Regularizing Portfolio Risk Analysis: A Bayesian Approach

Regularizing portfolio optimization

The optimization of large portfolios displays an inherent instability due to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theor...

Project Portfolio Risk Response Selection Using Bayesian Belief Networks

Risk identification, impact assessment, and response planning constitute three building blocks of project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remai...

Bayesian Portfolio Analysis

A general approach to Bayesian portfolio optimization

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 retu...

Bond Portfolio Optimization: A Risk-Return Approach

In this paper, we apply Markowitz’s approach of portfolio selection to government bond portfolios. As a main feature of our analysis, we use term structure models to estimate expected returns, return variances, and covariances of different bonds. Our empirical study for the German market shows that a small number of risky bonds is sufficient to reach very promising predicted risk-return profile...