Stock prices are dynamic and changing frequently whenever the financial markets are opened. Many people aspire to know where prices are likely to be at future times. One of the basic problems, connected with stock valuation is to create an appropriate model of stock prices dynamics. This paper deals with the stock valuation models, such as binomial, additive, multiplicative, Brownian motion and etc. These models appeals to hypothesis, declaring that returns of stock follows by normal distribution and stock prices by lognormal one. The aim of this work is to test these hypothesis. The research of this paper discloses the basic tendency, announcing that stock is not applicable for a real adaptability of methods in Lithuanian companies because the returns of stock do not follow by the normal distribution. Disputed situation is opposite in companies of the USA. The returns of stocks are calculated by the following ways. They are numbered in the area of MS Excel. In order to review the conditions of normality, the SAS software was chosen. Finally, Delphi 10 programming language was selected for realization of the binomial tree.

Made available in DSpace on 2016-06-02T20:06:02Z (GMT). No. of bitstreams: 1 2567.pdf: 3332442 bytes, checksum: 1e03b44e1c1f61f90b947fdca5682355 (MD5) Previous issue date: 2009-03-06 / Financiadora de Estudos e Projetos / Several techniques are proposed to determine the lag length of a dynamic regression model. However, none of them is completely satisfactory and a wrong choice could imply serious problems in the estimation of the parameters. This dissertation presents a review of the main criteria for models selection used in the classical methodology and presents a way for determining the lag length from the perspective Bayesian. A Monte Carlo simulation study is conducted to compare the performance of the significance tests, R2 adjusted, final prediction error, Akaike information criterion, Schwarz information criterion, Hannan-Quinn criterion, corrected Akaike information criterion and fractional Bayesian approach. Two estimation methods are also compared, the ordinary least squares and the Almon approach. / Na literatura, muitas técnicas são propostas para determinar o tamanho da defasagem de um modelo de regressão dinâmico. Entretanto, nenhuma delas é completamente satisfatória e escolhas erradas implicam em sérios problemas na estimação dos parâmetros. Este trabalho apresenta uma revisão dos principais critérios de seleção de modelos disponíveis na metodologia clássica, assim como aborda uma maneira de determinar o tamanho da defasagem sob a perspectiva Bayesiana. Um estudo de simulação Monte Carlo é conduzido para comparar a performance dos testes de significância, do R2 ajustado, do erro de predição final, dos critérios de informação de Akaike, Schwarz, Hannan-Quinn e Akaike corrigido e da aproximação Bayesiana fracionada. Também serão comparados os métodos de estimação de Mínimos Quadrados Ordinários e de Almon.