Functional principal component analysis of financial time series

Abstract

We introduce functional principal component techniques for the statistical analysis of a set of financial time series from an explorative point of view. We show that this approach highlights some relevant statistical features of such related datasets. A case study is here considered concerning the daily traded volumes of the shares in the MIB30 basket from January 3rd, 2000 to December 30th, 2002. Moreover, since the first functional principal component accounts for the 89.4% of the whole variabilitity, this approach suggests the construction of new financial indices based on functional indicators.