Statistical and forecasting techniques on financial markets

Abstract

In this project, several steps are taken to analyse and cluster the financial markets’ data. The first part is focused on a brief description of the statistical properties of the time series. Later on, the clustering with the k-Means method, using the rolling four statistical moments, is firstly focused on the NASDAQ index, then to extend the model, several financial products are selected. Moreover, the creation of a strategy based on clusters has been proposed in order to get profit, which has been successful. Finally, ARCH models backed by statistical tests and criteria, using standardized returns, are used to capture the volatility clustering and forecast it, and the GARCH (1,1) was selected among all since it has the most forecast accuracy.