Ordinal-response GARCH models for transaction data: A forecasting exercise |
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Abstract: | We use numerous high-frequency transaction data sets to evaluate the forecasting performances of several dynamic ordinal-response time series models with generalized autoregressive conditional heteroscedasticity (GARCH). The specifications account for three components: leverage effects, in-mean effects and moving average error terms. We estimate the model parameters by developing Markov chain Monte Carlo algorithms. Our empirical analysis shows that the proposed ordinal-response GARCH models achieve better point and density forecasts than standard benchmarks. |
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Keywords: | Conditional heteroscedasticity In-mean effects Leverage Markov chain Monte Carlo Moving average Ordinal responses |
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