Financial crises are typically characterized by highly positively correlated asset returns due to the simultaneous distress on almost all securities, high volatilities and the presence of extreme returns. In the aftermath of the 2008 crisis, investors were promptedeven further to look for portfolios that minimize risk and can better deal with estimation error in the inputs of the asset allocation models. The minimum variance portfolio à laMarkowitz is considered the reference model for risk minimization in equity markets, due to its simplicity in the optimization as well as its need for just one input estimate: the inverse of the covariance estimate, or the so-called precision matrix. In this paper, we propose a data-driven portfolio framework based on two regularization methods, glasso and tlasso, that provide sparse estimates of the precision matrix by penalizing its L1-norm. Glasso and tlasso rely on asset returns Gaussianity or t-Student assumptions, respectively. Simulation and real-world data results support the 14 proposed methods compared to state-of-art approaches, such as random matrix and Ledoit–Wolf shrinkage.

(2019). Sparse precision matrices for minimum variance portfolios [journal article - articolo]. In COMPUTATIONAL MANAGEMENT SCIENCE. Retrieved from http://hdl.handle.net/10446/135745

Sparse precision matrices for minimum variance portfolios

Torri, Gabriele;Giacometti, Rosella;
2019-01-01

Abstract

Financial crises are typically characterized by highly positively correlated asset returns due to the simultaneous distress on almost all securities, high volatilities and the presence of extreme returns. In the aftermath of the 2008 crisis, investors were promptedeven further to look for portfolios that minimize risk and can better deal with estimation error in the inputs of the asset allocation models. The minimum variance portfolio à laMarkowitz is considered the reference model for risk minimization in equity markets, due to its simplicity in the optimization as well as its need for just one input estimate: the inverse of the covariance estimate, or the so-called precision matrix. In this paper, we propose a data-driven portfolio framework based on two regularization methods, glasso and tlasso, that provide sparse estimates of the precision matrix by penalizing its L1-norm. Glasso and tlasso rely on asset returns Gaussianity or t-Student assumptions, respectively. Simulation and real-world data results support the 14 proposed methods compared to state-of-art approaches, such as random matrix and Ledoit–Wolf shrinkage.
articolo
2019
Torri, Gabriele; Giacometti, Rosella; Paterlini, Sandra
(2019). Sparse precision matrices for minimum variance portfolios [journal article - articolo]. In COMPUTATIONAL MANAGEMENT SCIENCE. Retrieved from http://hdl.handle.net/10446/135745
File allegato/i alla scheda:
File Dimensione del file Formato  
Torri2019_Article_SparsePrecisionMatricesForMini.pdf

Solo gestori di archivio

Versione: publisher's version - versione editoriale
Licenza: Licenza default Aisberg
Dimensione del file 402.33 kB
Formato Adobe PDF
402.33 kB Adobe PDF   Visualizza/Apri
paper_tlasso_portfolio_SSRN.pdf

Open Access dal 06/02/2020

Descrizione: This is a post-peer-review, pre-copyedit version of an article published in Computational Management Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10287-019-00344-6
Versione: postprint - versione referata/accettata senza referaggio
Licenza: Licenza default Aisberg
Dimensione del file 389.06 kB
Formato Adobe PDF
389.06 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/135745
Citazioni
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 10
social impact