Credit Portfolio Risk Evaluation with Non‐Gaussian One‐factor Merton Models and its Application to CDO Pricing

The goal of this paper is to develop the dynamic alpha (α)-stable method for collateralized debt obligation (CDO) pricing based on the α-stable distributions, which will resolve the two issues caused by using traditional static factor copula method in the practice, which means when pricing CDOs, the traditional static factor Copula method does not only exhibit the correlation smile phenomenon which is not inconsistent with the model's assumption, but also cannot be used in pricing CDOs or credit portfolio derivatives for the underlying portfolio with different maturities. As the applications, we present calibration and empirical numerical results for iTraxx Europe Tranches quotes from the market data on March 30, 2007. Thus, our new method under the framework of the dynamic α-stable model is the way for CDO pricing in the practice, and should be useful for the risk management in the practice too.

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Measuring and Managing Credit Portfolio Risk

Contributions to Economics - Risk Measurement, Econometrics and Neural Networks ◽

10.1007/978-3-642-58272-1_14 ◽

1998 ◽

pp. 259-306 ◽

Cited By ~ 6

Author(s):

Thomas C. Wilson

Keyword(s):

Portfolio Risk ◽

Credit Portfolio

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Credit Portfolio Risk and Diversification

Credit Securitizations and Derivatives ◽

10.1002/9781118818503.ch8 ◽

2013 ◽

pp. 153-170

Author(s):

Rudi Schäfer ◽

Alexander F. R. Koivusalo ◽

Thomas Guhr

Keyword(s):

Portfolio Risk ◽

Credit Portfolio

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An integrated eigenvector–DEA–TOPSIS methodology for portfolio risk evaluation in the FOREX spot market

Expert Systems with Applications ◽

10.1016/j.eswa.2009.05.041 ◽

2010 ◽

Vol 37 (1) ◽

pp. 509-516 ◽

Cited By ~ 35

Author(s):

M. Amiri ◽

M. Zandieh ◽

B. Vahdani ◽

R. Soltani ◽

V. Roshanaei

Keyword(s):

Risk Evaluation ◽

Spot Market ◽

Portfolio Risk

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Importance Sampling for Credit Portfolio Risk with Risk Factors Having t-Copula

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622017500201 ◽

2017 ◽

Vol 16 (04) ◽

pp. 1101-1124 ◽

Cited By ~ 6

Author(s):

Rongda Chen ◽

Ze Wang ◽

Lean Yu

Keyword(s):

Risk Factors ◽

Importance Sampling ◽

Variance Reduction ◽

Heavy Tails ◽

Risk Measure ◽

Optimization Technique ◽

Tail Probability ◽

Portfolio Risk ◽

Marquardt Algorithm ◽

Credit Portfolio

This paper proposes an efficient simulation method for calculating credit portfolio risk when risk factors have a heavy-tailed distributions. In modeling heavy tails, its features of return on underlying asset are captured by multivariate [Formula: see text]-Copula. Moreover, we develop a three-step importance sampling (IS) procedure in the [Formula: see text]-copula credit portfolio risk measure model for further variance reduction. Simultaneously, we apply the Levenberg–Marquardt algorithm associated with nonlinear optimization technique to solve the problem that estimates the mean-shift vector of the systematic risk factors after the probability measure change. Numerical results show that those methods developed in the [Formula: see text]-copula model can produce large variance reduction relative to the plain Monte Carlo method, to estimate more accurately tail probability of credit portfolio loss distribution.

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Default Correlations and Credit Portfolio Risk

Credit Risk Analytics ◽

10.1002/9781119449560.ch9 ◽

2017 ◽

pp. 237-270 ◽

Cited By ~ 2

Keyword(s):

Portfolio Risk ◽

Credit Portfolio

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GCPM: A ?exible package to explore credit portfolio risk

Austrian Journal of Statistics ◽

10.17713/ajs.v45i1.87 ◽

2016 ◽

Vol 45 (1) ◽

pp. 25-44 ◽

Cited By ~ 1

Author(s):

Kevin Jakob ◽

Matthias Fischer

Keyword(s):

Sensitivity Analysis ◽

Banking Industry ◽

The Novel ◽

Portfolio Risk ◽

Model Framework ◽

Model Risk ◽

General Importance ◽

Speed Up ◽

Credit Portfolio ◽

Credit Portfolio Model

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 quanti?cation 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 ?exibility, most of the models utilize a Monte Carlo simulation, which is implemented in C++, to achieve the loss dis- tribution. Furthermore, the package also o?ers 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 ?exibility of the package.

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Criteria for Classification of Risks of Project Financing in Оrder to Manage the Risks of Specialized Lending Portfolio

The world of new economy ◽

10.26794/2220-6469-2018-12-2-58-65 ◽

2018 ◽

Vol 12 (2) ◽

pp. 58-65

Author(s):

T. S. Gaibov

Keyword(s):

Relevant Literature ◽

Project Finance ◽

Counterparty Risk ◽

Portfolio Risk ◽

Management Tools ◽

Product Risk ◽

Risk Taxonomy ◽

Credit Portfolio ◽

Finance Risk

In the article we analyzed international and Russian methodological approaches for classification of risk in project finance and identified crucial criteria which provide further framework for development of principles and management mechanism of specialized credit portfolio at commercial bank. Considering relevant literature and taking into account the main purpose of the study, the practical use of aggregation of project finance risk taxonomy was concluded and three groups of risk were proposed: product risk, counterparty risk and portfolio risk. For each group, it was highlighted list of significant risks all of which shall be subsequently integrated into overall assessment of risks’ concentration of specialized credit portfolio together with some management tools.

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