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dc.contributor.supervisorMoyeed, Rana
dc.contributor.authorShahtahmassebi, Golnaz
dc.contributor.otherSchool of Engineering, Computing and Mathematicsen_US
dc.date.accessioned2011-12-08T13:47:07Z
dc.date.available2011-12-08T13:47:07Z
dc.date.issued2011
dc.date.issued2011
dc.identifier359263en_US
dc.identifier.urihttp://hdl.handle.net/10026.1/894
dc.description.abstract

The availability of ultra high-frequency (UHF) data on transactions has revolutionised data processing and statistical modelling techniques in finance. The unique characteristics of such data, e.g. discrete structure of price change, unequally spaced time intervals and multiple transactions have introduced new theoretical and computational challenges. In this study, we develop a Bayesian framework for modelling integer-valued variables to capture the fundamental properties of price change. We propose the application of the zero inflated Poisson difference (ZPD) distribution for modelling UHF data and assess the effect of covariates on the behaviour of price change. For this purpose, we present two modelling schemes; the first one is based on the analysis of the data after the market closes for the day and is referred to as off-line data processing. In this case, the Bayesian interpretation and analysis are undertaken using Markov chain Monte Carlo methods. The second modelling scheme introduces the dynamic ZPD model which is implemented through Sequential Monte Carlo methods (also known as particle filters). This procedure enables us to update our inference from data as new transactions take place and is known as online data processing. We apply our models to a set of FTSE100 index changes. Based on the probability integral transform, modified for the case of integer-valued random variables, we show that our models are capable of explaining well the observed distribution of price change. We then apply the deviance information criterion and introduce its sequential version for the purpose of model comparison for off-line and online modelling, respectively. Moreover, in order to add more flexibility to the tails of the ZPD distribution, we introduce the zero inflated generalised Poisson difference distribution and outline its possible application for modelling UHF data.

en_US
dc.language.isoenen_US
dc.publisherUniversity of Plymouthen_US
dc.subjectBayesian
dc.subjectZero inflated Poisson difference
dc.subjectOff-line
dc.subjectMarkov chain Monte Carlo
dc.subjectSequential Monte Carlo
dc.subjectParticle filters
dc.subjectOnline
dc.subjectFTSE100
dc.subjectProbability integral transform
dc.subjectSequential deviance information criterion
dc.subjectGeneralised Poisson difference
dc.subjectUltra high-frequencyen_US
dc.titleBAYESIAN MODELLING OF ULTRA HIGH-FREQUENCY FINANCIAL DATAen_US
dc.typeThesis
dc.identifier.doihttp://dx.doi.org/10.24382/3602


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