Special Issue "Credit Risk"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Risk".

Deadline for manuscript submissions: closed (30 July 2016).

Special Issue Editor

Prof. Dr. Jingzhi Huang
E-Mail Website
Guest Editor
The Smeal College of Business, Pennsylvania State University
Interests: derivatives; credit risk; fixed-income markets; mutual funds and hedge funds

Special Issue Information

Dear Colleagues,

Credit risk, one of the fundamental factors of financial risk, has received much attention from academics, practitioners and policy makers alike, especially in the aftermath of the global finance crisis. The literature has seen tremendous developments in the topical area of credit risk management with the introduction of new, sophisticated models and measures aimed at quantifying, managing and controlling credit risks as well as building better pricing systems, a variety of which have been implemented by financial institutions. Further studies would lead to better comprehension of the underpinnings of the subject and greater advances in methodologies that have important implications for the stability of the financial system. This special issue aims to present current theoretical and empirical work in the area of credit risk management, measurement, and modeling.

General topics of interest include, but are not limited to:
  • pricing and hedging of credit-sensitive instruments
  • credit risk and liquidity risk
  • credit-spread puzzle
  • credit ratings
  • sovereign debt
  • securitization and structured products

Prof. Dr. Jingzhi Huang
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. Journal of Risk and Financial Management 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.

Published Papers (5 papers)

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Research

Open AccessArticle
Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function
J. Risk Financial Manag. 2016, 9(4), 13; https://doi.org/10.3390/jrfm9040013 - 07 Nov 2016
Cited by 3
Abstract
Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied [...] Read more.
Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method. Full article
(This article belongs to the Special Issue Credit Risk)
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Open AccessArticle
The Design and Risk Management of Structured Finance Vehicles
J. Risk Financial Manag. 2016, 9(4), 12; https://doi.org/10.3390/jrfm9040012 - 28 Oct 2016
Abstract
Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by [...] Read more.
Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by short-term debt, thus bearing material rollover risk on their liabilities which led to defeasance. This paper models these vehicles, and shows that imposing leverage risk control triggers can be optimal for all capital providers, though they may not always be appropriate. The efficacy of these risk controls varies depending on anticipated asset volatility and fire-sale discounts on defeasance. Despite risk management controls, we show that a high failure rate is inherent in the design of these vehicles, and may be mitigated to some extent by including contingent capital provisions in the ex-ante covenants. Post the recent subprime financial crisis, we inform the creation of safer SIVs in structured finance, and propose avenues of mitigating risks faced by senior debt through deleveraging policies in the form of leverage risk controls and contingent capital. Full article
(This article belongs to the Special Issue Credit Risk)
Open AccessArticle
Probability of Default and Default Correlations
J. Risk Financial Manag. 2016, 9(3), 7; https://doi.org/10.3390/jrfm9030007 - 05 Jul 2016
Abstract
We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under [...] Read more.
We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under the independent asset values assumption. At any time, the distance-to-default for a single firm is derived in the system, and this distance-to-default should provide a different measure for credit rating with the correlated asset values into consideration. Then we derive a closed formula for the joint default probability and a general closed formula for the default correlation via the correlated multivariate process of the first-passage-time default correlation model. Our structural model encodes the sensitivities of default correlations with respect to the underlying correlation among firms’ asset values. We propose the disparate credit risk management from our result in contrast to the commonly used risk measurement methods considering default correlations into consideration. Full article
(This article belongs to the Special Issue Credit Risk)
Open AccessArticle
Application of Vine Copulas to Credit Portfolio Risk Modeling
J. Risk Financial Manag. 2016, 9(2), 4; https://doi.org/10.3390/jrfm9020004 - 07 Jun 2016
Cited by 5
Abstract
In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50 [...] Read more.
In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50 and the S&P 500 companies, respectively. Our study includes D-vines and R-vines where the bivariate building blocks are chosen from the Gaussian, the t and the Clayton family. Our findings are (i) the conventional Gauss copula is deficient in modeling the dependence structure of a credit portfolio and economic capital is seriously underestimated; (ii) D-vine structures offer a better statistical fit to the data than classical copulas, but underestimate economic capital compared to R-vines; (iii) when mixing different copula families in an R-vine structure, the best statistical fit to the data can be achieved which corresponds to the most reliable estimate for economic capital. Full article
(This article belongs to the Special Issue Credit Risk)
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Open AccessArticle
Revisiting Structural Modeling of Credit Risk—Evidence from the Credit Default Swap (CDS) Market
J. Risk Financial Manag. 2016, 9(2), 3; https://doi.org/10.3390/jrfm9020003 - 10 May 2016
Cited by 1
Abstract
The ground-breaking Black-Scholes-Merton model has brought about a generation of derivative pricing models that have been successfully applied in the financial industry. It has been a long standing puzzle that the structural models of credit risk, as an application of the same modeling [...] Read more.
The ground-breaking Black-Scholes-Merton model has brought about a generation of derivative pricing models that have been successfully applied in the financial industry. It has been a long standing puzzle that the structural models of credit risk, as an application of the same modeling paradigm, do not perform well empirically. We argue that the ability to accurately compute and dynamically update hedge ratios to facilitate a capital structure arbitrage is a distinctive strength of the Black-Scholes-Merton’s modeling paradigm which could be utilized in credit risk models as well. Our evidence is economically significant: We improve the implementation of a simple structural model so that it is more suitable for our application and then devise a simple capital structure arbitrage strategy based on the model. We show that the trading strategy persistently produced substantial risk-adjusted profit. Full article
(This article belongs to the Special Issue Credit Risk)
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