Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito

ilustraciones, diagramas, tablas

Autores:: Miranda Bolaños, Bryan Alexander

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
dc.title.translated.eng.fl_str_mv	Machine probabilities and applications to the case of default in credit portfolios
title	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
spellingShingle	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito 330 - Economía::332 - Economía financiera 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Accounts receivable Cuentas por cobrar Cartera de crédito Amortización Modelo de Mezclas Bernoulli Valor en Riesgo Simulación de variables Cadenas de Markov Regresión Beta Probabilidades de incumplimiento Modelo de probabilidades de máquina Puntaje de corte Loan portfolio Variable simulation Markov chains Amortization Beta regression Default probabilities Machine probability model Bernoulli Mixtures Model Cut-off score and Value at Risk
title_short	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
title_full	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
title_fullStr	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
title_full_unstemmed	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
title_sort	Probabilidades de máquina y aplicaciones al caso de default en portafolios de crédito
dc.creator.fl_str_mv	Miranda Bolaños, Bryan Alexander
dc.contributor.advisor.none.fl_str_mv	Giraldo Gomez, Norman Diego
dc.contributor.author.none.fl_str_mv	Miranda Bolaños, Bryan Alexander
dc.subject.ddc.spa.fl_str_mv	330 - Economía::332 - Economía financiera 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	330 - Economía::332 - Economía financiera 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Accounts receivable Cuentas por cobrar Cartera de crédito Amortización Modelo de Mezclas Bernoulli Valor en Riesgo Simulación de variables Cadenas de Markov Regresión Beta Probabilidades de incumplimiento Modelo de probabilidades de máquina Puntaje de corte Loan portfolio Variable simulation Markov chains Amortization Beta regression Default probabilities Machine probability model Bernoulli Mixtures Model Cut-off score and Value at Risk
dc.subject.lemb.none.fl_str_mv	Accounts receivable Cuentas por cobrar
dc.subject.proposal.spa.fl_str_mv	Cartera de crédito Amortización Modelo de Mezclas Bernoulli Valor en Riesgo Simulación de variables Cadenas de Markov Regresión Beta Probabilidades de incumplimiento Modelo de probabilidades de máquina Puntaje de corte
dc.subject.proposal.eng.fl_str_mv	Loan portfolio Variable simulation Markov chains Amortization Beta regression Default probabilities Machine probability model Bernoulli Mixtures Model Cut-off score and Value at Risk
description	ilustraciones, diagramas, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-12
dc.date.accessioned.none.fl_str_mv	2022-03-23T15:18:59Z
dc.date.available.none.fl_str_mv	2022-03-23T15:18:59Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
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dc.relation.references.spa.fl_str_mv	Bauer, E. & Kohavi, R. (1999), ‘An empirical comparison of voting classification algorithms: Bagging, boosting, and variants’, Machine learning 36(1-2), 105–139 Beling, P., Covaliu, Z. & Oliver, R. (2005), ‘Optimal scoring cutoff policies and efficient frontiers’, Journal of the Operational Research Society 56(9), 1016–1029. Blank, L. T., Tarquin, A. J. & B., C. F. M. (2012), Ingenier´ıa econ´omica, number 658.15/B64eE, McGraw-Hill. Block, S. B., Hirt, G. A. & Gómez Mont Araiza, J. T. (2013), Fundamentos de administración financiera., McGraw-Hill Brasil, P. (2010), ‘Diagnosismed: Diagnostic test accuracy evaluation for medical professionals’, R package version 0.2 3, 837–845. Breiman, L. (1996), ‘Bagging predictors’, Machine learning 24(2), 123–140. Breiman, L. (2001), ‘Random forests’, Machine learning 45(1), 5–32. Brémaud, P. (2013), Markov chains: Gibbs fields, Monte Carlo simulation, and queues, Vol. 31, Springer Science & Business Media. Carstensen, B., Plummer, M., Laara, E., Hills, M. & Carstensen, M. B. (2019), ‘Package epi’. Daykin, C. D., Pentikainen, T. & Pesonen, M. (1993), Practical risk theory for actuaries, CRC Press. Dutang, C., Goulet, V., Pigeon, M. et al. (2008), ‘actuar: An r package for actuarial science’, Journal of Statistical software 25(7), 1–37. Efron, B. & Tibshirani, R. J. (1994), An introduction to the bootstrap, CRC press Everitt, B. (2013), Finite mixture distributions, Springer Science & Business Media. Ferrari, S. & Cribari-Neto, F. (2004), ‘Beta regression for modelling rates and proportions’, Journal of applied statistics 31(7), 799–815. Fix, E. & Hodges Jr, J. L. (1951), Discriminatory analysis-nonparametric discrimination: consistency properties, Technical report, California Univ Berkeley. Fluss, R., Faraggi, D. & Reiser, B. (2005), ‘Estimation of the youden index and its associated cutoff point’, Biometrical Journal: Journal of Mathematical Methods in Biosciences 47(4), 458–472. Freeman, E. (2007), ‘Presenceabsence: an r package for presence-absence model evaluation’, USDA Forest Service, Rocky Mountain Research Station 507. Hastie, T., Tibshirani, R. & Friedman, J. (2009), The elements of statistical learning: data mining, inference, and prediction, Springer Science & Business Media Hogg, R. V. & Klugman, S. A. (2009), Loss distributions, Vol. 249, John Wiley & Sons. Kaas, R., Goovaerts, M., Dhaene, J. & Denuit, M. (2008), Modern actuarial risk theory: using R, Vol. 128, Springer Science & Business Media. Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. (2004), ‘kernlab-an s4 package for kernel methods in r’, Journal of statistical software 11(9), 1–20. Khan, M. R. A. A. (2019), ‘Rocit-an r package for performance assessment of binary classifier with visualization’. Kruppa, J., Liu, Y., Biau, G., Kohler, M., K¨onig, I. R., Malley, J. D. & Ziegler, A. (2014a), ‘Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory’, Biometrical Journal 56(4), 534–563. Kruppa, J., Liu, Y., Diener, H.-C., Holste, T., Weimar, C., K¨onig, I. R. & Ziegler, A. (2014b), ‘Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications’, Biometrical Journal 56(4), 564–583. Kruppa, J., Schwarz, A., Arminger, G. & Ziegler, A. (2013), ‘Consumer credit risk: Individual probability estimates using machine learning’, Expert Systems with Applications 40(13), 5125–5131. Kuhn, M. (2009), ‘The caret package’, Journal of Statistical Software 28(5). Liaw, A., Wiener, M. et al. (2002), ‘Classification and regression by randomforest’, R news 2(3), 18–22. Lien, D., Stroud, C. & Ye, K. (2016), ‘Comparing var approximation methods that use the first four moments as inputs’, communications in Statistics-Simulation and Computation 45(2), 491–503. López-Ratón, M., Rodríguez-Alvarez, M. X., Cadarso-Suárez, C., Gude-Sampedro, F. et al. (2014), ‘Optimalcutpoints: an r package for selecting optimal cutpoints in diagnostic tests’, J Stat Softw 61(8), 1–36. Louzada, F., Ara, A. & Fernandes, G. B. (2016), ‘Classification methods applied to credit scoring: Systematic review and overall comparison’, Surveys in Operations Research and Management Science 21(2), 117–134 Novomestky, F., Nadarajah, S. & Novomestky, M. F. (2016), ‘Package ‘truncdist”. Palomares, J. & Peset, M. (2015), Estados financieros Interpretación y an´alisis, España: Pirámide (Grupo Anaya, SA). Ramsay, C. M. (1991), ‘A note of the normal power approximation’, ASTIN Bulletin: The Journal of the IAA 21(1), 147–150. Ripley, B., Venables, W. & Ripley, M. B. (2019), ‘Package class’. Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., M¨uller, M., Siegert, S., Doering, M. & Robin, M. X. (2021), Package ‘proc’, Technical report, 2012-09-10 09: 34. Rolski, T., Schmidli, H., Schmidt, V. & Teugels, J. L. (2009), Stochastic processes for insurance and finance, Vol. 505, John Wiley & Sons Ross, S. A., Westerfield, R., Jordan, B. D. & Biktimirov, E. N. (2016), Essentials of corporate finance, McGraw-Hill/Irwin. Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. (2005), ‘Rocr: visualizing classifier performance in r’, Bioinformatics 21(20), 3940–3941. Spedicato, G. A. (2017), ‘Discrete time markov chains with r’, The R Journal . R package version 0.6.9.7. URL: https://journal.r-project.org/archive/2017/RJ-2017-036/index.html Thiele, C. (2018), ‘Cutpointr: Determine and evaluate optimal cutpoints in binary classification tasks’. Thomopoulos, N. T. (2012), Essentials of Monte Carlo simulation: Statistical methods for building simulation models, Springer Science & Business Media Tse, Y.-K. (2009), Nonlife actuarial models: theory, methods and evaluation, Cambridge University Press. Wright, M. N. & Ziegler, A. (2015), ‘ranger: A fast implementation of random forests for high dimensional data in c++ and r’, arXiv preprint arXiv:1508.04409 . Zeileis, A., Cribari-Neto, F., Gr¨un, B. & Kos-midis, I. (2010), ‘Beta regression in r’, Journal of statistical software 34(2), 1–24.
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dc.format.extent.spa.fl_str_mv	62 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.department.spa.fl_str_mv	Escuela de estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Giraldo Gomez, Norman Diego6fc7af8366501b4f70963c907682a0b2Miranda Bolaños, Bryan Alexandera8e5732cd66aadeb8a1c8673d1b3c24e2022-03-23T15:18:59Z2022-03-23T15:18:59Z2021-12https://repositorio.unal.edu.co/handle/unal/81325Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, tablasEste trabajo final de maestría, modalidad de profundización, consiste en la elaboración de un problema de simulación de carteras de crédito utilizando distribuciones de probabilidad aplicadas a conceptos de matemática financiera, con ello se busca estimar probabilidades de incumplimiento por medio de modelos de probabilidades de maquina como son k-NN, bosques aleatorios y máquinas de soporte vectorial. El trabajo pretende comparar los resultados de cada modelo a través de la optimización de los puntajes de corte pb(0)j, el cálculo de medidas de precisión que evalúen el rendimiento y el valor de las provisiones calculadas usando la cuantificación del riesgo de crédito por medio del Valor en Riesgo (VaR). Así mismo se quiere ilustrar: (1) Los efectos econ+omicos y monetarios derivados de la estimación de probabilidades de incumplimiento incorrecta, (2) las implicaciones de la optimalidad de pb(0)j y (3) el comportamiento de los costos totales o agregados (S) de la cartera de crédito simulada. (Texto tomado de la fuente)This final master’s work, deepening modality, consists of the elaboration of a simulation problem of loan portfolios using probability distributions applied to financial mathematics concepts with this aim to estimate default probabilities using machine probability models such as k-NN, random forests, and vector support machines. The work intends to compare the results of each model through the optimization of the cutoff scores pb(0)j, the calculation of precision measures that evaluate the performance and the value of the provisions calculated using the quantification of credit risk through Value at Risk (V aR). Likewise, we want to illustrate: (1) The economic and monetary effects derived from estimating the probability of incorrect default, (2) the implications of the optimization of pb(0)j and (3) the behavior of the total or aggregate costs (S) of the simulated loan portfolioMaestríaMagíster en Ciencias - EstadísticaÁrea Curricular Estadística62 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Ciencias - Maestría en Ciencias - EstadísticaEscuela de estadísticaFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín330 - Economía::332 - Economía financiera510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasAccounts receivableCuentas por cobrarCartera de créditoAmortizaciónModelo de Mezclas BernoulliValor en RiesgoSimulación de variablesCadenas de MarkovRegresión BetaProbabilidades de incumplimientoModelo de probabilidades de máquinaPuntaje de corteLoan portfolioVariable simulationMarkov chainsAmortizationBeta regressionDefault probabilitiesMachine probability modelBernoulli Mixtures ModelCut-off score and Value at RiskProbabilidades de máquina y aplicaciones al caso de default en portafolios de créditoMachine probabilities and applications to the case of default in credit portfoliosTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBauer, E. & Kohavi, R. (1999), ‘An empirical comparison of voting classification algorithms: Bagging, boosting, and variants’, Machine learning 36(1-2), 105–139Beling, P., Covaliu, Z. & Oliver, R. (2005), ‘Optimal scoring cutoff policies and efficient frontiers’, Journal of the Operational Research Society 56(9), 1016–1029.Blank, L. T., Tarquin, A. J. & B., C. F. M. (2012), Ingenier´ıa econ´omica, number 658.15/B64eE, McGraw-Hill.Block, S. B., Hirt, G. A. & Gómez Mont Araiza, J. T. (2013), Fundamentos de administración financiera., McGraw-HillBrasil, P. (2010), ‘Diagnosismed: Diagnostic test accuracy evaluation for medical professionals’, R package version 0.2 3, 837–845.Breiman, L. (1996), ‘Bagging predictors’, Machine learning 24(2), 123–140.Breiman, L. (2001), ‘Random forests’, Machine learning 45(1), 5–32.Brémaud, P. (2013), Markov chains: Gibbs fields, Monte Carlo simulation, and queues, Vol. 31, Springer Science & Business Media.Carstensen, B., Plummer, M., Laara, E., Hills, M. & Carstensen, M. B. (2019), ‘Package epi’.Daykin, C. D., Pentikainen, T. & Pesonen, M. (1993), Practical risk theory for actuaries, CRC Press.Dutang, C., Goulet, V., Pigeon, M. et al. (2008), ‘actuar: An r package for actuarial science’, Journal of Statistical software 25(7), 1–37.Efron, B. & Tibshirani, R. J. (1994), An introduction to the bootstrap, CRC pressEveritt, B. (2013), Finite mixture distributions, Springer Science & Business Media.Ferrari, S. & Cribari-Neto, F. (2004), ‘Beta regression for modelling rates and proportions’, Journal of applied statistics 31(7), 799–815.Fix, E. & Hodges Jr, J. L. (1951), Discriminatory analysis-nonparametric discrimination: consistency properties, Technical report, California Univ Berkeley.Fluss, R., Faraggi, D. & Reiser, B. (2005), ‘Estimation of the youden index and its associated cutoff point’, Biometrical Journal: Journal of Mathematical Methods in Biosciences 47(4), 458–472.Freeman, E. (2007), ‘Presenceabsence: an r package for presence-absence model evaluation’, USDA Forest Service, Rocky Mountain Research Station 507.Hastie, T., Tibshirani, R. & Friedman, J. (2009), The elements of statistical learning: data mining, inference, and prediction, Springer Science & Business MediaHogg, R. V. & Klugman, S. A. (2009), Loss distributions, Vol. 249, John Wiley & Sons.Kaas, R., Goovaerts, M., Dhaene, J. & Denuit, M. (2008), Modern actuarial risk theory: using R, Vol. 128, Springer Science & Business Media.Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. (2004), ‘kernlab-an s4 package for kernel methods in r’, Journal of statistical software 11(9), 1–20.Khan, M. R. A. A. (2019), ‘Rocit-an r package for performance assessment of binary classifier with visualization’.Kruppa, J., Liu, Y., Biau, G., Kohler, M., K¨onig, I. R., Malley, J. D. & Ziegler, A. (2014a), ‘Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory’, Biometrical Journal 56(4), 534–563.Kruppa, J., Liu, Y., Diener, H.-C., Holste, T., Weimar, C., K¨onig, I. R. & Ziegler, A. (2014b), ‘Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications’, Biometrical Journal 56(4), 564–583.Kruppa, J., Schwarz, A., Arminger, G. & Ziegler, A. (2013), ‘Consumer credit risk: Individual probability estimates using machine learning’, Expert Systems with Applications 40(13), 5125–5131.Kuhn, M. (2009), ‘The caret package’, Journal of Statistical Software 28(5).Liaw, A., Wiener, M. et al. (2002), ‘Classification and regression by randomforest’, R news 2(3), 18–22.Lien, D., Stroud, C. & Ye, K. (2016), ‘Comparing var approximation methods that use the first four moments as inputs’, communications in Statistics-Simulation and Computation 45(2), 491–503.López-Ratón, M., Rodríguez-Alvarez, M. X., Cadarso-Suárez, C., Gude-Sampedro, F. et al. (2014), ‘Optimalcutpoints: an r package for selecting optimal cutpoints in diagnostic tests’, J Stat Softw 61(8), 1–36.Louzada, F., Ara, A. & Fernandes, G. B. (2016), ‘Classification methods applied to credit scoring: Systematic review and overall comparison’, Surveys in Operations Research and Management Science 21(2), 117–134Novomestky, F., Nadarajah, S. & Novomestky, M. F. (2016), ‘Package ‘truncdist”.Palomares, J. & Peset, M. (2015), Estados financieros Interpretación y an´alisis, España: Pirámide (Grupo Anaya, SA).Ramsay, C. M. (1991), ‘A note of the normal power approximation’, ASTIN Bulletin: The Journal of the IAA 21(1), 147–150.Ripley, B., Venables, W. & Ripley, M. B. (2019), ‘Package class’.Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., M¨uller, M., Siegert, S., Doering, M. & Robin, M. X. (2021), Package ‘proc’, Technical report, 2012-09-10 09: 34.Rolski, T., Schmidli, H., Schmidt, V. & Teugels, J. L. (2009), Stochastic processes for insurance and finance, Vol. 505, John Wiley & SonsRoss, S. A., Westerfield, R., Jordan, B. D. & Biktimirov, E. N. (2016), Essentials of corporate finance, McGraw-Hill/Irwin.Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. (2005), ‘Rocr: visualizing classifier performance in r’, Bioinformatics 21(20), 3940–3941.Spedicato, G. A. (2017), ‘Discrete time markov chains with r’, The R Journal . R package version 0.6.9.7. URL: https://journal.r-project.org/archive/2017/RJ-2017-036/index.htmlThiele, C. (2018), ‘Cutpointr: Determine and evaluate optimal cutpoints in binary classification tasks’.Thomopoulos, N. T. (2012), Essentials of Monte Carlo simulation: Statistical methods for building simulation models, Springer Science & Business MediaTse, Y.-K. (2009), Nonlife actuarial models: theory, methods and evaluation, Cambridge University Press.Wright, M. N. & Ziegler, A. (2015), ‘ranger: A fast implementation of random forests for high dimensional data in c++ and r’, arXiv preprint arXiv:1508.04409 .Zeileis, A., Cribari-Neto, F., Gr¨un, B. & Kos-midis, I. (2010), ‘Beta regression in r’, Journal of statistical software 34(2), 1–24.ORIGINAL1085326267.2022.pdf1085326267.2022.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf776687https://repositorio.unal.edu.co/bitstream/unal/81325/3/1085326267.2022.pdf61cd91f317a89d3efac22b691d290193MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81325/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1085326267.2022.pdf.jpg1085326267.2022.pdf.jpgGenerated Thumbnailimage/jpeg3966https://repositorio.unal.edu.co/bitstream/unal/81325/5/1085326267.2022.pdf.jpg4b4410c2b7c4dfadfc6d09df80f4c699MD55unal/81325oai:repositorio.unal.edu.co:unal/813252023-08-02 23:04:06.784Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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