Abstract
Abstract We apply a signal processing technique known as independent component analysis (ICA) to multivariate financial time series. The main idea of ICA is to decompose the observed time series into statistically independent components (ICs). We further assume that the ICs follow the variance gamma (VG) process. The VG process is Brownian motion with drift evaluated at a random time given by a gamma process. We build a portfolio, using closed form expressions, that maximizes expected exponential utility when returns are driven by a mixture of independent factors with VG returns. The performance of this investment is compared with the Markowitz model as a benchmark.
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