Hierarchical Markov normal mixture models with applications to financial asset returns

Gianni Amisano and John Geweke

No 831, Working Paper Series from European Central Bank

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. JEL Classification: C53, G12, C11, C14

Keywords: Asset returns; Bayesian; forecasting; MCMC; mixture models (search for similar items in EconPapers)
Date: 2007-11
Note: 337895
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

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Related works:
Journal Article: Hierarchical Markov normal mixture models with applications to financial asset returns (2011)
Working Paper: Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:2007831

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