Next Article in Journal
Measures of Efficiency of Agricultural Insurance in Italy, Economic Evaluations
Previous Article in Journal
Alpha Beta Risk and Stock Returns—A Decomposition Analysis of Idiosyncratic Volatility with Conditional Models
Previous Article in Special Issue
A General Framework for Portfolio Theory. Part II: Drawdown Risk Measures
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessArticle
Risks 2018, 6(4), 125; https://doi.org/10.3390/risks6040125

Target Matrix Estimators in Risk-Based Portfolios

Department of Economics and Business Studies, University of Genova, Via Vivaldi, 5, 16126 Genova, Italy
Received: 14 October 2018 / Revised: 29 October 2018 / Accepted: 2 November 2018 / Published: 5 November 2018
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
Full-Text   |   PDF [2048 KB, uploaded 5 November 2018]   |  
  |   Review Reports

Abstract

Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation, and the Exponential Weighted Moving Average target matrices. Using an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance (MV), Inverse Volatility (IV), Equal-Risk-Contribution (ERC), and Maximum Diversification (MD) portfolios. We find out that the Identity and Variance Identity have very good statistical properties, also being well conditioned in high-dimensional datasets. In addition, these two models are the best target towards which to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps to reduce weight misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification and so benefit less from shrinkage. View Full-Text
Keywords: estimation error; shrinkage; target matrix; risk-based portfolios estimation error; shrinkage; target matrix; risk-based portfolios
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Neffelli, M. Target Matrix Estimators in Risk-Based Portfolios. Risks 2018, 6, 125.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top