Journal articles: 'Systemic risk measure'

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Engle, Robert. "Systemic Risk 10 Years Later." Annual Review of Financial Economics 10, no. 1 (November 2018): 125–52. http://dx.doi.org/10.1146/annurev-financial-110217-023056.

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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.

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Nekhili, Ramzi. "Systemic risk and interconnectedness in Gulf Cooperation Council banking systems." Banks and Bank Systems 15, no. 1 (March 25, 2020): 158–66. http://dx.doi.org/10.21511/bbs.15(1).2020.15.

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Nowadays, financial interconnectedness is the main driver of systemic risk. Thus, there is a constant need for tools to assess and manage systemic risk. This paper offers an alternative model framework to measure systemic risk and examine interconnectedness between direct exposures across banking systems in the emerging markets of the Gulf Cooperation Council (GCC). To ensure consistency and efficiency of systemic risk estimates and to capture its multifaceted nature, the methodology measures systemic risk using a combination of Filtered Historical Simulation and nonparametric regression and then examines the interconnectedness using a network analysis. The results reveal that shocks originating in the banking systems in Saudi Arabia may potentially cause a cascade of failures in the banking systems of most GCC countries. The banking system in Oman, however, is robust enough to withstand any ripple effect from adverse shocks affecting GCC’s major banking systems. Such results present some policy implications for regulators and supervisors and may benefit asset managers and investors in making portfolio allocation decisions.

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Ivanov, Katerina, James Schulte, Weidong Tian, and Kevin Tseng. "An Equilibrium-Based Measure of Systemic Risk." Journal of Risk and Financial Management 14, no. 9 (September 2, 2021): 414. http://dx.doi.org/10.3390/jrfm14090414.

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This paper develops and implements an equilibrium model of systemic risk. The model derives a systemic risk measure, loss beta, in characterizing all too-big-to-fail banks using a capital insurance equilibrium. By constructing each bank’s loss portfolio with a recent accounting approach, we perform a comprehensive empirical study of this loss beta measure and document all TBTF banks from 2002 to 2019. Our empirical findings suggest a significant number of too-big-to-fail banks in 2018–2019.

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Hadad, Elroi, Tomer Shushi, and Rami Yosef. "Measuring Systemic Governmental Reinsurance Risks of Extreme Risk Events." Risks 11, no. 3 (February 23, 2023): 50. http://dx.doi.org/10.3390/risks11030050.

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This study presents an easy-to-handle approach to measuring the severity of reinsurance that faces a system of dependent claims, where the reinsurance contracts are of excess loss or proportional loss. The proposed approach is a natural generalization of common reinsurance methodologies providing a conservative framework that deals with the fundamental question of how much money should a government hold to prepare for natural or human-made extreme risk events that the government will cover? Although the ruin theory is commonly used for extreme risk events, we suggest a new risk measure to deal with such events in a new framework based on multivariate risk measures. We analyze the results for the log-elliptical model of dependent claims, which are commonly used in risk analysis, and illustrate our novel risk measure using a Monte Carlo simulation.

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Kritzman, Mark, Yuanzhen Li, Sébastien Page, and Roberto Rigobon. "Principal Components as a Measure of Systemic Risk." Journal of Portfolio Management 37, no. 4 (July 31, 2011): 112–26. http://dx.doi.org/10.3905/jpm.2011.37.4.112.

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Parker, Edgar. "The Relationship between the US Economy’s Information Processing and Absorption Ratios: Systematic vs Systemic Risk." Entropy 20, no. 9 (September 2, 2018): 662. http://dx.doi.org/10.3390/e20090662.

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After the 2008 financial collapse, the now popular measure of implied systemic risk called the absorption ratio was introduced. This statistic measures how closely the economy’s markets are coupled. The more closely financial markets are coupled the more susceptible they are to systemic collapse. A new alternative measure of financial market health, the implied information processing ratio or entropic efficiency of the economy, was derived using concepts from information theory. This new entropic measure can also be useful in predicting economic downturns and measuring systematic risk. In the current work, the relationship between these two ratios and types of risks are explored. Potential methods of the joint use of these different measures to optimally reduce systemic and systematic risk are introduced.

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Pârţachi, Ion, and Eugeniu Gârlă. "Economic Insecurity as Systemic Risk." Annals of the Alexandru Ioan Cuza University - Economics 62, s1 (October 1, 2015): 29–36. http://dx.doi.org/10.1515/aicue-2015-0034.

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Abstract Difficulties related to the problem of evaluating the economic security / insecurity, including the threshold of economic security / insecurity, namely the impossibility of giving an analytical description of a criterion entirely made up of a set of indicators describing the degree of economic security / insecurity, makes more and more researchers, including the authors, to seek indirect ways of finding solutions, for example considering systemic risk., as a measure of evaluation. Thus, starting from a new approach, and given the specific components of systemic risk to financial stability: the banking sector, corporate sector, public sector, volume of credits, economic activity index the threshold vector of economic security / insecurity can be developed. The study shows that systemic risk can be used to measure the threshold of economic security /insecurity.

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Brownlees, Christian, and Robert F. Engle. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk." Review of Financial Studies 30, no. 1 (August 6, 2016): 48–79. http://dx.doi.org/10.1093/rfs/hhw060.

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Mwamba, John Weirstrass Muteba, and Serge Angaman. "Systemic risk and real economic activity: A South African insurance stress index of systemic risk." Asian Academy of Management Journal of Accounting and Finance 18, no. 1 (July 29, 2022): 195–218. http://dx.doi.org/10.21315/aamjaf2022.18.1.8.

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This study investigates the link between systemic risk in the South African insurance sector real economic activity in South Africa. To this end, we use six systemic risk measures, the Conditional Value at Risk (CoVaR), the Marginal Conditional Value at Risk (ΔCoVaR), the Comovement and Interconnectedness of the South African insurance sector (Eigen), the Dynamic Mixture Copula Marginal Expected Shortfall (DMC-MES), the Average Conditional Volatility (Ave-vol), and the South African Volatility Index (SAVI). We first evaluate the significance of each measure by assessing its ability to forecast future economic downturns in South Africa. We find that only two systemic risk measures possess the ability to predict future economic downturns in South Africa. We then use principal component quantile regression analysis to aggregate these measures into a composite stress index of systemic risk for the South African insurance sector and assess the ability of the proposed index to predict future economic downturns in South Africa. Our results reveal that the proposed index is a good predictor of future economic downturns in South Africa. Thus, our results suggest that regulators and risk managers must develop an analysis of systemic risk in the insurance sector with particular attention to its effects on real economic activity. In addition, our index can potentially be used as an instrument to monitor and mitigate systemic risk in the insurance sector in order to ensure the stability of the financial system and the economy in South Africa.

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Brunnermeier, Markus K., and Patrick Cheridito. "Measuring and Allocating Systemic Risk." Risks 7, no. 2 (April 26, 2019): 46. http://dx.doi.org/10.3390/risks7020046.

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In this paper, we develop a framework for measuring, allocating and managing systemic risk. SystRisk, our measure of total systemic risk, captures the a priori cost to society for providing tail-risk insurance to the financial system. Our allocation principle distributes the total systemic risk among individual institutions according to their size-shifted marginal contributions. To describe economic shocks and systemic feedback effects, we propose a reduced form stochastic model that can be calibrated to historical data. We also discuss systemic risk limits, systemic risk charges and a cap and trade system for systemic risk.

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Yun, Tae-Sub, Deokjong Jeong, and Sunyoung Park. "“Too central to fail” systemic risk measure using PageRank algorithm." Journal of Economic Behavior & Organization 162 (June 2019): 251–72. http://dx.doi.org/10.1016/j.jebo.2018.12.021.

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Kleinow, Jacob, and Tobias Nell. "Determinants of systemically important banks: the case of Europe." Journal of Financial Economic Policy 7, no. 4 (November 2, 2015): 446–76. http://dx.doi.org/10.1108/jfep-07-2015-0042.

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Purpose – This paper aims to investigate the drivers of systemic risk and contagion among European banks from 2007 to 2012. The authors explain why some banks are expected to contribute more to systemic events in the European financial system than others by analysing the tail co-movement of banks’ security prices. Design/methodology/approach – First, the authors derive a systemic risk measure from the concepts of marginal expected shortfall and conditional value at risk analysing tail co-movements of daily bank stock returns. The authors then run panel regressions for the systemic risk measure using idiosyncratic bank characteristics and a set of country and policy control variables. Findings – The results comprise highly significant drivers of systemic risk in the European banking sector with important implications for research and banking regulation. Using a set of panel regressions, the authors identify bank size, asset and income structure, loss and liquidity coverage, profitability and several macroeconomic conditions as drivers of systemic risk. Research limitations/implications – Analysing the tail co-movement of security prices excludes a number of “smaller” institutions without publicly listed securities. The other shortfall is that we do not assess the systemic impact of non-bank financial institutions. Practical implications – Regulators have to consider a broad variety of indicators for assessing systemic risks. Existing microprudential-oriented rules are less effective, and policymakers may consider new measures like asset diversification to mitigate systemic risks in the banking system. Originality/value – The authors contribute to existing empirical analyses in three ways. First, they propose a method to identify systemically important banks (SIBs). Second, they develop two measures to assess their potential negative impact on the system. Third, they contribute to the closing of the research gaps by analysing which macroprudential regulations for SIBs are most effective without hampering free market forces.

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Ding, Rui, and Stan Uryasev. "CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions." Journal of Risk and Financial Management 13, no. 11 (November 3, 2020): 270. http://dx.doi.org/10.3390/jrfm13110270.

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Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web.

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Rivera-Escobar, Orlando, John Willmer Escobar, and Diego Fernando Manotas. "Measurement of Systemic Risk in the Colombian Banking Sector." Risks 10, no. 1 (January 13, 2022): 22. http://dx.doi.org/10.3390/risks10010022.

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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.

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Fissler, Tobias, Jana Hlavinová, and Birgit Rudloff. "Elicitability and identifiability of set-valued measures of systemic risk." Finance and Stochastics 25, no. 1 (December 30, 2020): 133–65. http://dx.doi.org/10.1007/s00780-020-00446-z.

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AbstractIdentification and scoring functions are statistical tools to assess the calibration of risk measure estimates and to compare their performance with other estimates, e.g. in backtesting. A risk measure is called identifiable (elicitable) if it admits a strict identification function (strictly consistent scoring function). We consider measures of systemic risk introduced in Feinstein et al. (SIAM J. Financial Math. 8:672–708, 2017). Since these are set-valued, we work within the theoretical framework of Fissler et al. (preprint, available online at arXiv:1910.07912v2, 2020) for forecast evaluation of set-valued functionals. We construct oriented selective identification functions, which induce a mixture representation of (strictly) consistent scoring functions. Their applicability is demonstrated with a comprehensive simulation study.

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Gehrig, Thomas, and Maria Chiara Iannino. "Capital regulation and systemic risk in the insurance sector." Journal of Financial Economic Policy 10, no. 2 (May 8, 2018): 237–63. http://dx.doi.org/10.1108/jfep-11-2017-0105.

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Purpose This paper aims to analyze systemic risk in and the effect of capital regulation on the European insurance sector. In particular, the evolution of an exposure measure (SRISK) and a contribution measure (Delta CoVaR) are analyzed from 1985 to 2016. Design/methodology/approach With the help of multivariate regressions, the main drivers of systemic risk are identified. Findings The paper finds an increasing degree of interconnectedness between banks and insurance that correlates with systemic risk exposure. Interconnectedness peaks during periods of crisis but has a long-term influence also during normal times. Moreover, the paper finds that the insurance sector was greatly affected by spillovers from the process of capital regulation in banking. While European insurance companies initially at the start of the Basel process of capital regulation were well capitalized according to the SRISK measure, they started to become capital deficient after the implementation of the model-based approach in banking with increasing speed thereafter. Practical implications These findings are highly relevant for the ongoing global process of capital regulation in the insurance sector and potential reforms of Solvency II. Systemic risk is a leading threat to the stability of the global financial system and keeping it under control is a main challenge for policymakers and supervisors. Originality/value This paper provides novel tools for supervisors to monitor risk exposures in the insurance sector while taking into account systemic feedback from the financial system and the banking sector in particular. These tools also allow an evidence-based policy evaluation of regulatory measures such as Solvency II.

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Wang, Lu. "Bank Rating Gaps as Proxies for Systemic Risk." International Journal of Accounting and Financial Reporting 12, no. 2 (June 6, 2022): 1. http://dx.doi.org/10.5296/ijafr.v12i2.19678.

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Banks receive two types of ratings from major rating agencies: an “all-in” and a “stand-alone” rating. This paper investigates whether rating gaps between all-in ratings and stand-alone ratings could serve as a useful measure for the systemic risk of banks. Using US data from 1994 to 2007, the link between the rating gaps and a quantitative systemic risk measure, Co-independent Value at Risk (CoVar), is examined. The conclusion is that rating gaps are good proxies for systemic risk of large banks.

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Eratalay, Mustafa Hakan, and Ariana Paola Cortés Ángel. "The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms." Journal of Risk and Financial Management 15, no. 4 (March 28, 2022): 153. http://dx.doi.org/10.3390/jrfm15040153.

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There are diverging results in the literature on whether engaging in ESG related activities increases or decreases the financial and systemic risks of firms. In this study, we explore whether maintaining higher ESG ratings reduces the systemic risks of firms in a stock market context. For this purpose we analyse the systemic risk indicators of the constituent stocks of S&P Europe 350 for the period of January 2016–September 2020, which also partly covers the COVID-19 period. We apply a VAR-MGARCH model to extract the volatilities and correlations of the return shocks of these stocks. Then, we obtain the systemic risk indicators by applying a principle components approach to the estimated volatilities and correlations. Our focus is on the impact of ESG ratings on systemic risk indicators, while we consider network centralities, volatilities and financial performance ratios as control variables. We use fixed effects and OLS methods for our regressions. Our results indicate that (1) the volatility of a stock’s returns and its centrality measures in the stock network are the main sources contributing to the systemic risk measure, (2) firms with higher ESG ratings face up to 7.3% less systemic risk contribution and exposure compared to firms with lower ESG ratings and (3) COVID-19 augmented the partial effects of volatility, centrality measures and some financial performance ratios. When considering only the COVID-19 period, we find that social and governance factors have statistically significant impacts on systemic risk.

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Liu, Yuhao, Petar M. Djurić, Young Shin Kim, Svetlozar T. Rachev, and James Glimm. "Systemic Risk Modeling with Lévy Copulas." Journal of Risk and Financial Management 14, no. 6 (June 5, 2021): 251. http://dx.doi.org/10.3390/jrfm14060251.

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We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula based on the Lévy process. We also propose a novel backtesting method for CoVaR by a joint distribution correction. We test the proposed NTS model on the daily S&P 500 index and Dow Jones index with in-sample and out-of-sample tests. The results show that the NTS copula outperforms traditional copulas in the accuracy of both tail dependence and marginal processes modeling.

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Dziwok, Ewa, and Marta A. Karaś. "Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets." Risks 9, no. 7 (July 1, 2021): 124. http://dx.doi.org/10.3390/risks9070124.

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The paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson methodology in which we utilize the curve-fitting error as an indicator of financial system illiquidity. We empirically apply our method to a set of 10 divergent Central and Eastern Europe countries—Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia—in the period of 2006–2020. The results show three periods of increased risk in the sample period: the global financial crisis, the European public debt crisis, and the COVID-19 pandemic. They also allow us to identify three divergent sets of countries with different systemic liquidity risk characteristics. The analysis also illustrates the impact of the introduction of the euro on systemic illiquidity risk. The proposed methodology may be of consequence for financial system regulators and macroprudential bodies: it allows for contemporaneous monitoring of discussed risk at a minimal cost using well-known models and easily accessible data.

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Engle, Robert F., and Tianyue Ruan. "Measuring the probability of a financial crisis." Proceedings of the National Academy of Sciences 116, no. 37 (August 27, 2019): 18341–46. http://dx.doi.org/10.1073/pnas.1903879116.

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When financial firms are undercapitalized, they are vulnerable to external shocks. The natural response to such vulnerability is to reduce leverage, and this can endogenously start a financial crisis. Excessive credit growth, the main cause of financial crises, is reflected in the undercapitalization of the financial sector. Market-based measures of systemic risk such as SRISK, which stands for systemic risk, enable monitoring how such weakness emerges and progresses in real time. In this paper, we develop quantitative estimates of the level of systemic risk in the financial sector that precipitates a financial crisis. Common approaches to reduce leverage correspond to specific scaling of systemic risk measures. In an econometric framework that recognizes financial crises represent left tail events for the economy, we estimate the relationship between SRISK and the financial crisis severity for 23 developed countries. We develop a probability of crisis measure and an SRISK capacity measure based on our estimates. Our analysis highlights the important global externality whereby the risk of a crisis in one country is strongly influenced by the undercapitalization of the rest of the world.

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Scott, Cathy. "Practical Applications of Principal Components as a Measure of Systemic Risk." Practical Applications 1, no. 2 (October 31, 2013): 1.16–3. http://dx.doi.org/10.3905/pa.2013.1.2.016.

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Hanif, Hasan, Muhammad Naveed, and Mobeen Ur Rehman. "Extending the forward systemic risk measure: Do sector level variables matter?" Cogent Business & Management 7, no. 1 (January 1, 2020): 1809944. http://dx.doi.org/10.1080/23311975.2020.1809944.

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Lê Hải, Trung, Hằng Đỗ Thu, and Huyền Tạ Thanh. "Systemic Risk of The Vietnamese Commercial Banks: A New Approach Using CoVaR and SRISK Measurements." JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES 33, no. 8 (August 1, 2022): 102–20. http://dx.doi.org/10.24311/jabes/2022.33.08.07.

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One of the main lessons of the global financial crisis in 2007–2009 is keeping individual financial institutions sound would be not enough for the stability of the financial system, given the increasing complexity of the banking activities and systemic risk. In this paper, the authors measure the systemic risk of 12 listed Vietnamese commercial banks from April 2008 to June 2021 based on two market risk measures, namely the CoVaR and SRISK. The use of market price in the estimation of bank systemic risk results in timely and forward-looking risk measures, which is particularly important during volatile periods. The authors also provide several policy discussions on the measurement and supervision of systemic risk in the Vietnamese banking sector.

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Strobl, Sascha. "Stand-alone vs systemic risk-taking of financial institutions." Journal of Risk Finance 17, no. 4 (August 15, 2016): 374–89. http://dx.doi.org/10.1108/jrf-05-2016-0064.

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Purpose This study investigates the risk-taking behavior of financial institutions in the USA. Specifically, differences between taking risks that affect primarily the shareholders of the institution and risks contributing to the overall systemic risk of the financial sector are examined. Additionally, differences between risk-taking before, during and after the financial crisis of 2007/2008 are examined. Design/methodology/approach To analyze the determinants of stand-alone and systemic risk, a generalized linear model including size, governance, charter value, business cycle, competition and control variables is estimated. Furthermore, Granger causality tests are conducted. Findings The results show that systemic risk has a positive effect on valuation and that corporate governance has no significant effect on risk-taking. The influence of competition is conditional on the state of the economy and the risk measure used. Systemic risk Granger-causes idiosyncratic risk but not vice versa. Research limitations/implications The major limitations of this study are related to the analyzed subset of large financial institutions and important risk-culture variables being omitted. Practical implications The broad policy implication of this paper is that systemic risk cannot be lowered by market discipline due to the moral hazard problem. Therefore, regulatory measures are necessary to ensure that individual financial institutions are not endangering the financial system. Originality/value This study contributes to the empirical literature on bank risk-taking in several ways. First, the characteristics of systemic risk and idiosyncratic risk are jointly analyzed. Second, the direction of causality of these two risk measures is examined. Moreover, this paper contributes to the discussion of the effect of competition on risk-taking.

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Karkowska, Renata. "What Kind Of Systemic Risks Do We Face In The European Banking Sector? The Approach Of CoVaR Measure." Folia Oeconomica Stetinensia 14, no. 2 (December 1, 2014): 114–24. http://dx.doi.org/10.1515/foli-2015-0017.

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Abstract We measure a systemic risk faced by European banking sectors using the CoVaR measure. We propose the conditional value-at-risk for measuring a spillover risk which demonstrates the bilateral relation between the tail risks of two financial institutions. The aim of the study is to estimate the contribution systemic risk of the bank i in the analyzed banking sector of a country in conditions of its insolvency. The study included commercial banks from 8 emerging markets from Europe, which gave a total of 40 banks, traded on the public market, which provided a market valuation of the bank’s capital. The conclusions are that the CoVaR seems to be a better measure for systemic risk in the banking sector than the VaR, which is more individual. And banks in developing countries in Europe do not provide significant risk for the banking sector as a whole. But it must be taken into account that some individuals that may find objectionable. Our results hence tend to a practical use of the CoVaR for supervisory purposes.

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Dastkhan, Hossein. "What are the most effective and vulnerable firms in financial crisis? A network representation of CoVaR in an emerging market." International Journal of Financial Engineering 06, no. 01 (March 2019): 1950007. http://dx.doi.org/10.1142/s2424786319500075.

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In this paper, financial networks are applied to develop new measures of systemic risk. Using the CoVaR as a well-defined systemic risk measure, risk spillovers among different firms are estimated in Tehran Stock Exchange. Networks of systemic risk exposures are represented across time and two indices of vulnerability and systemic importance are introduced to define the most effective and vulnerable firms. The results show that the proposed network-based indices have a good performance to identify the vulnerable and systemically important firms. The results also show that when the market is in periods of financial downturn/crisis, the network interconnectedness was maximized.

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Fresno, Musa, and Dewi Hanggraeni. "Impact of diversification on systemic risk of conventional banks listed on the Indonesia Stock Exchange." Banks and Bank Systems 15, no. 4 (December 9, 2020): 80–87. http://dx.doi.org/10.21511/bbs.15(4).2020.07.

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It is believed that bank diversification increases financial stability. However, several theories argue that diversification can trigger the spread of failure because of the increased interconnectivity between institutions. The aim of this study is to determine the impact of diversification on the systemic risk of banks. The sample of the study consists of 21 conventional banks listed on the Indonesia Stock Exchange from 2009 to 2018. The study uses firm-year fixed effect panel regression and an instrumental variable approach to examine how firm-specific variables determine the level of systemic risk. Diversification is measured by bank assets, funding, and revenue diversification. To measure the systemic risk, the Conditional Value-at-Risk (ΔCoVaR) methodology is applied. The results show that an increase in funding diversification leads to a decrease in ΔCoVaR, indicating that funding diversification exacerbates the level of systemic risk, whereas asset diversification and revenue diversification do not have significant effects on the level of systemic risk. The empirical findings suggest that the interconnectivity between banks should be reduced by limiting the diversification of funding in the banks to minimize their systemic risks.

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Raz, Arisyi Fariza. "Risk and capital in Indonesian large banks." Journal of Financial Economic Policy 10, no. 1 (April 3, 2018): 165–84. http://dx.doi.org/10.1108/jfep-06-2017-0055.

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Purpose The purpose of this paper is to examine the behavior of banking risk in the emerging economies, particularly Indonesia and contribute to the discussion on the existing policy debate regarding the impact of capital on bank risk. Design/methodology/approach This study investigates the relationship between bank risk and capital using data on 15 Indonesian large banks between 2008 and 2015, using z-score and Delta-CoVaR to measure both idiosyncratic and systemic risks. Findings The empirical investigation suggests that capital has a negative and significant relationship with these risk measures. The authors also find that higher systemic risk encourages banks to increase their capital. However, similar evidence is not found in the idiosyncratic risk models. Finally, the role of capital in reducing risk is considered robust only during the normal periods, as banks may increase their assets risk during times of financial distress. Originality/value Systemic risk (CoVaR) is used to represent bank risk. This study focuses on the Indonesian banking sector (capture institutional arrangements and regulatory environment). It covers the period of 2008 GFC and post-crisis period.

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Sheu, Her-Jiun, and Chien-Ling Cheng. "SYSTEMIC RISK IN TAIWAN STOCK MARKET." Journal of Business Economics and Management 13, no. 5 (October 4, 2012): 895–914. http://dx.doi.org/10.3846/16111699.2011.620168.

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Recent financial crises resulted from systemic risk caused by idiosyncratic distress. In this research, taking Taiwan stock market as an example and collecting data from 2000 to 2010 which contained the 2001 dot-com bubble and the 2007–2009 financial crisis, we adopt the CoVaR model to empirically explore the impact of sector-specific idiosyncratic risk on the systemic risk of the system and attempt to investigate the links between financial crises, systemic risk and the idiosyncratic risk of a sector-specific anomaly. The result showed sector-specific marginal CoVaR, i.e., ΔCoVaR, perfectly explained Taiwan stock market disturbance during the 2001 dot-com bubble and 2007–2008 financial crisis. Thus, by identifying the larger ΔCoVaR sectors, i.e. the systemic importance sectors, and by exploring the risk indicators, independent variables, of these systemic importance sectors, investors could practically employ the sector-specific ΔCoVaR measure to deepen the systemic risk scrutiny from a macro into a micro prudential perspective.

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Karkowska, Renata. "Measuring Systemic Risk in the Polish Banking System by Means of the Risk-Based Balance Sheets Method." Folia Oeconomica Stetinensia 12, no. 2 (December 1, 2012): 7–18. http://dx.doi.org/10.2478/v10031-012-0035-4.

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Abstract The complex connections, spillovers and feedbacks of the global financial crisis remind how important it is to improve the analysis of risk modeling. This article introduces a new framework for mitigating systemic risk by using a risk-adjusted balance sheet approach. In this regard, the analysis of individual banks in Poland shows potential risk which could threaten all the financial system. Traditional banking models do not adequately measure risk position of financial institutions and cannot be used to understand risk within and between balance sheets in the financial sector. A fundamental subject is that accounting balance sheets do not indicate risk exposures, which are forward-looking. The paper concludes new directions for measuring systemic risk by using Merton’s model. It shows how risk management tools can be applied in new ways to measure and analyze systemic risk in the Polish banking system.

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Jian, Zhihong, and Xupei Li. "Skewness-based market integration: A systemic risk measure across international equity markets." International Review of Financial Analysis 74 (March 2021): 101664. http://dx.doi.org/10.1016/j.irfa.2021.101664.

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Liu, Ruicheng, and Chi Seng Pun. "Machine-Learning-enhanced systemic risk measure: A Two-Step supervised learning approach." Journal of Banking & Finance 136 (March 2022): 106416. http://dx.doi.org/10.1016/j.jbankfin.2022.106416.

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Garratt, Rodney, Lewis Webber, and Matthew Willison. "Using Shapley’s asymmetric power index to measure banks’ contributions to systemic risk." Journal of Network Theory in Finance 2, no. 2 (June 2016): 35–55. http://dx.doi.org/10.21314/jntf.2016.018.

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35

Lin, Junshan. "Using Weighted Shapley Values to Measure the Systemic Risk of Interconnected Banks." Pacific Economic Review 23, no. 2 (April 6, 2016): 244–70. http://dx.doi.org/10.1111/1468-0106.12155.

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Foglia, Matteo, and Eliana Angelini. "The Time-Spatial Dimension of Eurozone Banking Systemic Risk." Risks 7, no. 3 (July 6, 2019): 75. http://dx.doi.org/10.3390/risks7030075.

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In this paper, we measure the systemic risk with a novel methodology, based on a “spatial-temporal” approach. We propose a new bank systemic risk measure to consider the two components of systemic risk: cross-sectional and time dimension. The aim is to highlight the “time-space dynamics” of contagion, i.e., if the CDS spread of bank i depends on the CDS spread of other banks. To do this, we use an advanced spatial econometrics design with a time-varying spatial dependence that can be interpreted as an index of the degree of cross-sectional spillovers. The findings highlight that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present and persistent. Moreover, we analyse the role of the European Central Bank in managing contagion risk. We find that monetary policy has been effective in reducing systemic risk. However, the results show that systemic risk does not imply a policy intervention, highlighting how financial stability policy is not yet an objective.

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Ye, Xingxing, and Raphael Douady. "Systemic Risk Indicators Based on Nonlinear PolyModel." Journal of Risk and Financial Management 12, no. 1 (December 20, 2018): 2. http://dx.doi.org/10.3390/jrfm12010002.

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The global financial market has become extremely interconnected as it demonstrates strong nonlinear contagion in times of crisis. As a result, it is necessary to measure financial systemic risk in a comprehensive and nonlinear approach. By establishing a large set of risk factors as the main bones of the financial market network and applying nonlinear factor analysis in the form of so-called PolyModel, this paper proposes two systemic risk indicators that can prognosticate the advent and trace the development of financial crises. Through financial network analysis, theoretical simulation, empirical data analysis and final validation, we argue that the indicators suggested in this paper are proved to be very effective in forecasting and tracing the financial crises from 1998 to 2017. The economic benefit of the indicator is evidenced by the enhancement of a protective put/covered call strategy on major stock markets.

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Huang, Xin. "Persistence of Bank Credit Default Swap Spreads." Risks 7, no. 3 (August 26, 2019): 90. http://dx.doi.org/10.3390/risks7030090.

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Credit default swap (CDS) spreads measure the default risk of the reference entity and have been frequently used in recent empirical papers. To provide a rigorous econometrics foundation for empirical CDS analysis, this paper applies the augmented Dickey–Fuller, Phillips–Perron, Kwiatkowski–Phillips–Schmidt–Shin, and Ng–Perron tests to study the unit root property of CDS spreads, and it uses the Phillips–Ouliaris–Hansen tests to determine whether they are cointegrated. The empirical sample consists of daily CDS spreads of the six large U.S. banks from 2001 to 2018. The main findings are that it is log, not raw, CDS spreads that are unit root processes, and that log CDS spreads are cointegrated. These findings imply that, even though the risks of individual banks may deviate from each other in the short run, there is a long-run relation that ties them together. As these CDS spreads are an important input for financial systemic risk, there are at least two policy implications. First, in monitoring systemic risk, policymakers should focus on long-run trends rather than short-run fluctuations of CDS spreads. Second, in controlling systemic risk, policy measures that reduce the long-run risks of individual banks, such as stress testing and capital buffers, are helpful in mitigating overall systemic risk.

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Liu, Jianxu, Quanrui Song, Yang Qi, Sanzidur Rahman, and Songsak Sriboonchitta. "Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions." Sustainability 12, no. 10 (May 14, 2020): 4000. http://dx.doi.org/10.3390/su12104000.

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The global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct a trade decision model for investors and financial institutions to assist them in forecasting risk and potential returns based on the results of the analysis of systemic risk. The factor copula-generalized autoregressive conditional heteroskedasticity (GARCH) models and component expected shortfall (CES) were combined for the first time in this study to measure systemic risk and the contribution of individual countries to global systemic risk in global financial markets. The use of factor copula-based models enabled the estimation of joint models in stages, thereby considerably reducing computational burden. A high-dimensional dataset of daily stock market indices of 43 countries covering the period 2003 to 2019 was used to represent global financial markets. The CES portfolios developed in this study, based on the forecasting results of systemic risk, not only allow spreading of systemic risk but may also enable investors and financial institutions to make profits. The main policy implication of our study is that forecasting systemic risk of global financial markets and developing portfolios can provide valuable insights for financial institutions and policy makers to diversify portfolios and spread risk for future investments and trade.

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Giudici, Paolo, and Laura Parisi. "Bail-In or Bail-Out? Correlation Networks to Measure the Systemic Implications of Bank Resolution." Risks 7, no. 1 (January 5, 2019): 3. http://dx.doi.org/10.3390/risks7010003.

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We propose a statistical measure, based on correlation networks, to evaluate the systemic risk that could arise from the resolution of a failing or likely-to-fail financial institution, under three alternative scenarios: liquidation, private recapitalization, or bail-in. The measure enhances the observed CDS spreads with a risk premium that derives from contagion effects across financial institutions. The empirical findings reveal that the recapitalization of a distressed bank performed by the other banks in the system and the bail-in resolution minimize the potential losses for the banking sector with respect to the liquidation scenario, thus posing limited systemic risks. A closer comparison between the private intervention recapitalization and the bail-in tool shows that the latter slightly reduces contagion effects with respect to the private intervention scenario.

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Saidane, Dhafer, Babacar Sène, and Kouamé Désiré Kanga. "Pan-African banks, banking interconnectivity: A new systemic risk measure in the WAEMU." Journal of International Financial Markets, Institutions and Money 74 (September 2021): 101405. http://dx.doi.org/10.1016/j.intfin.2021.101405.

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Tabak, Benjamin M., Marcelo Takami, Jadson M. C. Rocha, Daniel O. Cajueiro, and Sergio R. S. Souza. "Directed clustering coefficient as a measure of systemic risk in complex banking networks." Physica A: Statistical Mechanics and its Applications 394 (January 2014): 211–16. http://dx.doi.org/10.1016/j.physa.2013.09.010.

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43

Jobst, Andreas A. "Multivariate dependence of implied volatilities from equity options as measure of systemic risk." International Review of Financial Analysis 28 (June 2013): 112–29. http://dx.doi.org/10.1016/j.irfa.2013.01.005.

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Gurgul, Henryk, and Robert Syrek. "The dependencies of subindexes of Stoxx 600 during the Covid-19 pandemic." Managerial Economics 22, no. 2 (July 3, 2022): 73. http://dx.doi.org/10.7494/manage.2021.22.2.73.

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In this index study, the relationships between Stoxx Europe 600 and sector indices are analyzed. This research uses DCoVar and MES as analytical tools developed as a measure of systemic risk and applied to financial institutions, to sectoral subindexes. For the sake of systemic risk assessment we calculate the dynamic correlation model with bivariate t copula distribution. We focus on the impact of sectors on the market. Despite the similarity between the time series plots of both measures, with maximum values on similar days, the compatibility of daily rankings, measured as a percentage of concordant pairs, is equal to about 50%. The rankings of the most and least risky sectors are different and depend on the choice of measure, but in the case of both we observe poor stability. When sectors are ranked in terms of the highest and lowest mean values at specific intervals (designated by the structural break estimation method, which surpisingly detects very similar dates of structural changes) we draw the same conclusions. For both measures we note huge percentage changes in mean values of risk, especially in the period from February 24, 2020 till August 20, 2020 with respect to the previous period. The percentage changes for both intervals indicate the same most risky sectors, but the indications of both measures are not consistent.

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Khan, Mohammed Arshad, Preeti Roy, Saif Siddiqui, and Abdullah A. Alakkas. "Systemic Risk Assessment: Aggregated and Disaggregated Analysis on Selected Indian Banks." Complexity 2021 (July 8, 2021): 1–14. http://dx.doi.org/10.1155/2021/8360778.

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Exposure of the banking system to the Global Financial Crisis attracted attention to the study of riskiness and spillover. This paper studies the pattern of systemic risk and size effect in the Indian banking sector. Based on market capitalization, three public sector banks and three from the private sector were taken. Data are taken from the year 2007 to 2020. The analysis is done through quantile- CoVaR (Conditional Value at Risk) and TENET (Tail-Event-Driven Network) measure. State variables like Indian market volatility and global risk measures negatively influence the Indian banks’ returns. Liquidity risk is a crucial aspect of private banks. Public banks experience public confidence even in the distress period. Large banks like HDFC and SBI bank offer the highest degree of systemic risk contribution. The role of private banks in transmitting systemic risk has been intensifying since 2015. Small-sized banks like PNB and BOB have become significant receivers and transmitters of risk.

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46

Adrian, Tobias, and Markus K. Brunnermeier. "CoVaR." American Economic Review 106, no. 7 (July 1, 2016): 1705–41. http://dx.doi.org/10.1257/aer.20120555.

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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, forward-looking 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. (JEL C58, E32, G01, G12, G17, G20, G32)

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Koike, Takaaki, and Marius Hofert. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations." Risks 8, no. 1 (January 15, 2020): 6. http://dx.doi.org/10.3390/risks8010006.

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In this paper, we propose a novel framework for estimating systemic risk measures and risk allocations based on Markov Chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution given the so-called crisis event. By considering a crisis event as an intersection of linear constraints, this class of allocations covers, for example, conditional Value-at-Risk (CoVaR), conditional expected shortfall (CoES), VaR contributions, and range VaR (RVaR) contributions as special cases. For this class of allocations, analytical calculations are rarely available, and numerical computations based on Monte Carlo (MC) methods often provide inefficient estimates due to the rare-event character of the crisis events. We propose an MCMC estimator constructed from a sample path of a Markov chain whose stationary distribution is the conditional distribution given the crisis event. Efficient constructions of Markov chains, such as the Hamiltonian Monte Carlo and Gibbs sampler, are suggested and studied depending on the crisis event and the underlying loss distribution. The efficiency of the MCMC estimators is demonstrated in a series of numerical experiments.

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48

Cipollini, Fabrizio, Alessandro Giannozzi, Fiammetta Menchetti, and Oliviero Roggi. "Financial Companies’ Failures: Early Warning Information from Systematic and Systemic Risk Measures." Quarterly Journal of Finance 08, no. 04 (September 24, 2018): 1840007. http://dx.doi.org/10.1142/s2010139218400074.

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Following the 2007–2008 financial crisis, advanced risk measures were proposed with the specific aim of quantifying systemic risk, since the existing systematic (market) risk measures seemed inadequate to signal the collapse of an entire financial system. The paper aims at comparing the systemic risk measures and the earlier market risk measures regarding their predictive ability toward the failure of financial companies. Focusing on the 2007–2008 period and considering 28 large US financial companies (among which nine defaulted in the period), four systematic and four systemic risk measures are used to rank the companies according to their risk and to estimate their relationship with the company’s failure through a survival Cox model. We found that the two groups of risk measures achieve similar scores in the ranking exercise, and that both show a significant effect on the time-to-default of the financial institutions. This last result appears even stronger when the Cox model uses, as covariates, the risk measures evaluated one, three and six months before. Considering this last case, the most predictive risk measures about the default risk of financial institutions were the Expected Shortfall, the Value-at-Risk, the [Formula: see text] and the [Formula: see text]. We contribute to the literature in two ways. We provide a way to compare risk measures based on their predictive ability toward a situation, the company’s failure, which is the most catastrophic event for a company. The survival model approach allows to map each risk measure in terms of probability of default over a given time horizon. We note, finally, that although focused on the Great Recession in US, the analysis can be applied to different periods and countries.

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Gao, Ming Shi, Yong Wu, and Chen Zhao. "Research on Models of Technology Innovation Systemic Risk and Early Warning of Materials and Manufacturing in SMEs." Advanced Materials Research 655-657 (January 2013): 2344–47. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.2344.

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The prime task for limiting SMEs’ technology innovation systemic risks in materials and manufacturing is to exactly measure and warn the systemic risks.Before the SMEs’ technology innovation crisis in materials and manufacturing ,most of the research focused on how the macro economy shocked the l system,while few of them were interested in the correlation between and among different sectors and markets.This article tried to analyze the latest development of systemic risk measuring method based on the data the models needed,especially for the correlation measurement.

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Ivanov, Katerina, and Julia Jiang. "Does securitization escalate banks’ sensitivity to systemic risk?" Journal of Risk Finance 21, no. 1 (January 27, 2020): 1–22. http://dx.doi.org/10.1108/jrf-12-2018-0184.

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Purpose The purpose of this paper is to test empirically the impact of asset securitization and sale activities as well as the holdings of sub-prime related securitized products on the US bank holding companies’ (BHC) exposure to systemic risk. Design/methodology/approach This paper adopts a robust econometric method to estimate the conditional value-at-risk as a measure of BHCs' institutional sensitivity to market crushes. Using the data over the period of 2004-2016, the study also uses OLS with robust standard errors and panel estimation with random effects as two alternative estimation techniques to assess the impact of securitization activities on the sensitivity of BHCs to systemic risk. Findings Residential mortgage and other forms of securitization activities are positively related to an increase in the US BHCs' sensitivity to systemic distress. The significant cross effects of both securitized loans and holdings of securitized products play a crucial role in determining risks in financial sector. Originality/value This study contributes to the empirical literature on the effects of securitization on BHCs' risk exposures in several ways. First, the paper considers the complexity of the bank's risk profile; it focuses on BHCs' individual sensitivity to systemic distress and its dependence on the size of securitization and assets sold activities considering both supply and demand sides of securitization. Second, the time horizon under investigation sheds a light on the relationship between securitization and banks' risk exposures including the pre-crisis, crisis and post-crisis periods.

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Journal articles on the topic 'Systemic risk measure'

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