Abstract
We propose a new method for estimating the covariance matrix of a multivariate time series of financial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the final covariance estimate. We extend the idea of (model) covariance averaging offered in the covariance shrinkage approach by means of greater ease of use, flexibility and robustness in averaging information over different data segments. The suggested approach does not suffer from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization.
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Notes
Note that the \(\displaystyle \widehat{\alpha }_{1}\) estimate of the smoothing parameter is, by construction, less than 1 and positive.
This is the average cost that one of the major online brokerage houses charges the individual investor.
We perform a standard estimation and test for zero mean differences using a GMM-based approach and standard errors. Details of the calculations and many other statistics beyond those presented are available on request.
The maximum drawdown is defined as the largest peak-to-trough drop in the portfolio value during the underlying evaluation period.
Counting the number of times that a table entry is greater than 50 %.
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The authors are very grateful for helpful comments from the editor and an anonymous referee. Any remaining errors are our responsibility.
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Papailias, F., Thomakos, D.D. Covariance averaging for improved estimation and portfolio allocation. Financ Mark Portf Manag 29, 31–59 (2015). https://doi.org/10.1007/s11408-014-0242-0
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DOI: https://doi.org/10.1007/s11408-014-0242-0