A new Bayesian multi-chain Markov Switching GARCH model for dynamic hedging in energy futures markets is developed by constructing a system of simultaneous equations for the return dynamics on the hedged portfolio and futures. More specifically, both the mean and variance of the hedged portfolio...

This paper builds on Asai and McAleer (2009) and develops a new multivariate Dynamic Conditional Correlation (DCC) model where the parameters of the correlation dynamics and those of the log-volatility process are driven by two latent Markov chains. We outline a suitable Bayesian inference...

Interactions between eurozone and United States booms and busts and among major eurozone economies are analyzed by introducing a panel Markov-switching VAR model. The model is well suitable for a multi-country cyclical analysis and accommodates changes in low and high data frequencies and...

We investigate the latent volatility structures of the fluctuations in the US business cycle and stock market valuations. The technical novelty of this work lies in the estimation of a Markov-switching stochastic-volatility model that allows for Bayesian sequential evaluation on both the...

We study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden volatility regimes, these models have many financial applications like asset allocation, option pricing and risk management. The Markov...

Modelling of the financial variable evolution represents an important issue in financial econometrics. Stochastic dynamic models allow to describe more accurately many features of the financial variables, but often there exists a trade-off between the modelling accuracy and the complexity....

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights...

In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, model selection is a difficult issue and standard procedures may often be...

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...

This discussion paper resulted in a publication in the 'Journal of Econometrics', 2013, 177(2), 213-232.<P> We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several...</p>