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.
Date of Award | 2011 |
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Original language | English |
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Awarding Institution | |
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Supervisor | Rana Moyeed (Other Supervisor) |
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- Bayesian
- Zero inflated Poisson difference
- Off-line
- Markov chain Monte Carlo
- Sequential Monte Carlo
- Particle filters
- Online
- FTSE100
- Probability integral transform
- Sequential deviance information criterion
- Generalised Poisson difference
- Ultra high-frequency
BAYESIAN MODELLING OF ULTRA HIGH-FREQUENCY FINANCIAL DATA
Shahtahmassebi, G. (Author). 2011
Student thesis: PhD