In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations...

This paper introduces a no-arbitrage framework to assess how macroeconomic factors help explain the risk-premium agents require to bear the risk of .uctuations in stock market volatility. We develop a model in which return volatility and volatility risk-premia are stochastic and derive...

We study a simple model of public opinion formation that posits that interaction between neighbouring agents leads to bandwagons in the dynamics of individual opinions, as well as in that of the aggregate process. We show that in different specifications of the model, there is a tendency for the...

The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen et al. [Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2003. Modelling and forecasting realized...

In recent years it has become apparent that many of the classical testing procedures used to select amongst alternative economic theories and economic models are not realistic. In particular, researchers have become more aware of the fact that parameter estimation error and data dependence play...

This paper proposes a new approach for detecting the number of structural breaks in a time series when estimation of the breaks is performed one at the time. We consider the case of shifts in the mean of a possibly nonlinear process, allowing for dependent and heterogeneous observations. This is...

It is a well accepted fact that stock returns data are often contaminated by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover, and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model...

It is a well accepted fact that stock returns data are often characterized by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model...