Robust mean-variance portfolio through the weighted \(L^p\) depth function
type: Item
instance of: scholarly article
title: Robust mean-variance portfolio through the weighted \(L^p\) depth function
zbMATH Open document ID: 1455.91240
author: Giuseppe Pandolfo
author: Carmela Iorio
author: R. Siciliano
author: A. D'Ambrosio
published in: Annals of Operations Research
publication date: 2021-01-06T00:00:00Z
review text: The main contribution of this paper is the extension of statistical data depth-based estimators to financial portfolio selection as it was already done with some other well-known robust techniques. To start with, the authors recall the classical mean-variance portfolio model. Then they offer some background on the notion of statistical depth functions along with their main properties and the estimation of the mean and covariance matrix through the weighted \(L^p\)-depth function. Such approach has the advantage to be independent of parametric assumptions, and less sensitive to changes in the asset return distribution than traditional techniques. The exposition is concluded with an out-of-sample evaluation through simulated data and an application to a real data set.
reviewed by: Yuliya S. Mishura
Mathematics Subject Classification ID: 91G10
Mathematics Subject Classification ID: 62P05
zbMATH DE Number: 7290747
zbMATH Keywords: finance
zbMATH Keywords: portfolio selection
zbMATH Keywords: outliers
zbMATH Keywords: weight function
zbMATH Keywords: robust statistics
zbMATH Keywords: statistical data
zbMATH Keywords: depth-based estimators
zbMATH Keywords: out-of-sample
describes a project that uses: FinTS
MaRDI profile type: Publication
full work available at URL: s10479-019-03474-x
OpenAlex ID: W2990965643
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DOI: 10.1007/S10479-019-03474-X
description: scientific article; zbMATH DE number 7290747