Robust estimation of efficient mean---variance frontiers

Authors:
Luigi Grossi;Fabrizio Laurini
Affiliations:
University of Verona, Verona, Italy 37129;University of Parma, Parma, Italy 43100
Venue:
Advances in Data Analysis and Classification
Year:
2011

Citing 7
Cited 0

Robust regression and outlier detection

Robust regression and outlier detection
Algorithms, routines, and S functions for robust statistics: the FORTRAN library ROBETH with an interface to S-PLUS

Algorithms, routines, and S functions for robust statistics: the FORTRAN library ROBETH with an interface to S-PLUS
Computing efficient frontiers using estimated parameters

Annals of Operations Research
Robust bivariate boxplots and multiple outlier detection

Computational Statistics & Data Analysis
A fast algorithm for the minimum covariance determinant estimator

Technometrics
A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity

Computational Statistics & Data Analysis
Portfolio Selection with Robust Estimation

Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Standard methods for optimal allocation of shares in a financial portfolio are determined by second-order conditions which are very sensitive to outliers. The well-known Markowitz approach, which is based on the input of a mean vector and a covariance matrix, seems to provide questionable results in financial management, since small changes of inputs might lead to irrelevant portfolio allocations. However, existing robust estimators often suffer from masking of multiple influential observations, so we propose a new robust estimator which suitably weights data using a forward search approach. A Monte Carlo simulation study and an application to real data show some advantages of the proposed approach.