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Essays on Bayesian inference in financial economics
Descriptive
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Text
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Title
Essays on Bayesian inference in financial economics
Identifier
ETD_1749
Identifier
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051374
Language
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eng
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theses
Subject
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Topic
Economics
Subject
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(authority = ETD-LCSH)
Topic
Bayesian statistical decision theory
Subject
(ID = SBJ-1);
(authority = ETD-LCSH)
Topic
Finance--Econometric models
Subject
(ID = SBJ-1);
(authority = ETD-LCSH)
Topic
Markov processes
Abstract
This dissertation consists of three essays on Bayesian inference in financial economics. The first essay explores the impact of discretization errors on the parametric estimation of continuous-time financial models. Euler and other discretization schemes cause discretization errors in solving stochastic differential equations. The empirical impact of these discretization errors on estimating two continuous-time financial models is investigated by using Monte Carlo experiments to compare the "exact" estimator and "Euler" estimator for the Euler scheme. The primary finding is that reducing the discretization interval to reduce the discretization error does not necessarily improve the performance of the estimators. This implies that discretization schemes may yield reliable results when the sampling interval is regularly small and shortening the discretization intervals or using data augmentation techniques may be redundant in practice.
The second essay examines the identification problem in state-space models under the Bayesian framework. Underidentifiability causes no real difficulty in the Bayesian approach in that a legitimate posterior distribution might be achieved for unidentified parameters when appropriate priors are imposed. When estimating unidentified parameters, Markov chain Monte Carlo algorithms may yield misleading results even if the algorithms seem to converge successfully. In addition, the identification problem does really not matter when the prediction of state-space models instead of parameter estimation is concerned.
The third essay extensively studies credit risk models using Bayesian inference. Bayesian inference is conducted and Markov chain Monte Carlo algorithms are developed for three popular credit risk models. Empirical results show that these three models in which the same PD (probability of default) can be estimated using different information may yield quite different results. Motivated by the empirical results about credit risk model uncertainty, I propose a "combined" Bayesian estimation method to incorporate information from different datasets and model structure for estimating the PD. This new approach provides an insight in dealing with two practical problems, model uncertainty and data insufficiency, in credit risk management.
The second essay examines the identification problem in state-space models under the Bayesian framework. Underidentifiability causes no real difficulty in the Bayesian approach in that a legitimate posterior distribution might be achieved for unidentified parameters when appropriate priors are imposed. When estimating unidentified parameters, Markov chain Monte Carlo algorithms may yield misleading results even if the algorithms seem to converge successfully. In addition, the identification problem does really not matter when the prediction of state-space models instead of parameter estimation is concerned.
The third essay extensively studies credit risk models using Bayesian inference. Bayesian inference is conducted and Markov chain Monte Carlo algorithms are developed for three popular credit risk models. Empirical results show that these three models in which the same PD (probability of default) can be estimated using different information may yield quite different results. Motivated by the empirical results about credit risk model uncertainty, I propose a "combined" Bayesian estimation method to incorporate information from different datasets and model structure for estimating the PD. This new approach provides an insight in dealing with two practical problems, model uncertainty and data insufficiency, in credit risk management.
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electronic resource
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x, 107 p.
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Note
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Ph.D.
Note
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Includes bibliographical references (p. 100-106)
Note
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by Xianghua Liu
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Liu
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Xianghua
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1977
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Xianghua Liu
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Tsurumi
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Hiroki
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Hiroki Tsurumi
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Bruce
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Bruce Mizrach
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Landon-Lane
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John
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John Landon-Lane
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Zhou
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Xing
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Xing Zhou
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-05
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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Identifier
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doi:10.7282/T30P107H
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ETD doctoral
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The author owns the copyright to this work.
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Open
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Non-exclusive ETD license
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Author Agreement License
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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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