Summary:
The debt to GDP ratio has increased significantly worldwide in the post Covid-19 era, makingmaintaining financial stability a great challenge to regulatory authorities globally. As conventional stress testing models do not consider the contagion of financial risks and thus tend to underestimate the impact of shocks on financial resilience, the central banks in advanced economies have started to develop macroprudential stress test models with specific focus on financial contagion channels. However, studies on the contagion effects within China's banking sector remain very limited. This paper aims to fill this gap and lay the foundation for China's macroprudential stress test framework. Based on the granular balance sheet data of listed Chinese banks, we present a micro-founded model to capture the financial contagion effects within the Chinese banking sector. We focus on the mark-to-market price channel of fire sales, as it is proven to be a critical contagion channel during financial crises. We calibrate the demand curves of multiple asset classes using the bond data, model banks' optimization problems in fire sales with regulatory constraints and simulate the model to exogeneous shocks. Specifically, we model both the first-round and the second-round effects of financial risks spreading in the banking sector. The first-round effect is the direct impact of a shock on banks, characterized by banks' losses (e.g., credit losses) and the changes in banks' capital adequacy ratios due to the losses. The second-round effect is the financial contagion effect that arises from banks' responses. When a bank breaches its capital requirement due to the initial shock, it has to sell financial assets to boost the capital adequacy ratio. This behavior causes mark-to-market losses of other banks with common asset holdings, which may cause them to breach capital requirements and start another round of fire sale. That depresses asset prices further and generates greater mark-to-market losses. The second-round effect is the contagion channel through which financial risks are amplified and spread in the banking sector. We also model banks' optimal behaviors in response to shocks. To minimize losses of fire sales, banks consider multiple factors when choosing assets to sell. The first is the risk weight of the assets, as assets with higher risk weights weigh more in calculating capital adequacy ratios. The second is the market depth of the asset. Illiquid assets are typically sold at price discounts which can be quite drastic amid fire sales. Selling illiquid assets leads to greater investment losses and banks need to sell more to meet the capital requirements. The third is the banks' balance sheet structure. Given the price discounts at fire sales, a bank suffers greater losses from selling a class of assets if that asset class accounts for a Large share on the bank's balance sheet. Our results and policy implications are as follows. First, market depth is critically important in the transmission of contagion risks. As the depth of the Chinese bond market grew from 2017 to 2019, the financial contagion effects were attenuated over the period. In other words, the banking sector in China became more resilient during these three years. Second, individual banks' optimal behaviors may amplify the financial contagion effects. This is because banks all choose to sell the same kind of relatively liquid assets and hence cause sharp price falls of that particular asset, which causes greater mark-to-market losses of other banks holding the common asset. Third, an external shock can generate contagion effects in a non-linear pattern. It is therefore very difficult to discern the emergence and the end of financial risks, which poses a serious challenge for regulators to decide when to act or exit. Forth, it is essential for the regulators to build a macroprudential stress test framework that captures the financial contagion effects. A conventional static bank stress test only captures the direct effects of shocks, and our results suggest that this will greatly underestimate the impact of shocks. Our paper contributes to the literature in three ways. First, we consider multiple asset classes with different levels of market depth and estimate their demand curve based on the data from the Chinese bond market. That forms the basis upon which we model banks' optimal behaviors in response to shocks. Second, we model banks' optimization problem with regulatory constraint, and analyze the key factors contributing to banks' optimal behaviors. This lays the micro-foundations for stress testing models, which has been omitted in the literature. Third, we investigate quantitatively the effects of financial contagion when banks face the constraint of capital adequacy ratio. The simulation results can help regulators identify the sources of emerging financial risks and assess their impact on the financial system.
马骏, 何晓贝. 金融风险传染机制研究—— 基于中国上市银行数据的模拟[J]. 金融研究, 2021, 495(9): 12-29.
MA Jun, HE Xiaobei. A Study on Financial Contagion: A Simulation Based on the Chinese Banking Sector Data. Journal of Financial Research, 2021, 495(9): 12-29.
Aikman, D., P. Alessandri, B. Eklund, P. Gai, S. Kapadia, E. Martin, N. Mora, G. Sterne and M. Willison, 2009, “Funding Liquidity Risk in a Quantitative Model of Systemic Stability”, Bank of England Working Paper, No. 372.
[12]
Anand, K., G. Bédard-Pagé and V. Traclet, 2014, “Stress Testing the Canadian Banking System: A System-Wide Approach”, Financial System Review, 61.
[13]
Bouchaud,Jean-Philippe, 2010. “The Endogenous Dynamics of Markets: Price Impact and Feedback Loops,”Papers 1009.2928, arXiv.org.
[14]
Burrows, O., D. Learmonth, J. Mckeown and R. Williams, 2012, “RAMSI: A Top-Down Stress-Testing Model Developed at the Bank of England”. Bank of England Quarterly Bulletin, Bank of England, vol. 52(3), pages 204~212.
[15]
Caballero, R., and A. Simsek, 2013. “Fire Sales in a Model of Complexity,” Journal of Finance, American Finance Association, vol. 68(6), pages 2549~2587, December.
[16]
Cateau, G., T. Roberts and J. Zhou, 2015, “Indebted Households and Potential Vulnerabilities for the Canadian Financial System: A Microdata Analysis”, Financial System Review, Bank of Canada.
[17]
Churm, R., 2017, “Stress Test Modeling at the Bank of England: Past, Present and Future”, Bank of England.
[18]
Cifuentes, R., G. Ferrucci and H. S. Shin, 2005, “Liquidity Risk and Contagion”, Journal of the European Economic Association, 3(2-3): 556~566.
[19]
Cont, R. and E. Schaanning, 2017, “Fire Sales, Indirect Contagion and Systemic Stress Testing”, Working Paper 2/2017, Norges Bank.
[20]
Gauthier, C. and M. Souissi, 2012, “Understanding Systemic Risk in the Banking Sector: A Macrofinancial Risk Assessment Framework”, Bank of Canada Review, 2012(Spring): 29~38.
[21]
Gorton, G. and L. Huang, 2004. “Liquidity, Efficiency, and Bank Bailouts,” American Economic Review, American Economic Association, Vol. 94(3), pages 455~483, June.
[22]
Henry, J., C. Kok, A. Amzallag, P. Baudino, I. Cabral, M. Grodzicki, M. Gross, G. Halaj, M. Kolb and M. Leber, 2013, “A Macro Stress Testing Framework for Assessing Systemic Risks in the Banking Sector”. Occasional Paper Series 152, European Central Bank.
[23]
Mitchell, M. and T. Pulvino, 2012. “Arbitrage crashes and the speed of capital,” Journal of Financial Economics, Elsevier, Vol. 104(3), pages 469~490.
[24]
Pyoun, D., 2015, “Systemic Risk Assessment Model for Macroprudential Policy (SAMP)”, Bank of Korea.
[25]
Schnabel, I., and H. S. Shin, 2004. “Liquidity and Contagion: The Crisis of 1763,” Journal of the European Economic Association, MIT Press, Vol. 2(6), pages 929~968, December.
[26]
Shleifer, A. and R. Vishny, 2011. “Fire Sales in Finance and Macroeconomics,” Journal of Economic Perspectives, American Economic Association, Vol. 25(1), pages 29~48.