The aim of this paper consists in testing the profitability of simple technical trading rules in the Italian stock market. By means of a recently developed bootstrap methodology we assess whether technical rules based on moving averages are capable of producing excess returns with respect to the...

In this paper we deal with the use of multivariate normal mixture distributions to model asset returns, In particular, by modelling daily asset returns as a mixture of a low-volatility and a high-volatility distribution, we obtain three main results: (i) we can use posterior probabilities to...

Continuous-time stochastic volatility models are becoming increasingly popular in finance because of their flexibility in accommodating most stylized facts of financial time series. However, their estimation is difficult because the likelihood function does not have a closed-form expression. In...

This paper develops a multivariate statistical model for the analysis of credit default swap spreads. Given the large excess kurtosis of the univariate marginal distributions, it is proposed to model them by means of a mixture of distributions. However, the multivariate extension of this...

In this paper we compare the relative efficiency of different forecasting methods of space-time series when variables are spatially and temporally correlated. We consider the case of a space-time series aggregated into a single time series and the more general instance of a space-time series...

In sampling theory the large concentration of the population with respect to most surveyed variables constitutes a problem which is difficult to tackle by means of classical tools. One possible solution is given by cut-off sampling, which explicitly prescribes to discard part of the population;...

Maximum likelihood estimation of spatial models based on weight matrices typically requires a sizeable computational capacity, even in rel- atively small samples. The unilateral approximation approach to spatial models estimation has been suggested in Besag (1974) as a viable alternat- ive to...

Approximate Maximum Likelihood Estimation (AMLE) is a simple and general method recently proposed for approximating MLEs without evaluating the likelihood function. The only requirement is the ability to simulate the model to be estimated. Thus, the method is quite appealing for spatial models...