The Underlying Return Generating Factors for REIT Returns: An Application of Independent Component Analysis
AbstractMulti-factor approaches to analysis of real estate returns have, since the pioneering work of Chan, Hendershott and Sanders (1990), emphasised a macro-variables approach in preference to the latent factor approach that formed the original basis of the arbitrage pricing theory. With increasing use of high frequency data and trading strategies and with a growing emphasis on the risks of extreme events, the macro-variable procedure has some deficiencies. This paper explores a third way, with the use of an alternative to the standard principal components approach – independent components analysis (ICA). ICA seeks higher moment independence and maximises in relation to a chosen risk parameter. We apply an ICA based on kurtosis maximisation to weekly US REIT data using a kurtosis maximising algorithm. The results show that ICA is successful in capturing the kurtosis characteristics of REIT returns, offering possibilities for the development of risk management strategies that are sensitive to extreme events and tail distributions.
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Bibliographic InfoPaper provided by Henley Business School, Reading University in its series Real Estate & Planning Working Papers with number rep-wp2006-12.
Length: 26 pages
Date of creation: 2006
Date of revision:
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More information through EDIRC
Real Estate Returns; REIT; ICA; Independent Component Analysis;
Other versions of this item:
- Colin Lizieri & Stephen Satchell & Qi Zhang, 2007. "The Underlying Return-Generating Factors for REIT Returns: An Application of Independent Component Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(4), pages 569-598, December.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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