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PhD Series No. 218
University of Copenhagen, Department of Economics, Copenhagen
In this thesis, we study a class of multivariate generalized autoregressive heteroskedasticity (GARCH) models, denoted the Dynamic Conditional Eigenvalue GARCH (or λ-GARCH) model. Multivariate GARCH models are useful for estimating and filtering time varying(co-)variances, which are used e.g. in empirical asset pricing, Markovitz-type portfoliooptimization and value-at-risk estimation. GARCH models have long been a staple inempirical finance and financial econometrics. This thesis contains three self-containedchapters on the λ-GARCH, covering large-sample properties and bootstrap-based inference.
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Doctoral Thesis

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