• Selected
• Entire List

Abstract:
This paper proposes a method to overcome the classical drawbacks of the Monte Carlo methods for the asset allocation, namely resampling, deeply dependent upon the multinormal assumption. The proposed approach allows to set a barrier against joint extreme negative returns (tail-dependence) and extreme (negative) returns (univariate tail risk) not included in the multivariate normal distribution. The dangerous tail-dependence between asset returns is considered by using a copula based approach instead of the multinormal Monte Carlo simulation. Then the proposed model has been applied on a sample of eleven euro-denominated asset classes with historical inputs and the consequent asset weights have been tested on multivariate Student's t returns and on a set of out-of-the sample real returns. The results of this model provide evidence of a barrier against extreme negative returns occurring simultaneously. The proposed model is distribution-free and therefore it does not involve any a priori decision on the marginal distributions for asset returns.

copula, simulation, tail index, EVT, asset allocation

Abstract:
In this paper we propose a new measure for the marginal contribution of each view to the ex-ante tracking error volatility (TEV).

The issue of the TEV sensitivity to the views is relevant for several purposes: 1. provide the asset managers with a method for revising the portfolio consistently with a given TEV constraint; 2. Make the specialists responsible for the generation process of the views; 3. set a mechanism to connect the incentive fees not only to the excess return but also to the marginal contribution of each view to the TEV.

We provide also an empirical investigation in the Black-Litterman framework in order to modify the views to achieve a TEV goal.

Black-Litterman, TEV, marginal contribution, views, sensitivity