Restricted Access
Reason: Access restricted by the author. A copy can be requested for private research and study by contacting your institution's library service. This copy cannot be republished
Analysis of financial time series: estimation and forecasting by non- and semi-parametric methods
thesis
posted on 2017-02-21, 05:04 authored by Chen, Xiang Jin BruceThis 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
AustraliaPrincipal supervisor
Paramsothy SilvapulleAdditional supervisor 1
Jiti GaoAdditional supervisor 2
Degui LiYear of Award
2015Department, School or Centre
Econometrics and Business StatisticsCourse
Doctor of PhilosophyDegree Type
DOCTORATEFaculty
Faculty of Business and EconomicsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC