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Covariance Prediction in Large Portfolio Allocation

São Paulo School of Economics, FGV, São Paulo 01332-000, Brazil
Department of Statistics, University of Campinas, Campinas 13083-859, Brazil
UC3M-Santander Big Data Institute, Universidad Carlos III de Madrid, Getafe 28903, Spain
Department of Economics, Universidade Federal de Santa Catarina, Florianópolis 88040-970, Brazil
Author to whom correspondence should be addressed.
Econometrics 2019, 7(2), 19;
Received: 12 November 2018 / Revised: 23 April 2019 / Accepted: 2 May 2019 / Published: 9 May 2019
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on stocks belonging to the S&P500 index from 2000 to 2017 and sub-periods. The analysis is done through several metrics, including standard deviation, turnover, net average return, information ratio and Sortino’s ratio. We find that no method is the best in all scenarios and the performance depends on the criterion, the period of analysis and the rebalancing strategy. View Full-Text
Keywords: Minimum variance portfolio; risk; shrinkage; S&P 500 Minimum variance portfolio; risk; shrinkage; S&P 500
MDPI and ACS Style

Trucíos, C.; Zevallos, M.; Hotta, L.K.; Santos, A.A.P. Covariance Prediction in Large Portfolio Allocation. Econometrics 2019, 7, 19.

AMA Style

Trucíos C, Zevallos M, Hotta LK, Santos AAP. Covariance Prediction in Large Portfolio Allocation. Econometrics. 2019; 7(2):19.

Chicago/Turabian Style

Trucíos, Carlos, Mauricio Zevallos, Luiz K. Hotta, and André A.P. Santos. 2019. "Covariance Prediction in Large Portfolio Allocation" Econometrics 7, no. 2: 19.

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