Sparse precision matrices for minimum variance portfolios

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10242950" target="_blank" >RIV/61989100:27510/19:10242950 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007%2Fs10287-019-00344-6" target="_blank" >https://link.springer.com/article/10.1007%2Fs10287-019-00344-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10287-019-00344-6" target="_blank" >10.1007/s10287-019-00344-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparse precision matrices for minimum variance portfolios

Popis výsledku v původním jazyce

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 prompted even 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 a la Markowitz 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 proposed methods compared to state-of-art approaches, such as random matrix and Ledoit-Wolf shrinkage. (C) 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Název v anglickém jazyce

Sparse precision matrices for minimum variance portfolios

Popis výsledku anglicky

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 prompted even 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 a la Markowitz 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 proposed methods compared to state-of-art approaches, such as random matrix and Ledoit-Wolf shrinkage. (C) 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50200 - Economics and Business

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computational Management Science

ISSN

1619-697X

e-ISSN

—

Svazek periodika

16

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

26

Strana od-do

375-400

Kód UT WoS článku

000476740000002

EID výsledku v databázi Scopus

2-s2.0-85061043573