A numerical simulation approach to study systemic risk in banking systems

Banking systems are at the center of the financial infrastructure of any country. It has become apparent after the subprime crisis that such systems cannot be studied by looking at their components individually (that is, in isolation). Thus, an integrated approach is needed.In this paper we introduce a numerically friendly, yet general, algorithm that allows us to represent a banking system in a realistic way. We start with a detailed description of the banks' balance sheets and we incorporate two different features to account for connectivity effects: interbank loans and correlated-exposures to a common universe of credit-risk loans. The driving force behind the model is the progressive deterioration of the loan portfolios to which the banks are exposed. This method is useful to identify the weaknesses of a given banking system, and it is also helpful to assess the merits of different potential responses from the regulator's viewpoint. An example based on the banking system of a Latin American country is used to demonstrate the merits of this approach.Some of our findings are in agreement with those of previous authors, namely, that the probability of developing cascades is proportional to the degree of connectivity of the banking networks, and that the maximum resilience for a given set on banks is achieved for some "intermediate" (optimal?) level of connectivity. However, we also find -- in contrast with a few earlier researchers -- that investigating the features of a banking system by collapsing banks individually (and keeping other features unaltered) can give a misleading view of the system resilience.

Much of the existing research on banking systemic risk focuses on static single-risk exposures, and there is a lack of research on multiple-risk exposures. The reality is that the banking system is facing an increasingly complex environment, and dynamic measures of multiple-risk integration are essential. To reveal the risk accumulation process under the multi-risk exposures of the banking system, this article constructs a dynamic banking system as the research object and combines geometric Brownian motion, the BSM model, and the maximum likelihood estimate method. This article also aims to incorporate three types of exposures (interbank lending market risk exposures, entity industry credit risk exposures, and market risk exposures) within the same framework for the first time and builds a model of the dynamic evolution of banking systemic risk under multiple exposures. This study included the collection of a large amount of real data on banks, entity industries, and market risk factors, and used the ΔCoVaR model to evaluate the systemic risk of the China banking system from the point of view of the accumulation of risk from different exposures, revealing the dynamic process of risk accumulation under the integration of multiple risks within the banking system, as well as the contribution of different exposures to banking systemic risk. The results showed that the banking systemic risk of China first increased and then decreased with time, and the rate of risk accumulation is gradually slowing down. In terms of the impact of different kinds of exposures on system losses, the credit risk exposure of the entity industry had the greatest impact on the banking systemic risk among the three kinds of exposures. In terms of the contribution of the interbank lending market risk to the systemic risk, the Bank of Communications, China Everbright Bank, and Bank of Beijing contributed the most. In terms of the contribution of the bank-entity industry credit risk to the systemic risk, the financial industry, accommodation and catering industry, and manufacturing industry contributed the most. Considering the contribution of market risk to the systemic risk, the Shanghai Composite Index, the Hang Seng Composite Index, and the Dow Jones Index contributed the most. The research in this paper enriches the existing banking systemic risk research perspective and provides a reference for the regulatory decisions of central banks.

In the wake of the 2008 financial tsunami, existing methods and tools for managing financial risk have been criticized for weaknesses in monitoring and alleviating risks at the systemic level. A 2009 article in Nature suggested new approaches to modeling economic meltdowns are needed to prevent future financial crises. However, existing studies have not focused on analysis of systemic risk at the individual bank level in a banking network, which is essential for monitoring and mitigating contagious bank failures. To this end, we develop a network approach to risk management (NARM) for modeling and analyzing systemic risk in banking systems. NARM views banks as a network linked through financial relationships. It incorporates network and financial principles into a business intelligence (BI) algorithm to analyze systemic risk attributed to each individual bank via simulations based on real-world data from the Federal Deposit Insurance Corporation. Our research demonstrates the feasibility of modeling and analyzing systemic risk at the individual bank level in a banking network using a BI-based approach. In terms of business impact, NARM offers a new means for predicting contagious bank failures and determining capital injection priorities in the wake of financial crises. Our simulation study shows that under significant market shocks, the interbank payment relationship becomes more influential than the correlated bank portfolio relationship in determining an individual bank's survival. These insights should help financial regulators devise more effective policies and mechanisms to prevent the collapse of a banking system. Further, NARM and the simulation procedure driven by real-world data proposed in this study have instructional value to similar research areas such as bank stress testing, where time series data and business networks may be studied.

Concentrated commonalities and systemic risk in China's banking system: A contagion network approach

Strong boards, ownership concentration and EU banks’ systemic risk-taking: Evidence from the financial crisis

Recent financial crisis raised the issue of understanding the liquidity risk of financial assets and institutions. This paper studies the ability that exposure and sensitivity to liquidity risk has in banking system in Malaysia. Purpose - This paper aims to analyze the liquidity risks and disclosure as well as to draw the relationship between liquidity risks and financial performance measures using deposits, cash, liquidity gap and also non-performing loans as the indicator to the banking system in Malaysia and evaluate the effect on banks’ capital and reserve. Design/methodology/approach - Data are retrieved from the utilizing journals, books, Thompson Data Stream, balance sheet, income statements and report by Bank Negara Malaysia for the period 1997-2012. Multiple regressions are applied to assess the impact of liquidity risk on banks’ capital and reserve. Findings - The results of the multiple regressions showed that liquidity risk affects banks capital and reserve significantly, with non-performing loan (NPLs), as the exacerbating the liquidity risk. They have a negative relationship with deposit, cash and liquidity gap. Research limitations/implications - The period studied in this paper is one year, due to availability of data. However, the sample period does not impair the findings since the sample includes 56 banks, which constitute the main part of the Malaysian banking system. Moreover, only NPLs do not used to measure of performance. Economic factors contributing to liquidity risk are not covered in this paper. Originality/value - This is paper are refer to journal who is research about the Pakistani banking system but the result from that journal are not influence the result in this paper. This paper helps in understanding the factors of liquidity risk and performance of banking system. Consequently, understanding their impact on the bank’s capital and reserve of the banking system. Paper type - Research paper

Objective: The purpose of this paper is to estimate the systemic risk of the banking industry, considering the structure of banks' balance sheets in the interbank money market. To do this research, it develops a network model based on banks' balance-sheet interdependence to analyze how the liquidity risk in a bank moves to other banks. The model shows that liability of the typical bank is an asset in the balance sheet of other banks. When a borrower bank runs out of liquidity and fails to commit to settling its debts in the interbank money markets, lenders run short and reduce their activities in the interbank money market, leaving other banks with liquidity constraints. In fact, a crisis in one bank moves to other banks, which may pose a risk of transmission. In this paper, to measure the systemic risk, the liquidity index in the interbank money market is introduced that is the weighted average of banks' liquidity position. We use daily transactions in the interbank money market from 2013 to 2019 and apply Adrian and Brunnermeier (2016) ∆CoVaR measure and quantile regression to estimate the systemic risk. Method: The method of collecting data includes document information and record data from interbank money market transactions extracted from the central bank website. In this study, the sample consists of daily transactions of reciprocal deposits of 32 active banks in the interbank money market between November 2013 and July 2019. The interbank money market started its operation in 2009 with two banks. The volume of interbank transactions has increased significantly in the last decade so that in 2019 the volume of transactions is 2.4 times the total liquidity of the economy. The method of data analysis is a quantitative one using E-views software and MATAB software, respectively. To estimate △CoVaR, we first considered two Quantile regression models for 95 and 50 percent Quantiles using ordinary least squares (OLS). All parameters were statistically significant. To estimate the VaR of the stock market and banking system liquidity indices, we first considered the conditional variances of these two indices using the GARCH family models. We estimated different GARCH models and the best model fitted to the data. In the next stage, we used estimated conditional variances to estimate conditional VaR(CoVaR) for two different 95 and 50 percent quantiles based on the estimated parameters of quantile regressions. At the last phase, we calculated △CoVaR based on estimated CoVaR for two quantiles. Findings: The results of the research showed that the null hypothesis of normality of distribution of both stock market index and liquidity status of the interbank money market is rejected based on Jarque-Bera, Andersen-Darling, and Cramer–Von Mises statistics. Dickey-Fuller and Philips-Peron unit root test results showed that both variables are stationary. The results also indicated that the distribution of ∆CoVaR is not normal. Based on the average of ∆CoVaR, in the case of a banking system liquidity crisis, the stock market index will decrease by 2081 units per day on average. The maximum decrease in the stock market is 7088 units in the case of a banking crisis. Concluding: The research result indicates the interdependent structure of banks' balance sheets in the interbank money market. According to the results, the central bank, as a market monitoring authority, should control daily transactions in the interbank money market. It also should take the structure of banks' balance sheet interdependence into consideration in the estimation of the banking system systemic risk.

International experiences have underscored the dual implications of financial openness. Given China’s unique circumstances and its escalating level of financial openness, it is crucial to assess potential impacts on the country’s bank systemic risk. This paper uses the quarterly data of 37 listed banks in China from 2010 to 2022 to explore the relationship between financial openness and systemic risk of the banking system, the mechanism of action, and the moderating effect of macroprudential policy on the two. The findings indicate an inverted “U”-shaped correlation between financial openness and bank systemic risk. On one side of this shape, financial openness primarily exacerbates funding mismatch, thereby increasing the systemic risk of banks. Conversely, on the other side, it primarily alleviates systemic risk by optimizing capital management. Moreover, with the help of macroprudential supervision, the inverted “U”-shaped relationship between financial openness and bank systemic risk leads to a lower level of systemic risk and, at the same time, promotes the early arrival of the inverted “U”-shaped inflection point between financial openness and bank systemic risk. Notably, the impact of financial openness on the systemic risk of joint-stock commercial banks, urban commercial banks, and rural commercial banks is more significant. The above research results provide a regulatory reference for effectively preventing and resolving systemic risk while achieving high-quality openness to the outside world. In deepening financial openness, the banking industry needs to pay attention to the funding mismatch and the efficiency of capital management and implement differential risk supervision and prevention mechanisms for banks with different ownership, which is conducive to the reduction of bank systematic risk.

In this study we present a comprehensive forward-looking portfolio simulation methodology for assessing the correlated impacts of market risk, private sector and Sovereign credit risk, and inter-bank default risk. In order to produce better integrated risk assessment for banks and systemic risk assessments for financial systems, we argue that reasonably detailed modeling of bank asset and liability structures, loan portfolio credit quality, and loan concentrations by sector, region and type, as well as a number of financial and economic environment risk drivers, is required. Sovereign and inter-bank default risks are increasingly important in the current economic environment and their inclusion is an important model extension. This extended model is demonstrated through an application to both individual Brazilian banks (i.e., 28 of the largest banks) and groups of banks (i.e., the Brazilian banking system) as of December 2004. When omitting Sovereign risk, our analysis indicates that none of the banks face significant default risk over a 1-year horizon. This low default risk stems primarily from the large amount of government securities held by Brazilian banks, but also reflects the banks' adequate capitalizations and extraordinarily high interest rate spreads. We note that none of the banks which we modeled failed during the very stressful 2007-2008 period, consistent with our results. Our results also show that a commonly used approach of aggregating all banks into one single bank, for purposes of undertaking a systemic banking system risk assessment, results in a misestimate of both the probability and the cost of systemic banking system failures. Once Sovereign risk is considered and losses in the market value of government securities reach 10% (or higher), we find that several banks could fail during the same time period. These results demonstrate the well known risk of concentrated lending to a borrower, or type of borrower, which has a non-zero probability of default (e.g., the Government of Brazil). Our analysis also indicates that, in the event of a Sovereign default, the Government of Brazil would face constrained debt management alternatives. To the best of our knowledge no one else has put forward a systematic methodology for assessing bank asset, liability, loan portfolio structure and correlated market and credit (private sector, Sovereign, and inter-bank) default risk for banks and banking systems. We conclude that such forward-looking risk assessment methodologies for assessing multiple correlated risks, combined with the targeted collection of specific types of data on bank portfolios, have the potential to better quantify overall bank and banking system risk levels, which can assist bank management, bank regulators, Sovereigns, rating agencies, and investors to make better informed and proactive risk management and investment decisions.

The gradual weakening and subsequent repeal of most provisions of the Glass-Steagall Act in 1999 allowed commercial banks to acquire investment banking subsidiaries, to grow substantially in size, and to access even more information through more diverse banking activities. At the same time, proprietary trading became a major source of revenue for the banks. The subsequent financial crisis of 2008 exposed another glaring weakness of banking in the post-Glass-Steagall era. Banks had grown too big, too risky and too interconnected, many surpassing trillions of dollars in assets, interbank loans and liabilities on and off balance sheet. The sheer size, risk and interconnectedness of banking alone raised concerns about systemically important and too-big-to-fail banks. After numerous attempts to bring back Glass-Steagall failed, Congress attempted to contain banking systemic banking risk by passing the Volcker rule to prohibit proprietary trading, and enacting consumer protection and other ring-fencing and fire-wall provisions in the Dodd-Frank Act. To test the potential importance of the Volcker Rule, we would need to know the amount of profits banks make from using proprietary adverse information about their clients. However, the source of the proprietary information banks use to execute their proprietary trading programs is typically confidential. Furthermore, banks do not disclose where and how they obtain this confidential information, which helps them create billions of dollars of profits every year. In this paper we investigate one possible source of this information. Specifically, we investigate the importance of the private information banks acquire as part of their financial intermediary and financial advisory role for their client firms. Banks often attain insider trading status and become subject to insider trading reporting requirements and trading restrictions when they are hired to provide financial advice to their client firms. When banks become temporary insiders, they must also report all of these trades executed on Forms 3, 4, and 5 alongside other legal insiders. Using this insider trading database, we demonstrate that banks can and do access important, private, material information about their clients and trade on this information. On average, the inside information that banks acquire and trade on is highly valuable, allowing the banks to earn more on 25% on their proprietary trades. Furthermore, we find that relaxation and elimination of the Glass-Steagall restrictions allowed the banks to trade more frequently and earn greater amount of abnormal profits. Since 2002, banks tend to trade and earn more than 40% abnormal profits from adverse information about their client firms. Consequently, we demonstrate that an added benefit of enforcement of the Volcker Rule would be to eliminate the incentives to trade on material, non-public information about their clients by eliminating proprietary trading by banks. Thus, we argue that enforcing the Volcker Rule would also help contain some the current conflicts of interest in the banking system resulting from the elimination of Glass-Steagall restrictions.

A thorough analysis of risks in the banking system requires incorporating banks’ inherent heterogeneity and adaptive behavior in response to shocks and changes in business conditions and the regulatory environment. ABBA is an agent-based model for analyzing risks in the banking system in which banks’ business decisions drive the endogenous formation of interbank networks. ABBA allows for a rich menu of banks’ decisions, contingent on banks’ balance sheet and capital position, including dividend payment rules, credit expansion, and dynamic balance sheet adjustment via risk-weight optimization. The platform serves to illustrate the effect of changes on regulatory requirements on solvency, liquidity, and interconnectedness risk. It could also constitute a basic building block for further development of large, bottom-up agent-based macro-financial models.

We investigate the relationship of central bank independence and banks’ systemic risk measures. Our results support the case for central bank independence, revealing that central bank independence has a robust, negative, and significant impact on the contribution and exposure of a bank to systemic risk. Moreover, the impact of central bank independence is similar for the stand-alone risk of individual banks. Secondarily, we study how the central bank independence affects the impact of selected country and banking system indicators on these systemic measures. The results show that central bank independence may exacerbate the effect of a crisis on the contribution of banks to systemic risk. However, central bank independence seems to mitigate the harmful effect of a bank’s high market power on its systemic risk contribution.

How does competition affect bank systemic risk?

This paper uses three methodologies for measuring the existence of systemic risk in the Colombian banking system. The determination of its existence is based on implementing three systemic risk measures widely referenced in academic works after the subprime crisis, known as CoVaR, MES and SRISK. Together, the three methodologies were implemented for the case of Colombian Banks during the 2008–2017 period. The findings allow us to establish that the Colombian banking sector did not present a systemic risk scenario, despite having suffered economic losses due to external shocks, mainly due to the subprime crisis. The results and findings show the efficiency of the systemic risk measures implemented in this study as an alternative to measure systemic risk in banking systems.

Purpose This paper aims to offer an empirical study of the impact of institutional quality on the banking system risk and credit risk. Design/methodology/approach Applying cross-sectional dependent tests and stationary tests to check the property of our sample, the panel corrected standard errors model is recruited as the main estimator, while feasible generalized least squares, pool ordinary least squares (OLS), robust pool OLS and other estimators are used as a robustness check for an unbalanced panel data for 56 economies divided into three subsamples between 2002 and 2015. Findings The empirical results show several significant contributions. First, an improvement in institutional quality is an important factor to reduce the banking system risk. This effect of the institutions is less important in well-capitalized, highly profitable and in high-economic growth countries. This effect is also stronger in highly liquid banking systems. Notably, a better institutional quality helps to reduce the banking system risk in the highly concentrated banking system. Second, institutional quality has a significant negative relationship with the banking credit risk, especially in highly concentrated banking systems and in high-growth countries. This influence is weaker in highly liquid and well-capitalized banking systems. Finally, better institutions reduce the positive effect of trade openness, but it induces a higher credit risk for the banking system from the trade openness. Notably, a better institutional quality enhances the negative effect of foreign direct investment (FDI) inflow on both banking system risk and credit risk. These findings are documented for a global sample and three subsamples: low and lower-middle-income economies, upper-middle-income economies and high-income economies. Originality/value This study provides some recommendations, for policymakers, on the roles of institutions in the banking system and financial stability.