Journal articles on the topic 'Conditional systemic risk measure'
To see the other types of publications on this topic, follow the link: Conditional systemic risk measure.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Conditional systemic risk measure.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Doldi, Alessandro, and Marco Frittelli. "Conditional Systemic Risk Measures." SIAM Journal on Financial Mathematics 12, no. 4 (January 2021): 1459–507. http://dx.doi.org/10.1137/20m1370616.
Full text
2
Dhaene, Jan, Roger J. A. Laeven, and Yiying Zhang. "Systemic risk: Conditional distortion risk measures." Insurance: Mathematics and Economics 102 (January 2022): 126–45. http://dx.doi.org/10.1016/j.insmatheco.2021.12.002.
Full text
3
Hoffmann, Hannes, Thilo Meyer-Brandis, and Gregor Svindland. "Risk-consistent conditional systemic risk measures." Stochastic Processes and their Applications 126, no. 7 (July 2016): 2014–37. http://dx.doi.org/10.1016/j.spa.2016.01.002.
Full text
4
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.
Full textAbstract:
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.
5
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.
Full text
6
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.
Full textAbstract:
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.
7
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.
Full textAbstract:
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.
8
Fan, Yiting, and Rui Fang. "Some Results on Measures of Interaction among Risks." Mathematics 10, no. 19 (October 2, 2022): 3611. http://dx.doi.org/10.3390/math10193611.
Full textAbstract:
It has become a common understanding that financial risk can spread rapidly from one institution to another, and the stressful status of one institution may finally result in a systemic crisis. One popular method to assess and quantify the risk of contagion is employing the co-risk measures and risk contribution measures. It is interesting and important to understand how the underlining dependence structure and magnitude of random risks jointly affect systemic risk measures. In this paper, we mainly focus on the conditional value-at-risk, conditional expected shortfall, the delta conditional value-at-risk, and the delta conditional expected shortfall. Existing studies mainly focus on the situation with two random risks, and this paper makes some contributions by considering the scenario with possibly more than two random risks. By employing the tools of stochastic order, positive dependence concepts and arrangement monotonicity, several results concerning the usual stochastic order, increasing convex order, dispersive order and excess wealth order are presented. Concisely speaking, it is found that for a large enough stress level, a larger random risk tends to lead to a more severe systemic risk. We also performed some Monte Carlo experiments as illustrations for the theoretical findings.
9
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.
Full textAbstract:
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.
10
Doldi, Alessandro, and Marco Frittelli. "Real-Valued Systemic Risk Measures." Mathematics 9, no. 9 (April 30, 2021): 1016. http://dx.doi.org/10.3390/math9091016.
Full textAbstract:
We describe the axiomatic approach to real-valued Systemic Risk Measures, which is a natural counterpart to the nowadays classical univariate theory initiated by Artzner et al. in the seminal paper “Coherent measures of risk”, Math. Finance, (1999). In particular, we direct our attention towards Systemic Risk Measures of shortfall type with random allocations, which consider as eligible, for securing the system, those positions whose aggregated expected utility is above a given threshold. We present duality results, which allow us to motivate why this particular risk measurement regime is fair for both the single agents and the whole system at the same time. We relate Systemic Risk Measures of shortfall type to an equilibrium concept, namely a Systemic Optimal Risk Transfer Equilibrium, which conjugates Bühlmann’s Risk Exchange Equilibrium with a capital allocation problem at an initial time. We conclude by presenting extensions to the conditional, dynamic framework. The latter is the suitable setup when additional information is available at an initial time.
11
Aste, Tomaso. "Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities." Journal of Risk and Financial Management 14, no. 5 (May 10, 2021): 213. http://dx.doi.org/10.3390/jrfm14050213.
Full textAbstract:
Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach.
12
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.
Full textAbstract:
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.
13
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.
Full textAbstract:
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.
14
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.
Full textAbstract:
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)
15
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.
Full textAbstract:
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.
16
Das, Bikramjit, and Vicky Fasen-Hartmann. "Conditional excess risk measures and multivariate regular variation." Statistics & Risk Modeling 36, no. 1-4 (December 1, 2019): 1–23. http://dx.doi.org/10.1515/strm-2018-0030.
Full textAbstract:
Abstract Conditional excess risk measures like Marginal Expected Shortfall and Marginal Mean Excess are designed to aid in quantifying systemic risk or risk contagion in a multivariate setting. In the context of insurance, social networks, and telecommunication, risk factors often tend to be heavy-tailed and thus frequently studied under the paradigm of regular variation. We show that regular variation on different subspaces of the Euclidean space leads to these risk measures exhibiting distinct asymptotic behavior. Furthermore, we elicit connections between regular variation on these subspaces and the behavior of tail copula parameters extending previous work and providing a broad framework for studying such risk measures under multivariate regular variation. We use a variety of examples to exhibit where such computations are practically applicable.
17
Chu, Yongqiang, Saiying Deng, and Cong Xia. "Bank Geographic Diversification and Systemic Risk." Review of Financial Studies 33, no. 10 (December 24, 2019): 4811–38. http://dx.doi.org/10.1093/rfs/hhz148.
Full textAbstract:
Abstract Exploiting staggered interstate banking deregulation as exogenous shocks to bank geographic expansion, we examine the causal effect of geographic diversification on systemic risk. Using the gravity-deregulation approach, we find that bank geographic diversification leads to higher systemic risk measured by the change in conditional value at risk ($\Delta$CoVaR) and financial integration (Logistic($R^{2}))$. Furthermore, we document that geographic diversification affects systemic risk via its impact on asset similarity. The impact of geographic diversification on systemic risk is stronger in BHCs located in states comoving less with the U.S. aggregate economy.
18
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.
Full textAbstract:
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.
19
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.
Full textAbstract:
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.
20
Shushi, Tomer, and Jing Yao. "Multivariate risk measures based on conditional expectation and systemic risk for Exponential Dispersion Models." Insurance: Mathematics and Economics 93 (July 2020): 178–86. http://dx.doi.org/10.1016/j.insmatheco.2020.04.014.
Full text
21
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.
Full textAbstract:
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.
22
Nabella, Rihana Sofie, Ghozali Maski, and Setyo Tri Wahyudi. "Analisis Risiko Sistemik dan Keterkaitan Keuangan: Studi pada Bank Umum Syariah di Indonesia." Journal of Business and Banking 10, no. 1 (October 30, 2020): 19. http://dx.doi.org/10.14414/jbb.v10i1.2048.
Full textAbstract:
Islamic banking in Indonesia has developed as indicated marked by the establishment of Bank Muamalat Indonesia as the first Islamic bank in Indonesia. Islamic banks—Besides the conventional bansk— are an alternative source of financing which are expected to support the country's economic growth. Banks are also known as risk-prone institutions, one of which is systemic risk. This study aims to measure systemic risk and financial linkages in Islamic commercial banks in Indonesia. This study uses the Conditional Value at Risk (CoVaR) model developed by Adrian and Brunnermeier (2009) with data samples of 8 Islamic banks in Indonesia from January 2012 to December 2018. The results isobtained are the contribution of systemic risk is not determined by the size of bank assets and individual risk. k, sBo that both small banks and large banks can threaten financial system stability. So that it can be a reference for regulators to always supervise all banks, not only large banks but also small banks that have high individual risks.
23
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.
Full textAbstract:
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.
24
Hakim, Arief, A. N. M. Salman, Yeva Ashari, and Khreshna Syuhada. "Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets." PLOS ONE 17, no. 11 (November 29, 2022): e0277756. http://dx.doi.org/10.1371/journal.pone.0277756.
Full textAbstract:
In a financial system, entities (e.g., companies or markets) face systemic risk that could lead to financial instability. To prevent this impact, we require quantitative systemic risk management we can carry out using conditional value-at-risk (CoVaR) and a network model. The former measures any targeted entity’s tail risk conditional on another entity being financially distressed; the latter represents the financial system through a set of nodes and a set of edges. In this study, we modify CoVaR along with its multivariate extension (MCoVaR) considering the joint conditioning events of multiple entities. We accomplish this by first employing a multivariate Johnson’s SU risk model to capture the asymmetry and leptokurticity of the entities’ asset returns. We then adopt the Cornish–Fisher expansion to account for the analytic higher-order conditional moments in modifying (M)CoVaR. In addition, we attempt to construct a conditional tail risk network. We identify its edges using a corresponding Delta (M)CoVaR reflecting the systemic risk contribution and further compute the strength and clustering coefficient of its nodes. When applying the financial system to global foreign exchange (forex) markets before and during COVID-19, we revealed that the resulting expanded (M)CoVaR forecast exhibited a better conditional coverage performance than its unexpanded version. Its superior performance appeared to be more evident over the COVID-19 period. Furthermore, our network analysis shows that advanced and emerging forex markets generally play roles as net transmitters and net receivers of systemic risk, respectively. The former (respectively, the latter) also possessed a high tendency to cluster with their neighbors in the network during (respectively, before) COVID-19. Overall, the interconnectedness and clustering tendency of the examined global forex markets substantially increased as the pandemic progressed.
25
Ben Ameur, Hachmi, Fredj Jawadi, Wael Louhichi, and Abdoulkarim Idi Cheffou. "MODELING INTERNATIONAL STOCK PRICE COMOVEMENTS WITH HIGH-FREQUENCY DATA." Macroeconomic Dynamics 22, no. 7 (November 21, 2017): 1875–903. http://dx.doi.org/10.1017/s1365100516000924.
Full textAbstract:
This paper studies stock price comovements in two key regions [the United States and Europe, which is represented by three major European developed countries (France, Germany, and the United Kingdom)]. Our paper uses recent high-frequency data (HFD) and investigates price comovements in the context of “normal times” and crisis periods. To this end, we applied a non-Gaussian Asymmetrical Dynamic Conditional Correlation (ADCC)-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model and the Marginal Expected Shortfall (MES) approach. This choice has three advantages: (i) With the development of high-frequency trading (HFT), it is more appropriate to use HFD to test price linkages for overlapping and nonoverlapping data. (ii) The ADCC-GARCH model captures further asymmetry in price comovements. (iii) The use of the MES enables to measure systemic risk contributions around the distribution tails. Accordingly, we offer two interesting findings. First, while the hypothesis of asymmetrical and time-varying stock return linkages is not rejected, the MES approach indicates that both European and US indices make a considerable contribution to each other's systemic risk, with significant input from Frankfurt to the French and US markets, especially following the collapse of Lehman Brothers. Second, we show that the propagation of systemic risk is higher during the crisis period and overlapping trading hours than during nonoverlapping hours. Thus, the MES test is recommended as an indicator to help monitor market exposure to systemic risk and to gauge expected losses for other markets.
26
Ayomi, Sri, and Bambang Hermanto. "SYSTEMIC RISK AND FINANCIAL LINKAGES MEASUREMENT IN THE INDONESIAN BANKING." Buletin Ekonomi Moneter dan Perbankan 16, no. 2 (April 5, 2014): 91–114. http://dx.doi.org/10.21098/bemp.v16i2.439.
Full textAbstract:
This paper measures the insolvency risk of bank in Indonesia. We apply Merton model to identify the probability of defaul tover 30 banks during the period of 2002-2013. This paper also identify role of financial linkage a cross banks on transmitting from one bank to another; which enable us to assess if the risk is systemic or not. The results showed the larger total asset of the bank, the larger they contribute to systemic risk. Keywords : Conditional Value at Risk; Probability of Default; systemic risk and financial linkages;Value at Risk.JEL Classification: D81, G21, G33
27
Ayomi, Sri, and Bambang Hermanto. "MENGUKUR RISIKO SISTEMIK DAN KETERKAITAN FINANSIAL PERBANKAN DI INDONESIA." Buletin Ekonomi Moneter dan Perbankan 16, no. 2 (April 4, 2014): 103–25. http://dx.doi.org/10.21098/bemp.v16i2.24.
Full textAbstract:
This paper measures the insolvency risk of bank in Indonesia. We apply Merton model to identify the probability of defaul tover 30 banks during the period of 2002-2013. This paper also identify role of financial linkage a cross banks on transmitting from one bank to another; which enable us to assess if the risk is systemic or not. The results showed the larger total asset of the bank, the larger they contribute to systemic risk. Keywords : Conditional Value at Risk; Probability of Default; systemic risk and financial linkages;Value at Risk. JEL Classification: D81, G21, G33
28
Hakim, Arief, and Khreshna Syuhada. "Formulating MCoVaR to Quantify Joint Transmissions of Systemic Risk across Crypto and Non-Crypto Markets: A Multivariate Copula Approach." Risks 11, no. 2 (February 7, 2023): 35. http://dx.doi.org/10.3390/risks11020035.
Full textAbstract:
Evidence that cryptocurrencies exhibit speculative bubble behavior is well documented. This evidence could trigger global financial instability leading to systemic risk. It is therefore crucial to quantify systemic risk and investigate its transmission mechanism across crypto markets and other global financial markets. We can accomplish this using the so-called multivariate conditional value-at-risk (MCoVaR), which measures the tail risk of a targeted asset from each market conditional on a set of multiple assets being jointly in distress and on a set of the remaining assets being jointly in their median states. In this paper, we aimed to find its analytic formulas by considering multivariate copulas, which allow for the separation of margins and dependence structures in modeling the returns of the aforementioned assets. Compared to multivariate normal and Student’s t benchmark models and a multivariate Johnson’s SU model, the copula-based models with non-normal margins produced a MCoVaR forecast with superior conditional coverage and backtesting performances. Using a corresponding Delta MCoVaR, we found the crypto assets to be potential sources of systemic risk jointly transmitted within the crypto markets and towards the S&P 500, oil, and gold, which was more apparent during the COVID-19 period encompassing the recent 2021 crypto bubble event.
29
Di Clemente, Annalisa. "Comparing Different Systemic Risk Measures for European Banking System." International Business Research 12, no. 1 (December 6, 2018): 35. http://dx.doi.org/10.5539/ibr.v12n1p35.
Full textAbstract:
This research examines and compares the performances in terms of systemic risk ranking for three different systemic risk metrics based on daily frequency publicly available data, specifically: Marginal Expected Shortfall (ES), Component Expected Shortfall (CES) and Delta Conditional Value-at-Risk (ΔCoVaR). We compute ΔCoVaR, MES and CES by utilizing EVT principles for modelling marginal distributions and Student’s t copula for describing the dependence structure between every bank and the banking system. Our objective is to attest whether different systemic risk metrics detect the same banks as systemically dangerous institutions with refer to a sample of European banks over the time span 2004-2015. For each bank in the sample we also calculate three traditional market risk measures, like Market VaR, Sharpe’s beta and the correlation between every bank and the banking system (European STOXX 600 Banks Index). Another aim is to explore the existence of a link among systemic risk measures and traditional risk metrics. In addition, the classification results obtained by the different risk metrics are compared with the ranking in terms of systemic riskiness (for European banks) calculated by Financial Stability Board (2015) using end-2014 data and collected in its list of Global Systemically Important Banks (G-SIBs). With refer to the entire sample period, we find a good coherence of ranking results among the three different systemic risk metrics, in particular between CES and ΔCoVaR. Moreover, we find for MES and ΔCoVaR a strong linkage with beta and correlation metrics respectively. Finally, CES metric shows the highest level of concordance with the list of G-SIBs by FSB with refer to European banks.
30
Choudhury, Tonmoy, Simone Scagnelli, Jaime Yong, and Zhaoyong Zhang. "Non-Traditional Systemic Risk Contagion within the Chinese Banking Industry." Sustainability 13, no. 14 (July 16, 2021): 7954. http://dx.doi.org/10.3390/su13147954.
Full textAbstract:
Systemic risk contagion is a key issue in the banking sector in maintaining financial system stability. This study is among the first few to use three different distance-to-risk measures to empirically assess the domestic interbank linkages and systemic contagion risk of the Chinese banking industry, by using bivariate dynamic conditional correlation GARCH model on data collected from eight prominent Chinese banks for the period 2006–2018. The results show a relatively high correlation among almost all the banks, suggesting an interconnectedness among the banks. We found evidence that the banking system is exposed to significant domestic contagion risks arising from systemic defaults. Given that Chinese markets deliver weak signals of forthcoming stress in banking sectors, new policy intervention is crucial to resolve the hidden stress in the system. The results have important policy implications and will provide scholars and policymakers further insight into the risk contagion originating from interbank networks.
31
Grut, Viktor, Martin Biström, Jonatan Salzer, Pernilla Stridh, Anna Lindam, Lucia Alonso-Magdalena, Oluf Andersen, et al. "Systemic inflammation and risk of multiple sclerosis – A presymptomatic case-control study." Multiple Sclerosis Journal - Experimental, Translational and Clinical 8, no. 4 (October 2022): 205521732211397. http://dx.doi.org/10.1177/20552173221139768.
Full textAbstract:
Background C-reactive protein (CRP) is a marker of systemic inflammation. Increased levels of CRP in young persons have been suggested to decrease the risk of multiple sclerosis (MS). Objectives To assess CRP as a risk factor for MS. Methods Levels of CRP were measured with a high-sensitive immunoassay in biobank samples from 837 individuals who later developed MS and 984 matched controls. The risk of developing MS was analysed by conditional logistic regression on z-scored CRP values. Results Levels of CRP were not associated with MS risk. Conclusions We found no association between CRP levels and risk of MS development.
32
Gupta, Juhi, and Smita Kashiramka. "Identification of systemically important banks in India using SRISK." Journal of Financial Regulation and Compliance 29, no. 4 (August 7, 2021): 387–408. http://dx.doi.org/10.1108/jfrc-07-2020-0067.
Full textAbstract:
Purpose Systemic risk has been a cause of concern for the bank regulatory authorities worldwide since the global financial crisis. This study aims to identify systemically important banks (SIBs) in India by using SRISK to measure the expected capital shortfall of banks in a systemic event. The sample size comprises a balanced data set of 31 listed Indian commercial banks from 2006 to 2019. Design/methodology/approach In this study, the authors have used SRISK to identify banks that have a maximum contribution to the systemic risk of the Indian banking sector. Leverage, size and long-run marginal expected shortfall (LRMES) are used to compute SRISK. Forward-looking LRMES is computed using the GJR-GARCH-dynamic conditional correlation methodology for early prediction of a bank’s contribution to systemic risk. Findings This study finds that public sector banks are more vulnerable to macroeconomic shocks owing to their capital inadequacy vis-à-vis the private sector banks. This study also emphasizes that size should not be used as a standalone factor to assess the systemic importance of a bank. Originality/value Systemic risk has attracted a lot of research interest; however, it is largely limited to the developed nations. This paper fills an important research gap in banking literature about the identification of SIBs in an emerging economy, India. As SRISK uses both balance sheet and market-based information, it can be used to complement the existing methodology used by the Reserve Bank of India to identify SIBs.
33
Wijoyo, Nugroho Agung, Sri Adiningsih, Irwan Adi Ekaputra, and Buddi Wibowo. "Identifying Systemically Important Banks in Indonesia: CoVaR Approach." Journal of Hunan University Natural Sciences 49, no. 2 (February 28, 2022): 84–93. http://dx.doi.org/10.55463/issn.1674-2974.49.2.8.
Full textAbstract:
The Global Financial Crisis of 2008 had a significant impact on banks' bailout decisions due to its high cost of Rp. 6.76 trillion (USD 499.5 million). That shifted the focus from analyzing bank size to a broader notion of systemic risk. Therefore, this research aims to systemically identify important banks in Indonesia using Conditional Value-at-Risk (CoVaR) approach. We conduct the following three steps measurement for population-based on all commercial banks data. Firstly, this study uses Merton Model to gauge the probability of default of commercial banks. Secondly, this study quantifies the value at risk of each bank, including its contribution to the whole banking systemic risk. Finally, this study measures financial linkage among banks and the individual bank value at risk contribution, conditional on other banks being in financial distress, i.e., at its Value at Risk (ΔCoVaR A|B). A threshold of 20% ΔCoVaR (A|B) was used to determine 12 out of 119 Indonesian commercial banks that are systemically important and the size ability to affect CoVaR positively. The novelty of the research is to integrate Probability of Default derived from the Merton Model into the CoVaR Model developed by Adrian & Brunnermeier.
34
Sun, Huayu, Fanqi Zou, and Bin Mo. "Does FinTech drive asymmetric risk spillover in the traditional finance?" AIMS Mathematics 7, no. 12 (2022): 20850–72. http://dx.doi.org/10.3934/math.20221143.
Full textAbstract:
<abstract><p>The rapid development of fintech has caused a great impact on traditional financial industries. It improves the quality of financial services but also buries potential risks at the same time. This paper takes China's FinTech and traditional financial industry as the research objects based on the daily yield data from 2019 to 2022. First, we measure the systemic risk index ∆CoVaR (Conditional Value at Risk) of the FinTech industry and traditional financial industries after effectively fitting the marginal distribution of industry return data. Second, we decompose the systemic risk sequences of FinTech and traditional financial industries to obtain the data at different frequencies with the combination of the frequency decomposition method. Finally, we use the quantile-on-quantile regression model to analyze the risk spillover effect of the FinTech industry driving traditional financial industries in different frequencies under different risk states. The article draws the following conclusion: first, in general, the peak of the positive risk spillover impact of FinTech on the traditional industries is mainly concentrated in the high quantile of FinTech, while the peak of the negative impact is mainly concentrated in the low quantile of FinTech. Second, the risk spillover impact direction of FinTech on the five traditional financial industries mainly changes from negative to positive under high trading frequency and low trading frequency, and takes a U-shape in medium trading frequency.</p></abstract>
35
Takeda, Akiko, and Takafumi Kanamori. "A robust approach based on conditional value-at-risk measure to statistical learning problems." European Journal of Operational Research 198, no. 1 (October 2009): 287–96. http://dx.doi.org/10.1016/j.ejor.2008.07.027.
Full text
36
Coke, Latanya N., Hongxiu Wen, Mary Comeau, Mustafa H. Ghanem, Andrew Shih, Christine N. Metz, Wentian Li, Carl D. Langefeld, Peter K. Gregersen, and Kim R. Simpfendorfer. "Arg206Cys substitution in DNASE1L3 causes a defect in DNASE1L3 protein secretion that confers risk of systemic lupus erythematosus." Annals of the Rheumatic Diseases 80, no. 6 (January 17, 2021): 782–87. http://dx.doi.org/10.1136/annrheumdis-2020-218810.
Full textAbstract:
Objectives To determine if the polymorphism encoding the Arg206Cys substitution in DNASE1L3 explains the association of the DNASE1L3/PXK gene locus with systemic lupus erythematosus (SLE) and to examine the effect of the Arg206Cys sequence change on DNASE1L3 protein function. Methods Conditional analysis for rs35677470 was performed on cases and controls with European ancestry from the SLE Immunochip study, and genotype and haplotype frequencies were compared. DNASE1L3 protein levels were measured in cells and supernatants of HEK293 cells and monocyte-derived dendritic cells expressing recombinant and endogenous 206Arg and 206Cys protein variants. Results Conditional analysis on rs35677470 eliminated the SLE risk association signal for lead single-nucleotide polymorphisms (SNPs) rs180977001 and rs73081554, which are found to tag the same risk haplotype as rs35677470. The modest effect sizes of the SLE risk genotypes (heterozygous risk OR=1.14 and homozygous risk allele OR=1.68) suggest some DNASE1L3 endonuclease enzyme function is retained. An SLE protective signal in PXK (lead SNP rs11130643) remained following conditioning on rs35677470. The DNASE1L3 206Cys risk variant maintained enzymatic activity, but secretion of the artificial and endogenous DNASE1L3 206Cys protein was substantially reduced. Conclusions SLE risk association in the DNASE1L3 locus is dependent on the missense SNP rs35677470, which confers a reduction in DNASE1L3 protein secretion but does not eliminate its DNase enzyme function.
37
Bodnar, Taras, Mathias Lindholm, Erik Thorsén, and Joanna Tyrcha. "Quantile-based optimal portfolio selection." Computational Management Science 18, no. 3 (April 2, 2021): 299–324. http://dx.doi.org/10.1007/s10287-021-00395-8.
Full textAbstract:
AbstractIn this paper the concept of quantile-based optimal portfolio selection is introduced and a specific portfolio connected to it, the conditional value-of-return (CVoR) portfolio, is proposed. The CVoR is defined as the mean excess return or the conditional value-at-risk (CVaR) of the return distribution. The portfolio selection consists solely of quantile-based risk and return measures. Financial institutions that work in the context of Basel 4 use CVaR as a risk measure. In this regulatory framework sufficient and necessary conditions for optimality of the CVoR portfolio are provided under a general distributional assumption. Moreover, it is shown that the CVoR portfolio is mean-variance efficient when the returns are assumed to follow an elliptically contoured distribution. Under this assumption the closed-form expression for the weights and characteristics of the CVoR portfolio are obtained. Finally, the introduced methods are illustrated in an empirical study based on monthly data of returns on stocks included in the S&P index. It is shown that the new portfolio selection strategy outperforms several alternatives in terms of the final investor wealth.
38
Pasieczna, Aleksandra Helena, and Magdalena Joanna Szydłowska. "Estimating Model Risk of VaR under Different Approaches: Study on European Banks." Współczesne Problemy Zarządzania 9, no. 2(19) (December 31, 2021): 65–76. http://dx.doi.org/10.52934/wpz.151.
Full textAbstract:
The objective of this research is to estimate the model risk, represented as precision, and the accuracy of the Value at Risk (VaR) measure, under three different approaches: historical simulation (HS), Monte Carlo (MC), and generalized ARCH (GARCH). In this work, to analyze the VaR model, the accuracy and precision were used. Estimation of the accuracy and precision was done under the three approaches for four European banks at 95 and 99% confidence levels. The percentage crossings and Kupiec POF were used to judge the model accuracy, whereas the ratio of the maximum and minimum VaR estimates, and the spread between the maximum and minimum VaR estimates were used to estimate the model risk. This was achieved by changing input parameters, specifically, the estimation time window (125, 250, 500 days). Implications/Recommendations: The accuracy alone is not sufficient to evaluate a model and precision is also required. The temporal evolution of the precision metrics showed that the VaR approaches were inconsistent under different market conditions. This article focuses on the accuracy and precision concepts applied to estimate model risk of the Value at Risk (VaR). VaR is the foundation for sophisticated risk metrics, including systemic risk measures like Marginal Expected Shortfall and Delta Conditional Value at Risk. Thus, understanding the risk associated with the use of VaR is crucial for finance practitioners.
39
PUJOL, J., P. GODOY, N. SOLDEVILA, J. CASTILLA, F. GONZÁLEZ-CANDELAS, J. M. MAYORAL, J. ASTRAY, et al. "Social class based on occupation is associated with hospitalization for A(H1N1)pdm09 infection. Comparison between hospitalized and ambulatory cases." Epidemiology and Infection 144, no. 4 (August 14, 2015): 732–40. http://dx.doi.org/10.1017/s0950268815001892.
Full textAbstract:
SUMMARYThis study aimed to analyse the existence of an association between social class (categorized by type of occupation) and the occurrence of A(H1N1)pmd09 infection and hospitalization for two seasons (2009–2010 and 2010–2011). This multicentre study compared ambulatory A(H1N1)pmd09 confirmed cases with ambulatory controls to measure risk of infection, and with hospitalized A(H1N1)pmd09 confirmed cases to asses hospitalization risk. Study variables were: age, marital status, tobacco and alcohol use, pregnancy, chronic obstructive pulmonary disease, chronic respiratory failure, cardiovascular disease, diabetes, chronic liver disease, body mass index >40, systemic corticosteroid treatment and influenza vaccination status. Occupation was registered literally and coded into manual and non-manual worker occupational social class groups. A conditional logistic regression analysis was performed. There were 720 hospitalized cases, 996 ambulatory cases and 1062 ambulatory controls included in the study. No relationship between occupational social class and A(H1N1)pmd09 infection was found [adjusted odds ratio (aOR) 0·97, 95% confidence interval (CI) 0·74–1·27], but an association (aOR 1·53, 95% CI 1·01–2·31) between occupational class and hospitalization for A(H1N1)pmd09 was observed. Influenza vaccination was a protective factor for A(H1N1)pmd09 infection (aOR 0·41, 95% CI 0·23–0·73) but not for hospitalization. We conclude that manual workers have the highest risk of hospitalization when infected by influenza than other occupations but they do not have a different probability of being infected by influenza.
40
Lee, Jae Yeon, Payam Hosseinzadeh Kasani, Sung Ok Kwon, and Jeong-Ah Kim. "Inter-eye Comparison of the Lamina Cribrosa Depth in Patients with Bilateral Normal-tension Glaucoma with Asymmetrical Damage." Journal of the Korean Ophthalmological Society 63, no. 5 (May 15, 2022): 446–54. http://dx.doi.org/10.3341/jkos.2022.63.5.446.
Full textAbstract:
Purpose: To evaluate the association between lamina cribrosa depth (LCD) and the severity of glaucomatous damage in patients with normal-tension glaucoma (NTG).Methods: The optic nerve heads (ONHs) of patients with bilateral NTG showing asymmetrical damage were scanned using spectral-domain optical coherence tomography. LCDs were measured on ONH horizontal B-scan images at three locations equidistant across the vertical optic disc diameter and compared between the more damaged and contralateral eyes. Conditional logistic regression analysis was performed to identify ocular risk factors associated with more severe damage between the eyes.Results: One hundred and four eyes of 52 patients with bilateral NTG were included. The mean age was 66.0 ± 15.0 years; there were 31 males and 21 females. The more damaged eyes exhibited a higher baseline intraocular pressure (IOP) (p < 0.001), a thinner global retinal nerve fiber layer thickness (p < 0.001), and worse visual field mean deviation (p < 0.001) and a pattern standard deviation (p < 0.001), than the contralateral eyes. In contrast, we found no significant inter-eye difference in either the spherical equivalent or the axial length. The average LCD was significantly larger in the more damaged eyes (529.4 ± 116.7 vs. 482.9 ± 107.5 μm, p < 0.001). On conditional logistic regression analysis, the higher the baseline IOP (p = 0.006) and the larger the LCD (p = 0.003), the higher the risk of having more severe damage compared to the contralateral eye under similar systemic conditions.Conclusions: The LCD was significantly larger in the more damaged eyes of patients with bilateral NTG, suggesting that LC deformation might be associated with glaucomatous ONH damage in NTG eyes.
41
Becker, Claudia, Susan S. Jick, and Christoph R. Meier. "ACE inhibitor use and risk of cataract: a case–control analysis." British Journal of Ophthalmology 103, no. 11 (February 7, 2019): 1561–65. http://dx.doi.org/10.1136/bjophthalmol-2018-312980.
Full textAbstract:
Background/AimUse of ACE inhibitors (ACEIs) has been associated with an increased risk of cataract in a previous observational study in humans. In contrast, ACEIs were associated with beneficial effects on cataract development in experimental studies. We assessed the risk of cataract in relation to exposure to ACEI and other antihypertensive drugs.MethodsThis is a case-control study based on data from the UK-based Clinical Practice Research Datalink (CPRD). We included first-time cataract patients aged ≥40 years between 1995 and 2015 and an equal number of cataract-free controls. We matched the controls to cases on age, sex, general practice, date of first cataract (ie, index date) and years of history in the CPRD prior to the index date. We assessed the number of prescriptions for ACEI and other antihypertensive drugs in detail and explored the use of single ACEI substances. We performed conditional logistic regression and conducted various sensitivity analyses to test the robustness of our findings. We calculated the risk of cataract associated with previous exposure to ACEI, measured as OR with 95% CIs, and adjusted the multivariable model for body mass index, smoking, diabetes, hypertension, prescriptions of systemic corticosteroids and other antihypertensive drugs.ResultsWe identified 206 931 cataract cases and the same number of matched controls. Use of ACEI was not associated with a materially altered risk of cataract compared with non-use of ACEI, neither in the main analysis (OR 1.06, 95% CI 1.04 to 1.08) nor in any of the sensitivity or stratified analyses.ConclusionIn our large observational study, use of ACEI was not associated with an altered risk of cataract.
42
KRISTIANSEN, MONICA, RUNE WINTHER, and BENT NATVIG. "ON COMPONENT DEPENDENCIES IN COMPOUND SOFTWARE." International Journal of Reliability, Quality and Safety Engineering 17, no. 05 (October 2010): 465–93. http://dx.doi.org/10.1142/s0218539310003895.
Full textAbstract:
Predicting the reliability of software systems based on a component approach is inherently difficult, in particular due to failure dependencies between the software components. Since it is practically difficult to include all component dependencies in a system's reliability calculation, a more viable approach would be to include only those dependencies that have a significant impact on the assessed system reliability. This paper starts out by defining two new concepts: data-serial and data-parallel components. Then, this paper illustrates how the components' marginal reliabilities put direct restrictions on the components' conditional probabilities, and proves that the degrees of freedom are much fewer than first anticipated when it comes to conditional probabilities. At last, a test system, consisting of five components, is investigated to identify possible rules for selecting the most important component dependencies. To do this, three different techniques are applied: (1) direct calculation, (2) Birnbaum's measure and (3) Principal Component Analysis (PCA). The results from the analyses clearly show that including partial dependency information may give substantial improvements in the reliability predictions, compared to assuming independence between all software components. The analyses also indicate that including only dependencies between data-parallel components may give predictions close to the system's true failure probability, as long as the dependency between the most unreliable components is included. Including only dependencies between data-serial components may however result in predictions even worse than by assuming independence between all software components.
43
Hou, R. "Immune-endocrine biomarkers associated with mental health: a 9-year longitudinal investigation from the Hertfordshire Ageing Study." European Psychiatry 65, S1 (June 2022): S118. http://dx.doi.org/10.1192/j.eurpsy.2022.327.
Full textAbstract:
Introduction Ageing is accompanied by the development of low-grade systemic inflammation which may promote changes in the neural systems predisposing to geriatric depression via the hypothalamic-pituitary-adrenal (HPA) axis. Objectives The aim of this study was to investigate the longitudinal associations between baseline values and conditional changes in immune-endocrine biomarkers and mental health status in a population-based cohort of older adults. Methods Data from 347 subjects(200 men, 147 women) who participated in the Hertfordshire Ageing Study at baseline(mean age 67.3 years) and at 9-year follow-up were analysed. Serum samples for analysis of inflammatory and endocrinological measures were collected at baseline and follow-up. At follow-up, depression (Hospital Anxiety and Depression Scale) and mental health(Short Form-36 questionnaire) were assessed. Baseline values and changes in biomarkers in relation to risk of high depression scores and low mental health scores were examined using logistic regression. Results Lower baseline cortisol was related to greater risk of high depression scores; higher baseline cortisol: Dehydroepiandrosterone Sulphate ratio(men only) and higher baseline CRP(women only) were related to greater risk of poor mental health scores. In addition, greater decline in cortisol was related to increased risk of high depression scores among men. These relationships were robust(p<0.05) after controlling for sex, age, BMI, smoking, alcohol consumption and number of systems medicated. Conclusions This study provides further evidence of the role of the HPA and inflammation in older adults with poor mental health. In addition, the findings highlight sex differences where increased inflammation in women and declines in cortisol in men was linked to poorer mental health. Disclosure No significant relationships.
44
Woo, Yu-Ri, Sehee Wang, Kyung-Ah Sohn, and Hei-Sung Kim. "Epidemiology, Comorbidities, and Prescription Patterns of Korean Prurigo Nodularis Patients: A Multi-Institution Study." Journal of Clinical Medicine 11, no. 1 (December 24, 2021): 95. http://dx.doi.org/10.3390/jcm11010095.
Full textAbstract:
Prurigo nodularis (PN) is a chronic dermatosis typified by extraordinarily itchy nodules. However, little is known of the nature and extent of PN in Asian people. This study aimed to describe the epidemiology, comorbidities, and prescription pattern of PN in Koreans based on a large dermatology outpatient cohort. Patients with PN were identified from the Catholic Medical Center (CMC) clinical data warehouse. Anonymized data on age, sex, diagnostic codes, prescriptions, visitation dates, and other relevant parameters were collected. Pearson correlation analysis was used to calculate the correlation between PN prevalence and patient age. Conditional logistic regression modeling was adopted to measure the comorbidity risk of PN. A total of 3591 patients with PN were identified at the Catholic Medical Center Health System dermatology outpatient clinic in the period 2007–2020. A comparison of the study patients with age- and sex-matched controls (dermatology outpatients without PN) indicated that PN was associated with various comorbidities including chronic kidney disease (adjusted odds ratio (aOR), 1.48; 95% confidence interval (CI), 1.29–1.70), dyslipidemia (aOR, 1.88; 95% CI, 1.56–2.27), type 2 diabetes mellitus (aOR, 1.37; 95% CI, 1.22–1.54), arterial hypertension (aOR, 1.50; 95% CI, 1.30–1.73), autoimmune thyroiditis (aOR, 2.43; 95% CI, 1.42–4.16), non-Hodgkin’s lymphoma (aOR, 1.95; 95% CI, 1.23–3.07), and atopic dermatitis (aOR, 2.16, 95% CI, 1.91–2.45). Regarding prescription patterns, topical steroids were most favored, followed by topical calcineurin inhibitors; oral antihistamines were the most preferred systemic agent for PN. PN is a relatively rare but significant disease among Korean dermatology outpatients with a high comorbidity burden compared to dermatology outpatients without PN. There is great need for breakthroughs in PN treatment.
45
Das, Arup, Subhojit Dawn, Sadhan Gope, and Taha Selim Ustun. "A Risk Curtailment Strategy for Solar PV-Battery Integrated Competitive Power System." Electronics 11, no. 8 (April 15, 2022): 1251. http://dx.doi.org/10.3390/electronics11081251.
Full textAbstract:
Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers connected to the network at the lowest cost without violating the system security limits. Violations of any security limit may result in system risk. The unstable and non-reliable system always has some drawbacks and is not desirable from the consumer’s point of view. A deregulated power market always keeps the consumer on the advantage side by giving stable, reliable, and less costly power. By using risk assessment tools, we identify the fault conditions and we try to minimize the risk by various uses of sequential programming methods. In this paper, a novel power system risk analysis and congestion management approach are introduced with considering meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA) and Artificial Bee Colony Algorithm (ABC) in renewable energy integrated electricity market. The proposed power system risk analysis is constructed with the help of two risk valuation tools named Conditional-Value-at-risk (CVaR) and Value-at-risk (VaR). The higher negative value of VaR and CVaR represents the higher risk system and lower negative value or towards a positive value of VaR and CVaR denotes the less risk or stable system. The projected method has been experienced on the IEEE 14-bus test system and IEEE 30-bus test system to examine the usefulness of the meta-heuristic algorithm in system risk analysis under the deregulated environment. The importance of renewable energy integration in system risk curtailment has also been depicted in this work: basically, to measure the system’s risk, hence enhancing the system’s reliability and societal welfare. As a result, it will benefit both supply and demand-side participants.
46
Xun, Li, Renqiao Jiang, and Jianhua Guo. "The conditional Haezendonck–Goovaerts risk measure." Statistics & Probability Letters 169 (February 2021): 108968. http://dx.doi.org/10.1016/j.spl.2020.108968.
Full text
47
Cerchiello, Paola, and Paolo Giudici. "Conditional graphical models for systemic risk estimation." Expert Systems with Applications 43 (January 2016): 165–74. http://dx.doi.org/10.1016/j.eswa.2015.08.047.
Full text
48
Brechmann, Eike C., Katharina Hendrich, and Claudia Czado. "Conditional copula simulation for systemic risk stress testing." Insurance: Mathematics and Economics 53, no. 3 (November 2013): 722–32. http://dx.doi.org/10.1016/j.insmatheco.2013.09.009.
Full text
49
Nicola, Giancarlo, Paola Cerchiello, and Tomaso Aste. "Information Network Modeling for U.S. Banking Systemic Risk." Entropy 22, no. 11 (November 23, 2020): 1331. http://dx.doi.org/10.3390/e22111331.
Full textAbstract:
In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF (USA Dollar/Swiss Franc) exchange rate. The information theory measures are extracted from a Gaussian Graphical Model constructed from daily stock time series of the top 74 listed US banks. The graphical model is calculated with a recently developed algorithm (LoGo) which provides very fast inference model that allows us to update the graphical model each market day. We therefore can generate daily time series of mutual information and transfer entropy for each bank of the network. The Granger causality between the bank related measures and the financial stress indexes is investigated with both standard Granger-causality and Partial Granger-causality conditioned on control measures representative of the general economy conditions.
50
Khiari, Wided, and Salim Ben Sassi. "On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the △CoVaR Measures." Risks 7, no. 4 (December 12, 2019): 122. http://dx.doi.org/10.3390/risks7040122.
Full textAbstract:
The aim of this work is to assess systemic risk of Tunisian listed banks. The goal is to identify the institutions that contribute the most to systemic risk and that are most exposed to it. We use the CoVaR that considered the systemic risk as the value at risk (VaR) of a financial institution conditioned on the VaR of another institution. Thus, if the CoVaR increases with respect to the VaR, the spillover risk also increases among the institutions. The difference between these measurements is termed △CoVaR, and it allows for estimating the exposure and contribution of each bank to systemic risk. Results allow classifying Tunisian banks in terms of systemic risk involvement. They show that public banks occupy the top places, followed by the two largest private banks in Tunisia. These five banks are the main systemic players in the Tunisian banking sector. It seems that they are the least sensitive to the financial difficulties of existing banks and the most important contributors to the distress of the other banks. This work aims to add a broader perspective to the micro prudential application of regulation, including contagion, proposing a macro prudential vision and strengthening of regulatory policy. Supervisors could impose close supervision for institutions considered as potentially systemic banks. Furthermore, regulations should consider the systemic contribution when defining risk requirements to minimize the consequences of possible herd behavior.