Application of Vine Copulas to Credit Portfolio Risk Modeling

Geidosch, Marco; Fischer, Matthias

doi:10.3390/jrfm9020004

Cited by 21 publications

(15 citation statements)

References 45 publications

(40 reference statements)

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“…In addition, the effect of tail dependence of bivariate linking copulas on that of a vine copula is investigated. Geidosch and Fisher (2016) [19] show the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. Fischer et al [20] use vine copula based quantile regression to stress testing German industry sectors.…”

Section: Background and Modelsmentioning

confidence: 99%

Risk Measurement and Risk Modelling Using Applications of Vine Copulas

2017

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This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite financial risk. Copula-based dependence modelling is a popular tool in financial applications, but is usually applied to pairs of securities. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. The paper features the use of Regular Vine copulas in an analysis of the co-dependencies of 10 major European Stock

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Section: Background and Modelsmentioning

confidence: 99%

Risk Measurement and Risk Modelling Using Applications of Vine Copulas

2017

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“…So and Yeung (2014) discussed the construction+ of Vine Copula structure and studied the relationship between financial markets and Vine Copula theory. Geidosch and Fischer (2016) confirmed the advantages of Vine Copulas over traditional Copula in simulating the dependent structure of credit portfolios. Aiming to measure risk and finding the optimal weights of portfolios containing three financial instruments, Pastpipatkul et al 2018used C-D vine Copulas method to establish the dependence relationship of each pair of financial instruments and used Monte Carlo simulation technology to generate simulation data to calculate risk value (VaR) and expected shortfall.…”

Section: The Dependence Of Financial Datamentioning

confidence: 54%

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Sun¹,

Liu

et al. 2019

JRFM

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The literature on portfolio selection and risk measurement has considerably advanced in recent years. The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk measurement. In the summary of the characteristics of financial data, we summarize the literature on fat tail and dependence characteristic of financial data. In the portfolio selection model part, we cover three models: mean-variance model, global minimum variance (GMV) model and factor model. In the portfolio risk measurement part, we first classify risk measurement methods into two categories: moment-based risk measurement and moment-based and quantile-based risk measurement. Moment-based risk measurement includes time-varying covariance matrix and shrinkage estimation, while moment-based and quantile-based risk measurement includes semi-variance, VaR and CVaR.

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“…Dorfleitner et al (2012) deal with specification risk and calibration effects of a multifactor credit portfolio model, whereas Pfeuffer et al (2018) provide a detailed simulation study on different estimation methods. For a discussion of non-Gaussian dependence structures in terms of copulas, we refer to Jakob and Fischer (2014) and Fischer and Jakob (2015), or with focus on vine copulas to the work of Geidosch and Fischer (2016). Jovan and Ahčan (2017) and Hainaut and Colwell (2016) discuss alternative processes driving the creditworthiness of the counterparties.…”

Section: Credit Risk and Credit Portfolio Modelingmentioning

confidence: 99%

A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

Fischer

Moser²,

Pfeuffer

2018

Risks

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In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler's allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that-in principle-all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures.

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Application of Vine Copulas to Credit Portfolio Risk Modeling

Cited by 21 publications

References 45 publications

Risk Measurement and Risk Modelling Using Applications of Vine Copulas

Risk Measurement and Risk Modelling Using Applications of Vine Copulas

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

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