Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?

Abstract

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial crisis. HF-based covariance matrix predictions are obtained by applying a blocked realized kernel estimator, different smoothing windows, various regularization methods and two forecasting models. We show that HF-based predictions yield a significantly lower portfolio volatility than methods employing daily returns. Particularly during the volatile crisis period, these performance gains hold over longer horizons than previous studies have shown and translate into substantial utility gains from the perspective of an investor with pronounced risk aversion.

Keywords: portfolio optimization, spectral decomposition, regularization, blocked realized kernel, covariance prediction

JEL Classification: G11, G17, C58, C14, C38