scholarly journals Measuring Systemic Risk: Common Factor Exposures and Tail Dependence Effects

2013 ◽  
Author(s):  
Wan-Chien Chiu ◽  
Juan Ignacio Peña ◽  
Chih-Wei Wang
Keyword(s):  
Systemic Risk ◽  
Common Factor ◽  
Tail Dependence

scholarly journals Measuring Systemic Risk: Common Factor Exposures and Tail Dependence Effects

2015 ◽  
Vol 21 (5) ◽  
pp. 833-866
Author(s):  
Wan-Chien Chiu ◽  
Juan Ignacio Peña ◽  
Chih-Wei Wang
Keyword(s):  
Systemic Risk ◽  
Common Factor ◽  
Tail Dependence

Diversifying systemic risk in agriculture

2016 ◽  
Vol 76 (4) ◽  
pp. 512-531 ◽  
Author(s):  
Xiaoguang Feng ◽  
Dermot Hayes
Keyword(s):  
Crop Yield ◽  
Systemic Risk ◽  
Crop Insurance ◽  
Tail Dependence ◽  
Model Potential ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Content Type ◽  
Insurance Portfolio ◽  
Systemic Nature

Purpose Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into “poolable” risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.

scholarly journals SYSTEMIC RISK: AN ASYMPTOTIC EVALUATION

2017 ◽  
Vol 48 (02) ◽  
pp. 673-698 ◽  
Author(s):  
Alexandru V. Asimit ◽  
Jinzhu Li
Keyword(s):  
Systemic Risk ◽  
Financial Distress ◽  
Mathematical Formulation ◽  
Tail Dependence ◽  
Value Theory ◽  
Asymptotic Independence ◽  
Regulatory Capital ◽  
Asymptotic Dependence ◽  
Asymptotic Evaluation ◽  
Mathematical Definitions

AbstractSystemic risk (SR) has been shown to play an important role in explaining the financial turmoils in the last several decades and understanding this source of risk has been a particular interest amongst academics, practitioners and regulators. The precise mathematical formulation of SR is still scrutinised, but the main purpose is to evaluate the financial distress of a system as a result of the failure of one component of the financial system in question. Many of the mathematical definitions of SR are based on evaluating expectations in extreme regions and therefore, Extreme Value Theory (EVT) represents the key ingredient in producing valuable estimates of SR and even its decomposition per individual components of the entire system. Without doubt, the prescribed dependence model amongst the system components has a major impact over our asymptotic approximations. Thus, this paper considers various well-known dependence models in the EVT literature that allow us to generate SR estimates. Our findings reveal that SR has a significant impact under asymptotic dependence, while weak tail dependence, known as asymptotic independence, produces an insignificant loss over the regulatory capital.

scholarly journals Dynamic Time Warping Algorithm in Modeling Systemic Risk in the European Insurance Sector

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1022
Author(s):  
Anna Denkowska ◽  
Stanisław Wanat
Keyword(s):  
Systemic Risk ◽  
Dynamic Time Warping ◽  
Spanning Trees ◽  
Tail Dependence ◽  
Risk Identification ◽  
Insurance Companies ◽  
Time Warping ◽  
Insurance Sector ◽  
Dynamic Time ◽  
European Insurance

We are looking for tools to identify, model, and measure systemic risk in the insurance sector. To this aim, we investigated the possibilities of using the Dynamic Time Warping (DTW) algorithm in two ways. The first way of using DTW is to assess the suitability of the Minimum Spanning Trees’ (MST) topological indicators, which were constructed based on the tail dependence coefficients determined by the copula-DCC-GARCH model in order to establish the links between insurance companies in the context of potential shock contagion. The second way consists of using the DTW algorithm to group institutions by the similarity of their contribution to systemic risk, as expressed by DeltaCoVaR, in the periods distinguished. For the crises and the normal states identified during the period 2005–2019 in Europe, we analyzed the similarity of the time series of the topological indicators of MST, constructed for 38 European insurance institutions. The results obtained confirm the effectiveness of MST topological indicators for systemic risk identification and the evaluation of indirect links between insurance institutions.

scholarly journals A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 39 ◽  
Author(s):  
Anna Denkowska ◽  
Stanisław Wanat
Keyword(s):  
Time Series ◽  
Systemic Risk ◽  
Spanning Trees ◽  
Hybrid Approach ◽  
Market Price ◽  
Garch Model ◽  
Tail Dependence ◽  
Insurance Companies ◽  
Insurance Sector ◽  
European Insurance

In the present work, we analyze the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis, we assume that the stock quotations of insurance companies reflect market sentiments, which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. Herein, we propose a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and minimum spanning trees (MST). Using the copula-DCC-GARCH model, we determine the tail dependence coefficients. Then, for each analyzed period we construct MST based on these coefficients. The dynamics are analyzed by means of the time series of selected topological indicators of the MSTs in the years 2005–2019. The contribution to systemic risk of each institution is determined by analyzing the deltaCoVaR time series using the copula-DCC-GARCH model. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk (SR) in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring in the market. We check whether the analyzed MST topological indicators can be considered as systemic risk predictors.

scholarly journals ON THE FAILURE AND SYSTEMIC RISK OF INNOVATION CLUSTER: COPULA APPROACH

2021 ◽  
Vol 19 (01) ◽  
pp. 24-33
Author(s):  
Laura Gudelytė
Keyword(s):  
Systemic Risk ◽  
Innovation Management ◽  
Joint Probability ◽  
Tail Dependence ◽  
External Factors ◽  
Reduced Form ◽  
Innovation Activity ◽  
Innovation Cluster ◽  
Asymmetric Dependence ◽  
The Impact

In order to assess and parameterize the risk of innovation activity implemented by innovation clusters, it is necessary to determine the reliable tools of measuring of systemic risk. Purpose – to propose an adequate approach to evaluate the systemic risk with regard to the impact of interlinkages between cluster entities and other external factors. Research methodology – general overview of research papers and documents presenting concepts and methodologies of evaluation of systemic risk and performance of networked structures as approach to evaluate the systemic risk with regard to the impact of interlinkages between cluster entities and other external factors, applied research. Findings – it is suggested to develop the further parameterization of intensity. Modelling of the tail dependence and asymmetric dependence between pairs of networked positions remains an important task. Research limitations – the lack of information concerning the structure and types of interactions and relationship between the members of innovation cluster. There are made some additional assumptions related to reduced-form approach of credit risk modelling. Practical implications – proposed conceptual model of evaluation of systemic risk should be useful for understanding and further treatment of measuring risk in a case of innovation management. Originality/Value – the concept of the measuring the systemic risk in innovation cluster as a joint probability of correlated failure of commercialization of innovative activity results is proposed and analysed in this paper.

scholarly journals Systemic Risk Modeling with Lévy Copulas

2021 ◽  
Vol 14 (6) ◽  
pp. 251
Author(s):  
Yuhao Liu ◽  
Petar M. Djurić ◽  
Young Shin Kim ◽  
Svetlozar T. Rachev ◽  
James Glimm
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Joint Distribution ◽  
Risk Measure ◽  
Tail Dependence ◽  
Risk Modeling ◽  
Jones Index ◽  
Out Of Sample ◽  
Copula Approach

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

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