Covariance Prediction in Large Portfolio Allocation
Carlos Trucíos (),
Mauricio Zevallos (),
Luiz Hotta and
Andre Santos ()
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Carlos Trucíos: São Paulo School of Economics, FGV, São Paulo 01332-000, Brazil
Mauricio Zevallos: Department of Statistics, University of Campinas, Campinas 13083-859, Brazil
Econometrics, 2019, vol. 7, issue 2, 1-24
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.
Keywords: Minimum variance portfolio; risk; shrinkage; S&P 500 (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:7:y:2019:i:2:p:19-:d:229754
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