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Models in finance and medicine using Bayesian inference
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Title
Models in finance and medicine using Bayesian inference
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Ibuka
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Yoko
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Yoko Ibuka
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Tsurumi
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Hiroki
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Advisory Committee
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Hiroki Tsurumi
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Russell
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Louise
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Advisory Committee
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Louise B Russell
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co-chair
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John
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John Landon-Lane
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Green
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Edwin
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Edwin J Green
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Rutgers University
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Graduate School - New Brunswick
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theses
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2008
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2008-05
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English
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electronic
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xi, 100 pages
Abstract
The purpose of this dissertation is to analyze three models in medicine and finance using Bayesian inference with the Markov chain Monte Carlo method. The model in medicine addresses cost-effectiveness analysis using copulas, and the two models in finance include discrete-time asset pricing models and a short-term interest rate model with stochastic volatility.
The first chapter develops the model that allows dependence between cost and effectiveness using copulas in cost-effectiveness analysis. The model was applied with sample of adults from the NHANES I Epidemiologic Follow-up Study, assuming a lognormal distribution for cost and a Weibull distribution for effectiveness as the marginals. Cost-effectiveness analysis is conducted for two types of patients using the estimated posterior densities of parameters regarding the hypothetical intervention for hypertension. A simulation based on Bayesian predictive densities is also performed to analyze cost and effectiveness at an individual patient level. The empirical result indicated a negative dependence between measures of effectiveness and cost.
The second chapter conducts a Bayesian analysis of discrete-time asset pricing model. The chapter particularly discusses the naive discretization problem, which arises from using discrete-time data to estimate continuous-time models. Our results using generated data showed that the naive discretization would not work well when data generating process is unknown, when the data is sampled at low frequency, and averaged data is used.
The final chapter develops a Bayesian analysis of a short-term interest rate model with stochastic volatility. The model was developed based on the CKLS model (Chan et al. 1992). We constructed MCMC algorithms suitable for the model based on the Jacquire, Polson and Rossi(1994) algorithm. The empirical results with the 3-month Treasury constant maturity rate suggested that there was high autocorrelation in volatility of the error terms. Finally, the developed model was compared with the model with a GARCH error, using Bayesian predictive densities. The predictive densities obtained by CKLS with stochastic volatility have wider variance than the ones from CKLS-GARCH,
and the realized value did not fall in the support of the predicted values for the CKLS GARCH model because of the tight variance in prediction.
The first chapter develops the model that allows dependence between cost and effectiveness using copulas in cost-effectiveness analysis. The model was applied with sample of adults from the NHANES I Epidemiologic Follow-up Study, assuming a lognormal distribution for cost and a Weibull distribution for effectiveness as the marginals. Cost-effectiveness analysis is conducted for two types of patients using the estimated posterior densities of parameters regarding the hypothetical intervention for hypertension. A simulation based on Bayesian predictive densities is also performed to analyze cost and effectiveness at an individual patient level. The empirical result indicated a negative dependence between measures of effectiveness and cost.
The second chapter conducts a Bayesian analysis of discrete-time asset pricing model. The chapter particularly discusses the naive discretization problem, which arises from using discrete-time data to estimate continuous-time models. Our results using generated data showed that the naive discretization would not work well when data generating process is unknown, when the data is sampled at low frequency, and averaged data is used.
The final chapter develops a Bayesian analysis of a short-term interest rate model with stochastic volatility. The model was developed based on the CKLS model (Chan et al. 1992). We constructed MCMC algorithms suitable for the model based on the Jacquire, Polson and Rossi(1994) algorithm. The empirical results with the 3-month Treasury constant maturity rate suggested that there was high autocorrelation in volatility of the error terms. Finally, the developed model was compared with the model with a GARCH error, using Bayesian predictive densities. The predictive densities obtained by CKLS with stochastic volatility have wider variance than the ones from CKLS-GARCH,
and the realized value did not fall in the support of the predicted values for the CKLS GARCH model because of the tight variance in prediction.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 96-99).
Subject
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Topic
Economics
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Topic
Bayesian statistical decision theory
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Topic
Econometrics
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Topic
Medical statistics
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T38P60VZ
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ETD doctoral
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Yoko Ibuka
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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