Specification Risk and Calibration Effects of a Multifactor Credit Portfolio Model

Dorfleitner, Gregor; Fischer, Matthias; Geidosch, Marco

doi:10.3905/jfi.2012.22.1.007

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…Once having specified the relevant factors for the underlying credit portfolio, 3 the unknown parameters of the factor model need to be estimated for a given default data set (calibration step) (see, for instance, Frye (2008)). 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).…”

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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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

Self Cite

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“…Other studies add additional risk factors like commodity prices (see, for example, Misina, Tessier, and Dey (2006)) or credit spreads (see, for example, Avouyi-Dovi, Bardos, Jardet, Kendaoui, and Moquet (2009)). Of course, many other macroeconomic risk factors might also be relevant to explaining defaults (such as industry production or money supply indicators; see, for example, Dorfleitner et al (2012)). …”

Section: Model Calibrationmentioning

confidence: 99%

A macroeconomic reverse stress test

Grundke

Pliszka

2017

Rev Quant Finan Acc

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Reproduction permitted only if source is stated.ISBN 978-3-95729-185-1 (Printversion) Non-technical summary Research QuestionIn response to the financial crisis [2007][2008][2009], regulatory authorities have strengthened the importance of stress test methodologies and particularly emphasized the role of reverse stress tests. Reverse stress tests look exactly for those scenarios which lead to a very unfavorable event for a bank, for example, an equity-exhausting loss, a non-fulfillment of the capital adequacy requirements or illiquidity. More generally, scenarios shall be identified that lead to an outcome in which the bank's business plan becomes unviable and the bank insolvent. In this paper, we show how a fully-fledged macroeconomic reverse stress test for credit and interest rate risk can be implemented which is in line with the new regulatory requirements. ContributionThis paper contributes to the sparse quantitative reverse stress test literature and sketches a framework which allows to model interactions between different risk factors at the level of individual financial instruments and risk factors. The focus lies on the presentation of the calibration procedure and on a detailed discussion of practical implementation issues. ResultsWe search for the most likely scenario which exhausts the bank's equity. It turns out that this so-called reverse stress test scenario, which is given by a combination of macroeconomic variables, is economically reasonable for the assumed bank portfolio. In particular, for a bank which engages in maturity-transformation, the most likely reverse stress test scenario implies a steeper interest rate curve and an economic downturn. However, the paper also reveals that due to high data requirements and intensive computational efforts, reverse stress tests are exposed to considerable model and estimation risk which makes numerous robustness checks necessary. AbstractReverse stress tests are a relatively new stress test instrument that aims at finding exactly those scenarios that cause a bank to cross the frontier between survival and default. Afterward, the scenario which is most probable has to be identified. This paper sketches a framework for a quantitative reverse stress test for maturity-transforming banks that are exposed to credit and interest rate risk and demonstrates how the model can be calibrated empirically. The main features of the proposed framework are: 1) The necessary steps of a reverse stress test (solving an inversion problem and computing the scenario probabilities) can be performed within one model, 2) Scenarios are characterized by realizations of macroeconomic risk factors, 3) Principal component analysis helps to reduce the dimensionality of the space of systematic risk factors, 4) Due to data limitations, the results of reverse stress tests are exposed to considerable model and estimation risk, which makes numerous robustness checks necessary.Keywords: copula functions, extreme value theory, principal component analysis, reverse stress testing JEL classi...

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“…However, a few examples are given on the following pages. For more information about the question of sector parametrization we refer to Hamerle and Rösch (2006) or Dorfleitner, Fischer, and Geidosch (2012).…”

Section: Modifying Distributional Assumptionsmentioning

confidence: 99%

GCPM: A ?exible package to explore credit portfolio risk

Jakob

Fischer

2016

AJS

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In this article we introduce the novel GCPM package, which represents a generalized credit portfolio model framework. The package includes two of the most popular mod- eling approaches in the banking industry namely the CreditRisk+ and the CreditMetrics model and allows to perform several sensitivity analysis with respect to distributional or functional assumptions. Therefore, besides the pure quantiﬁcation of credit portfolio risk, the package can be used to explore certain aspects of model risk individually for every arbitrary credit portfolio. In order to guarantee maximum ﬂexibility, most of the models utilize a Monte Carlo simulation, which is implemented in C++, to achieve the loss dis- tribution. Furthermore, the package also oﬀers the possibilities to apply simple pooling techniques to speed up calculations for large portfolios as well as a general importance sample approach. The article concludes with a comprehensive example demonstrating the ﬂexibility of the package.

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Specification Risk and Calibration Effects of a Multifactor Credit Portfolio Model

Cited by 11 publications

References 28 publications

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

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

A macroeconomic reverse stress test

GCPM: A ?exible package to explore credit portfolio risk

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