Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos
In this work, the parameters for the default intensity of observable covariates in the presence of an unobservable fragility factor are estimated. The observable information corresponds to the evolution In this work, the parameters for the default intensity of observable covariates in the presence o...
- Autores:
-
Bernal Berrio, Luis Alberto
- Tipo de recurso:
- Work document
- Fecha de publicación:
- 2019
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/75596
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/75596
- Palabra clave:
- Matemáticas::Probabilidades y matemáticas aplicadas
Intensidad de default
Proceso de Cox
Algoritmo EM
Muestreador de Gibbs
Default Intensity
Cox Process
EM Algorithm
Gibbs Sampler
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| title |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| spellingShingle |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos Matemáticas::Probabilidades y matemáticas aplicadas Intensidad de default Proceso de Cox Algoritmo EM Muestreador de Gibbs Default Intensity Cox Process EM Algorithm Gibbs Sampler |
| title_short |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| title_full |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| title_fullStr |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| title_full_unstemmed |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| title_sort |
Modelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltos |
| dc.creator.fl_str_mv |
Bernal Berrio, Luis Alberto |
| dc.contributor.advisor.spa.fl_str_mv |
Gómez Vélez, César Augusto |
| dc.contributor.author.spa.fl_str_mv |
Bernal Berrio, Luis Alberto |
| dc.subject.ddc.spa.fl_str_mv |
Matemáticas::Probabilidades y matemáticas aplicadas |
| topic |
Matemáticas::Probabilidades y matemáticas aplicadas Intensidad de default Proceso de Cox Algoritmo EM Muestreador de Gibbs Default Intensity Cox Process EM Algorithm Gibbs Sampler |
| dc.subject.proposal.spa.fl_str_mv |
Intensidad de default Proceso de Cox Algoritmo EM Muestreador de Gibbs |
| dc.subject.proposal.eng.fl_str_mv |
Default Intensity Cox Process EM Algorithm Gibbs Sampler |
| description |
In this work, the parameters for the default intensity of observable covariates in the presence of an unobservable fragility factor are estimated. The observable information corresponds to the evolution In this work, the parameters for the default intensity of observable covariates in the presence of an unobservable fragility factor are estimated. The observable information corresponds to the evolution of some macroeconomic variables over time, as well as the characteristic information of the individuals of a credit segment in a Colombian financial entity; a small modification to the Cox process proposed for intensity is made in Duffie et al. (2009), in order to include a jump component by means of which it is sought to describe the spontaneous clusters defaults, a program is finally implemented to estimate the parameters associated to the process for intensity by means of the EM algorithm and the Gibbs sampler. |
| publishDate |
2019 |
| dc.date.issued.spa.fl_str_mv |
2019 |
| dc.date.accessioned.spa.fl_str_mv |
2020-02-13T20:04:57Z |
| dc.date.available.spa.fl_str_mv |
2020-02-13T20:04:57Z |
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Documento de trabajo |
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info:eu-repo/semantics/workingPaper |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_8042 |
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Text |
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http://purl.org/redcol/resource_type/WP |
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http://purl.org/coar/resource_type/c_8042 |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/75596 |
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https://repositorio.unal.edu.co/handle/unal/75596 |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Ahn, J. J., Oh, K. J., Kim, T. Y., and Kim, D. H. (2011). Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Systems with Applications, 38(4):2966 – 2973 Bielecki, T. R., Cousin, A., Crépey, S., and Herbertsson, A. (2014). Dynamic hedging of portfolio credit risk in a markov copula model. Journal of Optimization Theory and Applications, 161(1):90–102. Bingham, N. (2007). Regular variation and probability: The early years. Journal of Computational and Applied Mathematics, 200(1):357 – 363. Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3):637–54. Company, M. G. T. (1996). Riskmetrics technical document. Technical Report 2, JP Morgan and Reuters, ttps://www.msci.com/documents/10199/5915b101-4206-4ba0-aee2- 3449d5c7e95ar. An optional note. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B, 39(1):1–38. Duffie, D. (2010). Dynamic Asset Pricing Theory. Princeton Series in Finance. Princeton University Press. Duffie, D., Eckner, A., Horel, G., and Saita, L. (2009). Frailty correlated default. Journal of Finance, 64(5):2089–2123 Edwin O. Fischer, Robert Heinkel, J. Z. (1989). Dynamic capital structure choice: Theory and tests. The Journal of Finance, 44(1):19–40. Gregoriou, G. (2006). Advances in Risk Management. Finance and Capital Markets Series. Palgrave Macmillan UK. Hillegeist, S. A., Keating, E. K., Cram, D. P., and Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1):5–34. Hull, J., Predescu, M., and White, A. (2004). The relationship between credit default swap spreads, bond yields, and credit rating announcements. Journal of Banking & Finance, 28(11):2789 – 2811. Recent Research on Credit Ratings. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671–680. Lamberton, D. and Lapeyre, B. (2011). Introduction to Stochastic Calculus Applied to Finance, Second Edition. Chapman and Hall/CRC Financial Mathematics Series. CRC Press. McNeil, A. J., Frey, R., and Embrechts, P. (2005). Quantitative risk management : concepts, techniques and tools. Princeton series in finance. Princeton University Press, Princeton (N.J.). Musiela, M. and Rutkowski, M. (2006). Martingale Methods in Financial Modelling. Stochastic Modelling and Applied Probability. Springer Berlin Heidelberg. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1):101–124. Spiliopoulos, K. (2015). Systemic Risk and Default Clustering for Large Financial Systems, pages 529–557. Springer International Publishing, Cham Tankov, P. (2003). Financial Modelling with Jump Processes. Chapman and Hall/CRC Financial Mathematics Series. CRC Press Wei, G. C. G. and Tanner, M. A. (1990). A monte carlo implementation of the em algorithm and the poor man’s data augmentation algorithms. Journal of the American Statistical Association,85(411):699–704. Zhou, C. (2001). The term structure of credit spreads with jump risk. Journal of Banking & Finance, 25(11):2015–2040 |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional |
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Acceso abierto |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia Acceso abierto http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
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123 |
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Escuela de estadística |
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Universidad Nacional de Colombia - Sede Medellín |
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Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados - Universidad Nacional de ColombiaAcceso abiertohttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Vélez, César Augusto279c0bd8-0c20-4053-aef6-ecb83498a954-1Bernal Berrio, Luis Alberto263a1b5e-71b1-4064-b75a-c40abe13be982020-02-13T20:04:57Z2020-02-13T20:04:57Z2019https://repositorio.unal.edu.co/handle/unal/75596In this work, the parameters for the default intensity of observable covariates in the presence of an unobservable fragility factor are estimated. The observable information corresponds to the evolution In this work, the parameters for the default intensity of observable covariates in the presence of an unobservable fragility factor are estimated. The observable information corresponds to the evolution of some macroeconomic variables over time, as well as the characteristic information of the individuals of a credit segment in a Colombian financial entity; a small modification to the Cox process proposed for intensity is made in Duffie et al. (2009), in order to include a jump component by means of which it is sought to describe the spontaneous clusters defaults, a program is finally implemented to estimate the parameters associated to the process for intensity by means of the EM algorithm and the Gibbs sampler.En este trabajo se estiman los parámetros para la intensidad de default de covariables observables en presencia de un factor de fragilidad no observable. La información observable corresponde a la evolución de algunas variables macroeconómicas en el tiempo, así como la información característica de individuos de un segmento de crédito en una entidad financiera colombiana; se realiza una pequeña modificación al proceso de Cox propuesto para la intensidad en Duffie et al. (2009), con el fin de incluir una componente de saltos a partir de la cual se busca describir los agrupamientos espontáneos de defaults, finalmente se implementa un programa para estimar los parámetros asociados al proceso para la intensidad por medio del algoritmo EM y el muestreador de GibbsMagister en Ciencias EstadísticaMaestría123spaMatemáticas::Probabilidades y matemáticas aplicadasIntensidad de defaultProceso de CoxAlgoritmo EMMuestreador de GibbsDefault IntensityCox ProcessEM AlgorithmGibbs SamplerModelo estructural de riesgo de crédito con intensidad estocástica de covariables observables y un factor de fragilidad determinado a partir de un proceso de saltosDocumento de trabajoinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_8042Texthttp://purl.org/redcol/resource_type/WPEscuela de estadísticaUniversidad Nacional de Colombia - Sede MedellínAhn, J. J., Oh, K. J., Kim, T. Y., and Kim, D. H. (2011). Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Systems with Applications, 38(4):2966 – 2973Bielecki, T. R., Cousin, A., Crépey, S., and Herbertsson, A. (2014). Dynamic hedging of portfolio credit risk in a markov copula model. Journal of Optimization Theory and Applications, 161(1):90–102.Bingham, N. (2007). Regular variation and probability: The early years. Journal of Computational and Applied Mathematics, 200(1):357 – 363.Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3):637–54.Company, M. G. T. (1996). Riskmetrics technical document. Technical Report 2, JP Morgan and Reuters, ttps://www.msci.com/documents/10199/5915b101-4206-4ba0-aee2- 3449d5c7e95ar. An optional note.Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B, 39(1):1–38.Duffie, D. (2010). Dynamic Asset Pricing Theory. Princeton Series in Finance. Princeton University Press.Duffie, D., Eckner, A., Horel, G., and Saita, L. (2009). Frailty correlated default. Journal of Finance, 64(5):2089–2123Edwin O. Fischer, Robert Heinkel, J. Z. (1989). Dynamic capital structure choice: Theory and tests. The Journal of Finance, 44(1):19–40.Gregoriou, G. (2006). Advances in Risk Management. Finance and Capital Markets Series. Palgrave Macmillan UK.Hillegeist, S. A., Keating, E. K., Cram, D. P., and Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1):5–34.Hull, J., Predescu, M., and White, A. (2004). The relationship between credit default swap spreads, bond yields, and credit rating announcements. Journal of Banking & Finance, 28(11):2789 – 2811. Recent Research on Credit Ratings.Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671–680.Lamberton, D. and Lapeyre, B. (2011). Introduction to Stochastic Calculus Applied to Finance, Second Edition. Chapman and Hall/CRC Financial Mathematics Series. CRC Press.McNeil, A. J., Frey, R., and Embrechts, P. (2005). Quantitative risk management : concepts, techniques and tools. Princeton series in finance. Princeton University Press, Princeton (N.J.).Musiela, M. and Rutkowski, M. (2006). Martingale Methods in Financial Modelling. Stochastic Modelling and Applied Probability. Springer Berlin Heidelberg.Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1):101–124.Spiliopoulos, K. (2015). Systemic Risk and Default Clustering for Large Financial Systems, pages 529–557. Springer International Publishing, ChamTankov, P. (2003). Financial Modelling with Jump Processes. Chapman and Hall/CRC Financial Mathematics Series. CRC PressWei, G. C. G. and Tanner, M. A. (1990). A monte carlo implementation of the em algorithm and the poor man’s data augmentation algorithms. Journal of the American Statistical Association,85(411):699–704.Zhou, C. (2001). The term structure of credit spreads with jump risk. Journal of Banking & Finance, 25(11):2015–2040ORIGINAL1152188078.2019.pdf1152188078.2019.pdfapplication/pdf977374https://repositorio.unal.edu.co/bitstream/unal/75596/1/1152188078.2019.pdf77405017f668705f3b99adb2caba3736MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83991https://repositorio.unal.edu.co/bitstream/unal/75596/2/license.txt6f3f13b02594d02ad110b3ad534cd5dfMD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.unal.edu.co/bitstream/unal/75596/3/license_rdf4460e5956bc1d1639be9ae6146a50347MD53THUMBNAIL1152188078.2019.pdf.jpg1152188078.2019.pdf.jpgGenerated Thumbnailimage/jpeg5206https://repositorio.unal.edu.co/bitstream/unal/75596/4/1152188078.2019.pdf.jpg587273b2eb844845cb66f6f251a6627bMD54unal/75596oai:repositorio.unal.edu.co:unal/755962023-06-28 23:11:17.83Repositorio Institucional Universidad Nacional de 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