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Modelling and forecasting volatility in Applied Econometrics

posted on 02.08.2021, 06:34 by armin pourkhanali
Here, I investigate a variety of volatility models with varying parameters. Then compare two new Conditional heteroscedasticity models with varying parameters. In the first proposed model, I allow the parameters, which are attached to the lagged squared error term and lagged conditional variance term in the conditional variance under a GARCH specification, to be time-varying. The second proposed model is to introduce functional parameters into the GARCH process, resulting in a new model called fGARCH, based on the temporal interactions between stock and bond returns. They both demonstrate strong improvement in volatility prediction relative to constant parameter GARCH models.


Campus location


Principal supervisor

Jonathan Macgregor Keith

Additional supervisor 1

Xibin Zhang

Year of Award


Department, School or Centre



Doctor of Philosophy

Degree Type



Faculty of Science

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