The specification of prior parameters is a common practical problem when implementing Bayesian approaches to portfolio optimization. The precision parameter of the prior on the expected asset returns reflects the confidence of the investor in the prior knowledge. Within the framework of the...

We propose an objective Bayesian method for the comparison of all Gaussian directed acyclic graphical models defined on a given set of variables. The method, which is based on the notion of fractional Bayes factor, requires a single default (typically improper) prior on the space of...

The specification of prior parameters is a common practical problem when implementing Bayesian approaches to portfolio optimization. The precision parameter of the prior on the expected asset returns reflects the confidence of the investor in the prior knowledge. Within the framework of the...

We propose an objective Bayesian method for the comparison of all Gaussian directed acyclic graphical models defined on a given set of variables. The method, which is based on the notion of fractional Bayes factor, requires a single default (typically improper) prior on the space of...

We propose an objective Bayesian method for the comparison of all Gaussian directed acyclic graphical models defined on a given set of variables. The method, which is based on the notion of fractional Bayes factor, requires a single default (typically improper) prior on the space of...

In this paper, we propose a Bayesian VAR model to examine the short term effects of monetary policy shocks on the Italian economy. Firstly, our BVAR model uses the Cholesky decomposition to identify four kinds of macroeconomic shocks, namely, supply, demand, interest rate and monetary shocks....

This paper analyzes the daily incidence of violence during the Second Intifada. We compare several alternative statistical models with different dynamic and structural stability characteristics while keeping modelling complexity to a minimum by only maintaining the assumption that the process...