Iqbal, F. and Triantafyllopoulos, K. orcid.org/0000-0002-4144-4092 (2019) Bayesian inference of multivariate rotated GARCH models with skew returns. Communications in Statistics - Simulation and Computation, 50 (10). pp. 3105-3123. ISSN 0361-0918
Abstract
Bayesian inference is proposed for volatility models, targeting financial returns, which exhibit high kurtosis and slight skewness. Rotated GARCH models are considered which can accommodate the multivariate standard normal, Student t, generalised error distributions and their skewed versions. Inference on the model parameters and prediction of future volatilities and cross-correlations are addressed by Markov chain Monte Carlo inference. Bivariate simulated data is used to assess the performance of the method, while two sets of real data are used for illustration: the first is a trivariate data set of financial stock indices and the second is a higher dimensional data set for which a portfolio allocation is performed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Taylor & Francis. This is an author-produced version of a paper accepted for publication in Communications in Statistics - Simulation and Computation. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Volatility; skew returns; GARCH; BEKK; rotated BEKK; multivariate time series; portfolio allocation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 May 2019 10:47 |
Last Modified: | 03 Dec 2021 09:55 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/03610918.2019.1620272 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146215 |