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Fall 2007
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John Geweke
Department of Statistics and Actuarial Science
Department of Economics
University of Iowa
"Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns"
3:00 Refreshments in 241 Schaeffer Hall
3:30 Talk in 140 Schaeffer Hall
Motivated by the common problem of constructing predictive distributions for daily asset
returns over horizons of one to several trading days, this article introduces a new model
for time series. This model is a generalization of the Markov normal mixture model in
which the mixture components are themselves normal mixtures, and it is a specific case
of an artificial neural network model with two hidden layers. The article characterizes the
implications of the model for time series in two ways. First, it derives the restrictions
placed on the autocovariance function 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. Second, it uses the prior predictive distribution of the model to study the
implications of the model for some interesting functions of asset returns. The article uses
the model to construct predictive distributions of daily S&P 500 returns 1971-2005, US
dollar -- UK pound returns 1972-1998, and one- and ten-year maturity bonds 1987-2006.
It compares the performance of the model for these returns with ARCH and stochastic
volatility models using the predictive likelihood function. 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 than its competitors for the dollar-pound returns. In- and
out-of-sample validation exercises with predictive distributions identify some
remaining deficiencies in the model and suggest potential improvements. The article
concludes by using the model to form predictive distributions of one- to ten-day returns
during volatile episodes for the S&P 500, dollar-pound and bond return series.
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All talks are free and open to the public.
To receive colloquium reminders via email, please send a request to statistics@uiowa.edu.
The 37th Annual Craig Lectures will be on October 11 and 12. Our speaker this year is Nancy Reid from the University of Toronto.
Past colloquia:
Spring 2007 |
Fall 2006 | Spring 2006 |
Fall 2005 |
Spring 2005 | Fall 2004 | Spring 2004 | Fall 2003 | Spring 2003 | Fall 2002 | Spring 2002 | Fall 2001 | Spring 2001 | Fall 2000 | Spring 2000 | Fall 1999 | Spring 1999 | Fall 1998 | Spring 1998
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