In this paper, we propose a methodology for building an estimator of the covariance matrix. We use a robust measure of moments called L-moments (see hosking, 1986), and their extension into a multivariate framework (see Serfling and Xiao, 2007). Random matrix theory (see Edelman, 1989) allows us...

In this paper, we propose a methodology for building an estimator of the covariance matrix. We use a robust measure of moments called L-moments (see hosking, 1986), and their extension into a multivariate framework (see Serfling and Xiao, 2007). Random matrix theory (see Edelman, 1989) allows us...

In this paper, we propose a methodology for building an estimator of the covariance matrix. We use a robust measure of moments called L-moments (see hosking, 1986), and their extension into a multivariate framework (see Serfling and Xiao, 2007). Random matrix theory (see Edelman, 1989) allows us...

We analyze the introduction of a diversification constraint into the portfolio optimization program. We show that such a constraint is equivalent to an unconstrained portfolio optimization program with a change of the sample covariance matrix by another matrix obtained as the sum of the sample...

The formulation of the Black-Litterman model as a Bayesian mixed estimation approach allows for computing the posterior expected returns taking into account the views of investor on future returns. When the views turn out to be wrong, the resulting portfolio may lead to losses. Sometimes, it may...

This book covers the latest advances in the theory and practice of public investment management. It includes the most up-to-date developments in the implementation of public asset management - including multiple contributions on portfolio allocation in varying interest-rate and credit-risk...