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Calendar Effects and Real Estate Securities

  • E. C. M. Hui
  • J. A. WrightEmail author
  • S. C. P. Yam
Article

Abstract

This paper examines twenty-seven international real estate securities indices from twenty countries and regions for calendar effects. Two methodologies are employed. The first is the standard approach which detects statistically significant anomalies via linear regression of returns. The second, new to the real estate securities literature, tests for economically significant effects through two tests specifically designed to compare multiple forecasts to a benchmark, White’s (Econometrica, 1097–1126, 2000) Reality Check and Hansen’s (J Bus Econ Stat 23(4):365–380, 2005) Superior Predictive Ability test. The standard approach tells us that while some effects have disappeared over time, statistically significant calendar anomalies persist. However, the tests of White and Hansen strongly suggest that they are not economically significant and thus should not be the basis of an investor’s trading strategy nor be considered as a challenge to market efficiency, as has been claimed previously.

Keywords

Calendar effects Real estate securities index Reality Check test Superior Predictive Ability test Market efficiency 

Notes

Acknowledgements

This study receives financial support from The Hong Kong Polytechnic Universitys Internal Funding (Project #: G-YH86, G-YH96, and 4-ZZC8). The second author, John Wright would like to thank the The Hong Kong Polytechnic University for their support, as much of his work for this paper was completed there. The third author, Phillip Yam, acknowledges financial support from The Hong Kong RGC GRF 502909, The Chinese University of Hong Kong Direct Grant 2010/2011 Project ID: 2060422, The Chinese University of Hong Kong Direct Grant 2011/2012 Project ID: 2060444. Phillip Yam also expresses his sincere gratitude to the hospitality of Mathematisches Forschungsinstitut Oberwolfach (MFO) in the German Black Forest during the preparation of the present work.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Building and Real EstateThe Hong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.Department of StatisticsThe Chinese University of Hong KongShatinHong Kong

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