title: Modelling and Forecasting of Realized Covariance Matrices
creator: Stollenwerk, Michael
subject: ddc-300
subject: 300 Social sciences
subject: ddc-310
subject: 310 General statistics
subject: ddc-330
subject: 330 Economics
subject: ddc-510
subject: 510 Mathematics
description: In this thesis, we use observation-driven models for time-series of daily RCs. That is, we assume a matrix-variate probability distribution for the daily RCs, whose parameters are updated based on the RC realizations from previous days.    In particular, Chapter 2 looks at different matrix-variate probability distributions for the RCs and their theoretical and empirical properties. Chapter 3 proposes a flexible observation-driven model to update all distribution-specific time-varying parameters, not just the expected value matrix as is done in the literature so far. Chapter 4 introduces an observation-driven updating mechanism that is applicable to high-dimensional time-series of RCs. Each of these three chapters is a self-contained paper.
date: 2023
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/33700/1/Dissertation_Michael_Stollenwerk.pdf
identifier: DOI:10.11588/heidok.00033700
identifier: urn:nbn:de:bsz:16-heidok-337006
identifier:   Stollenwerk, Michael  (2023) Modelling and Forecasting of Realized Covariance Matrices.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/33700/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng