Ordinal-Response GARCH Models for Transaction Data: A Forecasting Exercise

27 Pages Posted: 28 Jul 2018

See all articles by Stefanos Dimitrakopoulos

Stefanos Dimitrakopoulos

University of Leeds - Leeds University Business School (LUBS)

Efthymios G. Tsionas

Lancaster University

Date Written: March 8, 2018

Abstract

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

Suggested Citation

Dimitrakopoulos, Stefanos and Tsionas, Efthymios G., Ordinal-Response GARCH Models for Transaction Data: A Forecasting Exercise (March 8, 2018). Available at SSRN: https://ssrn.com/abstract=3210078 or http://dx.doi.org/10.2139/ssrn.3210078

Stefanos Dimitrakopoulos (Contact Author)

University of Leeds - Leeds University Business School (LUBS) ( email )

Leeds LS2 9JT
United Kingdom

Efthymios G. Tsionas

Lancaster University ( email )

Lancaster LA1 4YX
United Kingdom

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