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Covariance Prediction in Large Portfolio Allocation

Author

Listed:
  • 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)

  • Luiz K. Hotta

    (Department of Statistics, University of Campinas, Campinas 13083-859, Brazil)

  • André A. P. Santos

    (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)

Abstract

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.

Suggested Citation

  • Carlos Trucíos & Mauricio Zevallos & Luiz K. Hotta & André A. P. Santos, 2019. "Covariance Prediction in Large Portfolio Allocation," Econometrics, MDPI, vol. 7(2), pages 1-24, May.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:2:p:19-:d:229754
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    References listed on IDEAS

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    Cited by:

    1. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
    2. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    3. Prayut Jain & Shashi Jain, 2019. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification," Risks, MDPI, vol. 7(3), pages 1-27, July.

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