Ordinal-Response GARCH Models for Transaction Data: A Forecasting Exercise
27 Pages Posted: 28 Jul 2018
Date Written: March 8, 2018
Using high-frequency transaction data, we evaluate the forecasting performance of several dynamic ordinal-response time series models with generalized autoregressive conditional heteroscedasticity. The specifications account for three components; leverage effects, in-mean effects and moving average error terms. To estimate the model parameters we develop Markov chain Monte Carlo algorithms. Our empirical analysis showed that ordinal-response models achieve better point and density forecasts than standard benchmarks, when they incorporate at least one of the three components.
JEL Classification: C11, C13, C18, C22, C50, G00
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