Aplicación de Procesos Poisson-Gaussianos a los Rendimientos de los Activos en El: New York Stock Ex

Abstract

After more than 200 years since the birth of the stock market in the city of New York, USA (1792), inventors are looking for the best way to predict the behavior of the asset returns to maximize their profits. Bachelier (1900) did use the Brownian motion as a tandom element that would help us to have better models to forecast behavior of returns series. Unfortunately, the use of the Brownian motion have disadvantages: for example, we have to assume that the asset returns behave like log - normal. This paper proposes the use of a different modelling which includes normal distribution using the returns of a group of assets of the New York Stock Exchange, in New York, USA. An approach of Sanjiv Das (1998) which was first applied to the interest rates, is used in obtaining the likelihood function for the case of eleven assets belonging of the New York Stock Exchange using corresponding data from January 1st, 1994 to December 31th, 2004.