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dc.contributor.advisor Jiu, Brett
dc.creator Bai, Jingjing
dc.date.accessioned 2012-09-28T12:37:33Z
dc.date.available 2012-09-28T12:37:33Z
dc.date.created 2011-08
dc.date.issued 2012-09-28
dc.date.submitted August 2011
dc.identifier.uri http://hdl.handle.net/10657/ETD-UH-2011-08-205
dc.description.abstract The motivation of this paper is to study the estimation problems in large dimension systems in quantitative finance. The paper firstly presents principal component analysis to obtain the most important information in the data. Then, the orthogonal GARCH model introduced by Alexander and Chibumba (1997) and Alexander (2000) is provided to forecast five energy stocks’ monthly volatilities and correlations. I show that as long as the stocks are already highly correlated with one another, the orthogonal GARCH approach will reduce computational complexity, control the amount of ‘noise’, and produce volatility and correlations for all the assets. All the computation procedures were accomplished in Microsoft Excel. Forecasting of volatility and correlation of stock returns is significant in the analysis of option pricing, portfolio optimization and value-at-risk models.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Principal Component Analysis, GARCH Model, Covariance matrix
dc.title Using Orthogonal GARCH to Forecast Covarince Matrix of Stock Returns
dc.date.updated 2012-09-28T12:37:34Z
dc.identifier.slug 10657/ETD-UH-2011-08-205
dc.type.material text *
dc.type.genre thesis *
thesis.degree.name Applied Economics
thesis.degree.level Masters
thesis.degree.discipline Economics
thesis.degree.grantor University of Houston
thesis.degree.department Economics
dc.contributor.committeeMember Prodan, Ruxandra
dc.contributor.committeeMember Thornton, Rebecca A.

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