Stock market volatility forecasting at the Nairobi Securities Exchange: a comparison between asymmetric GARCH models and neural networks

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dc.contributor.authorRugut, Diana Chemutai
dc.date.accessioned2022-07-15T16:31:56Z
dc.date.available2022-07-15T16:31:56Z
dc.date.issued2021
dc.descriptionResearch thesis submitted to Strathmore University in fulfillment of the requirements for the Master of Science in Mathematical Financeen_US
dc.description.abstractIn this study, we conducted a comparative study on the volatility forecasting models focusing on the asymmetric GARCH models and the Artificial Neural Networks. The study focused on the Nairobi Securities Exchange and used the daily data from the NSE-20 Share Index for a period between January 2012 - February 2021. We took advantage of the year 2020 to investigate whether the models are still effective or they break during a period of turbulence. The parameters of the asymmetric GARCH models were estimated using Maximum Likelihood Estimation (MLE). The asymmetric GARCH models, namely the EGARCH and GJR-GARCH models were compared using AIC and BIC to determine the model with the best fit when it comes to modelling and based on the results of the AIC and BIC, the EGARCH was the better model. The study then compared the forecasting ability of the asymmetric GARCH models to that of the Artificial Neural Networks in terms of the standard deviations as a measure of the actual volatility to determine the better model. The RMSE and MAE measures were used to evaluate the forecasting abilities of the models and based on the results of the RMSE and MAE from the study, the Artificial Neural Networks outperformed the asymmetric GARCH models when it comes to forecasting.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12900
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectStock market volatilityen_US
dc.subjectNairobi Securities Exchangeen_US
dc.subjectGARCH modelsen_US
dc.subjectNeural networksen_US
dc.titleStock market volatility forecasting at the Nairobi Securities Exchange: a comparison between asymmetric GARCH models and neural networksen_US
dc.typeThesisen_US
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MSc.MF Theses and Dissertations (2021)