Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 4, 2019 DOI: 10.1016/j.ijforecast.2019.02.016
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Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Ordinal-response GARCH models for transaction data
T2 - A forecasting exercise
AU - Dimitrakopoulos, S.
AU - Tsionas, M.
N1 - This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 4, 2019 DOI: 10.1016/j.ijforecast.2019.02.016
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Conditional heteroscedasticity
KW - In-mean effects
KW - Leverage
KW - Markov chain Monte Carlo
KW - Moving average
KW - Ordinal responses
U2 - 10.1016/j.ijforecast.2019.02.016
DO - 10.1016/j.ijforecast.2019.02.016
M3 - Journal article
VL - 35
SP - 1273
EP - 1287
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 4
ER -