Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153265
Authors: 
Geweke, John
Amisano, Gianni
Year of Publication: 
2007
Series/Report no.: 
ECB Working Paper 831
Abstract: 
With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in terms of the number of components in the mixture and the roots of the Markov process. We use the model prior predictive distribution to study its implications for some interesting functions of returns. We apply the model to construct predictive distributions of daily S&P500 returns, dollarpound returns, and one- and ten-year bonds. We compare the performance of the model with ARCH and stochastic volatility models using predictive likelihoods. The model's performance is about the same as its competitors for the bond returns, better than its competitors for the S&P 500 returns, and much better for the dollar-pound returns. Validation exercises identify some potential improvements.
Subjects: 
Asset returns
Bayesian
forecasting
MCMC
mixture models
JEL: 
C53
G12
C11
C14
Document Type: 
Working Paper

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