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L'archivio è un'implementazione di IRIS, il sistema informativo per la ricerca sviluppato da Cineca.
EnglishFinancial 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
| Citazione: | (2019). Sparse precision matrices for minimum variance portfolios [journal article - articolo]. In COMPUTATIONAL MANAGEMENT SCIENCE. Retrieved from http://hdl.handle.net/10446/135745 | |
| Titolo: | Sparse precision matrices for minimum variance portfolios | |
| Tipologia specifica: | articolo | |
| Tutti gli autori: | Torri, Gabriele; Giacometti, Rosella; Paterlini, Sandra | |
| Data di pubblicazione: | 2019 | |
| Abstract (eng): | 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. | |
| Rivista: | ||
| Nelle collezioni: | 1.1.01 Articoli/Saggi in rivista - Journal Articles/Essays |
File allegato/i alla scheda:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Torri2019_Article_SparsePrecisionMatricesForMini.pdf | publisher's version - versione editoriale | Licenza default Aisberg | Testo non consultabile | |
| paper_tlasso_portfolio_SSRN.pdf | 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 | postprint - versione referata/accettata senza referaggio | Licenza default Aisberg | Open AccessVisualizza/Apri |
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