The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies.
The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance.
The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account.
The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective.
The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.
This research was conducted while Christian Fieberg was lecturer at the FOM Hochschule. The authors wish to thank anonymous referees for helpful comments.
Berger, T. and Fieberg, C. (2016), "On portfolio optimization: Forecasting asset covariances and variances based on multi-scale risk models", Journal of Risk Finance, Vol. 17 No. 3, pp. 295-309. https://doi.org/10.1108/JRF-09-2015-0094Download as .RIS
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