Please use this identifier to cite or link to this item: http://repository.kln.ac.lk/handle/123456789/15545
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRathnayaka, R.M.K.T.-
dc.contributor.authorSeneiratna, D.M.K.N.-
dc.contributor.authorArumawadu, H.I.-
dc.date.accessioned2016-12-20T08:59:36Z-
dc.date.available2016-12-20T08:59:36Z-
dc.date.issued2016-
dc.identifier.citationRathnayaka, R.M.K.T., Seneiratna, D.M.K.N. and Arumawadu, H.I. 2016. A New Financial Time Series Approach for Volatility Forecasting. Symposium on Statistical & Computational Modelling with Applications (SymSCMA – 2016), Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka. p 05-08.en_US
dc.identifier.urihttp://repository.kln.ac.lk/handle/123456789/15545-
dc.description.abstractThe investment in capital market is easiest, fastest and securable way for building healthy financial foundation today. Because of the economic outlooks causing directly on these market fluctuations, the making decisions in the equity market has been regarding as one of the biggest challenges in the modern economy. The main purpose of this study is to take an attempt to understand the behavioral patterns and seek to develop a new hybrid forecasting approach under the volatility. The results are successfully implemented on Colombo stock exchange (CSE), Sri Lanka over the three year period from January 2013 to December 2015. The empirical results indicated that the new proposed hybrid approach is more suitable for forecasting price indices than traditional time series forecasting methodologies under the high volatility.en_US
dc.language.isoenen_US
dc.publisherDepartment of Statistics & Computer Science, University of Kelaniya, Sri Lankaen_US
dc.subjectANNen_US
dc.subjectARIMAen_US
dc.subjectARIMA-ANNen_US
dc.subjectVolatilityen_US
dc.titleA New Financial Time Series Approach for Volatility Forecastingen_US
dc.typeArticleen_US
Appears in Collections:SymSCMA – 2016

Files in This Item:
File Description SizeFormat 
5-8.pdf584.35 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.