Journal article

Publication year: 2007

Pages: 403-418

Journal: Statistical Papers

Volume number: 48

Issue number: 3

ISSN: 0932-5026

eISSN: 1613-9798

DOI Link: https://doi.org/10.1007/s00362-006-0344-5

Publisher: Springer

Abstract: 
Portfolio optimization is very sensitive to the forecasts of returns and (co-)variances of the underlying assets, This paper applies a Bayesian vector-autoregression of the asset universe to predict the returns. Further, the co-variance matrix is forecasted by an Augmented GARCH estimation of the most volatile principle components of the return series. As an empirical illustration, the daily stock returns of the German stocks index DAX have been used to calculate some well-known mean-variance portfolios. Back-testing is used to evaluate the performance. The approach seems to be promising.

Harvard Citation style: Gohout, W. and Specht, K. (2007) Mean-variance portfolios using Bayesian vector-autoregressive forecasts, Statistical Papers, 48(3), pp. 403-418. https://doi.org/10.1007/s00362-006-0344-5

APA Citation style: Gohout, W., & Specht, K. (2007). Mean-variance portfolios using Bayesian vector-autoregressive forecasts. Statistical Papers. 48(3), 403-418. https://doi.org/10.1007/s00362-006-0344-5