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Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?
Author(s) -
Hautsch Nikolaus,
Kyj Lada M.,
Malec Peter
Publication year - 2015
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2361
Subject(s) - portfolio , econometrics , volatility (finance) , realized variance , smoothing , estimator , economics , portfolio optimization , asset allocation , covariance matrix , computer science , covariance , mathematics , statistics , financial economics , algorithm
Summary This paper addresses the debate about the usefulness of high‐frequency (HF) data in large‐scale portfolio allocation. We construct global minimum variance portfolios based on the constituents of the S&P 500. 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 2008 financial crisis, these performance gains hold over longer horizons than previous studies have shown, translating into substantial utility gains for an investor with pronounced risk aversion. Copyright © 2013 John Wiley & Sons, Ltd.