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

44 Pages Posted: 5 Mar 2013

See all articles by Nikolaus Hautsch

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Lada M. Kyj

Humboldt University of Berlin; Quantitative Products Laboratory

Peter Malec

University of Cambridge - Faculty of Economics

Date Written: February 28, 2013

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

Suggested Citation

Hautsch, Nikolaus and Kyj, Lada M. and Malec, Peter, Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (February 28, 2013). Available at SSRN: https://ssrn.com/abstract=2228772 or http://dx.doi.org/10.2139/ssrn.2228772

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Lada M. Kyj

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK 10099
Germany

Quantitative Products Laboratory ( email )

Alexanderstrasse 5
Berlin, 10099
Germany

Peter Malec (Contact Author)

University of Cambridge - Faculty of Economics ( email )

Sidgwick Avenue
Cambridge, CB3 9DD
United Kingdom

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