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dc.contributor.advisorMcMillan, David G.
dc.contributor.authorKambouroudis, Dimos S.
dc.coverage.spatial261en_US
dc.date.accessioned2012-10-17T14:14:54Z
dc.date.available2012-10-17T14:14:54Z
dc.date.issued2012-06-22
dc.identifier.urihttp://hdl.handle.net/10023/3191
dc.description.abstractStock market volatility has been an important subject in the finance literature for which now an enormous body of research exists. Volatility modelling and forecasting have been in the epicentre of this line of research and although more than a few models have been proposed and key parameters on improving volatility forecasts have been considered, finance research has still to reach a consensus on this topic. This thesis enters the ongoing debate by carrying out empirical investigations by comparing models from the current pool of models as well as exploring and proposing the use of further key parameters in improving the accuracy of volatility modelling and forecasting. The importance of accurately forecasting volatility is paramount for the functioning of the economy and everyone involved in finance activities. For governments, the banking system, institutional and individual investors, researchers and academics, knowledge, understanding and the ability to forecast and proxy volatility accurately is a determining factor for making sound economic decisions. Four are the main contributions of this thesis. First, the findings of a volatility forecasting model comparison reveal that the GARCH genre of models are superior compared to the more ‘simple’ models and models preferred by practitioners. Second, with the use of backward recursion forecasts we identify the appropriate in-sample length for producing accurate volatility forecasts, a parameter considered for the first time in the finance literature. Third, further model comparisons are conducted within a Value-at-Risk setting between the RiskMetrics model preferred by practitioners, and the more complex GARCH type models, arriving to the conclusion that GARCH type models are dominant. Finally, two further parameters, the Volatility Index (VIX) and Trading Volume, are considered and their contribution is assessed in the modelling and forecasting process of a selection of GARCH type models. We discover that although accuracy is improved upon, GARCH type forecasts are still superior.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.relationMcMillan, D. G., and Kambouroudis, D., (2009), “Are RiskMetrics forecasts good enough? Evidence from 31 stock markets”, International Review of Financial Analysis, Vol. 18, pp. 117-124.en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectVolatility forecastingen_US
dc.subjectGARCHen_US
dc.subjectBackward recursionen_US
dc.subjectVaRen_US
dc.subjectRiskmetricsen_US
dc.subjectVIXen_US
dc.subjectTrading volumeen_US
dc.subject.lccHG106.K2
dc.subject.lcshGARCH model--Evaluationen_US
dc.subject.lcshAccounting and price fluctuations--Mathematical modelsen_US
dc.subject.lcshFinancial instruments--Prices--Statistical methodsen_US
dc.subject.lcshTime-series analysis--Mathematical modelsen_US
dc.titleEssays on volatility forecastingen_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted within the work, this item's license for re-use is described as Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported