A Comparison of Traditional Models and Machine Learning Techniques for Volatility Forecasting

dc.contributor.advisorFeely, Ciara
dc.contributor.authorYoldas Ayan, Begum
dc.date.accessioned2024-03-27T11:31:10Z
dc.date.available2024-03-27T11:31:10Z
dc.date.issued2023
dc.description.abstractThe calculation of volatility plays an important role in many applications such as derivatives pricing, portfolio optimization, risk-based value modelling and risk management. This study aims investigate and compare the efficacy of different forecasting methods for financial market realized volatility, specifically focusing on the SPX and DAX Index for identified range of time. The methodological design incorporated both machine learning techniques, such as Support Vector Machines (SVM), Neural Networks, and Long Short-Term Memory (LSTM) networks, as well as traditional econometric models like GARCH. Procedures followed a structured evaluation process, which involved training the models on a designated dataset, followed by validation and testing phases to measure their forecasting accuracy. Based on the findings, any machine learning techniques offer a more efficacious approach for forecasting volatility in the financial markets, compared to the traditional volatility models such as ARCH&GARCH models.
dc.identifier.citationYoldas Ayan, B. (2023) A Comparison of Traditional Models and Machine Learning Techniques for Volatility Forecasting. Master's Thesis, Dublin Business School.
dc.identifier.urihttps://hdl.handle.net/10788/4450
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright, the Author
dc.rights.urihttp://www.esource.dbs.ie/copyright
dc.subjectStock price forecasting
dc.subjectMachine learning
dc.subjectNeural networks (Computer science)
dc.titleA Comparison of Traditional Models and Machine Learning Techniques for Volatility Forecasting
dc.typeThesis
dc.type.degreelevelMSc
dc.type.degreenameMSc in Financial Analytics
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