P. Galeano San Miguel, C. Garcia de la Fuente, M. P. Wiper

Financial data sets often present skewness and high kurtosis. As a consequence, it is natural to look for a model that is flexible enough to capture these characteristics. The proposal is to perform Bayesian inference and prediction for a generalized autoregressive conditional heteroskedastic (GARCH) model, where the innovations are assumed to follow a Skew Slash distribution. Gibbs sampling is used for parameter estimation and volatility prediction, and the method is illustrated using real financial data.

Palabras clave: financial time series Bayesian analysis Skew Slash distribution GARCH model Skewness Kurtosis

G. Núñez-Antonio

M. A. Gómez-Villegas, R. Susi García

M. Marin Martinez, J. M. del Río