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On the Benefits of Equicorrelation for Portfolio Allocation
Author(s) -
Clements Adam,
Scott Ayesha,
Silvennoinen Annastiina
Publication year - 2015
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2357
Subject(s) - portfolio , econometrics , modern portfolio theory , portfolio optimization , project portfolio management , variance (accounting) , set (abstract data type) , black–litterman model , stability (learning theory) , multivariate statistics , computer science , constant (computer programming) , post modern portfolio theory , economics , replicating portfolio , financial economics , machine learning , management , accounting , project management , programming language
Abstract The importance of modelling correlation has long been recognised in the field of portfolio management, with large‐dimensional multivariate problems increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating a number of models used to generate forecasts of the correlation matrix for large‐dimensional problems. We find evidence in favour of assuming equicorrelation across various portfolio sizes, particularly during times of crisis. During periods of market calm, however, the suitability of the constant conditional correlation model cannot be discounted, especially for large portfolios. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set are used to compare methods, while portfolio weight stability and relative economic value are also considered. Copyright © 2015 John Wiley & Sons, Ltd.