GCPM: A ?exible package to explore credit portfolio risk

Jakob, Kevin; Fischer, Matthias

doi:10.17713/ajs.v45i1.87

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The package includes a simulative framework with the link function (3) of the CreditMetrics type. The simulation process described in [17] contains the following steps for N > 0 simulations: Algorithm 1 Simulation Algorithm for n = 1, ..., N (simulation loop) draw sector realizations s n = (s n 1 , ..., s n K ) ∼ S for i = 1, ..., M (counterparty loop) calculate conditional PD: PD S i := Φ…”

Section: Overview Of Credit Portfolio Modelmentioning

confidence: 99%

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Fast Approximation Methods for Credit Portfolio Risk Calculations

Jakob,

Churt,

Nolte

et al. 2023

Preprint

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Credit risk is one of the main risks financial institutions are exposed to. Within the last two decades simulation-based credit portfolio models became extremely popular and replaced closed analytical ones as computers became more powerful. However, especially for non-homogenous and non-granular portfolios a full simulation of a credit portfolio model is still time consuming, which can be disadvantage within some use cases like credit pricing or within stress testing situations where results must be available very quickly. For this purpose, we investigate if methods based on artificial intelligence (AI) can be helpful to approximate a credit portfolio model. We compare the performance of AI-based methods within three different use cases with those of classical regression methods. As a result, we see that generally AI-based methods are able to capture portfolio characteristics and to and speed-up calculations but depending on the specific use case and the availability of training data they are not necessarily always the best choice. Particularly, considering the time and costs for collecting data and training of the complex algorithms, classical methods can be as good as or even better as AI-based ones with less effort.

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Section: Overview Of Credit Portfolio Modelmentioning

confidence: 99%

“…Within the GCPM package, it holds true that M i=1 RC ESα i = ES α . For more detailed information, please refer to [17] or the package documentation.…”

Section: Overview Of Credit Portfolio Modelmentioning

confidence: 99%

Fast Approximation Methods for Credit Portfolio Risk Calculations

Jakob,

Churt,

Nolte

et al. 2023

Preprint

Self Cite

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“…A technical implementation (see, in particular, Algorithm 1 from Jakob and Fischer (2016)) can be found in the R package GCPM, which was used to generate the loss distribution for the hypothetical portfolios in the empirical part.…”

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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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Fast approximation methods for credit portfolio risk calculations

Jakob,

Churt,

Fischer

et al. 2023

Digit Finance

View full text Add to dashboard Cite

Credit risk is one of the main risks financial institutions are exposed to. Within the last two decades, simulation-based credit portfolio models became extremely popular and replaced closed-form analytical ones as computers became more powerful. However, especially for non-homogenous and non-granular portfolios, a full simulation of a credit portfolio model is still time consuming, which can be disadvantageous within some use cases like credit pricing or within stress testing situations where results must be available very quickly. For this purpose, we investigate if methods based on artificial intelligence (AI) can be helpful to approximate a credit portfolio model. We compare the performance of AI-based methods within three different use cases with suitable non AI-based regression methods. As a result, we see that AI-based methods can generally capture portfolio characteristics and speed-up calculations but - depending on the specific use case and the availability of training data - they are not necessarily always the best choice. Particularly, considering the time and costs for collecting data and training of the complex algorithms, non-AI-based methods can be as good as or even better than AI-based ones, while requiring less computational effort.

show abstract

GCPM: A ?exible package to explore credit portfolio risk

Cited by 3 publications

References 18 publications

Fast Approximation Methods for Credit Portfolio Risk Calculations

Fast Approximation Methods for Credit Portfolio Risk Calculations

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

Fast approximation methods for credit portfolio risk calculations

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