Quantifying heteroskedasticity metrics HASSAN, MARWA HASSAN ALY 2016, Quantifying heteroskedasticity metrics, PhD. thesis, GTP Research, Deakin University. Attached Files Name Description MIMEType Size Downloads hassan-quantifyingheteroskedastic-2016A.pdf Connect to thesis application/pdf 5.20MB 221 Title Quantifying heteroskedasticity metrics Author HASSAN, MARWA HASSAN ALY Institution Deakin University School GTP Research Faculty Institute for Intelligent Systems Research and Innovation Degree type Research doctorate Degree name PhD. Thesis advisor Nahavandi Saeid orcid.org/0000-0002-0360-5270 Creighton Douglas orcid.org/0000-0002-9217-1231 Hossny Mohammed orcid.org/0000-0002-1593-6296 Date submitted 2016-11 Keyword(s) heteroskedasticity Statistics Mathematics Financial forecasting Summary This study proposes a quantification measure for heteroskedasticity in the time series. Two methods are introduced for quantifying heteroskedasticity: Slope of Local Variance Index (SoLVI) and a statistical divergence method using Bhattacharys coefficient. Both measures show reliability in measuring and quantifying heteroskedasticity in comparison to numerical and hypothesis heteroskedasticity tests. Language eng Field of Research 010406 - Stochastic Analysis and Modelling 50% 080110 - Simulation and Modelling 50% Socio Economic Objective 970109 - Expanding Knowledge in Engineering 100% Description of original xi, 95 pages : figures, some coloured. Copyright notice ©The Author. All Rights Reserved Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30089384 Document type: Thesis Collections: Higher degree theses (Open Access) Open Access Collection Centre for Integrative Ecology   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 25 Nov 2016, 12:51:56 EST Mon, 28 Nov 2016, 07:43:52 EST Mon, 12 Dec 2016, 08:52:03 EST Mon, 12 Dec 2016, 08:53:15 EST Mon, 12 Dec 2016, 08:54:20 EST Thu, 09 Mar 2017, 07:33:17 EST Thu, 09 Mar 2017, 09:33:13 EST Mon, 13 Mar 2017, 12:22:29 EST Thu, 09 Aug 2018, 11:23:07 EST Fri, 07 Sep 2018, 19:10:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 173 Abstract Views, 228 File Downloads  -  Detailed Statistics Created: Fri, 25 Nov 2016, 12:51:48 EST by Deb Gray