Quantifying heteroskedasticity using slope of local variances index

Hassan, Marwa, Hossny, Mohammed, Nahavandi, Saeid and Creighton, Douglas 2013, Quantifying heteroskedasticity using slope of local variances index, in UKSim 2013 : Proceedings of the 15th International Conference on Computer Modelling and Simulation, IEEE Computer Society, Piscataway, N.J., pp. 107-111, doi: 10.1109/UKSim.2013.75.

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Title Quantifying heteroskedasticity using slope of local variances index
Author(s) Hassan, Marwa
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Conference name Computer Modelling and Simulation. International Conference (15th : 2013 : Cambridge, England)
Conference location Cambridge, England
Conference dates 10-12 Apr. 2013
Title of proceedings UKSim 2013 : Proceedings of the 15th International Conference on Computer Modelling and Simulation
Editor(s) [Unknown]
Publication date 2013
Conference series Computer Modelling and Simulation International Conference
Start page 107
End page 111
Total pages 5
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Keyword(s) quantifying heteroskedasticity
local variance
Summary In econometrics, heteroskedasticity refers to the case when the variances of the error terms of the data in hand are not equal. Heteroskedastic time series are challenging to different forecasting models. However, all available solutions adopt the strategy of accommodating heteroskedasticity in the time series and consider it as a type of noise. Some statistical tests were developed over the past three decades to determine whether a time series features heteroskedastic behaviour. This paper presents a novel strategy to handle this problem by deriving a quantifying measure for heteroskedasticity. The proposed measure relies on the definition of heteroskedasticity as a time-variant variance in the time series. In this work, heteroskedasticity is measured by calculating local variances using linear filters, estimating variance trends, calculating changes in variance slopes, and finally obtaining the average slope angle. The results confirm that the proposed index complies with the widely popular heteroskedasticity tests.
ISBN 9780769549941
Language eng
DOI 10.1109/UKSim.2013.75
Field of Research 010401 Applied Statistics
Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30055219

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