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2018 | OriginalPaper | Chapter

Measuring Market Integration: US Stock and REIT Markets

Authors : Douglas W. Blackburn, N. K. Chidambaran

Published in: Complex Systems Modeling and Simulation in Economics and Finance

Publisher: Springer International Publishing

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Abstract

Tests of financial market integration traditionally test for equality of risk premia for a common set of assumed risk factors. Failure to reject the equality of market risk premia can arise because the markets do not share a common factor, i.e. the underlying factor model assumptions are incorrect. In this paper we propose a new methodology that solves this joint hypothesis problem. The first step in our approach tests for the presence of common factors using canonical correlation analysis. The second step of our approach subsequently tests for the equality of risk premia using Generalized Method of Moments (GMM). We illustrate our methodology by examining market integration of US Real Estate Investment Trust (REIT) and Stock markets over the period from 1985 to 2013. We find strong evidence that REIT and stock market integration varies through time. In the earlier part of our data period, the markets do not share a common factor, consistent with markets not being integrated. We also show that during this period, the GMM tests fail to reject the equality of risk premia, highlighting the joint hypothesis problem. The markets in the latter half of our sample show evidence of both a common factor and the equality of risk premia suggesting that REIT and Stock markets are integrated in more recent times.

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Appendix
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Footnotes
1
REITs are companies that invest in real estate. REITs are modeled after closed-end mutual funds with REIT shareholders able to trade their shares as do mutual fund shareholders. In addition to being able to sell and divest their shares, REIT shareholders receive dividend payouts representing income generated by the real estate properties owned by REITs.
 
2
The Canonical Correlation approach determines two factors, one from each data set, that have the highest correlation compared to any other two factors across the two data sets. In determining the set of factors with the highest correlation, the methodology recognizes that the principal components are not a unique representation of the factor model and that a rotation of the principal components is also a valid representation of the factor model. A rotation of the principal component is a set of orthogonal linear combination of the principal components and are called canonical variates. The search for a set of factors that has the highest correlation also includes all canonical variates, i.e. all possible rotations of the principal components. A canonical variate in the first data set that is highly correlated with a canonical variate in the second data set represents a common factor, if the correlation is statistically significant.
 
3
Liu et al. [15] find that equity REITs but not commercial real estate securities in general are integrated with the stock market.
 
4
Bekeart and Harvey [4] study international market integration over 1977 to 1992 using a conditional regime-switching model that nests the extreme cases of market segmentation and integration and find that most countries are neither completely integrated nor segmented but are instead somewhere in-between fluctuating between periods of being more integrated and periods of being more segmented. India and Zimbabwe are particularly interesting cases. Both countries exhibit extreme and sudden regime-shifts—in 1985, India instantaneously moves from being integrated to segmented, and likewise, Zimbabwe suddenly changes from being integrated to segmented in 1986 and then just as suddenly switches back to being integrated in 1991. Carrieri et al. [6] document similar results of a volatile integration process characterized by periods of increasing and decreasing integration.
 
5
See also [23].
 
6
Studies that have used a similar approach are [9] in their comparison NYSE and Nasdaq stocks, and Blackburn and Chidambaran [5] who study international return comovement derived from common factors.
 
7
This analysis was also performed on an annual basis. Results are similar.
 
8
It is common to use variables other than the Fama-French factors in studying REIT returns. For example, [17] use the excess returns on the value-weighted market portfolio, the difference between the 1-month T-bill rate and inflation, the 1-month T-bill rate relative to its past 12-month moving average, and the dividend yield (on an equally weighted market portfolio).
 
9
While it would be ideal to also include a standard set of economically motivated REIT factors, unlike the literature on explaining the cross-section of stock returns, the REIT literature has not yet agreed on such standard set of factors.
 
10
We also used the stock canonical variates and REIT canonical variates as common factor proxies and the results did not change.
 
11
Local factors are assumed to be orthogonal across markets and therefore do not affect covariance.
 
12
Pukthuanthong and Roll [20] argue this point as a critique of comovement as a measure of integration. This can also be true for R-square in markets characterized by high idiosyncratic risk.
 
13
Canonical correlations have been used in prior literature on factor analysis. For example, Bai and Ng [2] used the canonical correlation idea to test the equivalence of the space spanned by latent factors and by some observable time series. Our use of canonical correlations is very much in the flavour of Bai and Ng [2], but for very different purposes. We show that the methodology can be used to determine common and unique factors in panels of data that have intersecting factors and is able to identify the true factor structure in multilevel data.
 
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Metadata
Title
Measuring Market Integration: US Stock and REIT Markets
Authors
Douglas W. Blackburn
N. K. Chidambaran
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-99624-0_12