Medición del perfil de riesgo de inversión usando un modelo de Teoría de Respuesta al ítem bayesiano

dc.contributor.advisorMontenegro Díaz, Alvaro Mauriciospa
dc.contributor.authorMedina Cifuentes, David Hernandospa
dc.date.accessioned2021-01-20T02:08:34Zspa
dc.date.available2021-01-20T02:08:34Zspa
dc.date.issued2020-12-01spa
dc.description.abstractEn este trabajo se propone emplear un modelo en el marco de teoría de respuesta al ítem a través de estimación Bayesiana, con el objetivo de medir el trazo latente relacionado con el perfil de riesgo de inversión de las personas naturales que participan en el mercado financiero Colombiano. Para obtener los datos necesarios, se encuestaron a 144 individuos por medio de un cuestionario inicial compuesto por 18 ítems, construido a partir de una investigación rigurosa sobre los métodos y tipos de cuestionarios que emplean las principales entidades financieras del país para medir el perfil de riesgo de sus clientes. Se estimó un modelo Generalized Partial Credit Model (GPCM) en el lenguaje estadístico Stan, presentando buen desempeño en términos de convergencia de las cadenas de markov y de bondad de ajuste evaluado por medio del criterio predictivo p-valor bayesiano para los ítem finales y el modelo global.spa
dc.description.abstractIn this research we propose a model in the framework of item response theory through bayesian estimation, with the aim of measuring the latent trace related to the investment risk profile of natural persons participating in the Colombian financial market. For obtain the necessary data, 144 individuals were surveyed using an initial questionnaire made up of 18 items, constructed from rigorous research on the methods and types of questionnaires used by the country's main financial institutions to measure the risk profile of their clients. A Generalized Partial Credit Model was estimated in the statistical language Stan, showing good performance in terms of convergence of the markov chains and goodness of fit evaluated by means of the predictive bayesian p-value criterion for the finals items and the overall modell.spa
dc.description.additionalLínea de investigación: Teoría de respuesta al Ítem.spa
dc.description.degreelevelMaestríaspa
dc.format.extent60spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationMedina, D. (2020). Medición del perfil de riesgo de inversión usando un modelo de Teoría de Respuesta al ítem bayesiano [Tesis de Maestría en Estadística, Universidad Nacional de Colombia] Repositorio Institucionalspa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/78840
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Estadísticaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadísticaspa
dc.relation.referencesBock, R. and Lieberman, M. (1970). Fitting a response model for n dichotomously scored items. Psychometrika, 35:179-197.spa
dc.relation.referencesBrentari, E. and Golia, S. (2007). Unidimensionality in the rasch model: how to detect and interpret. Statistica, 67:253-261.spa
dc.relation.referencesCaviezel, V., Bertoli, L., and Lozza, S. (2011). Measuring risk pro le with a multidimensional rasch analysis. Journal of Applied Quantitative Methods, 6:14-29.spa
dc.relation.referencesD, L. (1943). On problems connected with item selection and test construction. Proceedings of the Royal Society of Edinburgh, Series A, 23:273{287.spa
dc.relation.referencesEdelen, M. and Reeve, B. (2007). Applying item response theory (irt) modeling to questionnaire development, evaluation, and re nement. Qual Life Res, 16:5.spa
dc.relation.referencesFox, J. P. (2010). Bayesian Item Response Modeling. Springer, New York.spa
dc.relation.referencesGao, G. (2018). Bayesian Claims Reserving Methods in Non-life Insurance with Stan. Springer.spa
dc.relation.referencesGilliam, J., Chatterjee, S., and Grable, J. (2010). Measuring the perception of nancial risk tolerance: A tale of two measures. Journal of Financial Counseling and Planning, 21.spa
dc.relation.referencesGrabble, J. and Lytton, R. (1999). Financial risk tolerance revisited: The development of a risk assesment instrument. Financial Services Review, 8:163-181.spa
dc.relation.referencesGrabble, J. and Schumm, W. (2010). An estimate of the reliability of the survey of consumer nances risk-tolerance question. Journa of Personal Finance, 9:117-31.spa
dc.relation.referencesHambleton, R., Swaminathan, H., and Rogers, H. (1991). Fundamentals of Item Response Theory. Sage, London.spa
dc.relation.referencesHoffman, M. and Gelman, A. (2014). The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15:1593-1623.spa
dc.relation.referencesJanseen, G., Meirer, M., and J, T. (2014). Classical test theory and item response theory:two understandings of one high-stakes performance exam. Colombian Applied Linguistics Journal, 16:167-184.spa
dc.relation.referencesLinacre J, M. (1994). Sample size and item calibration stability. Rasch Measurement Transactions, 7(4):328.spa
dc.relation.referencesLord, F. (1952). A theory of test scores. Psychometric Monograph, (No 7).spa
dc.relation.referencesMasters, G, M. (1982). A rasch model for partial credit scoring. Psychome- trika, 47:149-174.spa
dc.relation.referencesMetropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. (1953). Equations of state calculations by fast computing machines. The Journal of Chemical Physics, 21:1087-1092.spa
dc.relation.referencesMonila, J. (2016). Análisis de distribuciones a priori de los parámetros de escala del modelo de regresión poisson inflado con ceros. Master's thesis, Universidad Nacional de Colombia.spa
dc.relation.referencesMonnahan, C., Thorson, J., and Branch, T. (2017). Faster estimation of bayesian models in ecology using hamiltonian monte carlo. Methods in Ecology and Evolution, 8:339-348.spa
dc.relation.referencesMuraki, E. (1992). A generalized partial credit model: Aplication of the em algorithm. Applied Psychological Measurement, 16(2):159-176.spa
dc.relation.referencesOstini, R. and Nering, M. (2006). Polytomus item response theory models. Sage pub, London.spa
dc.relation.referencesRangel, H. (2019). Modelo politómico unidimensional de teoría de respuesta al ítem con distribución asimetrica de trazo latenten. Master's thesis, Universidad Nacional de Colombia.spa
dc.relation.referencesRasch, G. (1960). Probabilistic Models for some Intelligence Tests and Attain- ment Tests. Copenhagen: Danish Institute for Educational Research.spa
dc.relation.referencesRoszkowski, M. (1992). How to asses a client's nancial risk tolerance: The basics personal nancial risk tolerance. Master's thesis, Bryn Mawr, PA: The American College.spa
dc.relation.referencesRuiz, M. (2016). Usin Rasch Measurement Theory To Evaluate The Psycho- metric Quality Of a Financial Risk Tolerance Scale. Tesis de doctorado, Universidad de Georgia, Athens.spa
dc.relation.referencesSamemija, F. (1997). The graded response model In W. J. van der Lin- den and R. K. Hambleton (Eds.). Handbook of Modern Item Response Theory, New York:Springer.spa
dc.relation.referencesSherman, H., Gutter, M., and Fan, J. (2001). A mesaure of risk tolerance based on economic theory. Journal of Financial Counseling and Planning, 12.spa
dc.relation.referencesSpearman, C. (1994). The proof and measurement of assciation between two things. American Journal of Psychology, 15:72-101.spa
dc.relation.referencesSteve Brooks, Andrew Gelman, G. J. . X.-L. M. (2012). Handbook of Markov Chain Monte Carlo. Chapman and Hall/CRC Press.spa
dc.relation.referencesSwaminathan, H. and Gi ord, J. (1982). Bayesian estimation in the rasch model. Journal of Educational Statistics, 7:175{192.spa
dc.relation.referencesWiberg, M. (2004). Classical test theory vs item response theory: An evaluation of the theory test in the swedish driving-license test. EM: Educacional Measurement, 50.spa
dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc510 - Matemáticasspa
dc.subject.proposalTRIspa
dc.subject.proposalBayeseng
dc.subject.proposalBayesspa
dc.subject.proposalStaneng
dc.subject.proposalIRTeng
dc.subject.proposalStanspa
dc.subject.proposalModelo poltómicospa
dc.subject.proposalPolytomous modeleng
dc.titleMedición del perfil de riesgo de inversión usando un modelo de Teoría de Respuesta al ítem bayesianospa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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