Abstract
This paper develops a new portfolio selection method within an asset liability framework (ALM) using Bayesian theory. We propose the portfolio selection approach of Marschinski, Rossi, Tavoni and Cocco (2007) to overcome the drawbacks of classical optimization techniques based on maximization of expected utility (MEU). While there is no objective rule about how to scale the risk-aversion parameter, which stands in contrast to the strong effect this parameter causes on portfolio weights, we reinterpret the optimal portfolio as the logarithm of a probability distribution for optimal portfolios and no longer as the (single) extremum of a suitably chosen utility function. Numerical results illustrate that we are able to overcome the extreme sensitivity to external parameters and consider the uncertainty obtained from finite historical time series.
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