Dynamic Conditional Eigenvalues: Inference and testing in the λ-GARCH model

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.