DocumentCode :
120008
Title :
Modelling multiple REIT indices using TAR models based on aggregation functions
Author :
Komornik, Jozef ; Komornikova, Magda
Author_Institution :
Fac. of Manage., Comenius Univ. Bratislava, Bratislava, Slovakia
fYear :
2014
fDate :
11-13 Sept. 2014
Firstpage :
43
Lastpage :
47
Abstract :
The aim of this paper is to compare descriptive and predictive qualities of multivariate TAR models with threshold variables obtained via aggregation functions versus one-dimensional TAR models with endogenous as well as exogenous threshold variables. Time series of REIT indexes of 5 selected G7 countries (USA, Japan, Great Britain, France, Canada) were modelled. They manifest similar behaviour in the considered time period, January 1, 2000-May 8, 2012, divided into 3 sub-periods determined by the recent global financial markets crisis (July 1, 2008-April 30, 2009). The multivariate TAR models with threshold variables constructed via aggregation functions have in all cases better descriptive properties and in most cases they also show better prediction properties. A new subclass of those models, based on the OMA type of aggregation functions, exhibit promising properties both with respect to their descriptive and predictive performance.
Keywords :
investment; property market; time series; OMA aggregation function type; aggregation functions; exogenous threshold variables; global financial markets crisis; multiple REIT indices; multivariate TAR models; one-dimensional TAR models; threshold variables; time series; Biological system modeling; Data models; Indexes; Open wireless architecture; Predictive models; Testing; Time series analysis; OMA function; OWA function; Real Estate Investment Trust (REIT) index; aggregation functions; one-dimensional and multivariate TAR models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems and Informatics (SISY), 2014 IEEE 12th International Symposium on
Conference_Location :
Subotica
Type :
conf
DOI :
10.1109/SISY.2014.6923613
Filename :
6923613
Link To Document :
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