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Analysis of financial time series: estimation and forecasting by non- and semi-parametric methods

thesis
posted on 21.02.2017, 05:04 by Chen, Xiang Jin Bruce
This thesis proposes a semiparametic copula methodology for modelling, estimating and forecasting tail risks of stock-bond portfolios of Australia and G7 countries. These tail risks have vastly increased as a consequence of the recent global financial crisis. Additionally, this thesis introduces a nonparametric methodology for estimating and forecasting S&P 500 returns volatilities. An attractive feature of this method is that it does not make restrictive assumptions and data speaks for itself. In comparison to existing methods, our proposed method captures the underlying movements in the market volatilities and produces superior out-of-sample forecasts of global risk, particularly during the crises.

History

Campus location

Australia

Principal supervisor

Paramsothy Silvapulle

Additional supervisor 1

Jiti Gao

Additional supervisor 2

Degui Li

Year of Award

2015

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Business and Economics

Exports