Spatial dependence in credit risk and its improvement in credit scoring

Fernandes, Guilherme B.; Artes, Rinaldo

doi:10.1016/j.ejor.2015.07.013

Cited by 77 publications

(39 citation statements)

References 32 publications

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“…Although the central idea in this article of verifying whether space infl uences credit risk is similar to that of Stine (2011) andFernandes andArtes (2015), the target population and methodology used are diff erent, with no studies being found in the literature that used the GWLR technique for the development of credit scoring models. One advantage of applying the GWLR technique in relation to the others lies in estimating a model for each region in the study, allowing these models to be distinct in their variables and parameters (Atkinson et al, 2003), whereas a global model, represented by only one formula, may not represent local variations adequately.…”

Section: Introductionmentioning

confidence: 98%

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

Albuquerque

Medina

Silva

2017

Rev. contab. finanç.

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This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC), granted to clients residing in the Distrito Federal (DF), to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR) techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower's geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters), with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.

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Section: Introductionmentioning

confidence: 98%

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

Albuquerque

Medina

Silva

2017

Rev. contab. finanç.

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“…To investigate spatial dependence between the companies, Ref. applied an ordinary kriging model to defaults of Brazilian SMEs and found that it improves the credit scoring model. Spatial dependence was also established to be present for the growth of Brazilian SMEs …”

Section: Introductionmentioning

confidence: 99%

“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults

2017

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This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium-sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non-start-ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects.

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“…The method is however sometimes referred to using the terms "Gaussian Processes" and "machine learning". Some existing works using kriging methodology in actuarial sciences and finance concern for example dynamic lifetime adjustments (Debón et al, 2010), variable annuities valuation (Guojun, 2013;Gan and Sheldon Lin, 2015), nested simulation of expected shortfall (Liu and Staum, 2010), Vasicek model calibration (Sousa et al, 2012), stock market linkages (Asgharian et al, 2013) or credit scoring (Fernandes and Artes, 2015). Other works using spatial techniques are Kanevski et al (2008) on interest rates, Benth (2015) for energy futures prices.…”

Section: Introductionmentioning

confidence: 99%

Kriging of financial term-structures

Cousin

Maatouk

Rullière

2016

European Journal of Operational Research

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Due to the lack of reliable market information, building financial term-structures may be associated with a significant degree of uncertainty. In this paper, we propose a new term-structure interpolation method that extends classical spline techniques by additionally allowing for quantification of uncertainty. The proposed method is based on a generalization of kriging models with linear equality constraints (market-fit conditions) and shape-preserving conditions such as monotonicity or positivity (no-arbitrage conditions). We define the most likely curve and show how to build confidence bands. The Gaussian process covariance hyper-parameters under the construction constraints are estimated using cross-validation techniques. Based on observed market quotes at different dates, we demonstrate the efficiency of the method by building curves together with confidence intervals for term-structures of OIS discount rates, of zero-coupon swaps rates and of CDS implied default probabilities. We also show how to construct interest-rate surfaces or default probability surfaces by considering time (quotation dates) as an additional dimension. JEL classification C63; E43; G12

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Spatial dependence in credit risk and its improvement in credit scoring

Cited by 77 publications

References 32 publications

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults

Kriging of financial term-structures

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