Genetic algorithms for parameter estimation in modelling of index returns

The main aim for this paper is motivated by the usefulness of genetic algorithms (GAs) for the fitting of distribution models to financial market data. In detail, we use a GA along with the least squares method in order to achieve a more relatively accurate and robust approach for optimizing non-linear objective functions. The combination of these two methods is applied for fitting parametric distributions to a dataset of market index returns, improving the methodology of cumulative returns prediction. The process of extrapolation plays a fundamental role in this area of analysis, being essential to empirically fit a convenient distribution that describes the available data as closely as possible. For comparison and illustrative purpose, we analyse distribution models used in the financial literature for modelling such dataset, and then the practical application is carried out again on a more updated dataset from the same financial index. In addition, a brief simulation study is developed to illustrate the usefulness of the proposal procedure.