Anmerkungen:
In: Mathematical Methods of Operations Research 70, 337–356 (2009)
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 25, 2008 erstellt
Beschreibung:
We develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm. The portfolio optimization is done using the first two moments of the predictive discrete asset return distribution. For illustration purposes we apply our method to empirical stock market data where daily asset log-returns are assumed to follow an orthogonal MGARCH process with t-distributed perturbations. Our results are compared with other portfolios suggested by popular optimization strategies