Several previous works show that, in general, financial time series are characterized by periods of large volatility followed by periods of relative quitness. In this paper we consider the product partition model (PPM) to identify changes in the volatility extending it to identify multiple...

We extend the Large Homogeneous Portfolio (LHP) approximation to the case of the Student-t copula, and provide analytic formulae for the density and the cdf of the portfolio loss distribution. We compare the Value-at-Risk implied by the Student-t copula to that obtained using the Gaussian as...

In this paper we present a Bayesian solution to the multiple change point problems in the mean m and variance s2 of two Brazilian indexes: Índice Geral da Bolsa de Valores de São Paulo (IBOVESPA) and Índice da Bolsa de Valores de Minas Gerais, Espírito Santo e Brasília (IBOVMESB), both...

The multiple change point identification problem may be encountered in many subject areas, including disease mapping, medical diagnosis, industrial control, and finance. One appealing way of tackling the problem is through the product partition model (PPM), a Bayesian approach. Nowadays,...

This paper proposes a new model for autoregressive conditional heteroscedasticity and kurtosis. Via a time-varying degrees of freedom parameter, the conditional variance and conditional kurtosis are permitted to evolve separately. The model uses only the standard Student’s t density and...

The paper describes a continuous time share market model with a minimal number of factors. These factors are powers of Bessel processes. The asset prices are formed by ratios of the factors and have consequently leptokurtic return distributions. In this framework stochastic volatility with...

The paper describes a continuous time financial market model, where the basic factord are trading volumes per unit time. These are modelled by squared Bessel processes. The asset prices are formed by rations of these trading volumes. They have leptokurtic return distributions and stochastic...