Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults

Keywords

Logistic regression, Econometrics, Default, Statistics, Extreme value theory

References

• Extreme Value Theory for Risk ManagersAlexander J. McNeil — 1999
• Measuring Market RiskKevin Dowd — 2002
• Default and Asset Correlation: An Empirical Study for Italian SMEsPiero Vozzella, Giampaolo Gabbi — 2010
• Portfolio Credit RiskThomas C. Wilson — 1998
• Machine learning and statistics: the interfaceM. Turmon, Gholamreza Nakhaeizadeh, Charles Taylor — 1996
• Modelling Extremal Events for Insurance and FinanceJem N. Corcoran — 2002
• An Introduction to Generalized Linear ModelsEric R. Ziegel — 2002
• MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKETEdward I. Altman, Gabriele Sabato — 2013
• ZETATM analysis A new model to identify bankruptcy risk of corporationsEdward I. Altman, R. G. Haldeman, Paul Narayanan — 1977
• The Impact of Basel II on Lending to Small- and Medium-Sized Firms: A Regulatory Policy Assessment Based on Spanish Credit Register DataJesús Saurina, Carlos Trucharte — 2004
• The Estimation of Choice Probabilities from Choice Based SamplesCharles F. Manski, Steven R. Lerman — 1977
• Estimation of Response Probabilities From Augmented Retrospective ObservationsDavid A. Hsieh, Charles F. Manski, Daniel McFadden — 1985
• Extreme Values in Finance, Telecommunications, and the EnvironmentB. Finkenstädt, H. Rootzén — 2003
• Laws of Small Numbers: Extremes and Rare EventsMichael Falk, Rolf–Dieter Reiss, Jürg Hüsler — 1994
• Defining attributes for scorecard construction in credit scoringDavid J. Hand, Niall M. Adams — 2000
• A case-cohort design for epidemiologic cohort studies and disease prevention trialsRoss L. Prentice — 1986
• An Empirical Test of Financial Ratio Analysis for Small Business Failure PredictionRobert O. Edmister — 1972
• Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEsMichel Dietsch, Joël Petey — 2004
• Extreme value statistics for novelty detection in biomedical signal processingStephen Roberts — 2000
• A PRECIPITATION CLIMATOLOGY OF THE ALPS FROM HIGH-RESOLUTION RAIN-GAUGE OBSERVATIONSChristoph Frei, Christoph Schär — 1998

Cited by

• Cost-sensitive classification for rare events: an application to the credit rating model validation for SMEsRaffaella Calabrese, Geary Wp — 2011
• Improving Classifier Performance Assessment of Credit Scoring ModelsRaffaella Calabrese — 2012
• An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage marketTrevor Fitzpatrick, Christophe Mues — 2016
• Financial Performance in Manufacturing Firms: A Comparison Between Parametric and Non-Parametric ApproachesEleonora Bartoloni, Maurizio Baussola — 2014
• GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is RareArpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan — 2014
• Parameter estimation for the imbalanced credit scoring data using AUC maximizationChae‐Seon Hong, Chi Hwan Won — 2016
• Modeling Loan Defaults in Kenya Banks as a Rare Event Using the Generalized Extreme Value Regression ModelStephen Muthii Wanjohi — 2016
• GEV Regression with Convex Loss Applied to Imbalanced Binary ClassificationZhang Haolin, Gongshen Liu, Li Pan — 2016
• Solvency prediction for small and medium enterprises in bankingSilvia Figini, Federico Bonelli, Emanuele Giovannini — 2017
• Comparative Study on Logit and BGEVA Models in Assessing Corporate Default Risk: Based on Large Data of American Listed CompaniesHao-yue ZHOU, Yue Wang, Li Lu — 2019
• Prediction of default probability by using statistical models for rare eventsEmmanuel Ogundimu — 2019
• GEV-NN: A deep neural network architecture for class imbalance problem in binary classificationLkhagvadorj Munkhdalai, Tsendsuren Munkhdalai, Keun Ho Ryu — 2020
• Determinants of Gender-based Violence Against Women in Spain: An Asymmetric Bayesian ModelJosé María Pérez Sánchez, Nancy Dávila–Cárdenes, Emilio Gómez–Déniz — 2020
• Quantification of model risk that is caused by model misspecificationModisane Seitshiro, H.P. Mashele — 2020
• RiskLogitboost Regression for Rare Events in Binary Response: An Econometric ApproachJessica Pesantez-Narvaez, Montserrat Guillén, Manuela Alcañiz — 2021
• An improved GEV boosting method for imbalanced data classification with application to short-term rainfall predictionShuaida He, Zhouping Li, Xinwei Liu — 2022
• Predicting financial distress in Indonesian life insurance companies with classification methods and synthetic features generationDwi Purwanto, Dedy Dwi Prastyo — 2025
• Valuation of initial margin and model riskModisane Seitshiro — 2020
• Severity Modeling of Extreme Insurance ClaimsChristian Laudagg, Sascha Desmettre, Jörg Wenzel — 2018
• RA EXTREMOS DE PRECIPITAÇÃO. ESTUDO DEPrograma de Pós — 2014

Related papers

• Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression modelRaffaella Calabrese, Silvia Angela Osmetti — 2013
• Zero-inflated generalized extreme value regression model for binary data and application in health studyAba Diop, El Hadji Dème, Aliou Diop — 2021
• Improving Forecast of Binary Rare Events Data: A GAM-Based ApproachRaffaella Calabrese, Silvia Angela Osmetti — 2015
• SMOTE: Synthetic Minority Over-sampling TechniqueNitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall — 2002
• The Maximum Likelihood Estimates with Wrong Logistic Regression Model and Covariate Assumptions are ViolatedNuri H. Salem Badi, Mohamed M. Shakandli — 2021
• Bayesian networks with a logistic regression model for the conditional probabilitiesFrank Rijmen — 2008
• Censored Regression Models with Unobserved Stochastic Censoring ThresholdsForrest D. Nelson — 1977