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ADVANCEMENT OF AUTOREGRESSIVE CONDITIONAL DURATION MODELS INVOLVING LIQUIDITY AND PRICE DYNAMICS

Part of: Nonparametric inference Parametric inference Mathematical economics

Published online by Cambridge University Press:  22 March 2017

RASIKA PUSHPAMALI YATIGAMMANA
RASIKA PUSHPAMALI YATIGAMMANA*
Affiliation:
Discipline of Business Analytics, University of Sydney, New South Wales 2006, Australia email yatigammanar@hotmail.com
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Abstract

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Keywords

autoregressiveconditional durationliquidityprice dynamicsprobit frameworkextreme quantiles

MSC classification

Primary: 91B24: Price theory and market structure
Secondary: 62F15: Bayesian inference 62G32: Statistics of extreme values; tail inference 90C31: Sensitivity, stability, parametric optimization

Information

Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 95 , Issue 3 , June 2017 , pp. 523 - 524
Copyright
© 2017 Australian Mathematical Publishing Association Inc. 

References

Yatigammana, R. P., Choy, S. T. B. and Chan, J. S. K., ‘Autoregressive conditional duration model with an extended Weibull error distribution’, in: Causal Inference in Econometrics, Studies in Computational Intelligence, 622 (Springer International Publishing, Switzerland, 2016), 83–107.Google Scholar
Yatigammana, R. P., Peiris, M. S., Gerlach, R. H. and Allen, D., ‘Modelling and forecasting stock price movements with serially dependent determinants’, submitted.Google Scholar