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Portfolio Optimization Using a Block Structure for the Covariance Matrix
Author(s) -
Disatnik David,
Katz Saggi
Publication year - 2012
Publication title -
journal of business finance and accounting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.282
H-Index - 77
eISSN - 1468-5957
pISSN - 0306-686X
DOI - 10.1111/j.1468-5957.2012.02279.x
Subject(s) - portfolio , portfolio optimization , separation property , modern portfolio theory , post modern portfolio theory , covariance matrix , block structure , black–litterman model , variance (accounting) , selection (genetic algorithm) , econometrics , asset (computer security) , replicating portfolio , computer science , block (permutation group theory) , asset allocation , application portfolio management , rate of return on a portfolio , economics , project portfolio management , mathematics , financial economics , statistics , algorithm , artificial intelligence , combinatorics , accounting , computer security , estimator , management , project management
Implementing in practice the classical mean‐variance theory for portfolio selection often results in obtaining portfolios with large short sale positions. Also, recent papers show that, due to estimation errors, existing and rather advanced mean‐variance theory‐based portfolio strategies do not consistently outperform the naïve 1/ N portfolio that invests equally across N risky assets. In this paper, we introduce a portfolio strategy that generates a portfolio, with no short sale positions, that can outperform the 1/ N portfolio. The strategy is investing in a global minimum variance portfolio (GMVP) that is constructed using an easy to calculate block structure for the covariance matrix of asset returns. Using this new block structure, the weights of the stocks in the GMVP can be found analytically, and as long as simple and directly computable conditions are met, these weights are positive.