  Forecasting realized volatility of crude oil futures with equity market uncertainty , 2019, Applied Economics.

[3]  Qiang Ji,et al.  Dynamic network of implied volatility transmission among US equities, strategic commodities, and BRICS equities , 2018 .

[4]  Nikola Gradojevic,et al.  Predicting Systemic Risk with Entropic Indicators , 2017 .

[5]  Leonidas Sandoval,et al.  Structure of a Global Network of Financial Companies Based on Transfer Entropy , 2014, Entropy.

[6]  Huajiao Li,et al.  Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series , 2017, Scientific Reports.

[7]  Sjur Westgaard,et al.  Forecasting Volatility of the U.S. Oil Market , 2014 .

[8]  Simon X. B. Zhao Spatial Restructuring Of Financial Centers In Mainland China And Hong Kong , 2003 .

[9]  Michael J. Stutzer The Role of Entropy in Estimating Financial Network Default Impact , 2018, Entropy.

[10]  Yuan‐Teng Hsu,et al.  Measuring Systemic Risk: Capital Shortfall and CSRISK* , 2019, International Review of Finance.

[11]  Yazhen Wang,et al.  Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets , 2016 .

[12]  Francesco Audrino,et al.  Volatility Forecasting: Downside Risk, Jumps and Leverage Effect , 2011 .

[13]  H. Stanley,et al.  Dynamical Macroprudential Stress Testing Using Network Theory , 2014 .

[14]  A. Kim,et al.  A Study on Competitiveness Analysis of Ports in Korea and China by Entropy Weight TOPSIS , 2016 .

[15]  N. Shephard,et al.  Power and bipower variation with stochastic volatility and jumps , 2003 .

[16]  Kin Keung Lai,et al.  Time-varying Granger causality tests for applications in global crude oil markets , 2014 .

[17]  Zhengkai Liu,et al.  Big data analytics for financial Market volatility forecast based on support vector machine , 2020, Int. J. Inf. Manag..

[18]  A. Lo,et al.  Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors , 2011 .

[19]  Zhifeng Liu,et al.  Dynamic Properties of Foreign Exchange Complex Network , 2019, Mathematics.

[20]  Xin Huang,et al.  A Framework for Assessing the Systemic Risk of Major Financial Institutions , 2009 .

[21]  Chuangxia Huang,et al.  An empirical evaluation of the influential nodes for stock market network: Chinese A-shares case , 2020 .

[22]  Yuichi Kitamura,et al.  Connections between Entropic and Linear Projections in Asset Pricing Estimation , 2002 .

[23]  Wei-Xing Zhou,et al.  Tail dependence networks of global stock markets , 2018, International Journal of Finance & Economics.

[24]  Yann Braouezec,et al.  Risk-Based Capital Requirements and Optimal Liquidation in a Stress Scenario , 2016 .

[25]  Fenghua Wen,et al.  Efficient predictability of stock return volatility: The role of stock market implied volatility , 2020 .

[26]  Xu Gong,et al.  The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market , 2018, Energy Economics.

[27]  F. Diebold,et al.  Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility , 2005, The Review of Economics and Statistics.

[28]  Jonathan H. Wright,et al.  Bond risk premia and realized jump volatility , 2007 .

[29]  Giulio Girardi,et al.  Systemic Risk Measurement: Multivariate GARCH Estimation of CoVaR , 2012 .

[30]  Chao Li,et al.  Identification of influential spreaders based on classified neighbors in real-world complex networks , 2018, Appl. Math. Comput..

[31]  N. Shephard,et al.  Realised power variation and stochastic volatility models , 2003 .

[32]  N. Gradojevic,et al.  Heterogeneous investment horizons, risk regimes, and realized jumps , 2020 .

[33]  Abdelkader Mohamed Sghaier Derbali,et al.  Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Case of Greek Banks , 2017, Journal of the Knowledge Economy.

[34]  Cengiz Kahraman,et al.  Strategic Decision Selection Using Hesitant fuzzy TOPSIS and Interval Type-2 Fuzzy AHP: A case study , 2014, Int. J. Comput. Intell. Syst..

[35]  Ernst Schaumburg,et al.  Federal Reserve Bank of New York Staff Reports Jump-robust Volatility Estimation Using Nearest Neighbor Truncation Jump-robust Volatility Estimation Using Nearest Neighbor Truncation , 2010 .

[36]  T. Dimpfl,et al.  Using Transfer Entropy to Measure Information Flows Between Financial Markets , 2013 .

[37]  R. Bowden Directional entropy and tail uncertainty, with applications to financial hazard , 2011 .

[38]  Pierre Bajgrowicz,et al.  Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News , 2015, Manag. Sci..

[39]  M. Valahzaghard,et al.  Prediction of default probability in banking industry using CAMELS index: A case study of Iranian banks , 2013 .

[40]  Yann Braouezec,et al.  Strategic fire-sales and price-mediated contagion in the banking system , 2019, Eur. J. Oper. Res..

[41]  Huan Zhu,et al.  Stock return predictability from a mixed model perspective , 2020 .

[42]  Jie Liu,et al.  An efficient hybrid tridiagonal divide-and-conquer algorithm on distributed memory architectures , 2016, J. Comput. Appl. Math..

[43]  Ping Li,et al.  Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies , 2007 .

[44]  S. Mahadevan,et al.  A new method of identifying influential nodes in complex networks based on TOPSIS , 2014 .

[45]  Qi Zhang,et al.  Measure the structure similarity of nodes in complex networks based on relative entropy , 2018 .

[46]  Nikolaus Hautsch,et al.  Financial Network Systemic Risk Contributions , 2013 .

[47]  Keyi Zhang,et al.  Economic links and credit spreads , 2015 .

[48]  L. Pedersen,et al.  Measuring Systemic Risk , 2010 .

[49]  Xiaoqing Hao,et al.  The transmission of fluctuation among price indices based on Granger causality network , 2018, Physica A: Statistical Mechanics and its Applications.

[50]  Mark E. Paddrik,et al.  Interbank Contagion: An Agent-Based Model Approach to Endogenously Formed Networks , 2016, Journal of Banking & Finance.

[51]  H. An,et al.  Identifying influential energy stocks based on spillover network , 2020 .

[52]  Chuangxia Huang,et al.  Systemic importance of financial institutions: A complex network perspective , 2020 .

[53]  Chi Xie,et al.  Interconnectedness and systemic risk of China's financial institutions , 2017, Emerging Markets Review.

[54]  Zhifeng Dai,et al.  Forecasting stock market returns: New technical indicators and two-step economic constraint method , 2020 .

[55]  Monica Billio,et al.  Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors , 2011 .

[56]  F. Diebold,et al.  UNIVERSITY OF SOUTHERN CALIFORNIA Center for Applied Financial Economics (CAFE) On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms , 2011 .

[57]  Qiuhong Zheng,et al.  Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks , 2018, Discrete Dynamics in Nature and Society.

[58]  Chuangxia Huang,et al.  Analysis of Global Remittance Based on Complex Networks , 2020, Frontiers in Physics.

[59]  Liwen Liu,et al.  Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines , 2011 .

[60]  Xiong Xiong,et al.  Financial systemic risk measurement based on causal network connectedness analysis , 2019, International Review of Economics & Finance.

[61]  Fenghua Wen,et al.  Investigating the risk-return trade-off for crude oil futures using high-frequency data , 2017 .

[62]  N. Shephard Realized power variation and stochastic volatility models , 2003 .

[63]  Wolfgang Karl Härdle,et al.  TENET: Tail-Event Driven NETwork Risk , 2015 .

[64]  M. Konschake,et al.  Trade communities and their spatial patterns in the German pork production network. , 2011, Preventive veterinary medicine.

[65]  R. D. Haas,et al.  International Shock Transmission after the Lehman Brothers Collapse: Evidence from Syndicated Lending , 2012 .

[66]  Leonidas Sandoval Junior Structure and causality relations in a global network of financial companies , 2013, 1310.5388.