Contribution To Systemic Risk Research Articles

Monetary Policy, Industry Heterogeneity and Systemic Risk—Based on a High Dimensional Network Analysis

We utilized a high dimensional financial network to investigate the systemic risk contagion between different industries in China and to explore the impacts of monetary policy and industry heterogeneity factors. The empirical results suggest that the total level of systemic risk increased quite significantly during the 2008 global crisis and the 2015–2016 Stock Market Disaster. The energy, material, industrial, and financial sectors are the top systemic risk contributors. Industry heterogeneity variables such as the leverage ratio, book-to-market ratio, return on assets (ROA) and size have significant impacts on the systemic risk, but their effects on the systemic risk contribution are more pronounced than those on the systemic risk sensitivity. Moreover, monetary policy can effectively suppress the systemic risk diffusion derived from the leverage ratio. These results are essential for investors and regulators of risk management.

Measuring systemic risk contribution: The leave-one-out z-score method

We propose a new systemic risk measure based on the z-score, which defines a leave-one-out (LOO) contribution to systemic risk. The LOO z-score measure quantifies the systemic risk contribution of individual banks by the difference between the joint risk-taking of a banking system and the risk-taking of the same system when excluding a bank. The accounting-based LOO z-score measure can be used as a complement to market-based systemic risk measures, and it can also assess systemic risk contribution for unlisted banks. Empirical results show that the LOO z-score measure can identify the four largest New Zealand banks as systemically important.

Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach

The 2015–2016 China’s stock market crash raises awareness of risk contagion in financial system. How to investigate systemic risk from the perspective of network is still a challenging work especially for considering a large number of financial institutions. To this end, we introduce the least absolute shrinkage and selection operator (LASSO) method into the CoVaR estimation to construct a systemic risk network between financial institutions’ tail risk exposures. First, we apply the LASSO-CoVaR based systemic risk network to investigate the interconnectedness and systemic risk of financial institutions in China from 2010 to 2017. Our empirical results show that the interconnectedness among institutions is very important and cannot be ignored in estimating CoVaR of an individual institution. Second, the topology analysis shows that the system-level interconnectedness reaches a peak when the system is under distress, especially before and after the stock market crash occurred. Third, we rank institutions in terms of the systemic risk contribution and find that their systemic importance changes in four different sub-periods. To sum up, our empirical results reveal substantial relevant risk spillover channels and identify the systemically important financial institutions in China, providing useful information for regulators to formulate macro prudential supervision policy.

International trade, foreign direct investments, and firms’ systemic risk : Evidence from the Netherlands

This paper measures the contribution of firms in the financial and non-financial sectors to systemic risk. We quantify systemic risk as possible risk spillovers from individual firms to the economy by taking into account time-varying linkages between the firm and the economy. Based on a novel dataset that combines data on international trade and foreign direct investments with daily stock data for 67 Dutch listed companies from 2006–2015, our results indicate that high systemic risk contributions are not only present in the financial sector, but also occur in other sectors of the economy. We find that firms within the financial sector are more capable than non-financial firms of reverting to their pre-financial crisis level of systemic risk contribution. Having examined the potential role globalization fulfills in determining systemic risk, we find two main opposing effects. First, firms in internationally trade-intensive sectors contribute less to systemic risk than firms in sectors with low trade intensity. Second, systemic risk rises when firms are engaged in foreign direct investment activity, suggesting that international networks and global supply chains contribute to systemic risk propagation. Our empirical results imply that macro-prudential policy aimed at monitoring systemic risk should be extended to non-financial sectors and should take into consideration globalization measures, such as foreign direct investments and global supply chains.

Systemic Risks of Financial Institutions: Importance and Vulnerability

Since the outbreak of the global financial crisis, forestalling and defusing systemic financial risks has been a hot topic of social concerns. In China, with constant development and innovation of the financial system, higher level financial deepening and openness, and economic downside pressure under new normal” economy, risk-prevention becomes much more complicated. In this case, the financial system should better serve the real economy, reduce financial risks and deepen financial reforms—three tasks of China’s financial work. The report of the 19th National Congress of the Communist Party of China further emphasized that the government should improve the financial regulatory system to forestall systemic financial risks. Therefore, ensuring China’s financial stability and preventing systemic risks have become the priority and major challenges for China’s financial regulatory authorities. Accurate measurement of systemic risks is the basis for risk prevention, the improvement of financial regulations, and any effective regulatory actions. However, existing domestic studies measure financial institutions’ systemic risks from only one aspect—systemic risk contribution or systemic risk exposure, and lack a clear distinction between the two measures in theoretical and policy implications. Some scholars even use systemic risk exposure metrics to measure the systemic risk contribution of financial institutions and assess its systemic importance. Actually, the aggregate risks of financial institutions include both risk contribution and risk exposure—the former focuses on systemic importance while the latter underlines systemic vulnerability, so we should take both sides into risk measurement. This paper uses ΔCoVaR and Exposure-ΔCoVaR to comprehensively measure the systemic risks of financial institutions from both sides—systemic importance and systemic vulnerability. This paper finds no significant correlation between the systemic importance and vulnerability of financial institutions in the cross-sectional dimension, but significant correlation in the time-series dimension, which means the systemic importance and vulnerability of financial institutions change simultaneously and periodically. The results imply that, in China, the systemic importance of bank and insurance industry exceed that of securities industry, while the latter’s systemic vulnerability exceeds that of the former. These differences exist persistently in the time-series dimension. The big four” banks have high systemic importance but low systemic vulnerability, while a handful of financial institutions have both significantly high systemic importance and vulnerability. Furthermore, the size of financial institutions’ asset is an important influencing factor of systemic importance, and the leverage is an important influencing factor of systemic vulnerability, while the margin trading of securities has a significant positive effect on systemic vulnerability but no significant effect on systemic importance. This paper accurately measures the systemic risks of 33 listed financial institutions in China from two aspects—risk contribution and risk exposure, and makes a precise assessment on their systemic importance and vulnerability. We also investigate the influencing factors of financial institutions’ systemic importance and vulnerability. These findings help to understand the systemic risks of China’s financial institutions in cross-sectional and time-series dimensions and correct some wrong perceptions in existing academic studies, and further provide useful empirical references and policy suggestions to China’s financial regulatory authorities to forestall systemic risks and improve macro-regulation. The policy implications of the results are mainly reflected in the following three aspects. First, regulators need to select targeted regulatory objectives and policy tools to make differential regulations based on the features of institutions in systemic importance and vulnerability. Second, different institutions are different in systemic importance and vulnerability, so regulatory authorities should pick out key financial institutions through their performance in systemic importance and vulnerability, and enhance the supervision of key institutions. Third, financial regulators are able to choose proper and effective regulatory tools according to the main drivers of systemic importance and vulnerability.

Cyclical Behavior of Systemic Risk in the Banking Sector

This paper examines cyclical behavior of banks’ systemic risk contribution and exposure. Using an unbalanced panel of 787 banks from countries members of the Organisation for Economic Co-operation and Development and the European Union covering the period 2000:Q1-2017:Q4, we document that both systemic risk contribution and exposure are positively related to business cycle. That is, systemic risk starts to accumulate in the financial sector during periods of boom, i.e., when the output gap is positive. Furthermore, during periods of robust economic growth, the level of credit tends to increase dramatically, going hand in hand with asset and property prices developments. We also find that contribution and exposure to system-wide distress move procyclically during credit and house cycles, meaning that during upturns in credit and house cycles bank interconnectedness increases, but tend to fall during downturns. However, individual risk of the banks, proxied by Value at Risk, evolves countercyclically during business and financial cycles, i.e., decreasing in upturns and raising in downturns. Thus, Value at Risk is not a good proxy for signalling the buit-up of financial imbalances. Dynamic conditional beta, however, is a more appropriate measure to quantify individual risk of the banks in relation with the market, being procyclical during the financial cycle. Additionally, the empirical analysis shows that both bank-specific and macroeconomic factors influence banks’ systemic distress. Particularly, size, credit risk and inflation boost systemic risk contribution and exposure of the banks, whereas capitalization, the share of loans in total assets, the share of non-interest income in total revenue and economic freedom help banks in reducing their systemic importance. The findings remain robust after controlling for nesting, cross-sectional dependence and reverse causality issues.

Scaling the Twin Peaks: Systemic Risk and Dual Regulation

In April 2013, the UK implemented a dual-regulation approach to financial services often referred to as twin peaks. In this paper, we assess the impact of the introduction of twin peaks regulation on the systemic risk contributions of UK financial institutions. Using a matched sample of single- and dual-regulated financial institutions, we provide evidence that twin peaks regulation resulted in a relative reduction in systemic risk for dual-regulated firms.

Scaling the twin peaks: Systemic risk and dual regulation

In April 2013, the UK implemented a dual-regulation approach to financial services often referred to as twin peaks. In this paper, we assess the impact of the introduction of twin peaks regulation on the systemic risk contributions of UK financial institutions. Using a matched sample of single- and dual-regulated financial institutions, we provide evidence that twin peaks regulation resulted in a relative reduction in systemic risk for dual-regulated firms.

Relative performance and systemic risk contributions of small and large banks during the financial crisis

During the financial crisis of 2007-2009, large banks (BHCs with assets exceeding $1 billion) suffered a sharper decline in performance and a greater increase in systemic risk, than small banks (BHCs with assets less than $1 billion). Performance is measured by return on assets (ROA) and EBIT/TA. Systemic risk (SRISK) is measured by the capital shortfall indicator proposed by Brownless and Engle (2017) and total risk is standard deviation of monthly returns. We test four hypotheses associated with the dissimilarity in behavioral patterns of large and small banks during the crisis: (1) the nontraditional banking activity hypothesis, (2) the organizational structure hypothesis, (3) the accounting risk hypotheses, and (4) the accounting conservatism hypothesis. We find that behavioral dissimilarities between large and small banks can be, at least, partially related to the first three hypotheses. Non-nested test procedures show that factors associated with nontraditional activity hypothesis fit the data for performance (ROA and EBIT/TA) and total risk the best. Organizational complexity factors are the most relevant set of variables related to systemic risk of banks (SRISK). The implications of our findings are that regulators should focus on factors related these two hypotheses to optimally alter bank performance and bank contribution to systemic risk, rather than limiting bank size and bank acquisitions as proposed by Section 622 of the Dodd-Frank Act (effective January 1, 2015). Our recommendation is in accord with attempts by the Trump administration to relax the Dodd-Frank Act.

Systemic risk in financial institutions of BRICS: measurement and identification of firm-specific determinants

The aim of this paper is twofold. First, it measures the systemic risk contribution of banks, financial services, and insurance firms of each of BRICS member country for the period 2000–2015. Second, it empirically examines how firm-specific factors determine systemic risk in financial institutions of BRICS countries. To carry out the empirical analysis, the unbalanced firm-level data are used. To gauge the systemic risk of banks, financial services, and insurance firms, the Delta Conditional Value-at-Risk (∆CoVaR) methodology is applied. The panel regression approach is used to examine how firm-specific variables determine the level of systemic risk in different financial institutions of BRICS countries. The empirical findings suggest that the size of institution, the tier 1 ratio, the liquidity ratio, the operating profit margin ratio, and the market-to-book value ratio statistically significantly determine systemic risk in BRICS countries. The results are significant in devising financial regulations to decrease the influence of systemic risk factors in the respective economies.

Macroprudential Regulation for the Chinese Banking Network System with Complete and Random Structures

There has been little quantitative research on macro-prudential regulation for the Chinese banking system while the existing relevant research in other countries has not considered the network structure. Therefore, the present paper constructs a dynamic Chinese banking network system with complete and random structures and a quantitative model of macro-prudential regulation using four risk allocation mechanisms (Component VaR, Incremental VaR, Shapley value EL, and ΔCoVaR). Then we analyze empirically the macro-prudential regulation effect on the dynamic Chinese banking network system. The results show that the macro-prudential regulation focus on capital requirements for the Chinese banking network system is very effective in that most banks’ default probabilities have been reduced. Moreover, the regulation effect of the ΔCoVaR mechanism is the most significant and it has strong applicability because it is not affected by the two network structures. The next effective methods are Component VaR and Shapley value EL mechanisms. The last is the Incremental VaR mechanism. The Chinese banking system with random network is more stable in most years than that of the complete network. Lastly, our analysis suggests that setting up capital requirements based on each bank’s systemic risk contribution is able to promote the stability of the Chinese banking system.

Measuring network systemic risk contributions: A leave-one-out approach

The aim of this paper is to propose a new network measure of systemic risk contributions that combines the pair-wise Granger causality approach with the leave-one-out concept. This measure is based on a conditional Granger causality test and consists of measuring how far the proportion of statistically significant connections in the system breaks down when a given financial institution is excluded. We analyse the performance of our measure of systemic risk by considering a sample of the largest banks worldwide over the 2003–2018 period. We obtain three important results. First, we show that our measure is able to identify a large number of banks classified as global systemically important banks (G-SIBs) by the Financial Stability Board (FSB). Second, we find that our measure is a robust and statistically significant early-warning indicator of downside returns during the last financial crisis. Finally, we investigate the potential determinants of our measure of systemic risk and find similar results to the existing literature. In particular, our empirical results suggest that the size and the business model of banks are significant drivers of systemic risk.

Financial structure and determinants of systemic risk contribution

This paper investigates on what drives the different determinants of systemic risk contribution in different countries, based on a dataset for commercial banks in a bank-based system (the BRICs and Japan) and a market-based system (the US). In both separate and pooled systems, the determinants of systemic risk contribution are found to be conditional on the financial structure whether a country has a bank-based or market-based financial system. The impact of non-traditional banking activities on systemic risk contribution is enlarged when the markets rather than banks are more important to the economy. The systemic risk contribution is generally larger for banks in a market-based system. The findings remain robust to linearity of size, different lags, different exchange rate levels and risk measures. Our study provides insights on understanding the differences in systemic risks determinants in different countries, and justifies country-specific macro-prudential regulations by providing country-level determinants of systemic risks.

Do firm-level factors play forward-looking role for financial systemic risk: Evidence from China

This paper examines the firm-level driving factors of the systemic risk in the Chinese banking system. To more accurately estimate the systemic risk, we take asymmetric and fat-tailed financial returns into consideration. Our empirical results reveal that the Chinese banks' systemic risk contribution reached the highest level during the stock market crash in 2015. The panel regression results imply that banks' size, leverage, loans, non-performing loans and demand deposits positively drive their systemic risk contribution, and ROA, loan-loss provisions and time deposits shows a negative relationship. Moreover, individual bank's VaR increase its systemic risk contribution, which means that the riskier individual bank contributes more to the systemic risk. The forward-looking role of the firm-level factors can potentially be used for macro-prudential regulation.

Switching generalized autoregressive score copula models with application to systemic risk

SummaryRecent financial disasters have emphasized the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market. The ability of econometric models to predict extreme events strongly relies on their flexibility to account for the highly nonlinear and asymmetric dependence patterns observed in financial time series. In this paper, we develop a new class of flexible copula models where the dependence parameters evolve according to a Markov switching generalized autoregressive score (GAS) dynamics. Maximum likelihood estimation is performed using a two‐step procedure where the second step relies on the expectation–maximization algorithm. The proposed switching GAS copula models are then used to estimate the conditional value at risk and the conditional expected shortfall, measuring the impact on an institution of extreme events affecting another institution or the market. The empirical investigation, conducted on a panel of European regional portfolios, reveals that the proposed model is able to explain and predict the evolution of the systemic risk contributions over the period 1999–2015.

Bank capital, institutional environment and systemic stability

Using data on publicly traded banks in 61 countries, we examine how the institutional environment affects the relationship between bank capital and system-wide fragility. Consistent with prior studies, we find that bank capital is associated with a reduction in the systemic risk contribution of individual banks. This effect is more pronounced for banks located in countries with less efficient public and private monitoring of financial institutions and in countries with lower levels of information availability. Overall, our findings suggest that capital can act as a substitute for a weak institutional environment in reducing systemic risk.

Measuring bank downside systemic risk in Taiwan

In this study, both systemic and individual bank risks across domestic and international banks in Taiwan are analyzed given a risk event breakout, using the conditional value-at-risk (CoVaR) and the conditional expected shortfall (CoES) by estimating the bivariate quantile autoregression with asymmetric downside risk adjustment to the daily stock returns of banks. The estimation results reveal the significant external asymmetric downside risk in individual bank systemic risk in contrast to the internal asymmetric downside risk in system itself. The individual bank systemic risk contribution is idiosyncratic and incoherent, but the system risk impact on banks is consistent and co-integrated toward all banks. The empirical results show that the larger size or leverage of the individual banks causes higher system risk impact, as they are hit and go down jointly in shocking events. The foreign banks incur and spread more symmetric external risk, whereas the domestic banks suffer and release more asymmetric external risk. Accordingly, the banks in North America and Europe not only take system risk but contribute the most systemic risk during the subprime crisis and Greek debt crisis, respectively, leading to the episode of asymmetric volatility externality effect on the Taiwanese banks in Asia.

Systemic risk network of Chinese financial institutions

The Chinese stock market crash in June 2015 has demonstrated necessary to improve understanding of systemic risk from the perspective of financial network. This study constructs a tail risk network to present overall systemic risk of Chinese financial institutions, given the macroeconomic and market externalities. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) method of high-dimensional models, our results show that firm's idiosyncratic risk can be affected by its connectedness with other institutions. The risk spillover effect from other companies is the main driving factor of firm-specific risk, comparing with macroeconomic state, firm characteristics and historical price movement. The number of connections between institutions significantly increases during June 2014 to June 2016. Moreover, we utilize the Kolmogorov-Smirnov statistic to test significance of systemic risk beta based on tail risk and further rank the systemic risk contribution. Finally, we test the determinants of systemic risk contribution in a forward-looking way. Regulators could detect those firms that are most threatening to the stability of system.

Pitfalls in the Use of Systemic Risk Measures

We examine pitfalls in the use of return-based measures of systemic risk contributions (SRCs). For both linear and nonlinear return frameworks, assuming normal and heavy-tailed distributions, we identify nonexotic cases in which a change in a bank’s systematic risk, idiosyncratic risk, size, or contagiousness increases the risk of the system but lowers the measured SRC of the bank. Assessments based on estimated SRCs could thus produce false interpretations and incentives. We also identify potentially adverse side effects: A change in a bank’s risk structure can make the measured SRC of its competitors increase more strongly than its own.

Systemic Risk Network of Chinese Financial Institutions

The Chinese stock market crash in June 2015 has demonstrated necessary to improve understanding of systemic risk from the perspective of financial network. This study constructs a tail risk network to present overall systemic risk of Chinese financial institutions, given the macroeconomic and market externalities. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) method of high-dimensional models, our results show that firm’s idiosyncratic risk can be affected by its connectedness with other institutions. The risk spillover effect from other companies is the main driving factor of firm-specific risk, comparing with macroeconomic state, firm characteristics and historical price movement. The number of connections between institutions significantly increases during June 2014 to June 2016. Moreover, we utilize the Kolmogorov-Smirnov statistic to test significance of systemic risk beta based on tail risk and further rank the systemic risk contribution. Regulators could detect those firms that are most threatening to the stability of system.