University of Cambridge > > Signal Processing and Communications Lab Seminars > Improving ARMA-GARCH Forecasting

Improving ARMA-GARCH Forecasting

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If you have a question about this talk, please contact Rachel Fogg.

We exploit the partial exchangeability structure of the parameters of many ARMA -GARCH models to borrow strength for univariate forecasting. We adopt a challenging reversible jump MCMC scheme and we test our forecasts via a S&P100 dataset.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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