This paper presents an application of neural network models to predictive classification for data quality control. Our aim is to identify data affected by measurement error in the Bank of Italy�s business surveys. We build an architecture consisting of three feed-forward networks for...

In this paper we consider several time-varying volatility and/or heavy-tailed models to explain the dynamics of return time series and to fit the volatility smile for exchange-traded options where the underlying is the main �Borsa Italiana� stock index. Given observed prices for the...

Most of the important models in finance rest on the assumption that randomness is explained through a normal random variable because, in general, the use of alternative models is obstructed by the difficulty of calibrating and simulating them. In this paper, we empirically study models for...

This paper investigates how bank profitability is affected by corporate income tax (CIT) using aggregate data on the banking sector of the main industrialized countries for the period 1981-2003. Two main novelties emerge with respect to the existing literature. First, the paper explicitly...

When do financial markets help in predicting economic activity? With incomplete markets, the link between financial and real economy is state-dependent and financial indicators may turn out to be useful particularly in forecasting "tail" macroeconomic events. We examine this conjecture by...

We develop a dynamic multivariate default model for a portfolio of credit-risky assets in which default times are modelled as random variables with possibly different marginal distributions, and L�vy subordinators are used to model the dependence among default times. In particular, we...

This paper proposes the use of Bayesian model averaging (BMA) as a tool to select the predictors' set for bridge models. BMA is a computationally feasible method that allows us to explore the model space even in the presence of a large set of candidate predictors. We test the performance of BMA...

In this paper, we exploit the heterogeneity in the forecasts obtained by estimating different factor models to measure forecast uncertainty. Our approach is simple and intuitive. It consists first in selecting all the models that outperform some benchmark model, and then in constructing an...

This paper proposes a simple procedure to obtain monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines emerging and advanced countries� high frequency information to explain quarterly national accounts variables through bridge models. The union of all...

Quantile aggregation (or 'Vincentization') is a simple and intuitive way of combining probability distributions, originally proposed by S. B. Vincent in 1912. In certain cases, such as under Gaussianity, the Vincentized distribution belongs to the same family as that of the individual...