Exploratory projection pursuit for multivariate financial data

Projection pursuit is a multivariate statistical technique aimed at finding interesting low-dimensional data projections. It deals with three major challenges of multivariate analysis: the curse of dimensionality, the presence of irrelevant features and the limitations of visual perception. In particular, kurtosis-based projection pursuit looks for interesting data features by means of data projections with either minimal or maximal kurtosis. Its applications include independent component analysis, cluster analysis, discriminant analysis, multivariate normality testing and outliers detection. To the best of the author's knowledge, this paper constitutes the first application of kurtosis-based projection pursuit to the exploratory analysis of multivariate financial time series.