No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Banking networks, systemic risk, and the credit cycle in emerging markets
Abstract
We study how globalization impacts systemic risk in emerging markets. We extend a large literature on systemic risk in the US, Europe, and other developed countries to emerging markets, which are relatively under-researched. Our findings are based on a large-scale empirical examination of systemic risk among 1048 financial institutions in a sample of 23 emerging markets, broken down into 5 regions, along with 369 U.S. financial institutions. Using an additively decomposable systemic risk score that combines banking system interconnectedness with default probabilities, systemic risk is quantified for each region, across time. The empirical analyses suggest that emerging markets’ systemic risk is heterogeneous across regions, is strongly dependent on the interconnectedness of the banking system within each region, and drives the level of default risk in each region, while the regions are compartmentalized away from each other and insulated from the United States. The systemic risk score may be used as a policy variable in each emerging market region to manage the credit cycle. Our evidence is consistent with the notion that globalization engenders financial stability and does not lead to large systemic risk spillovers across emerging market regions.
... Economic theory suggests that systemic risk accumulates during the expansion phase of the financial cycle and materialises into financial crises during downturns (Borio et al., 2020a;Das et al., 2022;Danthine, 2012). During expansions, financial agents often become overly optimistic, leading to increased borrowing, lending, and investment in riskier assets, which appear less dangerous in a booming economy. ...
The stability of the financial cycle is paramount for the effective formulation and implementation of macroprudential policy in South Africa. The South African Reserve Bank (SARB) and the Prudential Authority strive to mitigate excessive fluctuations in the financial cycle, recognising that a stable cycle provides more reliable signals for financial sector activity and anchors macroprudential policy decisions. However, the tightening of macroprudential policy by the SARB and the Prudential Authority during the post-2009 recovery period, despite mild signs of recovery from the global financial crisis, raises concerns about the stability of the South African financial cycle. This study aims to construct a financial cycle volatility index to assess its stability and identify the key macroeconomic drivers of financial instability in South Africa. Employing monthly data from 1970 to 2024, the study utilises a dynamic conditional correlation model and a Markov switching regression model to analyse the relationship between macroeconomic variables and financial stability. The findings reveal heightened financial cycle volatility around crisis periods and demonstrate that macroeconomic variables such as exchange rate fluctuations, price level changes, and implementing monetary and macroprudential policies can significantly increase financial instability. These results suggest a need for proactive and aggressive macroprudential policy measures in the years preceding potential crises. Moreover, the study’s findings emphasise the importance of considering macroeconomic conditions when calibrating financial cycle policies.
... To this effect, researchers build financial networks to understand the behaviors and dynamics of banks because the banking system has shown itself to be complex with many interactive agents. Some studies that applied the network theory in the analysis of systemic risk and the transmission process of an institution's risk in a system include Nier et al. (2007), Gai and Kapadia (2010), Upper (2007), Minoru et al. (2013), Diebold and Yilmaz (2014), Martinez-Jaramillo et al. (2014), Guimarães-Filho and Hong (2016) Wang et al. (2022), Das et al. (2022), Zhang et al. (2023). ...
This study examines fifteen major banks’ network connectedness in the Nigerian banking system via its stock returns. The paper studies both the static and dynamic network connectedness of banks built on the generalized forecast error variance decomposition, using daily data from January 4, 2005, to June 28, 2019, of publicly traded banks. This study finds a substantial total connectedness, with a high pairwise connectedness among the system’s large banks. The dynamic evolution of connectedness in the network reveals that banks’ connectivity increases in response to certain economic episodes. The evolution of the global network's topological properties reveals that it is mainly susceptible to shocks threatening its stability. Additionally, the study computes a composite index of systemic importance for the Nigerian banking system by combining several network centrality metrics using the principal component analysis. The outcome shows that large banks are more centralized in the network, and the larger the scale of assets a bank has, the more systemically relevant the bank is in the network. Since systemic risk emanates from connectedness, frequent assessment of the banking system's connectedness and systemic importance will aid policy decisions. The proposed measure of systemic importance can be incorporated into the CBN’s stress testing mechanism for fast-tracking risk potential banks.
... Similarly, Le et al. (2022) use a high-dimensional VAR model to identify the global sovereign CDS network. Furthermore, Das et al. (2022) apply a VAR model to explore the time-series interdependencies among systemic risks in the banking systems across six global regions. These findings reveal that connections in the sovereign risk network tend to be stronger within regional groups and among countries with similar levels of economic development. ...
This paper examines the spillover effect of global economic policy uncertainty (EPU) on sovereign credit default swap (CDS) spreads in a sample of 21 countries. We use a multivariate quantile model to measure EPU spillovers for each country and find that global EPU spillovers have a significant and positive effect on subsequent CDS spreads in both developed and emerging markets. The spillover effect is stronger in developed markets compared to emerging markets. The positive relationship between EPU spillovers and CDS spreads remain significant when controlling for various economic, financial, and political risk factors. Our results are robust to alternative measures of EPU spillovers and sovereign credit risk, across different forecast horizons, and to potential endogeneity resulting from omitted variables.
... Both studies report FIs have become highly interrelated over time -increasing the level of systemic risk -with banks playing a much more important role in transmitting shocks than others. Das et al. (2022) examine emerging markets and find that systemic risk varies -but does not spill over -across regions, and is strongly dependent on the interconnectedness of banks within each region. ...
We evaluate changes in system-wide information transmission following US and European bank M&A. We generalize Granger causality network measures to consider spillover channels in the mean, volatility, and tail risk, each capturing a unique form of systemic risk transmission between acquirers and the system. We exploit our measure's directionality and granularity to offer novel results on the exact transmission channels within and across regions, and extend research to include bank-nonbank deals. Following M&A, US acquirers contribute more to local tail risk and become more vulnerable to local and cross-border shocks, while European acquirers increase US tail risk. Our findings also highlight the benefits of scale, diversification, and the negative externalities linked to pre-merger systemic relevance.
We study how consumer preferences affect the transmission of microeconomic price shocks to consumer price index (CPI) inflation. These preferences give rise to complementarities and substitutions between goods, generating demand-driven cross-price dependencies that either amplify or mitigate the impact of price shocks. Our results demonstrate that while both effects are present, positive spillovers due to complementarities dominate. The magnitude of these cross-price effects is significant, demonstrating their importance in shaping CPI inflation dynamics. Most importantly, demand-driven price linkages decisively shape the impact of producer prices on CPI inflation. These findings underscore the need to take into account demand-driven price dependencies when assessing the impact of price shocks on CPI inflation, rather than relying solely on supply-related ones.
In this paper, we propose a novel concept of correlation-based stable networks to empirically investigate the dynamic integration and network structure of the European
Monetary Union (EMU) sovereign bond markets. The obtained results uncover a high degree of market integration between sample markets over the period preceding the recent financial crises, while segmentation is found afterwards. The stable network analysis shows, for its part, the existence of two different network structures before and after the onset of the European debt crisis, where the in-crisis network structure is characterized by two groups of countries with respect to their fiscal performance. In particular,Belgium is the unique vertex connecting the two groups, making it the channel for shock transmission in the event of worsening debt crisis in the EMU.
We investigate the interconnectedness and systemic risk of China's financial institutions by constructing dynamic tail-event driven networks (TENETs) at 1% risk level based on weekly returns of 24 publicly-listed financial institutions from 2008 to 2016. Total connectedness reaches a peak when the system exhibits stress, especially during the recent period from mid-2014 to end-2016. Large commercial banks and insurers usually exhibit systemic importance, but some small firms are systemically important due to their high level of incoming (outgoing) connectedness. Our results provide useful information to regulators when they assess systemic risk of financial institutions and formulate macroprudential supervision policy.
We use LASSO methods to shrink, select, and estimate the high-dimensional network linking the publicly traded subset of the world's top 150 banks, 2003–2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statically, we find that global bank equity connectedness has a strong geographic component, whereas country sovereign bond connectedness does not. Dynamically, we find that equity connectedness increases during crises, with clear peaks during the Great Financial Crisis and each wave of the subsequent European Debt Crisis, and with movements coming mostly from changes in cross-country as opposed to within-country bank linkages.
We present an economic model of systemic risk in which undercapitalization of the financial sector as a whole is assumed to harm the real economy, leading to a systemic risk externality. Each financial institution’s contribution to systemic risk can be measured as its systemic expected shortfall (SES), that is, its propensity to be undercapitalized when the system as a whole is undercapitalized. SES increases in the institution’s leverage and its marginal expected shortfall (MES), that is, its losses in the tail of the system’s loss distribution. We demonstrate empirically the ability of components of SES to predict emerging systemic risk during the financial crisis of 2007–2009.
Received December 1, 2015; editorial decision August 5, 2016 by Editor Andrew Karolyi.
We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top 150 banks, 2003-2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statistically, we find that global banking connectedness is clearly linked to bank location, not bank assets. Dynamically, we find that global banking connectedness displays both secular and cyclical variation. The secular variation corresponds to gradual increases/decreases during episodes of gradual increases/decreases in global market integration. The cyclical variation corresponds to sharp increases during crises, involving mostly cross-country, as opposed to within-country, bank linkages.
Non-financial corporations from emerging market economies (EMEs) have increased their external borrowing significantly through the offshore issuance of debt securities. Having obtained funds abroad, the foreign affiliate of a non-financial corporation could transfer funds to its home country via three channels: it could lend directly to its headquarters (within-company flows), extend credit to unrelated companies (between-company flows) or make a cross-border deposit in a bank (corporate deposit flows). Cross-border capital flows to EMEs associated with all three of the above channels have grown considerably over the past few years, as balance of payments data reveal. To the extent that these flows are driven by financial operations rather than real activities, they could give rise to financial stability concerns.
This paper examines the domestic systemically important banks and regional systemically important banks in Asian emerging markets using four major market-based measures: (i) Delta conditional value at risk, (ii) Marginal expected shortfall, (iii) SRISK, and (iv) Component expected shortfall. We find that, first, the four systemic risk measures lead to consistent assessments in relation to the systemic risk contributions of both large and small banks at the regional level but not at the country level. Second, at the Asian regional level, most of the top 10 systemically important banks are large banks operating in China. The results also provide evidence of the similarities and significant correlations in the relative rankings of individual banks among the measures. Third, at the country level, the similarities and correlations in the relative rankings of individual banks vary across countries. The results imply that the identification of systemically important banks based on a single measure should be used cautiously.
We examine the impact of bank liquidity creation on systemic risk and its heterogeneous impact over the network connectedness. We find that excessive liquidity creation increases the systemic risk with a “U shape” relationship, while internal and external liquidity creation drives systemic risk in a different way. Network connectedness of banks strengthens the relationship between liquidity creation and systemic risk. Our results provide supporting evidence on regulating bank liquidity creation to enhance the financial stability.
In systemic risk measure, a large amount of literature has emerged, but few of them take into account the multi-scale natures of financial data. Considering these natures, we develop a novel W-QR-CoVaR method to measure systemic risk. To be specific, the W-QR-CoVaR method combines the wavelet multiresolution analysis (MRA) with the conditional value-at-risk (CoVaR) method based on the quantile regression (QR) framework. We then apply it to measure the systemic risk in the Chinese banking industry covering the period from September 2007 to September 2018. Our experiment results show that the hybrid W-QR-CoVaR method performs better than the traditional CoVaR method in terms of predictive accuracy. Furthermore, we also explore the relation between the systemic risk contribution of each individual bank and the bank-specific characteristics. Size and leverage appear to be the most robustness determinants. The findings suggest that regulators should pay more attention to the banks with smaller size and higher leverage.
Corporate leverage among emerging market firms went up considerably after the 2007–09 Global Financial Crisis (GFC). We investigate how the increased emerging market corporate leverage in the post-GFC period (2010–15) impacted the underlying credit risk, compared to the pre-GFC (2002–2006) and GFC (2007–09) periods. Using firm-level credit risk, financial, and balance sheet data for 350 firms in 23 emerging markets, we find that leverage growth leads to a significant increase in corporate credit default swap (CDS) spreads only in the post-GFC period, and the incremental effect is mainly evident among risky firms (firms with high leverage and idiosyncratic volatility). In contrast, emerging market CDS spreads during the GFC period are mainly driven by global market risk factors. The post-GFC corporate debt vulnerability is mitigated for high growth prospect firms and firms domiciled in countries with high net capital inflows and superior governance. While corporate leverage growth impacts aggregate corporate credit risk, there is no evidence that it increases sovereign credit risk. Our paper contributes to the recent literature on potential sources of default risk in emerging markets.
We show that, in the presence of correlated investment opportunities across firms, risk sharing between firm shareholders and firm managers leads to compensation contracts that include relative performance evaluation. These contracts bias investment choices toward correlated investment opportunities, and thus create systemic risk. Furthermore, we show that leverage amplifies all such effects. In the context of the banking industry, we analyze recent policy recommendations for firm managerial pay and show how shareholders optimally undo the policies’ intended effects.
Received October 31, 2017; editorial decision August 21, 2018 by Editor Wei Jiang. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
This paper offers novel evidence on the impact of raising bank capital requirements on lending in an emerging market and explores heterogeneous effects, depending on bank characteristics and economic conditions. Using quarterly bank-level data and exploiting the adoption of bank-specific capital buffers, we find that higher capital requirements are associated with lower credit growth in Peru. But the effect is short-lived and becomes insignificant in about half a year. The impact of capital requirements varies with economic conditions and bank characteristics. The effects is stronger during periods of lower economic growth. Weaker (less profitable, less capitalized and less liquid) banks react more to changes in capital requirements. Our findings are robust to estimating a variety of specification to address concerns about the endogeneity of capital requirements.
This paper sheds light on the effects of SME lending on banking system stability. Using a dataset of 32 economies during 2007–15, we find that higher growth in SME lending is associated with greater banking system stability, as measured by greater distance to default, but only in emerging market economies (EMEs). In EMEs, financial development tends to be lower and information asymmetries higher so that profitable projects may remain unfinanced.
The National Banking Acts (NBAs) of 1863–1864 established rules governing the amounts and locations of interbank deposits, thereby reshaping the bank networks. Using unique data on bank balance sheets and detailed interbank deposits in 1862 and 1867 in Pennsylvania, we study how the NBAs changed the network structure and quantify the effect on financial stability in an interbank network model. We find that the NBAs induced a concentration of interbank deposits at both the city and bank levels, creating systemically important banks. Although the concentration facilitated diversification, contagion would have become more likely when financial center banks faced large shocks. (JEL E44, G01, G21, G28, L14, N21)
Theory suggests that government aid to banks may either reduce or increase systemic risk. We are the first to address this issue empirically, analyzing the Troubled Assets Relief Program (TARP). Analysis suggests that TARP significantly reduced contributions to systemic risk, particularly for larger and safer banks, and those in better local economies. This occurred primarily through a capital cushion channel that reduced market leverage by increasing the value of common equity. Results are robust to endogeneity and selection bias checks. Findings yield policy conclusions about whether to aid banks, the best targets for future assistance, and short-term versus long-term effects.
This paper adopts the Tail-Event driven NETwork (TENET) risk model to assess the systemic risk of Indian banks. Building upon the Value at Risk (VaR), Conditional Value at Risk (CoVaR) and a Single Index Model (SIM) in a generalized quantile regression framework, the results suggest that the Indian banks exhibit high interconnectedness during the crisis period. The results also identify the systemically important banks and explain the banking networks.
With the establishment of the Banking Union, the European Central Bank has been granted the power to impose stricter regulations than the national regulator if systemic risks are not adequately addressed at the national level. We ask whether there is a cross-border externality in the sense that a bank's systemic risk differs when applying a national versus a European perspective. On average, banks’ contribution to systemic risk is similar at the two regional levels, and so is the ranking of banks. Generally, larger banks and banks with a lower share of loans are more systemically important. The effects of these variables are qualitatively but not quantitatively similar at the national versus the European level.
Ten years ago, the financial crisis spurred research focused on systemic risk. This article examines the history and application of the SRISK measure, which was developed at that time and is now widely used in monitoring systemic risk around the globe. The concept is explained and a variety of ways to measure SRISK are developed. In this article, new results are presented on the uncertainty associated with the SRISK measure and on how it compares with other related measures from both academics and regulators. By focusing on the mechanism by which undercapitalization of the financial sector initiates a financial crisis, new research examines how the probability of a financial crisis is affected by the level of SRISK and, consequently, how much SRISK a country can stand without having a high probability of crisis. The model used to evaluate this probability recognizes the externalities between financial institutions that make an undercapitalized firm or country more fragile if other firms and countries are also undercapitalized. Expected final online publication date for the Annual Review of Financial Economics Volume 10 is November 1, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
This study investigates how insurers contribute to systemic risk in the global financial system. In a modeling framework embracing publicly traded and nonpublic firms, the financial system is represented by 201 major banks and insurers over the period from 2004 through 2014. In the aggregate, the insurance sector contributes relatively little to systemic losses; during the financial crisis and the European sovereign debt crisis, its risk share averaged 9 percent. Individually, however, several multi‐line and life insurers appear to be as systemically risky as the riskiest banks. Our results, therefore, affirm that some insurers are systemically important and indicate that insurers’ level of systemic risk varies by line of business. We discuss several important implications of our results for managing systemic risk in insurance, arguing for a combination of entity‐ and activity‐based regulation.
In this paper, we identify several shortcomings in the systemic-risk scoring methodology currently used to identify and regulate Systemically Important Financial Institutions (SIFIs). Using newly-disclosed regulatory data for 119 US and international banks, we show that the current scoring methodology severely distorts the allocation of regulatory capital among banks. We then propose and implement a methodology that corrects for these shortcomings and increases incentives for banks to reduce their risk contributions.
We study the sources of corporate default clustering in the United States. We reject the hypothesis that firms’ default times are correlated only because their conditional default rates depend on observable and latent systematic factors. By contrast, we find strong evidence that contagion, through which the default by one firm has a direct impact on the health of other firms, is a significant clustering source. The amount of clustering that cannot be explained by contagion and firms’ exposure to observable and latent systematic factors is insignificant. Our results have important implications for the pricing and management of correlated default risk.
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.
We investigate why some banks are more exposed and contribute more to systemic risk in the global financial system than others. On average, European banks contribute more to global systemic risk than banks in the United States while banks on both continents have a comparable average exposure to systemic crises. In addition to being larger, European banks appear to contribute more to global systemic risk, because of the lower quality of their loan portfolios and their higher relative interconnectedness with the rest of the global financial system. Finally, more stringent capital regulations are found to decrease the average exposure of banks to systemic risk.
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.
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.
In this paper we analyse firm level systemic risk for US and European banks from 2004 to 2012. We observe that common systemic risk indicators are primarily driven by firm size which implies an overriding concern for “too-big-to-fail” institutions. However, smaller banks may still pose considerable systemic threats, as exemplified by the Northern Rock debacle in 2007. By introducing a simple standardisation, we obtain new risk measures that often prove to be superior predictors of financial distress during the 2007–2009 subprime crisis. We conclude that the new measures could be a valuable addition to the existing indicators employed in Basel III to identify systemically important banks.
Syndication increases the overlap of bank loan portfolios and makes them more vulnerable to contagious effects. We develop a novel measure of bank interconnectedness using syndicated corporate loan portfolios, overlap based on industry and region, and different weights such as equal weights, size and relationships. We find that interconnectedness is driven mainly by bank diversification, less by bank size or overall loan market size. Interconnectedness is positively correlated with different bank-level systemic risk measures including SRISK, DIP and CoVaR, and such a positive correlation mainly arises from an elevated effect of interconnectedness on systemic risk during recessions. Overall, our results highlight that institution-level risk reduction through diversification ignores the negative externalities of an interconnected financial system.
Emerging country governments increasingly issue local currency denominated bonds and foreign investors have been increasing their holdings of these assets. By issuing debt denominated in local currency, emerging country governments eliminate exchange rate risk. The growing stock of local currency government debt in the financial portfolios of foreign investors increases their diversification and exposure to fast growing economies. In this paper, we highlight some of the risks associated to this recent trend. First, we adopt the CoV aR risk-measure to estimate the vulnerability of individual countries to systemic risk in the market for local currency government debt. Second, we show that our country-level estimates of vulnerability increase with the share of local currency debt held by foreign investors. A version of the old adage "When New York sneezes, London catches a cold," used often to describe the relationship between the stock markets in these two cities, still applies between individual emerging countries and the aggregate market for local currency government debt.
Bank liquidity shortages associated with the growth of wholesale-funded credit intermediation has motivated the implementation of liquidity regulations. We analyze a dynamic stochastic general equilibrium model in which liquidity and capital regulations interact with the supply of risk-free assets. In the model, the endogenously time varying tightness of liquidity and capital constraints generates intermediaries' leverage cycle, influencing the pricing of risk and the level of risk in the economy. Our analysis focuses on liquidity policies' implications for households' welfare. Within the context of our model, liquidity requirements are preferable to capital requirements, as tightening liquidity requirements lowers the likelihood of systemic distress without impairing consumption growth. In addition, we find that intermediate ranges of risk-free asset supply achieve higher welfare.
We propose the realized systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential regulation.
Previous studies support the hypothesis that institutional ownership leads to an enhanced systematic liquidity risk by increasing the commonality in liquidity. By using a proprietary database of all incoming orders and ownership structure in an emerging stock market, we show that institutional ownership leads to an increase in commonality in liquidity for mid- to-large cap firms; however, only individual ownership can lead to such an increase for small cap firms, revealing a new source of systematic liquidity risk for a specific group of firms. We also reveal that commonality decreases with the increasing number of investors (for both individual and institutional) at any firm size level; suggesting that as the investor base gets larger, views of market participants become more heterogeneous, which provides an alternative way to decrease the systematic liquidity risk.
In May 2013, the U.S. Federal Reserve announced the beginning of the end of the program of monthly security purchases, the so-called “tapering.” The announcement was associated with large investor outflows to EM funds and large fund re-allocations across countries and stocks. Unexpected flow-implied fund upward (downward) allocations are associated with positive (negative) individual security returns following the announcement. The effect is pronounced EM stocks that had positive cumulative abnormal returns around earlier Federal Reserve asset-purchase announcements leading up to the taper. It is concentrated in more liquid, smaller capitalization stocks and is stronger for forced trades among active funds.
Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks.
This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.
We take a Bayesian perspective and provide a novel methodology to make robust system-wide inference on uncertain, time-varying, cross-fi rm fi nancial linkages. We use this framework to investigate contagion and systemic risk within the S&P100 blue chips, and find that fi rms interconnectedness increased across the period 1999/2003 (e.g. Gramm-Leach-Bliley act, fi nancial scandals, etc.), and the great fi nancial crisis. The empirical analysis shows that firm-level network centrality does not depend on market values, while is positively correlated with financial losses. Further, we show that systemic risk partly correlates with the business cycle and with exposures to sources of systematic risks.
We examine systemic risk in the Chinese banking system by estimating the conditional value at risk (CoVaR), the marginal expected shortfall (MES), the systemic impact index (SII) and the vulnerability index (VI) for 16 listed banks in China for the 2007–2014 period. We find that these measures show different patterns, capturing different aspects of systemic risk of Chinese banks. However, rankings of banks based on these measures are significantly correlated. The time-series results for the CoVaR and MES measures suggest that systemic risk in the Chinese banking system decreased after the global financial crisis but started rising in 2014.
This article presents an analysis of the literature on systemic financial risk. To that end, we analyze and classify 266 articles that were published no later than September 2016 in the databases Scopus and Web of Knowledge; these articles were identified using the keywords “systemic risk”, “financial stability”, “financial”, “measure”, “indicator”, and “index”. They were evaluated based on 10 categories, namely, type of study, type of approach, object of study, method, spatial scope, temporal scope, context, focus, type of data used, and results. The analysis and classification of this literature made it possible to identify the remaining gaps in the literature on systemic risk; this contributes to a future research agenda on the topic. Moreover, the most influential articles in this field of research and the articles that compose the mainstream research on systemic financial risk were identified.
Financial networks have shown to be important in understanding systemic events in credit markets. In this paper, we investigate how the structure of those networks can affect the capacity of regulators to assess the level of systemic risk. We introduce a model to compute the individual and systemic probability of default in a system of banks connected in a generic network of credit contracts and exposed to external shocks with a generic correlation structure. Even in the presence of complete knowledge, we identify conditions on the network for the emergence of multiple equilibria. Multiple equilibria give rise to uncertainty in the determination of the default probability. We show how this uncertainty can affect the estimation of systemic risk in terms of expected losses. We further quantify the effects of cyclicality, leverage, volatility and correlations. Our results are relevant to the current policy discussions on new regulatory framework to deal with systemic events of distress as well as on the desirable level of regulatory data disclosure.
We introduce SRISK to measure the systemic risk contribution of a financial firm. SRISK measures the capital shortfall of a firm conditional on a severe market decline, and is a function of its size, leverage and risk. We use the measure to study top financial institutions in the recent financial crisis. SRISK delivers useful rankings of systemic institutions at various stages of the crisis and identifies Fannie Mae, Freddie Mac, Morgan Stanley, Bear Stearns, and Lehman Brothers as top contributors as early as 2005-Q1. Moreover, aggregate SRISK provides early warning signals of distress in indicators of real activity.
Received June 7, 2011; accepted April 18, 2016 by Editor Geert Bekaert.
Many debt claims, such as bonds, are resaleable, whereas others, such as repos, are not. There was a fivefold increase in repo borrowing before the 2008 crisis. Why? Did banks’ dependence on non-resaleable debt precipitate the crisis? In this paper, we develop a model of bank lending with credit frictions. The key feature of the model is that debt claims are heterogeneous in their resaleability. We find that decreasing credit market frictions leads to an increase in borrowing via non-resaleable debt. Borrowing via non-resaleable debt has a dark side: it causes credit chains to form, since if a bank makes a loan via non-resaleable debt and needs liquidity, it cannot sell the loan but must borrow via a new contract. These credit chains are a source of systemic risk, since one bank’s default harms not only its creditors but also its creditors’ creditors. Overall, our model suggests that reducing credit market frictions may have an adverse effect on the financial system and may even lead to the failures of financial institutions.
The Lehman bankruptcy highlights the potential for interconnectedness to cause negative externalities through counterparty contagion, but the externalities may also arise from information contagion. We examine troubled financial firms and find that both channels are significant factors in creating spillover effects. Counterparty contagion is greater in cases of riskier firms and larger and more complex exposures. However, the counterparty exposures are small, especially among banks that face diversification regulations, and do not typically cause a cascade of failures. Information contagion is stronger for rivals in the same markets and has a larger impact in cases of distress than in bankruptcies.
We propose a measure of systemic risk, Δ CoVaR, defined as the change in the value at risk of the financial system conditional on an institution being under distress relative to its median state. Our estimates show that characteristics such as leverage, size, maturity mismatch, and asset price booms significantly predict Δ CoVaR. We also provide out-of-sample forecasts of a countercyclical, forwardlooking measure of systemic risk, and show that the 2006:IV value of this measure would have predicted more than one-third of realized Δ CoVaR during the 2007-2009 financial crisis.
This article studies how systemic risk and financial market distress affect the distribution of shocks to real economic activity. We analyze how changes in 19 different measures of systemic risk skew the distribution of subsequent shocks to industrial production and other macroeconomic variables in the US and Europe over several decades. We also propose dimension reduction estimators for constructing systemic risk indexes from the cross section of measures and demonstrate their success in predicting future macroeconomic shocks out of sample.
Using an integrated model to control for simultaneity, as well as new risk measurement techniques such as Adapted Exposure CoVaR and Marginal Expected Shortfall (MES), we show that the aggregate systemic risk exposure of financial institutions is positively related to sovereign debt yields in European countries in an episodic manner, varying positively with the intensity of the financial crisis facing a particular nation. We find evidence of a simultaneous relation between systemic risk exposure and sovereign debt yields. This suggests that models of sovereign debt yields should also include the systemic risk of a country's financial system in order to avoid potentially important mis-specification errors. We find evidence that systemic risk of a country's financial institutions and the risk of sovereign governments are inter-related and shocks to these domestic linkages are stronger and longer lasting than international risk spillovers. Thus, the channel in which domestic sovereign debt yields can be affected by another nation's sovereign debt is mostly an indirect one in that shocks to a foreign country's government finances are transmitted to that country's financial system which, in turn, can spill over to the domestic financial system and, ultimately, have a destabilizing effect on the domestic sovereign debt market.
This paper studies the significant variation in the cross-section of standalone and systemic risk of large banks during the recent financial crisis to identify bank specific factors that determine risk. We find that systemic risk grows with bank size and is inversely related to bank capital, and this effect exists above and beyond the effect of bank size and capital on standalone bank risk. Our results contribute to the ongoing debate on the merits of imposing systemic risk-based capital requirements on banks.
We review the extensive literature on systemic risk and connect it to the current regulatory debate. While we take stock of
the achievements of this rapidly growing field, we identify a gap between two main approaches. The first one studies different
sources of systemic risk in isolation, uses confidential data, and inspires targeted but complex regulatory tools. The second
approach uses market data to produce global measures which are not directly connected to any particular theory, but could
support a more efficient regulation. Bridging this gap will require encompassing theoretical models and improved data disclosure.
We develop a multivariate credit risk model that accounts for joint defaults of banks and allows us to disentangle how much of banks’ credit risk is systemic. We find that the United States and United Kingdom differ not only in the evolution of systemic risk but, in particular, in their banks’ systemic exposures. In both countries, however, systemic credit risk varies substantially, represents about half of total bank credit risk on average, and induces high risk premia. The results suggest that sovereign and bank systemic risk are particularly interlinked in the United Kingdom.
Using dynamic conditional correlations and networks, we bring a novel framework to define the integration and segmentation of emerging countries. The individual EMBI+ spreads of 13 emerging countries from 01/2003 to 12/2013 are used to compare their interaction structure before (phase 1) and after (phase 2) the global financial crisis. Accordingly, the average of dynamic correlations between cross country spreads significantly increases in phase 2. At first, the increased co-movement degree suggests an integration of the sample countries after the crisis. However, correlation based stable networks show that the increase is more likely to be caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Important implications for international investors and policymakers are discussed.