Dynamic data science applications in finance

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dc.contributor.authorZhu, Zimo
dc.contributor.examiningcommitteeTurgeon, Max (Statistics

) Thulasiram, Ruppa (Computer Science
)en_US
dc.contributor.supervisorThavaneswaran, Aerambamoorthy (Statistics)en_US
dc.date.accessioned2020-09-08T19:45:52Z
dc.date.available2020-09-08T19:45:52Z
dc.date.copyright2020-08-16
dc.date.issued2020-08en_US
dc.date.submitted2020-08-16T13:11:44Zen_US
dc.degree.disciplineStatisticsen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractIt has been shown that some financial data follows certain heavy tailed distributions. Thavaneswaran et al(2019) proposed a novel data-driven exponential weighted moving average (DDEWMA) volatility forecasting model to forecast the volatility directly. In this thesis, regularized DDEWMA volatility forecasting model and the data-driven neural volatility(DDNV) forecasting model are proposed to forecast volatility, value-at-risk (VaR)and expected shortfall (ES). Moreover, the regularized DDEWMA and DDNV help improve the volatility forecasts. The DDEWMA also helps enhance the performance of portfolio optimization and algorithmic trading.en_US
dc.description.noteOctober 2020en_US
dc.identifier.urihttp://hdl.handle.net/1993/34993
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectDynamic Data Science, Volatility forecasting, Risk forecasting, Portfolio optimization, Algorithmic tradingen_US
dc.titleDynamic data science applications in financeen_US
dc.typemaster thesisen_US
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