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dc.contributor.advisorMcMillan, David G.-
dc.contributor.advisorKambouroudis, Dimos-
dc.contributor.authorAl Rababa'A, Abdel Razzaq-
dc.description.abstractThis thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods.en_GB
dc.publisherUniversity of Stirlingen_GB
dc.rightsI, Abdel Razzaq Al Rababa'A, hereby certify that this thesis has been written by me, that it is the record of work carried out by me and that it has not been submitted in any previous application for a higher degree.en_GB
dc.subjectTime-Scale Analysisen_GB
dc.subjectMaximum Overlap Discrete Wavelet Transformen_GB
dc.subjectVolatility Forecastingen_GB
dc.subjectMacroeconomic Surprisesen_GB
dc.subjectDynamic Correlationen_GB
dc.subjectReturn-Volume Relationen_GB
dc.subject.lcshWavelets (Mathematics)en_GB
dc.subject.lcshTime-series analysisen_GB
dc.subject.lcshPrice-earnings ratioen_GB
dc.subject.lcshStock exchangesen_GB
dc.subject.lcshHarmonic analysisen_GB
dc.titleUncovering hidden information and relations in time series data with wavelet analysis: three case studies in financeen_GB
dc.typeThesis or Dissertationen_GB
dc.type.qualificationnameDoctor of Philosophyen_GB
dc.rights.embargoreasonI am writing articles for publication from my thesisen_GB
dc.contributor.funderYarmouk University- Jordanen_GB
Appears in Collections:Accounting and Finance eTheses

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