This thesis considers the modelling of ultra high frequency (UHF) nancial data from South African markets. The approach to be taken is that such irregularly spaced data can be viewed as a realization of a marked point process. We propose a statistical model that incorporates both the unequally spaced transaction times (the points) as well as the movements of the associated returns (the marks). In all data sets investigated, no change in the value of the mark accounts for more that half the observations. If no change is considered as the censoring of some underlying process, we can explicitly model both the censoring of marks and the underlying process by utilizing methods for Markov chains and missing values. All models considered hitherto in the literature assume homogeneity of structure within a UHF data set. Data analyses indicate strongly that such an assumption is not justi ed. The proposed model aims to exploit this observation. The diurnal (time of day) e¤ect is a form of non-stationarity commonly found in UHF data sets. We show that the method currently considered standard practice is inadequate and we will propose modi cations of it. Consideration is given to the classi cation of heterogeneous subsets that arises naturally in UHF data, for instance daily subsets of a UHF data set. We nd evidence in support of some market microstructure theories, but no theory is supported by all data sets considered. We pay attention to technical issues surrounding the application of certain tests to large samples. As large samples are common in UHF data sets methods that are sensitive to large sample size, for example the Ljung-Box test, are not suitable. / Professor Freek Lombard
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:10238 |
| Date | 07 July 2008 |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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