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dc.contributor.authorZhang, Q.*
dc.contributor.authorCai, Charlie X.*
dc.contributor.authorKeasey, K.*
dc.date.accessioned2014-04-28T11:28:16Z
dc.date.available2014-04-28T11:28:16Z
dc.date.issued2009
dc.identifier.citationZhang Q, Cai CX and Keasey K (2009) Forecasting using high-frequency data: a comparison of asymmetric financial duration models. Journal of Forecasting, 28(5): 371-386.
dc.identifier.urihttp://hdl.handle.net/10454/6250
dc.description.abstractThe first purpose of this paper is to assess the short-run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomial–Autoregressive Conditional Duration (ACM-ACD) model is better than the Asymmetric Autoregressive Conditional Duration (AACD) model. However, the ACM-ACD model is more complex in terms of the computational setting and is more sensitive to starting values. The second purpose is to examine the effects of market microstructure on the forecasting performance of the two models. The results indicate that the forecast performance of the models generally decreases as the liquidity of the stock increases, with the exception of the most liquid stocks. Furthermore, a simple filter of the raw data improves the performance of both models. Finally, the results suggest that both models capture the characteristics of the micro data very well with a minimum sample length of 20 days.
dc.relation.isreferencedbyhttp://dx.doi.org/10.1002/for.1100
dc.subjectREF 2014; Autoregressive duration model (ACD); Forecasting; High-frequency data; Market microstructure
dc.titleForecasting using high-frequency data: a comparison of asymmetric financial duration models
dc.typeArticle


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