Mean-Variance Models with Missing Data

Marius RĂDULESCU¹, Constanţa Zoie RĂDULESCU²
¹Institute of Mathematical Statistics and Applied Mathematics
Casa Academiei Romane, 13, Calea 13 Septembrie, 050711 Bucharest 5, Romania mradulescu.csmro@yahoo.com
²I C I Bucharest
(National Institute for R & D in Informatics)
8-10 Averescu Blvd.
011455 Bucharest 1, Romania
radulescucz@yahoo.com

Abstract: A common challenge in the theory of portfolio selection is that certain assets have shorter return histories than others. Consequently, historical data of the returns have missing data. This paper deals with portfolio selection models of mean-variance type in which missing data exist. Two simple methods for constructing a vector and a matrix starting from a matrix of rate of returns are presented. One considers a standard minimum variance model in which the vector and the matrix built replace the vector of means and the matrix of covariance. Several numerical experiments are made and the effect of missing data on the efficient frontiers associated to the minimum variance models is investigated.

Keywords: Mean-variance model, minimum variance model, missing data, NaN vector of means, NaN covariance matrix.

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CITE THIS PAPER AS:
Marius RĂDULESCU, Constanţa Zoie RĂDULESCU, Mean-Variance Models with Missing Data, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (4), pp. 299-306, 2013. https://doi.org/10.24846/v22i4y201305