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| Nonlinear Analysis of the Determinants of Tail Risk: New Evidence from the Panel Smooth Transition Regression Model |
| YANG Zihui, CHEN Yutian, LIN Shihan, GUAN Zihuan
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Lingnan College, Sun Yat-Sen University;
Institute of Advanced Finance, Sun Yat-Sen University |
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Abstract Small financial institutions frequently encounter tail risk events such as insolvency and significant decline in asset quality in the post-crisis period. These events challenge the traditional supervisory concept of “too big to fail.” There is currently growing uncertainty in the capital market and increasing economic downward pressure. The Chinese capital market is also undergoing accelerating reform. It is therefore academically and practically important to investigate the intrinsic tie between bank size and tail risk and to explore the determinants of tail risk.
This paper complements and expands the literature with a high level of originality. First, most domestic literature addresses risk contagion between financial institutions. There is little discussion of whether the “too big to fail” theory can be applied under China's actual economic conditions. Second, there is currently little consensus regarding the direction of effect of bank size on tail risk. The literature suggests that the fundamental variables of financial institutions actually play an important role in this relationship (Buch et al., 2019). Research highlights the need to include fundamental variables in the model to evaluate the heterogenous impacts of institution size on risk-taking more efficiently. Third, linear baseline regression models are often used when researching driving factors of tail risk. However, examining the relationships among variables under the traditional linear empirical framework may result in great bias, as indicated by Acemoglu et al. (2015) and De Vita et al. (2018). This bias makes it difficult to identify the risk sources in the financial system. Finally, research is likely to overlook the fact that the economic reform process exhibits an incremental trajectory in China when analyzing the nonlinear interconnectedness among variables. It is therefore more appropriate to discuss the smooth evolution of tail risk in China under the panel smooth transition regression (PSTR) model.
Our sample consists of 44 Chinese listed financial institutions, comprising 11 banks and 33 non-bank institutions. The sample period runs from January 2008 to June 2020. MES is constructed to represent the tail risk level in this paper. All the data come from the Wind database.
Our paper uses the dominance analysis method developed by Israeli (2007) and Givoly et al. (2019) to investigate the contributions of bank size and other fundamental variables to banks' tail risks. We find that bank size is not the only main determinant of bank risk; variables such as the non-performing loan ratio, personal housing loan ratio, and non-interest income ratio are also significant in the model. We next introduce the marginal effect analysis technique and provide strong evidence of the heterogeneous effects of fundamental variables on tail risk conditional on bank size. Using the PSTR model proposed by Cheikh and Zaied (2020) and González et al. (2017), this paper further discusses the nonlinear impact of bank size on tail risk and the roles of other fundamental variables in this relationship. The result indicates that an increase in the size of banks reduces the tail risk of the financial system in a highly nonlinear way. The reduction of tail risk depends on fundamental variables such as franchise value, asset quality, leverage, cost, income structure, and loan portfolio. The conclusions remain consistent and robust even when we extend our sample to 44 financial institutions. We also find that the evolution of tail risk is more volatile in financial institutions than in the banking sector.
Our findings yield three important policy implications. First, the tail risks of small financial institutions deserve stronger supervisory attention and differentiated regulatory responses, especially at the level of cost management. Second, it is more appropriate to deleverage the financial sector gradually than in a rush. Finally, stronger integrated financial supervision is urgently needed to meet the emerging trend of cross mixed operation in the Chinese financial market. This paper thereby enhances insights into how to deepen financial reform and achieve high-quality economic development in China both theoretically and empirically.
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Received: 04 August 2020
Published: 01 April 2021
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| [1] |
陈湘鹏、周皓、金涛和王正位,2019,《微观层面系统性金融风险指标的比较与适用性分析——基于中国金融系统的研究》,《金融研究》第5期,第17～36页。
|
| [2] |
邓可斌、关子桓和陈彬,2018,《宏观经济政策与股市系统性风险——宏微观混合β估测方法的提出与检验》,《经济研究》第8期,第68～83页。
|
| [3] |
刘晓星和石广平,2018,《杠杆对资产价格泡沫的非对称效应研究》,《金融研究》第3期,第53～70页。
|
| [4] |
杨子晖、陈雨恬和张平淼,2020,《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》,《管理世界》第5期,第13～35+7页。
|
| [5] |
杨子晖,2020,《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》,《中国社会科学》第12期,第160～180页。
|
| [6] |
杨子晖和周颖刚,2018,《全球系统性金融风险溢出与外部冲击》,《中国社会科学》第12期,第69～90+200～201页。
|
| [7] |
Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. 2015. “Systemic Risk and Stability in Financial Networks”, American Economic Review, 105(2):564～608.
|
| [8] |
Altunbas, Yener, Mahir Binici, and Leonardo Gambacorta. 2018. “Macroprudential Policy and Bank Risk”, Journal of International Money and Finance, 81:203～220.
|
| [9] |
Bakkar, Yassine, Clovis Rugemintwari, and Amine Tarazi. 2020. “Charter Value, Risk-Taking and Systemic Risk in Banking before and after the Global Financial Crisis of 2007-2008”, Applied Economics, 52(36):3898～3918.
|
| [10] |
Benbouzid, Nadia, Leone Leonida, and Sushanta K. Mallick. 2018. “The Non-Monotonic Impact of Bank Size on Their Default Swap Spreads: Cross-Country Evidence”, International Review of Financial Analysis, 55:226～240.
|
| [11] |
Berger, Allen N., Raluca A. Roman, and John Sedunov. 2019. “Did TARP Reduce or Increase Systemic Risk？ The Effects of Government Aid on Financial System Stability”, Journal of Financial Intermediation, 100810.
|
| [12] |
Bhagat, Sanjai, and Brian Bolton. 2019. “Corporate Governance and Firm Performance: The Sequel”, Journal of Corporate Finance, 58: 142～168.
|
| [13] |
Black, Lamont, Ricardo Correa, Xin Huang, and Hao Zhou. 2016. “The Systemic Risk of European Banks during the Financial and Sovereign Debt Crises”, Journal of Banking & Finance, 63:107～125.
|
| [14] |
Bostandzic, Denefa, and Gregor N. F. Weiss. 2018. “Why Do Some Banks Contribute More to Global Systemic Eisk?”, Journal of Financial Intermediation, 35: 17~40.
|
| [15] |
Buch, Claudia M., Thomas Krause, and Lena Tonzer. 2019. “Drivers of Systemic Risk: Do National and European Perspectives Differ?”, Journal of International Money and Finance, 91: 160~176.
|
| [16] |
Cheikh, Nidhaleddine B., and Younes B. Zaied. 2020. “Revisiting the Pass-Through of Exchange Rate in the Transition Economies: New Evidence from New EU Member States”, Journal of International Money and Finance, 100:102093.
|
| [17] |
De Vita, Glauco, Emmanouil Trachanas, and Yun Luo. 2018. “Revisiting the Bi-Directional Causality between Debt and Growth: Evidence from Linear and Nonlinear Tests”, Journal of International Money and Finance, 83:55～74.
|
| [18] |
Demirgüç-Kunt, Asli, and Harry Huizinga. 2013. “Are Banks too Big to Fail or too Big to Save？ International Evidence from Equity Prices and CDS Spreads”, Journal of Banking & Finance, 37(3):875～894.
|
| [19] |
De Jonghe, Olivier, Maaike Diepstraten, and Glenn Schepens. 2015. “Banks’ Size, Scope and Systemic Risk: What Role for Conflicts of Interest?” Journal of Banking & Finance, 61:S3~S13.
|
| [20] |
Dinger, Valeriya, and Daniel M. te Kaat. 2020. “Cross-Border Capital Flows and Bank Risk-Taking” Journal of Banking & Finance, 105842.
|
| [21] |
Givoly, Dan, Yifan Li, Ben Lourie, and Alexander Nekrasov. 2019. “Key Performance Indicators as Supplements to Earnings: Incremental Informativeness, Demand Factors, Measurement Issues, and Properties of Their Forecasts”, Review of Accounting Studies, 24(4):1147～1183.
|
| [22] |
González, Andrés, Timo Teräsvirta, Dick van Dijk, and Yukai Yang. 2017. Panel Smooth Transition Regression Models.
|
| [23] |
Hryckiewicz, Aneta, and Lukasz Kozł owski. 2017. “Banking Business Models and the Nature of Financial Crisis”, Journal of International Money and Finance, 71:1～24.
|
| [24] |
Israeli, Osnat. 2007. “A Shapley-based Decomposition of the R-Square of A Linear Regression”, Journal of Economic Inequality, 5(2):199～212.
|
| [25] |
Laeven, Luc, and Ross Levine. 2008. “Complex Ownership Structures and Corporate Valuations”, The Review of Financial Studies, 21(2):579～604.
|
| [26] |
López-Andión, Carmen, Ana Iglesias-Casal, Maria C. López-Penabad, and Jose Maside-Sanfiz.2019.“Securitization and Financial Solvency:Empirical Evidence from Portugal”,European Journal of Finance,25(2):155～166.
|
| [27] |
Matthys, Thomas, Elien Meuleman, and Rudi Vander Vennet. 2020. “Unconventional Monetary Policy and Bank Risk Taking”, Journal of International Money and Finance, 109:102233.
|
| [28] |
van Oordt, Maarten, and Chen Zhou. 2019, “Systemic Risk and Bank Business Models”, Journal of Applied Econometrics, 34(3):365～384.
|
| [29] |
Parrado-Martínez, Purificacion, Pilar Gómez-Fernández-Aguado, and Antonio Partal-Ureña. 2019. “Factors Influencing the European Bank's Probability of Default: An Application of SYMBOL Methodology”, Journal of International Financial Markets, Institutions and Money, 61:223～240.
|
| [30] |
Tabak, Benjamin M., Dimas M. Fazio, and Daniel O. Cajueiro. 2013. “Systemically Important Banks and Financial Stability: The Case of Latin America”, Journal of Banking & Finance, 37(10):3855～3866.
|
| [31] |
Varotto, Simone, and Lei Zhao. 2018. “Systemic Risk and Bank Size”, Journal of International Money and Finance, 82:45～70.
|
|
|
|
