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dc.contributor.author Haftel, Jared
dc.date.accessioned 2009-05-04T17:49:11Z
dc.date.available 2009-05-04T17:49:11Z
dc.date.issued 2009-05-04T17:49:11Z
dc.identifier.uri http://hdl.handle.net/10161/1279
dc.description.abstract In the past thirty years, academia and the marketplace have devoted signi cant e ort and resources toward gaining a better understanding of how volatility changes over time in the nancial markets and how changes in one market a ect changes in another. All of these attempts involve modeling the covariance matrix of two or more asset returns using the period-earlier covariance matrix. In this paper, we outline the volatility modeling process for an Antisymmetric Dynamic Covariance (ADC) multivariate Generalized Autoregressive Conditional Heteroskedacity (GARCH) model, explain the math involved, and attempt to estimate the parameters of the model using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm. We nd several barriers to estimating parameters using BFGS and suggest using alternative algorithms to estimate the ADC multivariate GARCH in the future. en_US
dc.format.extent 207528 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Antisymmetric Dynamic Covariance en_US
dc.subject Generalized Autoregressive Conditional Heteroskedacity en_US
dc.subject Broyden-Fletcher-Goldfarb-Shanno optimization algorithm en_US
dc.title A Closer Look at ADC Multivariate GARCH en_US
dc.department Mathematics

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