Ordinal-response GARCH models for transaction data: A forecasting exercise

Stefanos Dimitrakopoulos and Mike Tsionas

International Journal of Forecasting, 2019, vol. 35, issue 4, 1273-1287

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.

Keywords: Conditional heteroscedasticity; In-mean effects; Leverage; Markov chain Monte Carlo; Moving average; Ordinal responses (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1273-1287

DOI: 10.1016/j.ijforecast.2019.02.016

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