Covariance Prediction in Large Portfolio Allocation
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.
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Medicine
neoplasms. tumors. oncology. including cancer and carcinogens
business
economics as a science
diseases of the respiratory system
management. industrial management
mathematics
electronic computers. computer science
transportation and communications
transportation and communication
probabilities. mathematical statistics
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