This paper presents a novel theoretical framework to model the evolution of a dynamic portfolio (i.e., a portfolio whose weights vary over time), considering a given investment policy. The framework is based on graph theory and the quantum probability. Embedding the dynamics of a portfolio into...

The aim of this work is to investigate whether the combination of forecasts plays an important role in the improvement of forecast accuracy Particular attention is paid to: (a) the methods of forecasting (the methods compared are neural networks, fuzzy logic, GARCH models, switching regime and...

This paper aims at the production of a chronology for the EU15 business cycle by comparing parametric and non-parametric procedures on monthly and quarterly data as well in a combined approach. The main innovation is the joint use of the monthly series for the EU15 Gross Domestic Product (GDP)...

We propose several econometric measures of systemic risk to capture the interconnectedness among the monthly returns of hedge funds, banks, brokers, and insurance companies based on principal components analysis and Granger-causality tests. We find that all four sectors have become highly...

We propose a new approach for detecting turning points and forecasting the level of economic activity in the business cycle. We make use of coincident indicators and of nonlinear and non-Gaussian latent variable models. We thus combine the ability of nonlinear models to capture the asymmetric...

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions...

A regime-switching beta model is proposed to measure dynamic risk exposures of hedge funds to various risk factors during different market volatility conditions. Hedge fund exposures strongly depend on whether the equity market (S&P 500) is in the up, down, or tranquil regime. In the down-state...

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven...

We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over...

Identification of financial bubbles and crisis is a topic of major concern since it is important to prevent collapses that can severely impact nations and economies. Our analysis deals with the use of the recently proposed ‘delay vector variance’ (DVV) method, which examines local...