Is there volatility convergence in Asia-Pacific
securitized real estate markets?
Kim Hiang LIOW and Wei CHEN
Kim Hiang LIOW*
Department of Real Estate
National University of Singapore
4 Architecture Drive
Singapore 117566
Tel: (65)65163420
Fax: (65)67748684
Email : rstlkh@nus.edu.sg
and
Wei CHEN
July 25, 2011
1
Is there volatility convergence in Asia-Pacific
securitized real estate markets?
Abstract
This study considers whether a group of eight Asia-Pacific securitized real estate markets display
similar volatility trend over the past 15 years, 1995-2009, using an econometric model that incorporates
common volatility effects across the sample markets. The empirical results indicate the presence of at
least one common variance component, and thus partial volatility convergence, among the sample Asia
real estate securities markets. During the global financial crisis period, some real estate securities
markets are co-integrated in both their first and second moments and demonstrate partial return and
volatility convergence. Our analysis that focuses in capturing the common roots in the second moment
whilst accounting for time-varying variance has important implications for international real estate
portfolio investment.
Keywords
Asian real estate securities markets; common volatility; volatility convergence; GARCH co-integration;
portfolio diversification; global financial crisis
Background, Objective and Contribution of Research
Prior literature has considered various aspects of international stock market integration from
short run and long-term perspectives. While the short-run investigation focuses on returns and volatility
spillovers, as well as time-varying correlations across different national stock markets (e.g. Hamos et al.
1990; Yang, 2005), another strand of the literature considers long-run relationships and common
stochastic trends among various stock markets over time. In this regard, co-integration methodology
has been frequently applied to different international stock market datasets to detect the existence and
dynamics of long-run relationships and strength of the relationships among the stock markets (e.g.
Chan et al 1992). Moreover, Kasa (1992) and Manning (2002) investigate the presence of common
forces driving the long-run return movement of the stock market indexes. This latter approach is
superior because a combination of common trends analysis and co-integration analysis provides
investors with a more complete picture regarding the degree of portfolio diversification benefits across
the markets concerned over time.
In seeking to contribute to international real estate volatility literature, our paper extends this
“common trend” interest to investigate whether a group of Asia-Pacific securitized real estate markets
has displayed a similar common time-varying volatility (i.e. volatility convergence) over the period
1995-2009. Our motivation is derived from the Engle and Susmel (1993)’s work that has examined
explicitly whether there is a common component driving the volatility among international stock
markets using the bivariate common feature test of Engle and Kozicki (1993). By the same token, it is
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possible that a group of securitized real estate markets investigated shares a common volatility
component; and accordingly there is volatility convergence in these markets if this commonality exists.
Due to the strong growth and remarkable risk-adjusted performance of securitized real estate
over the last two decades, international investors have become increasingly interested in increasing real
estate allocations in their portfolios (Dhar and Goetzmann, 2006). Importantly, the level of securitized
property in Asia is about 12% of global property capitalization, which is significantly above that of the
mature markets (US: 6 percent; UK: 5 percent; France: 6 percent) and the global average of six percent
(EPRA, 2008). With Asian stock markets accounting for 28% of global stock market capitalization
(WFE, 2009) and Asian securitized real estate markets accounting for 48% of global property securities
(Macquarie Securities, 2009), the significance and performance of the Asian securitized real estate
sector deserve global investors’ attention. However, in comparison to the considerable amount of
literature that has examined stock market and bond market integration, far less is understood about the
presence of volatility convergence and its implication for integration among the Asian real estate
securities markets despite the significant interest in volatility convergence in securitized property
diversification in Asia. This is where our study intends to contribute.
This study, which is about Asia-Pacific securitized real estate markets, includes four focused
areas. With a sample covering eight Asia-Pacific real estate securities markets that include Australia,
Japan, Hong Kong, Singapore, China, Taiwan, Malaysia and the Philippines over a period of 15 years
beginning January 1995 and ending December 2009, first we search for a long-term equilibrium
relationship among the real estate securities indices. Second, we test whether these real estate securities
markets exhibit time-varying volatility characteristics. Third, we address the issue of common timevarying volatility across the sample real estate securities indexes using the modified multivariate cointegration test with generalized autoregressive conditional heteroskedascity (GARCH) effects
(henceforth termed as “GARCH-cointegration”) developed by Gannon (1996). With the investigation
conducted on both multivariate and bivariate basis, we hope to detect the presence of a common timevarying volatility factor among the real estate securities markets under examination. Evidence of this
nature is consistent with the notion that real estate securities markets are linked in their second
moments, as well as indicate that a global common time-varying volatility specification is adequate in
modeling real estate securities prices. Specifically, co-integration analysis evaluates only whether the
real estate securities market prices are integrated in the long run, while GARCH co-integration hopes to
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detect whether the markets are linked together through a common variance factor. Thus the two tests
complement each other. Finally, we repeat the volatility convergence analysis for the last five years
from January 2005 to Dec 2009. 1 It should be noted that the emphasis on the 2005-2009 period is very
specific. Not only did volatility in the capital markets generally rise considerably in this period but this
was particularly seen in some securitized real estate markets. In addition, the role of real estate in the
financial crisis was initially confined to the case of the subprime market but then extended to the
collapse in many residential and commercial markets, making this a very eventful period. In line with
the literature, if real estate securities markets have become more closely related in their second
moments during this “crisis” period, then it is possible that this period may be associated with more
common volatility components or a stronger common volatility factor compared with the full-period
model.
The analysis in this article has important implications for intra-regional volatility dynamics.
Specifically, if the group of Asia-Pacific securitized real estate markets is found to have volatility
convergence, this implies the existence of some common factors governing cross-market volatility
generating process, and has significance pricing implications in the spirit of Capital Asset Pricing
Model (CAPM) and Arbitrage Pricing Theory (APT). Specifically, even though the sample real estate
securities market prices might not be integrated through the first moments (see for example, Liow,
2008), it is still possible that the markets are linked together in the long run through their second
moment (i.e. volatility). Moreover, if the markets are converging towards few common stochastic
volatility components over time, it would imply that the benefits arising from investing in international
real estate securities portfolio could be (significantly) reduced due to the presence of one or more
common volatility factor(s) in the cross-market relationships. This knowledge is likely to benefit
portfolio managers who should be more cautious in understanding the cross-market return and
volatility linkages (and thus convergence) in the short-run as well as in the long term. For policy
makers, this additional knowledge on volatility convergence will be useful in their policy formulation
on cross-border real estate investment especially in periods of market turmoil such as the recent 2007
global financial crisis.
1
For the purpose of investigating volatility convergence during the global financial crisis period, we
divide the 15-year full study period from January 1995-December 2009 into three shorter sample
periods; (a) Jan95-Dec99, (b) Jan00-Dec04, and (c) Jan05-Dec09, which includes the crisis period. We
focus on (c).
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It is noted even though the GARCH co-integration test methodology, an extension of the
bivariate Engle and Kozicki (1993)’s “common-feature” methodology, has been employed in some
stock, bond and foreign exchange markets’ studies (e.g. Gannon, 1996; Alexakis and Apergis, 1996;
Pan et al. 1999; Fan, 2003; Thuraisamy and Gannon, 2008) to our knowledge, this is probably the first
study in securitized real estate arena that utilizes this multivariate “common feature” methodology to
study volatility convergence across the Asia-Pacific real estate securities markets. With the growing
economic importance of the Asian region, particularly the Greater China area (which includes
Mainland China) in recent years, we would expect this volatility convergence issue in Asia to become
increasingly significantly enough in affecting the level of real estate market informational efficiency,
due to the influence of globalization and real estate asset securitization in Asia.
The outline of the paper is as follows. The next two sections review the existing literature and
describe the real estate securities market sample and data characteristics used in the analysis. The
following section explains the “common features” and “GARCH co-integration” methodologies, as
well as outlines the empirical procedures. Thereafter, we present the empirical results and discuss
research implications. The last section presents some concluding remarks.
Related Literature
This study is related to the growing literature on stock markets’ and securitized real estate
markets’ integration. As in the stock market literature, short-term studies on international real estate
securities market linkages focus on return correlations and volatility spillovers. Cotter and Stevenson
(2006) use a multivariate GARCH model to examine tine-varying conditional volatilities and
correlations in the daily USA REIT and equity prices. Michayluk et al (2006) investigate daily
volatility spillover effects and time-varying correlation dynamics between the USA and UK securitized
real estate markets. Liow et al (2009) examine the time-varying correlations and volatility links of
several national /regional securitized real estate and stock markets. They find that real estate securities
markets’ conditional volatilities and stock markets’ volatilities are synchronous over time. Moreover,
the international correlation structure of real estate securities and the broader stock market are linked to
each either. In another study, Liow et al (2011) investigate the dynamics and transmission of
conditional return volatilities with multiple structural breaks using a multivariate regime-dependent
asymmetric dynamic covariance model. However, none of the above studies has examined the issue of
volatility convergence in the securitized real estate markets.
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Another group of real estate researchers use multivariate co-integration technique to
investigate the nature and extent of long-term price equilibrium relationships among international real
estate securities markets (e.g. Wilson and Okunev, 1996; Eichholtz et al, 1998; Garvey et al. 2001;
Kleiman et al. 2002; Liow et al. 2005; Yang et al. 2005; Yunus and Swanson, 2007 and Yunus, 2009).
For example, Yunus and Swanson (2007) examine long-run price relationships and short-run causal
return linkages among the real estate securities markets of Australia, Hong Kong, Japan and Singapore.
The find that US investors can derive diversification benefits from investing in these markets both in
the long run and in the short run.
In so far as this study is related, we gather four real estate studies that bear similar “commonfeature” theme to our work. Bond and Hwang (2003) identify the common (or permanent) component
of volatility shared by both the UK securitized and direct commercial property markets. Liow and
Webb (2009) examine the existence of common return factors in the securitized real estate markets of
Hong Kong, Singapore, the UK and the US over 1993-2003. Using factor analysis and canonical
correlation technique on monthly data, they detect a common return risk factor which is moderately
correlated with the global real estate market. Yunus (2009) investigates the dynamic interdependence
among the securitized real estate markets of Australia, France, Hong Kong, Japan, Netherlands, the UK
and US for the period 1990-2007. Using co-integration tests and common trends analysis, she finds that
international securitized real estate markets are becoming increasing converged over time. In addition,
the US and Japanese markets are the sources of the common stochastic trends that drive the cointegrated markets toward the long-run equilibrium relationships. Finally, covering 12 real estate
securities markets over 1994-2009, Liow and Ibrahim (2010) in their recent study examine the
correlation structure of “permanent” and “transitory” volatility series of the sample markets. In
particular, the summary of the “permanent” volatility dynamics using factor analysis indicates whether
the correlation in volatilities is caused by at least a “common factor “that spans across all real estate
“permanent” volatility series. They find that even though with the same numbers of “common factor”
derived from the “permanent” and “transitory” volatility series, their loadings are not similar and
consequently the long-run and short-term volatility linkages for some real estate securities markets are
different.
Sample and data characteristics
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Our sample consists of daily closing securitized property market indexes for Australia, Japan,
Hong Kong, Singapore, China, Taiwan, Malaysia and Philippines. It is noted that each of the eight real
estate securities markets is in different stages of development and has different market capitalizations,
institutional and regulatory frameworks, as well as market transparencies, trading systems and
transaction costs. The sample data, which come from Standard and Poor (S&P) Global Property
database, 2 consist of 3907 total return indexes covering the period from 6 January 1995 to 26
December 2009, the longest data period for which all eight real estate indices are available in an
international study. 3 Daily stock returns are computed as natural logarithmic of the total return indexes
relative,
I t in successive days; i.e. Ln( I t I t −1 ) . Moreover, as one data frequency might not
necessarily be more appropriate than another, this study also uses weekly data for comparison. One
shortcoming is the use of weekly data also leads to a relatively small sample (782 points), particularly
with respect to the sub-periods.
Some summary statistics for the eight real estate securities market index return series are
given in Table 1. Over the full study period, the best daily return performer is China (0.033%), and is
followed by Hong Kong (0.017%). In contrast, Malaysia real estate securities market has the worse
average daily return performance (-0.025%). Except for Malaysia, the other three Asia-developing
markets 4 are the most volatile with China tops the list (daily standard deviation is 2.594%). Finally, the
results indicate that the distribution of return for all eight real estate stock indexes is non-normal,
characterized by higher peakedness and fat tails relative to a normal distribution, whereas this evidence
for non-normality is weaker for weekly returns. Figure 1 plots the index movements of these eight
Asia-Pacific securitized real estate markets over the full study period.
(Table 1 and Figure 1 here)
Table 2 provides the Pearson correlation results for the squared return (volatility) series for the
full period – Panel A (upper triangle: weekly; lower triangle: daily), as well as for the last five years
2
This S&P global property database, the latest international public real estate database in the market, is
designed to reflect components of the broad universe of investable international real estate stocks
reflecting their risk and return characteristics. In total, the database has indices (both capitalization
weighted and float adjusted) comprised of over 500 companies from more than 35 developed and
emerging markets with a minimum market value of $100 million (Serrano and Hoesli, 2009).
3
Thailand and Indonesia were excluded from the study due to large number of missing time series data.
4
We follow S&P Property classification - Asia-Pacific developed (Australia, Japan, Hong Kong and
Singapore) and Asia-Pacific developing (China, Taiwan, Malaysia and the Philippines).
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(Jan05-Dec09) that includes the global financial crisis – Panel B (upper triangle: weekly; lower triangle:
daily). Most of the volatility correlations are low. For the full period estimates using daily data, only
one correlation figure is higher than 0.5 (Singapore/Hong Kong: 0.704) and none of the correlation
coefficients are higher than 0.3. During the global financial period (Jan05-Dec09), correlations are
higher for all 28 pairs. Moreover, 13 pairs of the correlation coefficients are higher than 0.3. The
correlation results obtained from using weekly data are mostly consistent to those estimated from daily
data. In addition, the correlations for weekly (long horizon) volatility appear to become larger in all
cases, except that Australia’s volatility correlation with Malaysia has decreased from 0.016 for daily
data to -0.0027 for weekly data
The volatility correlation results are in general agreement with what appears in the literature.
In contrast to international stock markets, international securitized real estate markets exhibit smaller
return correlations (Liow et al, 2009); however, some markets are interdependent through their second
moments (i.e. volatility). Moreover, it appears that there is a pattern of (much) higher volatility
correlation among the markets in “crisis” period, which is again in broad agreement with what is
reported in the stock market literature (Longin and Solnik, 1995). However, correlation analysis is not
suitable for analyzing long-term relationships because it utilizes return (or squared return) information
and neglects information contained in the level data.
(Table 2 here)
Methodology
Empirical framework for common time-varying volatility
This section presents the empirical framework designed to address the volatility convergence
issue in the Asia-Pacific real estate securities markets. Since the presence of GARCH effects (i.e. fat
tailed and possible time-varying volatility – Table 1) could affect the standard co-integration tests due
to the presence of time-varying volatility (Pan et al 1999), we first consider the “common ARCHfeature” testing procedure of Engle and Kozicki (1993) to document a common time-varying variance
present in the real estate securities markets. Specifically, there is a common ARCH feature if a linear
combination of two or more series does not display ARCH effects even though ARCH is present in
each of the individual series. Moreover, if two series share a common volatility process, it is also an
indicator of volatility convergence between the two markets which are responding to similar factors
that cause volatility in their respective real estate securities markets. However, as the Engle and
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Kozicki (1993) common ARCH-feature procedure is only applicable to a bivariate market-pair and is
therefore inappropriate for our sample of eight markets, we appeal to a multivariate testing
methodology; GARCH co-integration test developed by Gannon (1996), to examine whether our eight
Asia-Pacific securitized real estate data series share at least a common time-varying volatility. A
review of stock market literature reveals only Pan et al. (1999) and Fan (2003) have utilized this
methodology in their studies on Asia-Pacific stock markets. In addition to the multivariate version, we
also conduct bivariate common volatility investigation for all 28 market-pairs in order to unravel any
significant bivariate linkages that have contributed to the multivariate cross-market volatility
relationships under examination. The GARCH co-integration methodology is briefly explained below
in three steps:
(a)
As in Gannon (1996), our test for common time-varying volatility is built upon the popular
Johansen (1988)’s likelihood ratio test in multivariate co-integration. As documented in the
literature, the concept of co-integration is developed from the belief that certain economic
variables such as stock market price series should not diverge from each other without bound.
These variables may drift apart in the short-run but economic forces will bring them together
if they continue to be far apart in the long-run (granger 1986). Further, if the markets are cointegrated, it implies that diversification opportunities between the markets are reduced in
comparison to markets that are segmented. To incorporate the time-varying volatility effect
present in return series, the standard Johansen co-integration test has to be modified. We first
test the order of integration of the eight series in the system using the standard co-integration
test. Then, a vector of residuals
R0 (from the vector of differences of the series- 1a) and R1
(from the vector of lagged level of the series – 1b) is generated, i.e.
(b)
(1a)………… ∆Yt
= ∑ j =1 a 0 j ∆Yt − j + R0t
(1b)………… Yt − k
= ∑ j =1 a1 j ∆Yt − j + R1t
k −1
k −1
Estimate the canonical (multivariate) correlations from 1(a) and 1(b) and derive the canonical
weights
g1 j to g pj and h1 j to h pj . Then, generate two new canonical variates, named U and
V as follows:
(2a)…….
U j = g1 j R0 jt + + ........ + g pj R0 pt
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(2b)………….
By construction, the variates
V j = h1 j R1 jt + ...... + h pj R1 pt
U j and V j are created as linear combinations of original error
terms and hence have zero mean
(c):
Estimate GARCH (1, 1) models for U and V for those significant canonical pairs from (b):
(3a)………………………
(3b) ………… h jt
U jt = ψV jt + ς t
= ω j + α j ς t2−1 + β j ht −1
In the above, equation (3a) is equivalent to the standard ADF test for testing a unit root in
residuals from the co-integrating equation, with GARCH (1, 1) effects accounted for in calculating U j.
We then compare the t-value for the ψ coefficient in equation (3a) with the critical values derived
from McKinnon (1991). If the computed t-statistic is less than the relevant critical values at the
conventional probability levels (1%, 5% and 10%), then the null hypothesis of no common volatility
component in securitized real estate markets cannot be rejected.
Empirical implementation
We first test whether all individual series is stationary or not. The augmented Dickey-Fuller
(ADF) and Kwiatkowski-Philips-Schmidt-Shin (KPSS) tests will be utilized. Second we test for
univariate ARCH effects in each market’s return. Using squared returns as a proxy for realized
volatility, we perform Engle’s (1982)’s Lagrange Multiplier (LM) test by regressing each squared
return on a constant and four lags of its own squared return, as well as on a constant and eight lags of
its squared returns. These are the univariate ARCH (4) and ARCH (8) tests. In addition, the ARCH test
is conducted with multivariate information set that includes, respectively, one lagged (MARCH (1)),
four lagged (MARCH (4)) and eight lagged (MARCH (8)) squared returns of other markets. The null
hypothesis to be tested is that the series exhibits no ARCH effects. The test statistic, which has a chisquared distribution, is obtained by multiplying the regression R2 times the sample size (Engle, 1982).
Third, we conduct the GARCH co-integration test for three multivariate models (Model A: All AsiaPacific; Model B: Asia-Pacific developed; Model C: Asia-Pacific developing) as well for all 28
bivariate pairs. This will allow us examine closely which markets contribute to the co-integration space
in the context of common volatility.
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Results
The results of the unit root tests are presented in Table 3. Based on the ADF and KPSS tests,
and with one minor exception, the reported results indicate the rejection of non-stationarity in first
(natural logarithmic) differences. Therefore, all eight real estate securities series are integrated once -
I (1) . Because all the real estate indices are found to be non-stationary, subsequent co-integration tests
are appropriate.
(Table 3 here)
Johansen multivariate co-integration test results on long-run price equilibrium
We first conduct Johansen’s multivariate likelihood ratio co-integration analysis to examine
whether there are any common forces driving the long-term movements of the eight real estate stock
index series. Table 4 presents the Johansen’s trace and maximum eigen values, together with the 5%
critical values. As the numbers indicate, for both daily and weekly data, all test statistics are below the
5% critical values for a given hypothesis. Therefore, it appears that the eight sample real estate
securities index series are not co-integrated; implying these there is absence of long-term price comovement of the real estate securities markets under examination. The absence of co-integration also
implies price divergence among the sample markets in the long-run. Accordingly, there exists potential
long-run gain in risk reduction from diversifying in any of these Asia-Pacific markets, which is broadly
consistent with the finding reported in Garvey et al (2001) and Liow (2008).
(Table 4 here)
Time-varying volatility
Table 5 presents the ARCH test results for time-varying volatility: univariate [ARCH (4) and
ARCH (8)], as well as multivariate [MARCH (1), MARCH (4) and MARCH (8)] information set. The
null hypothesis tested is that the real estate securities series display no ARCH effects. For daily data, all
the markets except Taiwan (ARCH-8) and The Philippines (ARCH-8) indicate evidence of ARCH
effects. Moreover the MARCH tests provide consistent evidence of time-varying volatility in all of the
real estate securities markets under examination. The results from weekly data are slightly different
from those of daily data. While all univariate ARCH test results are highly significant, there is
insignificant MARCH effect in Taiwan.
(Table 5 here)
Common time-varying volatility trends
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Based on the three-step GARCH co-integration methodology outlined above, Table 6 contains
the full period results. Of key interest is the estimation of theψ coefficient (Equation (3a)) which is
based on a GARCH (1, 1) conditional variance equation. The row of r =1 corresponds with the test
results estimated from the maximum canonical correlation variates; whereas r =2 provides the test
results estimated from the second highest canonical correlation variates.
For daily data with all markets (Model A), there is evidence of two common co-integrating
volatility vectors from the GARCH (1, 1) adjusted variates significant at the one-percent level.
Furthermore, the estimated results indicate when the GARCH co-integration is applied to the four
developed (Model B) and four developing (Model C) markets respectively, we are able to detect a
common time-varying volatility among the two groups of four daily indices because the respective tstatistics are statistically significant at the one-percent level. Thus it appears that the eight Asian real
estate securities markets are linked together in the long-run through some similar volatility processes,
or partial volatility convergence.
The results from weekly data are somewhat different from those of daily data. Because the
Taiwan real estate securities market does not exhibit a MARCH effect (Table 5), the test is also
conducted on both the cases of with and without Taiwan. Whilst there is evidence of two significant
butt weaker common co-integrating volatility vectors when all eight markets are included in the test
(Model A), there is no evidence of a second co-integrating vector when Taiwan (without an MGARCH
effect) is excluded from the test (Model A1). In addition, the empirical results show that the two
models for the developing market group (with Taiwan – Model C; without Taiwan – Model C1) do not
have a common time-varying volatility since the respective t-statistics are not significant at any
conventional level.
(Table 6 here)
Finally, Table 7 contains the bivariate GARCH co-integration results for all 28 real estate
securities market pairs. The empirical results indicate six pairs of real estate securities markets have a
common time-varying volatility each. They are: Philippines / Singapore, Japan /Singapore, Philippines
/ Japan, Philippines / Malaysia, Hong Kong / Singapore, as well as China / Hong Kong. However, the
two Asia-Pacific groups’ index series do not have the same volatility process. The weekly results are
similar and yet weaker, with evidence of one significant common co-integrating vector in only three
market-pairs: Philippines / Singapore, Hong Kong / Singapore, as well as China / Hong Kong
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(Table 7 here)
Overall, this part of the modified co-integration analyses finds that the eight Asia-Pacific real
estate securities markets do share some common time-varying volatility. In addition, Singapore, Hong
Kong, China, and Philippines appear to be part of the common volatility relationships. Accordingly,
there is at least partial volatility convergence among the sample Asia-Pacific securitized real estate
markets. Finally, the real estate securities markets of Australia, Taiwan, and to lesser degree, Japan and
Malaysia can possibly be excluded from the volatility co-integration space. The implication is that
diversification benefits are still available within these excludable real estate securities markets in the
long run.
Common volatility during the global financial crisis period (Jan05 to Dec09)
In contrast to the full period co-integration results reported above, the standard Johansen cointegration test indicates that there is one co-integrating relationship significant at the 5% level during
the global financial crisis period (results not reported in order to conserve space) , implying these
indexes exhibit some tendencies to move together in the long run. This result is in broad agreement
with the findings reported in Liow (2008) that real estate securities markets have become more
interdependent following some turbulence (such as Asian financial crisis) in the markets because of the
contagion effect.
To understand the impact of the global financial crisis on the dynamics of volatility
convergence, we repeat the GARCH co-integration tests on the same dataset covering the period from
Jan05 to Dec09. Table 8 provides the multivariate results. Both the daily and weekly results are similar.
In addition, two major differences are observed: (a) there appears to be one significant statistical link in
volatility among the eight Asia-Pacific daily (and weekly) real estate stock market indexes; and (b) No
evidence of volatility convergence for both the developed group and the developing group is detected,
i.e. the respective t-statistics are not significant at any conventional levels.
(Table 8 here)
Finally, the bivariate results contained in Table 9 indicate that there are two significant
volatility links (China / Hong Kong and Hong Kong /Taiwan) when daily data are used. An additional
significant volatility link is detected between Philippines and Singapore when weekly data are used. It
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thus appear that volatility convergence during this crisis period is mainly caused by inter-group (i.e.
developed and developing) dynamics and is consistent with the multivariate results.
(Table 9 here)
Summarizing, this last five-year period that includes the global financial crisis period is
associated with co-integration in the first moment among the eight Asia-Pacific real estate securities
markets. In addition, a common time-varying volatility is detected among the eight real estate securities
markets. Hence some real estate securities markets during this “crisis” period are co-integrated in both
their first and second moments with other markets and thereby demonstrate partial return and volatility
convergence. Accordingly, international portfolio diversification benefits are further reduced compared
to the full period where no long-term price co-movement could be detected. An in-depth analysis of the
long-run price and volatility convergence of larger samples of securitized real estate markets and their
short-term temporal adjustment, as well as their potential changes triggered by a major crisis is
important to validate the Asia-Pacific results, as well as for international portfolio management and risk
diversification in real estate investing.
Conclusion
In the simple set up of this paper, we investigate whether there are long-run equilibrium
relationships among the eight Asia-Pacific securitized real estate market indices, including Australia,
Japan, Hong Kong, Singapore, China, Taiwan, Malaysia and the Philippines over the past 15 years
spanning from January 1995 to December 2009, in the context of “price convergence” and “volatility
convergence”. In contrast to the previous studies that evaluated only long-run price interdependence or
volatility spillover, we focus on whether the sample Asia-Pacific real estate securities markets have
long-term, common time-varying volatility. To our knowledge, this is probably the first “common
volatility” real estate study and supplements prior securitized real estate literature on return and
volatility spillover. In addition, we examine the impact of the global financial crisis on the dynamics of
price and volatility convergences among the real estate securities markets under examination.
Our empirical findings indicate the presence of ARCH effects in almost all real estate
securities index series, indicating that their time-varying volatilities need to be incorporated in
searching for volatility convergence. Although the sample Asia-Pacific real estate securities market
indexes are not co-integrated in the long run, we find these markets share long-term, common time-
14
varying volatility. Specifically, our GARCH co-integration tests reveal that the sample markets are
linked with at least one common time-varying variance factor. During the global financial crisis period
(January 2005-December2009), we find that not only these real estate securities markets display some
tendencies to co-move in the long run (i.e. price convergence), they are also linked through partial
volatility convergence.
To conclude, our study indicates the presence of at least one common time-varying variance
component, and thus partial volatility convergence among the eight Asia-Pacific real estate securities
markets that. One important lesson to learn from this study is that any effort to unravel the crossmarket dynamics and extent of integration among international real estate securities markets should
consider both price and volatility convergence; as well as from the short term perspective should
consider dynamic return correlation and volatility spillovers. Hopefully, this suggested effort will
provide investors with a better understanding of securitized real estate market integration from both the
long term and short run perspectives.
15
References
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17
Table 1
Summary statistics on real estate securities returns: Jan95-Dec09
Market
Mean (% )
Australia
Japan
Hong Kong
Singapore
China
Malaysia
Philippines
Taiwan
0.001
-0.001
0.017
0.002
0.033
-0.025
-0.003
-0.023
Australia
Japan
Hong Kong
Singapore
China
Malaysia
Philippines
Taiwan
0.121
0.020
0.151
0.066
0.202
-0.060
0.018
-0.072
Std dev(% )
Skewness
Panel A: Daily data
1.174
-0.968
2.063
0.244
1.955
0.334
2.037
0.941
2.594
-0.006
1.893
0.902
2.196
0.854
2.227
-0.220
Panel B: Weekly data
2.672
-2.093
4.567
-0.098
4.561
-0.396
4.961
-0.350
5.843
0.033
4.494
0.801
5.140
-0.012
5.007
-0.154
Kurtosis
Jarque-Bara
15.839
6.644
12.260
15.558
6.184
24.909
14.857
8.370
27436.3
2199.3
14026.9
26241.5
1650.2
78652.2
23354.1
4725.0
20.641
4.279
7.347
12.747
5.041
11.347
5.498
5.135
10697.5
54.5
635.4
3107.9
135.7
2350.9
203.1
151.4
Source: derived from S&P global property database
18
Table 2
Correlation results for squared returns (volatilities)
Panel A: Full period (January 1995 - December 2009; upper traingle (weekly data); lower triangle:(daily data))
Australia
Japan
Hong Kong
Singapoere
China
Malaysia
Philipinnes
Taiwan
Australia
1
0.251
0.221
0.111
0.185
0.016
0.069
0.047
Japan
0.391
1
0.201
0.162
0.132
0.079
0.085
0.042
Hong Kong
0.268
0.330
1
0.704
0.223
0.171
0.188
0.061
Singapore
0.169
0.204
0.771
1
0.129
0.186
0.184
0.038
China
0.247
0.256
0.311
0.293
1
0.049
0.070
0.040
Malaysia
-0.0027
0.113
0.286
0.444
0.154
1
0.054
0.027
Philipinnes
0.259
0.264
0.379
0.395
0.238
0.137
1
0.005
Taiwan
0.091
0.141
0.107
0.092
0.112
0.043
0.081
1
Panel B: Subperiod (January 2005 - December 2009; upper traingle (weekly data); lower triangle: (daily data))
Australia
Japan
Hong Kong
Singapoere
China
Malaysia
Philipinnes
Taiwan
Australia
1
0.383
0.403
0.383
0.323
0.084
0.231
0.201
Japan
0.493
1
0.471
0.409
0.367
0.169
0.309
0.198
Hong Kong
0.595
0.727
1
0.558
0.629
0.166
0.348
0.261
Singapore
0.525
0.542
0.669
1
0.461
0.214
0.286
0.272
China
0.372
0.565
0.615
0.491
1
0.172
0.281
0.318
Malaysia
0.05
0.152
0.131
0.146
0.299
1
0.229
0.141
Philipinnes
0.546
0.604
0.611
0.547
0.525
0.203
1
0.206
Taiwan
0.205
0.305
0.344
0.475
0.349
0.062
0.424
1
Notes: Each correlation panel contains two reporting frequencies: Figures for the upper triangle are the correlation coefficients for the weekly data; figure for the lower
triangle are correlation coefficients estimated using daily data
19
Table 3
Unit root tests
ADF
Market
Level
Australia
Japan
Hong Kong
Singapore
China
Malaysia
Philippines
Taiwan
-2.155
-1.696
-1.633
-1.212
-0.787
-1.706
-1.658
-1.272
Australia
Japan
Hong Kong
Singapore
China
Malaysia
Philippines
Taiwan
-2.107
-1.512
-1.500
-1.619
-0.640
-1.736
-1.561
-1.226
1st difference
Panel A: daily data
-19.965***
-45.936***
-56.779***
-57.082***
-57.056***
-57.497***
-56.308***
-58.888***
Panel B: weekly data
-17.565***
-30.874***
-26.608***
-13.247***
-27.945***
-26.536***
17.803***
-26.798***
KPSS
level
first difference
5.647***
2.762***
3.706***
2.206***
4.866***
1.696***
1.450***
3.060***
0.537**
0.089
0.061
0.091
0.123
0.281
0.147
0.321
2.552***
1.261***
1.742***
1.016***
2.232***
0.787***
0.668**
1.385***
0.413
0.095
0.049
0.115
0.096
0.216
0.126
0.241
Notes
ADF – the Augmented Dickey-Fuller test with a constant with test critical values as: -2.865 (5%) and
3.438 (1%). The null hypothesis is that the time series has a unit root.
KPSS – the Kwiatkowski-Philips-Schmidt-Shin test with test critical values as: 0.463 (5%) and 0.739
(1%). The null hypothesis is that the time series is stationary. ***- denotes statistical significance at
the 1% level
20
Table 4
Results of Johansen multivariate co-integration tests: January
1995-December 2009
Trace
Ho
statistic
r =1
r<=1
r<=2
r<=3
r<=4
r<=5
r<=6
r<=7
141.81
103.07
69.26
44.06
25.12
10.19
5.53
1.68
r =1
r<=1
r<=2
r<=3
r<=4
r<=5
r<=6
r<=7
142.41
104.91
72.58
48.52
27.91
12.78
7.41
2.36
5% CV
Panel A: Daily Data
159.53
125.62
95.73
69.82
47.86
29.79
15.49
3.84
Panel B: Weekly data
159.53
125.62
95.73
69.82
47.86
29.79
15.49
3.84
Maximum Eigenvalue
statistic
5% CV
38.74
33.81
25.21
18.93
14.94
4.66
3.84
1,.68
52.36
46.23
40.08
33.88
27.58
21.13
14.26
3.84
37.51
32.32
24.06
20.61
15.13
5.37
5.06
2.36
52.36
46.23
40.08
33.88
27.58
21.13
14.26
3.84
Notes: r is the number of co-integrating vectors. The critical values are from Mackinnon-HaugMichelis (1999).
21
Table 5
Lagrange Multiplier (LM) statistic for ARCH tests in securitized real estate markets: Jan 1995 – Dec 2009
Market
ARCH(4)
Japan
Australia
Hong Kong
Singapore
China
Taiwan
Malaysia
Philippines
218.08*
294.96*
122.61*
79.81*
159.21*
14.72*
33.77*
5.09*
Japan
Australia
Hong Kong
Singapore
China
Taiwan
Malaysia
Philippines
16.37*
44.81*
9.94*
21.79*
5.73*
4.66*
14.29*
6.59*
ARCH(8)
MARCH(1)
Panel A: Daily data
112.09*
254.28*
414.25*
683.11*
34.69*
338.38*
33.31*
164.02*
108.66*
241.72*
6.32
35.10*
14.54*
1068.50*
0.67
49.43*
Panel B: Week ly data
10.86*
134.59*
31.98*
171.22*
5.62*
47.15*
11.61*
30.92*
4.85*
33.44*
5.44*
9.73
8.22*
65.04*
4.42*
50.47*
MARCH(4)
MARCH(8)
662.84*
919.50*
528.31*
303.25*
481.14*
85.50*
1239.45*
104.22*
765.35*
1143.03*
650.42*
409.84*
564.98*
116.91*
1293.10*
175.43*
197.38*
352.30*
111.83*
131.69*
62.312**
37.18
320.21*
86.48*
256.64*
387.92*
183.58*
226.57*
139.76*
83.98
371.57*
138.19*
Notes: (a) ARCH(4) and ARCH(8) are tests for the ARCH effects with four lags and eight lags respectively for the own volatility (squared return); (b) MARCH(1), MARCH(4)
and MARCH(8) are multivariate ARCH tests that include respectively, one, four and eight lagged volatility of other markets, (c) * - indicates statistical significance at least
at the 5% level
22
Table 6
Results of Modified Multivariate Co-integration test with GARCH Effect for Securitized Real Estate Markets: Jan95- Dec 09 (Full-period)
Market
Model
A
All
N
8
B
C
Asia-Pacific (developed)
Asia-Pacific (emerging)
4
4
R
1
2
1
1
ψ
-0.0892***
-0.0853***
-0.0728***
-0.0642***
t-stat
-6.315
-5.834
-5.454
-5.021
10%
-5.235
-5.235
-3.812
-3.812
critical values
5%
-5.235
-5.235
-4.099
-4.099
1%
-5.711
-5.711
-4.648
-4.648
t-stat
-5.613
-5.047
-5.518
-4.983
-3.712
-3.287
10%
-4.974
-4.974
-4.717
-3.821
-3.821
-3.460
critical values
5%
-5.529
-5.529
-5.002
-4.110
-4.110
-3.752
1%
-5.805
-5.805
-5.550
-4.667
-4.667
-4.312
Panel B: Weekly data
Model
A
Market
All
N
8
A1
B
C
C1
All (w/out Taiwan)
Asia-Pacific (developed)
Asia-Pacific (emerging)
Asian emerging (w/out Taiwan)
7
4
4
3
R
1
2
1
1
1
1
ψ
-0.1761**
-0.1759*
-0.1595**
-0.1508***
-0.1094
-0.1032
Notes: (a) Based on S&P Global Property - All (Japan, Australia, Hong Kong, Singapore, China, Taiwan, Malaysia, Philippines); Asia-Pacific (developed) (Japan,
Australia, Hong Kong, Singapore); Asia-Pacific (developing) (China, Taiwan, Malaysia, Philippines); (b) N refers to the number of markets; R refers to the canonical
variates derived from step 2 (see methodology)-the maximum canonical correlation is used for the row of R=1; whereas the second highest (significant) canonical
correlation is used for the row of R=2; ψ is the portfolio weight coefficient in equation 4(a) with GARCH effect: (4a) U jt = ψV jt + ς t ; critical values are derived from
Table 2 in McKinnon (1991); ***, **, - denotes statistical significance at the 1% and 5% level
23
Table 7
Results of Bivariate Co-integration test with GARCH Effect for Securitized Real Estate
Markets: Jan95- Dec 09 (Full-period)
R=1
CN/HK
CN/TW
CN/PH
CN/JP
CN/SG
CN/MA
CN/AU
HK/TW
HK/PH
HK/JP
HK/SG
HK/MA
HK/AU
TW/PH
TW/JP
TW/SG
TW/MA
TW/AU
PH/JP
PH/SG
PH/MA
PH/AU
JP/SG
JP/MA
JP/AU
SG/MA
SG/AU
MA/AU
DEV/EMEG
Weekly data
Coefficient
t-statistic
-0.0944*
-3.084
-0.0386
-1.21
-0.0621
-1.851
-0.0683
-2.088
-0.0672
-2.173
-0.0599
-2.129
-0.0292
-1.31
-0.0656
1.876
-0.0778
-2.799
-0.0618
-1.876
-0.1063*
-3.067
-0.0537
-2.053
-0.0406
-1.537
-0.0865
-2.562
-0.0459
-1.385
-0.0308
-0.962
-0.0661
-2.557
-0.0371
-1.697
-0.065
-2.073
-0.1216***
-4.194
-0.0729
-2.596
-0.0358
-1.499
-0.0708
-2.122
-0.0395
-1.539
-0.0401
-1.635
-0.0321
-1.632
-0.0365
-1.499
-0.0286
-1.292
-0.0735
-2.297
Daily data
Coefficient
-0.0412*
-0.0224
-0.0275
-0.0331
-0.0377
-0.0161
-0.0201
-0.0185
-0.0306
-0.0283
-0.0388*
-0.0246
-0.0158
-0.0389
-0.0179
-0.0081
-0.0312
-0.0191
-0.0410*
-0.0659***
-0.0376*
-0.0186
-0.0420**
-0.0218
-0.0206
-0.0165
-0.0155
-0.0121
-0.0373
t-statistic
-3.242
-1.679
-2.037
-2.531
-2.975
-1.486
-2.042
-1.346
-2.297
-2.137
-3.167
-2.368
-1.441
-2.671
-1.272
-0.591
-2.601
-1.973
-3.112
-6.259
-3.241
-1.721
-3.379
-1.885
-1.759
-1.695
-1.596
-1.236
-2.119
Notes: (a) This table presents the ψ estimates, the portfolio weight coefficient and its t-statistics, in
equation 4(a) with GARCH effect: (4a)
U jt = ψV jt + ς t ; (b) Based on S&P property – Japan (JP),
Australia (AU), Hong Kong (HK), Singapore (SG), China (CH), Taiwan (TW), Malaysia (MA),
Philippines (PH); Asia-Pacific (developed) (DEV) (JP, AU, HK, SG); Asia-Pacific (developing)
(EMEG) (CH, TW, MA, PH) (c) R=1 refers to the first canonical variates derived from step 2 (see
methodology)-the maximum canonical correlation is used for the row of R=1; critical values are
derived from Table 2 in McKinnon (1991) (10%: 3.061; 5%: 3.360 and 1%: 3.939); ***, **, * denotes statistical significance at the 1%, 5% and 10% level; significant coefficients are bolded..
24
Table 8
Results of Modified Multivariate Co-integration test with GARCH Effect for Securitized Real Estate Markets: January 2005- December 2009
(includes the Global Financial Crisis period)
Panel A: Daily data
Model
A
B
C
Markets
All
Asia-Pacific (developed)
Asia-Pacific (emerging)
N
8
4
4
R
1
1
1
N
8
4
4
R
1
1
1
ψ
-0.1391***
-0.0654
-0.0705
t-stat
-6.5251
-3.145
-2.807
10%
-4.964
-3.187
-3.187
critical values
5%
-5.247
-4.105
-4.105
1%
-5.788
-4.657
-4.657
t-stat
-5.471
-3.556
-2.611
10%
-5.021
-3.843
-3.843
critical values
5%
-5.318
-4.139
-4.139
1%
-5.892
-4.714
-4.714
Panel B: Weekly data
Model
A
B
C
Markets
All
Asia-Pacific (developed)
Asia-Pacific (emerging)
ψ
-0.2904**
-0.2367
-0.1527
Notes: This table reports the common volatility results based on the modified co-integration test with GARCH effect from Jan 05 to Dec 09 which covers the global financial
crisis period. Based on S&P Global property - All (Japan, Australia, Hong Kong, Singapore, China, Taiwan, Malaysia, Philippines); Asia-Pacific (developed) (Japan,
Australia, Hong Kong, Singapore); Asia-Pacific (developing) (China, Taiwan, Malaysia, Philippines); N refers to the number of markets; R refers to the canonical variates
derived from step 2 (see methodology)-the maximum canonical correlation is used for the row of R=1; whereas the second highest canonical correlation is used for the row
of R=2; ψ is the portfolio weight coefficient in equation 4(a) with GARCH effect: (4a) U jt = ψV jt + ς t ; critical values are derived from Table 2 in McKinnon (1991); ***,
**, - denotes statistical significance at the 1% and 5% level.
25
Table 9
Results of Bivariate Co-integration test with GARCH Effect for Securitized Real Estate
Markets: January 2005- December 2009 (includes the Global Financial Crisis period)
R=1
CN/HK
CN/TW
CN/PH
CN/JP
CN/SG
CN/MA
CN/AU
HK/TW
HK/PH
HK/JP
HK/SG
HK/MA
HK/AU
TW/PH
TW/JP
TW/SG
TW/MA
TW/AU
PH/JP
PH/SG
PH/MA
PH/AU
JP/SG
JP/MA
JP/AU
SG/MA
SG/AU
MA/AU
DEV/EMEG
Weekly data
Coefficient
-0.2089*
-0.1534
-0.1253
-0.0689
-0.0863
-0.1036
-0.0671
-0.1894*
-0.0965
-0.1138
-0.0978
-0.0903
-0.0558
-0.0922
-0.0591
-0.0591
-0.1353
-0.0577
-0.8451
-0.2031**
-0.0966
-0.0877
-0.1071
-0.1381
-0.0775
-0.1082
-0.0811
-0.0667
-0.0011
Daily data
t-statistic
-3.318
-2.931
-1.901
-1.938
-1.777
-2.156
-1.441
3.304
-1.662
-2.211
-2.145
-1.685
-1.183
-1.471
-0.882
-0.882
-2.565
-1.013
-2.868
3.636
-1.471
-1.451
-2.487
-2.674
-2.146
-2.417
-1.902
-1.635
-0.026
Coefficient
-0.0874**
-0.0589
-0.0598
-0.0393
-0.0315
-0.0483
-0.0293
-0.0731*
-0.0418
-0.0463
-0.0458
-0.0383
-0.0225
-0.0488
-0.0168
-0.0495
-0.0675
-0.0285
-0.0582
-0.0834
-0.0473
-0.0461
-0.0513
-0.0506
-0.0306
-0.0234
-0.0361
-0.0271
-0.0052
t-statistic
-3.435
-2.438
-2.261
-2.101
-1.628
-2.155
-1.307
-3.054
-1.759
-2.301
-2.141
-1.831
-1.026
-1.776
-0.768
-2.171
-2.667
-1.198
-2.298
-2.979
-1.746
-1.822
-2.982
-2.666
-1.798
-1.071
-1.927
-1.406
-0.296
Notes: (a) This table presents the ψ estimates, the portfolio weight coefficient and its t-statistics, in
equation 4(a) with GARCH effect: (4a)
U jt = ψV jt + ς t ; (b) Based on S&P property – Japan (JP),
Australia (AU), Hong Kong (HK), Singapore (SG), China (CH), Taiwan (TW), Malaysia (MA),
Philippines (PH); Asia-Pacific (developed) (DEV) (JP, AU, HK, SG); Asia-Pacific (emerging) (EMEG)
(CH, TW, MA, PH) (c) R=1 refers to the first canonical variates derived from step 2 (see
methodology)-the maximum canonical correlation is used for the row of R=1; critical values are
derived from Table 2 in McKinnon (1991) (10%: 3.061; 5%: 3.360 and 1%: 3.939); **, * - denotes
statistical significance at the 5% and 10% level; significant coefficients are bolded..
26
Figure 1
Securitized real estate markets: logarithmic total return index movement
3.0
2.6
3.0
Australia
2.8
Japan
Hong Kong
2.8
2.4
2.6
2.2
2.6
2.4
2.4
2.0
2.2
2.2
1.8
2.0
2.0
1.8
96
98
00
02
04
06
08
3.0
1.6
96
98
00
02
04
06
08
3.2
98
00
02
04
06
08
06
08
2.6
China
Singapore
2.8
96
Malaysia
2.4
2.8
2.6
2.2
2.4
2.4
2.2
2.0
2.0
1.8
2.0
1.6
1.6
1.8
1.6
1.2
96
98
00
02
04
06
08
1.4
96
98
00
02
04
06
08
06
08
96
98
00
02
04
2.2
2.6
The Philippines
Taiwan
2.0
2.4
1.8
2.2
1.6
1.4
2.0
1.2
1.8
1.0
1.6
0.8
96
98
00
02
04
06
08
96
98
00
02
04
Source: S & P Global Property
27