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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Bayesian probability distribution over a class of autoregression models applied to financial time series
 Škerlík, Peter ; Šindelář, Jan (advisor) ; Hlávka, Zdeněk (referee)
In the present bachelor thesis we study the selection of appropriate autoregression models to forecast financial time series. We use Bayesian inference in statistics, which will be further explained. Consequently there is also given theoretical background which explains how to apply Bayesian inference to selection of models. The major contribution of the work is considered to be the application of this theoretical background to financial time series in programming environment C++ and the results of this application. The development of the probability of each autoregression model is shown in graphs. The results for Laplace and normal probability distribution of white noise in autoregression models are compared. The aim of the work is to provide the reader with enough theoretical information and to give him an practical overview of the usage of Bayesian statistics in data prediction. Also results of the work can be helpful to understand the mentioned models and to select the suitable model in practice.
Detailed record
Bayesian probability distribution over a class of autoregression models applied to financial time series
 Škerlík, Peter ; Šindelář, Jan (advisor) ; Hlávka, Zdeněk (referee)
In the present bachelor thesis we study the selection of appropriate autoregression models to forecast financial time series. We use Bayesian inference in statistics, which will be further explained. Consequently there is also given theoretical background which explains how to apply Bayesian inference to selection of models. The major contribution of the work is considered to be the application of this theoretical background to financial time series in programming environment C++ and the results of this application. The development of the probability of each autoregression model is shown in graphs. The results for Laplace and normal probability distribution of white noise in autoregression models are compared. The aim of the work is to provide the reader with enough theoretical information and to give him an practical overview of the usage of Bayesian statistics in data prediction. Also results of the work can be helpful to understand the mentioned models and to select the suitable model in practice.
Detailed record

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