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Relevant bibliographies by topics / Systemic risk

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Academic literature on the topic 'Systemic risk'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Systemic risk"

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Borzutzky, Daniel. "Systemic Risk." Critical Quarterly 62, no. 1 (2020): 29. http://dx.doi.org/10.1111/criq.12524.

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Kaal, Wulf. "The Systematic Risk of Private Funds After the Dodd-Frank Act." Michigan Business & Entrepreneurial Law Review, no. 4.2 (2015): 163. http://dx.doi.org/10.36639/mbelr.4.2.systemic.

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The Financial Stability Oversight Council (FSOC) was created under the Dodd-Frank Act with the primary mandate of guarding against systemic risk and correcting perceived regulatory weaknesses that may have contributed to the financial crisis of 2008-2009. The Securities and Exchange Commission (SEC) collects data pertaining to private fund advisers in order to facilitate FSOC’s assessment of non-bank financial institutions’ potential systemic risks. Evidence that the SEC’s data collection encounters accuracy and consistency problems might hamper FSOC’s ability to evaluate the systemic risk of private fund advisers. The author shows that while the SEC’s data plays a crucial role in all stages of FSOC’s systemic risk assessment of private fund advisers, FSOC relies most heavily on some of the most problematic disclosure items collected by the SEC.

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Huang, Xin, Hao Zhou, and Haibin Zhu. "Systemic Risk Contributions." Finance and Economics Discussion Series 2011, no. 08 (2011): 1–39. http://dx.doi.org/10.17016/feds.2011.08.

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Wood, William C. "Teaching Systemic Risk." International Journal of Risk and Contingency Management 4, no. 4 (2015): 49–52. http://dx.doi.org/10.4018/ijrcm.2015100104.

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This article shows how an in-class simulation can be used to teach the joint failure probability of statistically independent failures, and then to teach the more complex problem of system or “common-mode” failure. The technique has many potential applications, but here focuses on bank failures as a readily accessible application. This teaching simulation has been successfully presented to diverse audiences since 2011. The original audience consisted of high school, community college and university instructors and the case has since been taught to additional continuing education groups and 400-level Economics students.

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Mitroff, Ian I., and Abraham Silvers. "Assessing Systemic Risk." International Journal of Risk and Contingency Management 5, no. 2 (2016): 66–75. http://dx.doi.org/10.4018/ijrcm.2016040104.

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Far too many applications of Risk Analysis (RA) and Risk Management (RM) treat risks as though they are distinct and independent. Thus, risks are largely treated as though they can be evaluated and mitigated independently of one another. This paper takes a fundamentally different approach. The basic argument is that there are no such things as independent and separate risks. All risks are part of a larger system of interrelated issues, problems, and risks. Each risk is not only connected to all of the other risks that are parts of the system, but if only in part, each risk is responsible, in causing and triggering of all of the other risks. Using recent findings in the probability of implication (Mitroff and Silvers, 2013), a simple mathematical treatment of the interconnectedness of risks is developed. The treatment is capable of being expanded indefinitely to include more complex situations. Finally, the mathematical treatment shows how risks and crises are interrelated.

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Šimáček, Milan. "Systemic Risk Indicator." Český finanční a účetní časopis 2014, no. 4 (2014): 31–42. http://dx.doi.org/10.18267/j.cfuc.421.

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Guerra, Solange Maria, Thiago Christiano Silva, Benjamin Miranda Tabak, Rodrigo Andrés de Souza Penaloza, and Rodrigo César de Castro Miranda. "Systemic risk measures." Physica A: Statistical Mechanics and its Applications 442 (January 2016): 329–42. http://dx.doi.org/10.1016/j.physa.2015.09.013.

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Huang, Xin, Hao Zhou, and Haibin Zhu. "Systemic Risk Contributions." Journal of Financial Services Research 42, no. 1-2 (2011): 55–83. http://dx.doi.org/10.1007/s10693-011-0117-8.

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Acharya, Viral V., Lasse H. Pedersen, Thomas Philippon, and Matthew Richardson. "Measuring Systemic Risk." Review of Financial Studies 30, no. 1 (2016): 2–47. http://dx.doi.org/10.1093/rfs/hhw088.

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Kaspereit, Thomas, Kerstin Lopatta, Suren Pakhchanyan, and Jörg Prokop. "Systemic operational risk." Journal of Risk Finance 18, no. 3 (2017): 252–67. http://dx.doi.org/10.1108/jrf-11-2016-0141.

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Purpose The aim of this paper is to study the information content of operational loss events occurring at European financial institutions with respect to the announcing bank’s industry rivals from an equity investor’s perspective. Design/methodology/approach The authors conduct an event study to identify spillover effects of operational loss events using the Carhart (1997) four-factor model as a benchmark model. In addition, they conduct multiple regression analyses to investigate the extent to which firm-specific factors or the market environment affect abnormal returns. Findings They observe significant negative abnormal returns following operational loss announcements exceeding € 50 million for both the announcing firms and their competitors. In addition, they find that stock market reactions occur only within a very small event window around the announcement date, indicating a high degree of market efficiency. Finally, abnormal returns tend to be insignificant for smaller loss amounts. Originality/value While operational risk is often believed to be strictly firm-specific, the results show that large operational risk events are not purely idiosyncratic; rather, they are systemic in the sense that they have contagious effects on non-event banks. Thus, the authors shed new light on how operational risk affects equity investors’ investment behaviour in an opaque and highly interconnected banking market.

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Dissertations / Theses on the topic "Systemic risk"

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Tang, Zhaofeng. "Quantitative risk management under systematic and systemic risks." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/7035.

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The contemporary risk management practice emphasizes the interplay of multilevel risks, of which the systematic and systemic risks are considered the main culprits of catastrophic losses. With this in mind, this thesis investigates three important topics in quantitative risk management, in which the systematic and systemic risks play a devastating role. First of all, we center on the design of reinsurance policies that accommodate the joint interests of the insurer and reinsurer by drawing upon the celebrated notion of Pareto optimality in the context of a distortion-risk-measure-based model. Such a topic is of considerable practical interest in the current post financial crisis era when people have witnessed the significant systemic risk posed by the insurance industry and the vulnerability of insurance companies to systemic events. Specifically, we characterize the set of Pareto-optimal reinsurance policies analytically and introduce the Pareto frontier to visualize the insurer-reinsurer trade-off structure geometrically. Another enormous merit of developing the Pareto frontier is the considerable ease with which Pareto-optimal reinsurance policies can be constructed even in the presence of the insurer's and reinsurer's individual risk constraints. A strikingly simple graphical search of these constrained policies is performed in the special cases of value-at-risk and tail value-at-risk. Secondly, we propose probabilistic and structural characterizations for insurance indemnities that are universally marketable in the sense that they appeal to both policyholders and insurers irrespective of their risk preferences and risk profiles. We begin with the univariate case where there is a single risk facing the policyholder, then extend our results to the case where multiple possibly dependent risks co-exist according to a mixture structure capturing policyholder's exposure to systematic and systemic risks. Next, we study the asymptotic behavior of the loss from defaults of a large credit portfolio. We consider a static structural model in which latent variables governing individual defaults follow a mixture structure incorporating idiosyncratic, systematic, and systemic risks. The portfolio effect, namely the decrease in overall risk due to the portfolio size increase, is taken into account. We derive sharp asymptotics for the tail probability of the portfolio loss as the portfolio size becomes large and our main finding is that the occurrence of large losses can be attributed to either the common shock variable or systematic risk factor, whichever has a heavier tail. Finally, we extend the asymptotic study of loss from defaults of a large credit portfolio under an amalgamated model. Aiming at investigating the dependence among the risk components of each obligor, we propose a static structural model in which each obligor's default indicator, loss given default, and exposure at default are respectively governed by three dependent latent variables with exposure to idiosyncratic, systematic, and systemic risks. The asymptotic distribution as well as the asymptotic value-at-risk and expected shortfall of the portfolio loss are obtained. The results are further refined when a specific mixture structure is employed for latent variables.

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Panzica, Roberto Calogero <1984&gt. "Network connectivity, systematic and systemic risk." Doctoral thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/14091.

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The thesis collects five papers, each of them, except the last one, treats a different topics related to the asset interconnections and asset pricing. The first paper extends the classic factor-based asset pricing model by including network linkages, leading to a network-augmented linear factor models. The contribution of the paper is to show that the network presence affects the exposure on the common factor, the power of the diversification and the expected returns. The second paper generalizes the model used in the first work by allowing the number of network greater than one. This work has two contributions: how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints; and to demonstrate that Granger causality should be combined with quantile-based causality when the focus is on risk propagation. The third paper investigates on the determinants of the idiosyncratic volatility puzzle by allowing the contemporaneous linkages across asset returns. The first contribution is to show that the puzzle found by ang et al 2006, where stocks with high (low)idiosyncratic volatility relative to the FamaFrench1993 model have low (high) average returns, falls if the idiosyncratic volatility is filtered out from the impact coming from the network. The purpose of the fourth paper is to assert the different informative content between quantile based network measures and quantile based loss measures such as ΔCoVaR. Globally Systemically Important Banks and Insurers and several Hedge Fund indices are considered. The contribution of the paper is to show that quantile regression based on network measures capture the indirect effect of risk spillovers that is instead ignored by quantile based loss measures. Finally, the comparison between quantile based network measures and quantile based losses measures highlights the predicting power of the former during the global systemic crisis of 2007/2008. The fifth paper is a not network related topic, it analyses which are the causes to make a contract eligible to be cleared, by using probit analysis.

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Scquizzato, Gianmarco <1989&gt. "Systemic Risk Measures." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/9570.

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Considering the effects generated by the recent financial crisis, and, given the ease with which a situation of financial distress caused impact beyond and outwith financial system, the concept of systemic risk has gained even more attention in the worldwide community. Despite many studies, there is no recognised single definition of systemic risk and as demonstrated by the existence of numerose metrics in the relative literature, finding effective measures to assess systemic risk is one of the toughest challenges for many individuals and institutions. The aimo of this work is to provide a measure of systemic risk through the use of three econometric methods: principal component analysis, Granger causality test and nonlinear causality test of Diks and Panchenko.

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Liu, Yi. "Essays on systemic risk and risk spillovers." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7313/.

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This thesis studies the implications of risk spillover effects in the systemic risk regarding the financial institutions and the financial system. We study the risk spillovers from sovereign CDS market to financial CDS market and the systemic risk contributions of sovereign countries. We then extend the previous study to investigate the dynamics of sovereign risk spillovers to the sovereign bond market, sovereign CDS market, and the national banking sectors, and we examine the interdependence of these markets. Lastly, we study the implications of network interconnectedness of the financial institutions and its contributions to systemic risk. Our research provides deeper understanding regarding the systemic risk and risk spillovers, and offer practical empirical evidence regarding the regulation of financial institutions.

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Dang, Hieu. "Essays in Financial Systemic Risk." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1596018496809798.

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Silva, Walmir Geraldo da. "Essays on financial systemic risk." reponame:Repositório Institucional da UnB, 2018. http://repositorio.unb.br/handle/10482/32512.

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Tese (doutorado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade e Gestão Pública, Programa de Pós-Graduação em Administração, 2018.<br>Submitted by Raquel Viana (raquelviana@bce.unb.br) on 2018-08-22T18:49:30Z No. of bitstreams: 1 2018_WalmirGeraldodaSilva.pdf: 8732645 bytes, checksum: 6aba8d37d3e4596cb6988d4470ad0437 (MD5)<br>Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-08-27T22:15:13Z (GMT) No. of bitstreams: 1 2018_WalmirGeraldodaSilva.pdf: 8732645 bytes, checksum: 6aba8d37d3e4596cb6988d4470ad0437 (MD5)<br>Made available in DSpace on 2018-08-27T22:15:13Z (GMT). No. of bitstreams: 1 2018_WalmirGeraldodaSilva.pdf: 8732645 bytes, checksum: 6aba8d37d3e4596cb6988d4470ad0437 (MD5) Previous issue date: 2018-08-22<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).<br>This dissertation presented to obtain the Ph.D. degree in Business Administration is composed of two articles. The first one 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 main stream research on systemic financial risk were identified. In the second article, we model an indicator that aims to identify systemic risk in the financial markets. Using 93 assets from different classes and from both developed and emerging countries, we apply principal components analysis (PCA) to calculate an initial indicator that is then submitted to Markov switching (MS) technique. This procedure advances the use of PCA in systemic risk modelling by preventing the need for arbitrary definitions of normal and stressed regimes. Additionally, applying MS to the indicator extracted by PCA from the correlation matrix of a relevant number of assets of various classes supports the argument that the indicator is indeed systemic. The results show that the probabilities that the indicator is under stress, according to the MS model, can be used as a signal of systemic risk. We also verified that the average risk of assets, calculated by the average value-at-risk (VaR), is affected when the series of these assets are separated in the systemic risk and normal regimes. In addition, we measure the performance of the indicator compared to other metrics built with only an asset class, especially stock indices. The results show that our model adequately depicts periods of high systemic risk, being relatively thorough.

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Mosmann, Gabriela. "Axiomatic systemic risk measures forecasting." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/178875.

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Neste trabalho, aprofundamos o estudo sobre risco sistêmico via funções de agregação. Consideramos três carteiras diferentes como proxy para um sistema econômico, estas carteiras são consistidas por duas funções de agregação, baseadas em todos as ações do E.U.A, e um índice de mercado. As medidas de risco aplicadas são Value at Risk (VaR), Expected Shortfall (ES) and Expectile Value at Risk (EVaR), elas são previstas através do modelo GARCH clássico unido com nove funções de distribuição de probabilidade diferentes e mais por um método não paramétrico. As previsões são avaliadas por funções de perda e backtests de violação. Os resultados indicam que nossa abordagem pode gerar uma função de agregação adequada para processar o risco de um sistema previamente selecionado.<br>In this work, we deepen the study of systemic risk measurement via aggregation functions. We consider three different portfolios as a proxy for an economic system, these portfolios are consisted in two aggregation functions, based on all U.S. stocks and a market index. The risk measures applied are Value at Risk (VaR), Expected Shortfall (ES) and Expectile Value at Risk (EVaR), they are forecasted via the classical GARCH model along with nine distribution probability functions and also by a nonparametric approach. The forecasts are evaluated by loss functions and violation backtests. Results indicate that our approach can generate an adequate aggregation function to process the risk of a system previously selected.

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Tasca, Paolo <1976&gt. "Diversification, leverage and systemic risk." Doctoral thesis, Università Ca' Foscari Venezia, 2012. http://hdl.handle.net/10579/1188.

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The Thesis investigates from a theoretical perspective the relationship between leverage, diversification and systemic risk. Moving from the folk wisdom that asset diversification enhances financial stability by dispersing credit risks, we contribute to the debate shedding light on a critical facet of this strategy. First a representative leveraged investor is considered. Under the standard framework of asset pricing theory in a frictionless, arbitrage-free and complete market, we show that maximum diversification may increase the default risk. Then, we consider a financial system of interconnected banks whose assets include also securities outside the financial network. We show that diversification in external assets displays a knife-edge effect: beyond a certain range, it may induce financial instability. Finally, we found that in presence of procyclical capital requirements that create a positive feedback loop between leverage and asset prices, the knife-edge property is amplified.<br>La Tesi esamina da un punto di vista teorico la relazione tra leva finanziaria, diversificazione e rischio sistemico. Partendo dalla convinzione popolare che la diversificazione innalza la stabilità finanziaria attraverso la diluzione dei rischi, collaboriamo al dibattito facendo luce su un aspetto critico di questa strategia. Consideriamo dapprima un investitore in leva. Sotto le condizioni standard della teoria moderna della finanza, dimostriamo che la massima diversificazione può aumentare il rischio di fallimento. Successivamente, consideriamo un sistema finanziario composto da banche con mutua esposizione finanziaria tra di loro e che investono in attività esterne al sistema. Dimostriamo che la diversificazione in attività esterne oltre un certo livello può diminuire la stabilità finanziaria: effetto 'filo di rasoio'. Alla fine troviamo che in presenza di regolamenti di capitale che creano un ciclo tra leva e prezzi, la diversificazione amplifica l'effetto 'filo di rasoio'.

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Pasqualini, Andrea <1990&gt. "Approaching Systemic Risk with Entropy." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5208.

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This Thesis focuses on the Marginal Expected Shortfall (MES) and its application in Finance as early-warning reference. I will specify a logit model that allows to link MES to the conditional probability of financial crisis. The analysis includes a multinomial bayesian density estimation approach, and considers different crisis indicators as dependent variables.

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Salhi, Khadidja <1991&gt. "MONETARY POLICY AND SYSTEMIC RISK." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/13439.

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Books on the topic "Systemic risk"

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Kasser, Joseph Eli. Systemic and Systematic Risk Management. CRC Press, 2020. http://dx.doi.org/10.1201/9780429025389.

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Kemp, Malcolm H. D. Systemic Risk. Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-56587-7.

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Quantifying systemic risk. The University of Chicago Press, 2012.

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Radev, Deyan. Measuring Systemic Risk. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94281-6.

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Systemic operational risk. Risk Books, 2015.

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Jorion, Philippe. Bank trading risk and systemic risk. National Bureau of Economic Research, 2005.

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Jorion, Philippe. Bank trading risk and systemic risk. National Bureau of Economic Research, 2005.

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Fouque, Jean-Pierre, and Joseph A. Langsam, eds. Handbook on Systemic Risk. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9781139151184.

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1951-, Evanoff Douglas Darrell, Hoelscher David S, Kaufman George G, and Federal Reserve Bank of Chicago., eds. Globalization and systemic risk. World Scientific, 2009.

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Hochrainer-Stigler, Stefan. Extreme and Systemic Risk Analysis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2689-3.

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Book chapters on the topic "Systemic risk"

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Abu el Ata, Nabil, and Rudolf Schmandt. "Systemic and Systematic Risk." In The Tyranny of Uncertainty. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49104-1_7.

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Gould, Scot A. C., Stephen A. Naftilan, Sarkis J. Khoury, and Danae J. Wright. "Systemic Risk." In Financial Innovations and the Welfare of Nations. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1623-1_5.

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Arisson, Morten. "Systemic Risk." In Investing in the Age of Democracy. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95903-0_7.

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Hauptmann, Johannes, and Rudi Zagst. "Systemic Risk." In Quantitative Financial Risk Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19339-2_24.

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Kemp, Malcolm H. D. "Measuring Systemic Risk." In Systemic Risk. Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-56587-7_5.

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Kemp, Malcolm H. D. "Responding to Systemic Risk." In Systemic Risk. Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-56587-7_8.

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Hurd, T. R. "Systemic Risk Basics." In SpringerBriefs in Quantitative Finance. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33930-6_1.

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Wladis, Edward J., and Michael I. Rothschild. "Systemic Risk Factors." In Avoiding and Managing Complications in Cosmetic Oculofacial Surgery. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51152-4_2.

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Young, Carl S. "Systemic Security Risk." In Risk and the Theory of Security Risk Assessment. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30600-7_10.

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Cooley, Thomas F., Thomas Philippon, Viral V. Acharya, Lasse H. Pedersen, Thomas Philippon, and Matthew Richardson. "Regulating Systemic Risk." In Restoring Financial Stability. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118258163.ch13.

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Conference papers on the topic "Systemic risk"

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Sarlin, Peter, and Tuomas A. Peltonen. "Introduction to Systemic Risk Analytics Minitrack." In 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE, 2016. http://dx.doi.org/10.1109/hicss.2016.221.

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Basu, Sumanta, Sreyoshi Das, George Michailidis, and Amiyatosh Purnanandam. "Measuring Systemic Risk with Network Connectivity." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2951894.2951912.

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Feng, Yichen, Ming Min, and Jean-Pierre Fouque. "Deep Learning for Systemic Risk Measures." In ICAIF '22: 3rd ACM International Conference on AI in Finance. ACM, 2022. http://dx.doi.org/10.1145/3533271.3561669.

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Kavitha, Veeraruna, Indrajit Saha, and Sandeep Juneja. "Random Fixed Points, Limits and Systemic Risk." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619790.

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Basilio, Jorge, Amilcar Oliveira, and Rahim Mahmoudvand. "An overview of the systemic risk measures." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0027053.

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Esposito De Falco, Salvatore, Antonio Renzi, Giuseppe Sancetta, and Gianluca Vagnani. "Enterprise risk management, corporate governance and systemic risk: Some research perspectives." In New challenges in corporate governance: Theory and practice. Virtus Interpress, 2019. http://dx.doi.org/10.22495/ncpr_15.

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Cholez, Hervé, and Christophe Feltus. "Towards an Innovative Systemic Approach of Risk Management." In the 7th International Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2659651.2659734.

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Tsakayev, Alkhozur Kharonovich. "Regulation Mechanism Of Systemic Risk In Agricultural Insurance." In SCTCGM 2018 - Social and Cultural Transformations in the Context of Modern Globalism. Cognitive-Crcs, 2019. http://dx.doi.org/10.15405/epsbs.2019.03.02.138.

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Yang, Jingbiao, Jiong Zheng, Xufeng Li, and Weijian Luo. "Research on Systemic Risk of Pressure Special Equipment." In ASME 2012 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/pvp2012-78300.

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Traditional risk management or risk-based inspection focuses on the probability and the consequences of failure almost without considering the impact on society of the failure events. Social impact of a failure event resulting from a same type of equipment but with the occurrence of the event in the occasion with different public opinion and mental capacity is not the same. The social impact of an event resulting from a pressure special equipment, namely a risk intensity as a modified factor is introduced in this paper based on the traditional description of risk. A systemic risk model is developed after the consequences of failure is modified by the risk intensity. The model built explains the phenomenon well that the failure of the same equipment in different occasions has different social impact.

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Peruski, Johnathon, Caroline Lacy, Walter Goethel, et al. "Systemic Risk in the United States banking industry." In 2014 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2014. http://dx.doi.org/10.1109/sieds.2014.6829913.

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Reports on the topic "Systemic risk"

1

Jorion, Philippe. Bank Trading Risk and Systemic Risk. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11037.

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2

Allen, Franklin, Ana Babus, and Elena Carletti. Financial Connections and Systemic Risk. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w16177.

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3

Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. Networks, Shocks, and Systemic Risk. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w20931.

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4

Chan, Nicholas, Mila Getmansky, Shane Haas, and Andrew Lo. Systemic Risk and Hedge Funds. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11200.

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Battiston, Stefano, Domenico Delli Gatti, Mauro Gallegati, Bruce Greenwald, and Joseph Stiglitz. Liaisons Dangereuses: Increasing Connectivity, Risk Sharing, and Systemic Risk. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w15611.

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Laverde, Mariana, Esteban Gómez-González, and Miguel Ángel Morales-Mosquera. Measuring systemic risk in the Colombian financial system : a systemic contingent claims approach. Banco de la República, 2011. http://dx.doi.org/10.32468/tef.60.

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King, Thomas, Travis D. Nesmith, Anna Paulson, and Todd Prono. Central Clearing and Systemic Liquidity Risk. Federal Reserve Bank of Chicago, 2019. http://dx.doi.org/10.21033/wp-2019-12.

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8

Brunnermeier, Markus, and Martin Oehmke. Bubbles, Financial Crises, and Systemic Risk. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18398.

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9

Das, Sanjiv, Kris James Mitchener, and Angela Vossmeyer. Systemic Risk and the Great Depression. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w25405.

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Brunnermeier, Markus, Simon Rother, and Isabel Schnabel. Asset Price Bubbles and Systemic Risk. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25775.

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