Special Issue "Advances in Credit Risk Modeling and Management"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 March 2019).

Special Issue Editor

Prof. Dr. Frédéric Vrins
Website
Guest Editor
Louvain Finance Center & CORE, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Interests: credit risk modelling; counterparty risk; stochastic calculus; information theory

Special Issue Information

Dear Colleagues,

Correctly assessing credit risk still represents an important challenge for both practitioners and scholars. On the one hand, credit risk measures play a central role in the banking sector’s regulations, governing the profitability of financial institutions which remain at the heart of our economic system. On the other hand, effectively computing such measures in a sound and rigorous way triggers important challenges because of the lack of relevant information and/or models. In practice, computing a 99.9% VaR in a statistically meaningful way is not so easy, and typically requires a lot of assumptions that may significantly impact the figures. It is therefore important that academics pursue efforts to improve this.

This Special Issue aims at collating papers contributing methodologically and/or computationally, towards a more rigorous and reliable management of credit risk of financial institutions. Theoretical and empirical research works covering theoretical properties and/or computational aspects of risk measures are welcome.

Prof. Dr. Frédéric Vrins
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • recovery rate modeling
  • default models
  • computational techniques
  • economic capital
  • Basel regulation
  • counterparty risk
  • risk measures

Published Papers (9 papers)

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Open AccessArticle
Credit Valuation Adjustment Compression by Genetic Optimization
Risks 2019, 7(4), 100; https://doi.org/10.3390/risks7040100 - 29 Sep 2019
Abstract
Since the 2008–2009 financial crisis, banks have introduced a family of X-valuation adjustments (XVAs) to quantify the cost of counterparty risk and of its capital and funding implications. XVAs represent a switch of paradigm in derivative management, from hedging to balance sheet optimization. [...] Read more.
Since the 2008–2009 financial crisis, banks have introduced a family of X-valuation adjustments (XVAs) to quantify the cost of counterparty risk and of its capital and funding implications. XVAs represent a switch of paradigm in derivative management, from hedging to balance sheet optimization. They reflect market inefficiencies that should be compressed as much as possible. In this work, we present a genetic algorithm applied to the compression of credit valuation adjustment (CVA), the expected cost of client defaults to a bank. The design of the algorithm is fine-tuned to the hybrid structure, both discrete and continuous parameter, of the corresponding high-dimensional and nonconvex optimization problem. To make intensive trade incremental XVA computations practical in real-time as required for XVA compression purposes, we propose an approach that circumvents portfolio revaluation at the cost of disk memory, storing the portfolio exposure of the night so that the exposure of the portfolio augmented by a new deal can be obtained at the cost of computing the exposure of the new deal only. This is illustrated by a CVA compression case study on real swap portfolios. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessArticle
An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages
Risks 2019, 7(3), 76; https://doi.org/10.3390/risks7030076 - 07 Jul 2019
Abstract
We propose an alternative approach to the modeling of the positive dependence between the probability of default and the loss given default in a portfolio of exposures, using a bivariate urn process. The model combines the power of Bayesian nonparametrics and statistical learning, [...] Read more.
We propose an alternative approach to the modeling of the positive dependence between the probability of default and the loss given default in a portfolio of exposures, using a bivariate urn process. The model combines the power of Bayesian nonparametrics and statistical learning, allowing for the elicitation and the exploitation of experts’ judgements, and for the constant update of this information over time, every time new data are available. A real-world application on mortgages is described using the Single Family Loan-Level Dataset by Freddie Mac. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessArticle
Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania
Risks 2019, 7(2), 67; https://doi.org/10.3390/risks7020067 - 13 Jun 2019
Cited by 2
Abstract
In this research, trade credit is analysed form a seller (supplier) perspective. Trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time increases the risk of the supplier’s [...] Read more.
In this research, trade credit is analysed form a seller (supplier) perspective. Trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time increases the risk of the supplier’s insolvency. If the supplier is a small or micro-enterprise (SMiE), it is usually an issue of human and technical resources. Therefore, when dealing with these issues, the supplier needs a high accuracy but simple and highly interpretable trade credit risk assessment model that allows for assessing the risk of insolvency of buyers (who are usually SMiE). The aim of the research is to create a statistical enterprise trade credit risk assessment (ETCRA) model for Lithuanian small and micro-enterprises (SMiE). In the empirical analysis, the financial and non-financial data of 734 small and micro-sized enterprises in the period of 2010–2012 were chosen as the samples. Based on the logistic regression, the ETCRA model was developed using financial and non-financial variables. In the ETCRA model, the enterprise’s financial performance is assessed from different perspectives: profitability, liquidity, solvency, and activity. Varied model variants have been created using (i) only financial ratios and (ii) financial ratios and non-financial variables. Moreover, the inclusion of non-financial variables in the model does not substantially improve the characteristics of the model. This means that the models that use only financial ratios can be used in practice, and the models that include non-financial variables can also be used. The designed models can be used by suppliers when making decisions of granting a trade credit for small or micro-enterprises. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessArticle
Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model
Risks 2019, 7(2), 66; https://doi.org/10.3390/risks7020066 - 12 Jun 2019
Cited by 1
Abstract
One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian Motion (GBM) is a widely used [...] Read more.
One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian Motion (GBM) is a widely used method for modeling the evolution of exchange rates. An important limitation of GBM is that, due to the assumption of constant drift and volatility, stylized facts of financial time-series, such as volatility clustering and heavy-tailedness in the returns distribution, cannot be captured. We propose a model where volatility and drift are able to switch between regimes; more specifically, they are governed by an unobservable Markov chain. Hence, we model exchange rates with a hidden Markov model (HMM) and generate scenarios for counterparty exposure using this approach. A numerical study is carried out and backtesting results for a number of exchange rates are presented. The impact of using a regime-switching model on counterparty exposure is found to be profound for derivatives with non-linear payoffs. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessArticle
Default Ambiguity
Risks 2019, 7(2), 64; https://doi.org/10.3390/risks7020064 - 10 Jun 2019
Abstract
This paper discusses ambiguity in the context of single-name credit risk. We focus on uncertainty in the default intensity but also discuss uncertainty in the recovery in a fractional recovery of the market value. This approach is a first step towards integrating uncertainty [...] Read more.
This paper discusses ambiguity in the context of single-name credit risk. We focus on uncertainty in the default intensity but also discuss uncertainty in the recovery in a fractional recovery of the market value. This approach is a first step towards integrating uncertainty in credit-risky term structure models and can profit from its simplicity. We derive drift conditions in a Heath–Jarrow–Morton forward rate setting in the case of ambiguous default intensity in combination with zero recovery, and in the case of ambiguous fractional recovery of the market value. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessArticle
The Determinants of Market-Implied Recovery Rates
Risks 2019, 7(2), 57; https://doi.org/10.3390/risks7020057 - 18 May 2019
Abstract
In the presence of recovery risk, the recovery rate is a random variable whose risk-neutral expectation can be inferred from the prices of defaultable instruments. I extract market-implied recovery rates from the term structures of credit default swap spreads for a sample of [...] Read more.
In the presence of recovery risk, the recovery rate is a random variable whose risk-neutral expectation can be inferred from the prices of defaultable instruments. I extract market-implied recovery rates from the term structures of credit default swap spreads for a sample of 497 United States (U.S.) corporate issuers over the 2005–2014 period. I analyze the explanatory factors of market-implied recovery rates within a linear regression framework and also within a Tobit model, and I compare them with the determinants of historical recovery rates that were previously identified in the literature. In contrast to their historical counterparts, market-implied recovery rates are mostly driven by macroeconomic factors and long-term, issuer-specific variables. Short-term financial variables and industry conditions significantly impact the slope of market-implied recovery rates. These results indicate that the design of a recovery risk model should be based on specific market factors, not on the statistical evidence that is provided by historical recovery rates. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessFeature PaperArticle
Contingent Convertible Debt: The Impact on Equity Holders
Risks 2019, 7(2), 47; https://doi.org/10.3390/risks7020047 - 29 Apr 2019
Cited by 1
Abstract
Contingent Convertible (CoCo) is a hybrid debt issued by banks with a specific feature forcing its conversion to equity in the event of the bank’s financial distress. CoCo carries two major risks: the risk of default, which threatens any type of debt instrument, [...] Read more.
Contingent Convertible (CoCo) is a hybrid debt issued by banks with a specific feature forcing its conversion to equity in the event of the bank’s financial distress. CoCo carries two major risks: the risk of default, which threatens any type of debt instrument, plus the exclusive risk of mandatory conversion. In this paper, we propose a model to value CoCo debt instruments as a function of the debt ratio. Although the CoCo is a more expensive instrument than traditional debt, its presence in the capital structure lowers the cost of ordinary debt and reduces the total cost of debt. For preliminary equity holders, the presence of CoCo in the bank’s capital structure increases the shareholder’s aggregate value. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessArticle
Modelling Recovery Rates for Non-Performing Loans
Risks 2019, 7(1), 19; https://doi.org/10.3390/risks7010019 - 20 Feb 2019
Cited by 3
Abstract
Based on a rich dataset of recoveries donated by a debt collection business, recovery rates for non-performing loans taken from a single European country are modelled using linear regression, linear regression with Lasso, beta regression and inflated beta regression. We also propose a [...] Read more.
Based on a rich dataset of recoveries donated by a debt collection business, recovery rates for non-performing loans taken from a single European country are modelled using linear regression, linear regression with Lasso, beta regression and inflated beta regression. We also propose a two-stage model: beta mixture model combined with a logistic regression model. The proposed model allowed us to model the multimodal distribution we found for these recovery rates. All models were built using loan characteristics, default data and collections data prior to purchase by the debt collection business. The intended use of the models was to estimate future recovery rates for improved risk assessment, capital requirement calculations and bad debt management. They were compared using a range of quantitative performance measures under K-fold cross validation. Among all the models, we found that the proposed two-stage beta mixture model performs best. Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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Open AccessErratum
Erratum: Hui Ye, Anthony Bellotti. Modelling Recovery Rates for Non-Performing Loans. Risks 7 (2019): 19
Risks 2020, 8(2), 42; https://doi.org/10.3390/risks8020042 - 29 Apr 2020
Abstract
The authors wish to make the following corrections to this paper (Ye and Bellotti 2019): [...] Full article
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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