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Robust filtering
Mach, Tibor ; Dostál, Petr (advisor) ; Štěpán, Josef (referee)
This work is focused on the problem of filtering of random processes and on the construction of a stochastic integral with a measureable parameter. This integral is used to devise filtration equations for a random process which is based on a model motivated by a financial application. The method used to devise them and the equations themselves are then compared with the so called optional filtering from the book Markov processes and Martingales by Rogers and Williams, while the definition of the optional projection is extended so it is possible to correct a~mistake in a proposition in the aforementioned book. Powered by TCPDF (www.tcpdf.org)
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Bayesian modeling of market price using autoregression model
Šindelář, Jan
1 Bayesian modeling of market price using autoregression model 1Šindelář Jan Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Miroslav Kárný, DrSc. Abstract: In the thesis we present a novel solution of Bayesian filtering in autoregression model with Laplace distributed innovations. Estimation of regression models with lep- tokurtically distributed innovations has been studied before in a Bayesian framework [2], [1]. Compared to previously conducted studies, the method described in this article leads to an exact solution for density specifying the posterior distribution of parameters. Such a solution was previously known only for a very limited class of innovation distributions. In the text an algorithm leading to an effective solution of the problem is also proposed. The algorithm is slower than the one for the classical setup, but due to increasing com- putational power and stronger support of parallel computing, it can be executed in a reasonable time for models, where the number of parameters isn't very high. Keywords: Bayesian, Autoregression, Optimal Trading, Time Series References [1] P. Congdon. Bayesian statistical modelling. Wiley, 2006. [2] A. Zellner. Bayesian and Non-Bayesian analysis of the regression model with multivari- ate Student-t error term. Journal...
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Bayesian Approaches to Stochastic Reserving
Novotová, Simona ; Pešta, Michal (advisor) ; Branda, Martin (referee)
In the master thesis the issue of bayesian approach to stochastic reserving is solved. Reserving problem is very discussed in insurance industry. The text introduces the basic actuarial notation and terminology and explains the bayesian inference in statistics and estimation. The main part of the thesis is framed by the description of the particular bayesian models. It is focused on the derivation of estimators for the reserves and ultimate claims. The aim of the thesis is to show the practical uses of the models and the relations between them. For this purpose the methods are applied on a real data set. Obtained results are summarized in tables and the comparison of the methods is provided. Finally the impact of a prior distribution on the resulting reserves is showed. Powered by TCPDF (www.tcpdf.org)
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Bayesian modeling of market price using autoregression model
Šindelář, Jan
1 Bayesian modeling of market price using autoregression model 1Šindelář Jan Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Miroslav Kárný, DrSc. Abstract: In the thesis we present a novel solution of Bayesian filtering in autoregression model with Laplace distributed innovations. Estimation of regression models with lep- tokurtically distributed innovations has been studied before in a Bayesian framework [2], [1]. Compared to previously conducted studies, the method described in this article leads to an exact solution for density specifying the posterior distribution of parameters. Such a solution was previously known only for a very limited class of innovation distributions. In the text an algorithm leading to an effective solution of the problem is also proposed. The algorithm is slower than the one for the classical setup, but due to increasing com- putational power and stronger support of parallel computing, it can be executed in a reasonable time for models, where the number of parameters isn't very high. Keywords: Bayesian, Autoregression, Optimal Trading, Time Series References [1] P. Congdon. Bayesian statistical modelling. Wiley, 2006. [2] A. Zellner. Bayesian and Non-Bayesian analysis of the regression model with multivari- ate Student-t error term. Journal...
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Robust filtering
Mach, Tibor ; Dostál, Petr (advisor) ; Štěpán, Josef (referee)
This work is focused on the problem of filtering of random processes and on the construction of a stochastic integral with a measureable parameter. This integral is used to devise filtration equations for a random process which is based on a model motivated by a financial application. The method used to devise them and the equations themselves are then compared with the so called optional filtering from the book Markov processes and Martingales by Rogers and Williams, while the definition of the optional projection is extended so it is possible to correct a~mistake in a proposition in the aforementioned book. Powered by TCPDF (www.tcpdf.org)
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