Show simple item record

dc.contributor.authorIrungu, Irene W.
dc.contributor.authorMwita, Peter N.
dc.contributor.authorWaititu, Antony G.
dc.date.accessioned2018-10-23T08:35:38Z
dc.date.available2018-10-23T08:35:38Z
dc.date.issued2018-04
dc.identifier.isbn978-9966-117-37-3
dc.identifier.urihttp://ir.mksu.ac.ke/handle/123456780/772
dc.description.abstractGARCH models have been commonly used to capture volatility dynamics in financial time series. A key assumption utilized is that the series is stationary as this allows for model identifiability. This however violates the volatility clustering property exhibited by financial returns series. Existing methods attribute this phenomenon to parameter change. However, the assumption of fixed model order is too restrictive for long time series. This paper proposes a change-point estimator based on Manhattan distance.The estimator is applicable to GARCH model order change-point detection. Procedures are based on the sample autocorrelation function of squared series. The asymptotic consistency of the estimator is proven theoretically. Keywords:Autocorrelation Function, Change-Point, Consistency, Garch, Manhattan Distance, Model Orderen_US
dc.language.isoenen_US
dc.publisherMachakos Universityen_US
dc.subjectAutocorrelation Functionen_US
dc.subjectGARCH Modelsen_US
dc.titleConsistency of the Model Order Change-Point Estimator for GARCH Modelsen_US
dc.typeLearning Objecten_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record