Covariance Prediction in Large Portfolio Allocation

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ID: 69178
2019
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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.
Reference Key
trucos2019covarianceeconometrics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Trucíos, Carlos;Zevallos, Mauricio;Hotta, Luiz K.;Santos, André A. P.;
Journal econometrics
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/2225-1146/7/2/19
Keywords
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