Securitized real estate market integration in Greater China and
international linkages
This draft: 15 March 2013
By Liow Kim Hiang, Department of Real Estate, NUS, paper to be presented at the 2013 GCREC Conference,
July 5-7, Beijing, China; Email: rstlkh@nus.edu.sg
Abstract
This research examines the integration among three Greater China (GC) securitized real estate markets,
including Mainland China, Hong Kong and Taiwan, and five international developed markets , Singapore,
Japan, Australia, the united Kingdom and the United States from January 1995 to April 2012. Results indicate
that the three GC markets are neither co-integrated among themselves or with any international developed
public property markets but all securitized real estate markets share moderate but significant non-linear
relationships. In the short-run, the extent of conditional volatility interdependence within and across the GC
areas is moderate. Over time, the three GC securitized real estate markets have become more integrated by
themselves and with other regional/international developed public property markets from the volatility
transmission perspective. Additionally, each of the three GC public property markets has influenced and has
been influenced by other GC and international markets with similar magnitudes, thereby resulting in relatively
low average net directional volatility spillover index estimates obtained. Finally, Hong Kong public property
market appears to be the “volatility leader” within and across the GC context.
1.
Introduction and motivation of research
The growing interdependence of national stock markets has been the subject of extensive research
over the last two decades. Owing to the rapid growing size of the Chinese stock markets1, as well as the
growing presence of the Greater China (GC) economy (comprising Mainland China, Hong Kong and Taiwan),
a study on interaction between the three GC markets and the other markets within the region and with major
international stock markets, including the US and the UK, is of paramount interest. This study focuses on the
GC securitized real estate markets due to the continuing strong economic growth, massive urbanization and the
growth of private real estate ownership in China, the scope for Hong Kong real estate investment trusts
(REITs) to provide more pure-play property investment opportunities in China, as well as Taiwan’s growing
economic ties with the Chinese mainland. Consequently their direct real estate and securitized real estate
markets have attracted great interest of domestic and international investors.
1
In adding to the existing body of knowledge concerning global financial market integration, this study
makes important contribution. It utilizes recent advances in time series econometrics for a relatively long
period of time (January 1995 to April 2011) to analyze and compare long-run relationships and short-run
linkages among the three GC securitized real estate markets, as well their international relationships.
Specifically, it focuses whether the three GC public property markets2 have become more integrated by
themselves and/or with other international developed markets (the US, the UK, Australia, Japan and
Singapore) from the co-integration perspective. Using multivariate modeling and volatility spillover index
methodology developed by Diebold and Yilmaz (2012), it analyzes whether the three GC markets have
influenced or have been more influenced by the international markets from the conditional volatility
perspective during the entire study period that covers the period of global financial crisis (GFC). To our
knowledge, with the notable exception of Liow and Newell (2012), less formal attention has been focused on
the GC securitized real estate market integration. Given China’s fast growing economic influence, this current
research’s evaluation on the changing level of integration and spillover among public property markets of the
GC economies and with their major international developed markets is important and timely.
It must be pointed out that our contribution to the research field is not to provide a new methodology
to study international market linkages, but rather to draw on earlier research studies conducted for stock
markets (CG, 2005) and real estate securities markets (LN, 2012). However we expand the existing literature
in the following ways. First, whilst CG only include three GC, the US and Japanese stock markets and LN
cover only three GC and the US real estate securities markets; findings from this study that utilizes a GC and
larger international real estate securities dataset (the US, the UK, Australia, Hong Kong and Singapore) should
be more generalizable to other global public property markets and over an extended period of time. Second,
CG examine the long run price equilibrium and unconditional return linkages; LN investigate the conditional
volatility interdependence; in contrast this study is of wider scope that embraces both the long-run price
equilibrium and short-term conditional volatility interdependence that controlled for multivariate GARCH
effect. Third, with international financial markets become more correlated and connected than ever before, an
understanding of the nature of the cross-market volatility transmission, volatility correlation and
2
interdependence, as well as the intensity and direction of spillovers over time is crucial for investors, financial
institutions and policy makers. One can also infer such analysis which of the markets is the most influential in
transmitting volatilities to others in international investing. This aspect was not addressed by both CG (2005)
and LN (2012) and represents another contribution of the study. And lastly, since international investors
incorporate into their portfolio allocation not only the long-term price relationship but also the market
volatility interaction and short-term return correlation structure, the results of this study can shed more light on
the extent to which investors can benefit from regional and international diversification in the long run and
short-term within the GC securitized property sector.
The structure of the paper is as follows. Section 2 provides an update on the significance of securitized
real estate in the GC areas. This is followed by a brief review of recent stock and securitized real estate market
literature on market integration. The next section describes the data and empirical steps involved in the present
study. This is followed by a presentation of the empirical results. The final section concludes the study.
2.
Significance of Real Estate Securities in Greater China
The significance of real estate in GC has taken on increased importance in recent years, given its
strong economic growth and improved real estate market maturity and transparency over the last few years
(Chin et al 2006). Consequently, the GC region has attracted more global real estate investors during the GFC
period, accounting for 13.0% of global commercial real estate transactions in 2008 compared to only 7.6% in
2007 (Newell and Razali, 2009).This sees the major cities in GC of Hong Kong, Shanghai, Beijing and Taipei
as becoming major international cities, with significant regional and international economic functions (Newell
et al. 2009).
The listed real estate securities markets in GC account for 29.0% of global real estate securities at
September 2009, comprising Hong Kong (20.3%), China (7.8%) and Taiwan (0.9%) (Macquarie Securities,
2009). This sees Hong Kong as the largest real estate securities market globally, China as the 3rd largest and
Taiwan as the 16th largest. This high level of securitized real estate in GC is further reflected in the GC stock
markets accounting for 11% of global stock markets (WFE, 2009). REIT markets in GC are still in the early
3
stages, with REITs in Hong Kong and Taiwan only established in 2005 and only accounting for 1.9% of REITs
globally (Macquarie Securities, 2009). REITs are yet to be established in China.
In China, state owned enterprises dominate its real estate securities market, with real estate
development being the major real estate activity. China Vanke (listed on Shenzhen Exchange), China Overseas
Land & Investment (listed on Hong Kong) and Hopson Development (listed on HK) top this list. The major
significance of listed real estate companies to the Hong Kong stock market is reflected in six of the top 20
listed companies being real estate companies. Due to the relative maturity and small scale of the Hong Kong
real estate market, as well as the rapid economic development and an active real estate market in China, many
Hong Kong firms are now increasing their real estate business activities in China. Moreover, a number of
Hong Kong developers have expanded their activities in the Chinese mainland, and increasing attention is now
being placed on tier-2 cities (e.g. Chongqing and Tianjin). The other major players in real estate development
in China are an increasing number of Asian real estate developers from Taiwan, Thailand, Malaysia, Indonesia,
Singapore, South Korea and Japan. In contrast, the involvement of the US and European firms in China real
estate sector remains limited, as compared to Asian developers, because it takes a period of time for them to
become familiar with the local real estate markets due to regulatory and legal complexities and other
investment risks involved. Foreign investors also face risks arising from China’s deficient legal system,
undeveloped contract law, corruption in government, foreign currency control, the government’s monopoly on
land supply and frequent rule changes and cultural differences.
Statistics from Bloomberg indicate, over 2005 to 2009, real estate securities market accounts for an
average of 6.29% (China), 9.98% (Hong Kong) and 1.02% (Taiwan) of local stock market capitalization. The
numbers of listed real estate firms as at end 2009 was 114 (China), 122 (Hong Kong) and 10 Taiwan). In
addition, an estimate of 16.5% of the Chinese incorporated real estate firms (30% of market capitalization) are
listed on the Hong Kong Stock Exchange. Finally, the stature of the GC real estate securities sector is further
reinforced by its strong performance relative to the other major real estate securities markets and global stocks
in recent years. In particular, a strong recovery from the GFC is evident; significantly ahead of the mature US,
UK and global markets (Macquarie Securities, 2009).
4
3.
Literature review
A review of literature indicates two main types of interdependence exist among international stock
markets. First a number of studies have applied co-integration tests and Granger causality tests to address the
issue of long run equity market integration within Mainland China as well as between the Chinese and Hong
Kong markets (e.g. Kim and Shin, 2000). The second line of investigation has focused on the short term
linkages with respect to volatility transmission and interdependence among international markets. Groenewold
et al. (2004) find a strong contemporaneous relationship between the Shanghai and Shenzhen stock markets;
but the two mainland markets are relatively isolated from the Hong Kong and Taiwan stock markets. Qiao et
al. (2008) analyze the long-run equilibrium, short-term adjustment and spillover effects across the Chinese
segmented stock markets (A-share and B-share) and HK stock markets using a bivariate FIVECM-BEKK
GARCH model.. Two main findings from their analyses are: (a) the A-share markets are most influential in
contributing to the spillover effects, and (b) the effects of the Asian crisis on the dynamic return correlations
vary across the stock markets. Although Johansson and Ljungwall (2009) find no long-run relationship among
the three GC stock markets, there are short-run spillover effects in both returns and volatility in the region from
their VAR-MVGARCH model. These results lead them to conclude that there are significant
interdependencies among the three stock markets. In a different context, Fan et al. (2009) investigate the
dynamic linkages between the Chinese and the US, the UK, Japan and Hong Kong stock markets using a
Markov-Switching Vector Error Correction Model (MS-VECM) which considers three regimes of depression,
boom and speculation in the market. In the short term, they find that the Chinese stock market has been
influenced by the four international stock markets and the impacts vary under different regimes. Tao and
Zhang (2009) investigate the spillovers from the US to Chinese and Hong Kong stock markets during the
subprime crisis. Using both univariate and multivariate GARCH models, they find that the Chinese stock
market has experienced price and volatility spillovers from the US. Moreover, the price and volatility
spillovers from the US are more significant to the Hong Kong’s equity returns than China’s returns. The
conditional correlation between China and Hong Kong is stronger than their conditional correlations with the
US, indicating increasing financial integration between China and Hong Kong. Finally, Cheng and Glascock
(2005) investigate the long-term price equilibrium relationship among the three GC stock markets and the US.
5
Using non-linear co-integration techniques, they detect a weak nonlinear relationship between the three stock
markets; however, these three markets are not co-integrated with either US or Japan. Additionally, they include
the unconditional return spillover results from the innovation accounting analysis, and indicate that Hong
Kong is the most influential among the three GC markets. After the Asian Financial Crisis, both Hong Kong
and Taiwan have strong contemporaneous relationships with each other. While CG have assessed the dynamic
long term relationship between the GC and the US stock markets, fuller studies involving the volatility
linkages of the stock markets have not been carried out. Specifically, two important components of market
integration; i.e. time-varying conditional correlation and conditional volatility spillover, have not been
addressed by CG in their study.
In the real estate literature, considerable attention has been given regarding the diversification benefits
from the US securitized real estate markets (e.g. Worzala and Sirmans, 2003; Michayluk et al. 2006). In
contrast, empirical research regarding the dynamics of the Chinese commercial real estate markets is still
limited. Newell et al (2009) demonstrate the risk-adjusted performance, added value and portfolio
diversification benefits of Chinese commercial real estate in a pan-Asia portfolio for both listed and direct real
estate, while Ke (2008) assesses the performance of state-owned listed real estate companies in China. Most
other studies concerning China have not been China-specific, but are largely concerning Chinese real estate
securities in a pan-Asia real estate securities portfolio (e.g.: Liow and Adair, 2009). Similarly, studies
regarding listed real estate securities in Taiwan have focused on their role in a pan-Asia portfolio (e.g. Liow
and Sim, 2006). Most of the previous property research regarding Hong Kong commercial property has
focused on Hong Kong property companies, including linkages between the Hong Kong listed and direct
property markets (e.g. Newell et al, 2004), Hong Kong property company performance (e.g.: Chau et al, 2003),
the role of Hong Kong property companies in a pan-Asia or global property company portfolio (e.g.: Liow,
2007) and the dynamics of the Hong Kong direct property market (e.g.: Brown and Chau, 1997). LN (2012)
consider on two important issues of short-term conditional cross-market relationships; i.e. volatility
interactions and time-varying correlations, whose dynamics could be very different from those that exist in the
long - run. They investigate simultaneously the effects of volatility spillover and conditional correlation on the
6
cross-market relationships among the three GC real estate securities markets, as well as their international links
with the US securitized real estate market over 1995-2009. Overall, their results indicate that the three GC
markets are integrated among themselves and with the US markets from the volatility and correlation
perspectives. Additionally, the conditional correlations between the GC markets have outweighed their
conditional correlations with the US market, indicating closer integration between the GC markets due to
geographical proximity and closer economic links. Finally, higher levels of volatility spillovers and
correlations are detected across all markets during the 2007 global financial crisis period.
4.
Data and methodology
We use Standard & Poor’s weekly closing real estate stock indexes for three GC {China (CH), Hong
Kong (HK), Taiwan (TW)} and five non-GC real estate securities markets {Australia (AU), Japan (JP),
Singapore (SG)., the US and the UK}3 from Jan 4, 1995 through Apr 25, 2012, the longest time period for
which all data are available. This 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.
As noted by LN (2012), the three GC real estate securities markets are quite different in terms of their
macroeconomic conditions, degree of market openness, informational transparency, legal system, size and
maturity level, as well as levels of government intervention on the real estate market. Moreover, the Chinese
real estate securities market is still relatively undeveloped and needs a longer time to mature; for example,
there is no REIT market in CH. In contrast, the HK securitized real estate market is one of the world’s largest
markets and its listed real estate companies have long established track records on the HK stock exchange.
Finally, TW has relatively stable economic growth and its real estate securities market is the smallest among
the three and with the establishment of a REIT (TW-REIT or T-REIT) market in 2005.The five international
markets were chosen because they are the obvious candidates (i.e. the most developed in Asia, Europe and
North America). The US, UK, JP and AU have well-developed financial markets and open capital accounts. In
particular, the US and JP are the main investors and trading partners with the GC economies. SG, due to its
geographical proximity and cultural similarity, has had close economic ties with the three GC economies.
7
Additionally, we extract four regional real estate securities market indices: Panda (GC regional index),
Asia-Pacific (AP), Europe (EU) and North America (NM) for similar regional analysis where appropriate.
Weekly real estate index returns are calculated as the natural logarithm of the total return index relative,
measured in a common currency - US dollar returns. This common currency approach would make the
comparison more sensible and rule out trading due to rebalancing that is driven by the exchange rate
fluctuations. Exhibit 1 plots the time series trend of these eight real estate securities market total return indices
over the full study period. In addition, Exhibit 2 provides some descriptive statistics for the sample. The
Jarque-Bara (JB) test shows that all indexes are not normally distributed during the full study period. Hence,
GARCH modeling is useful.
(Exhibits 1 and 2 here)
Our empirical methodology comprises four main steps. We first employ co-integration tests to assess
the whether the markets are in long-term equilibrium. Johansen’s multivariate approach is utilized to test cointegration among securitized real estate market price indices. The null hypothesis is that there is no cointegration among the securitized real estate price indices. The basis of Johansen method is essentially a VAR
model with cross-equation restrictions. 4Second, since the market could be linked in a nonlinear manner and
that Johansen test can only detect linear relationship, we appeal to a bivariate partial co-integration model
developed by Okunev and Wilson (1997) to detect possible nonlinear relationship between the markets. Third,
we conduct multivariate GARCH estimation to extract conditional volatility series to assess their volatility
interdependence. And lastly, using Diebold and Yilmaz (2012)’s generalized VAR and volatility spillover
index methodologies, we assess whether the three GC markets have influenced to or have been more
influenced by other markets from the conditional volatility spillover perspective across the full sample period
and around the GFC period.
5.
Empirical results
5.1
Unconditional correlations
8
A simple test for integration between securitized real estate indices is done by estimating the
correlation coefficients of the weekly indices’ returns (and variances) across markets. Exhibit 3 reports the
results for the full period. With two minor exceptions, the correlations in returns and variances among various
markets are positive. Among the three GC markets, both the return and variance correlations are highest
between CH and HK (0.3643 for return correlation and 0.1780 for variance correlation).On the international
linkage, HK has the highest return correlation with SG (0.5855), followed by HK and AU (0.3998) and HK
and JP (0.3323). Similarly, HK has the highest variance correlation with SG (0.4039). All other return and
variance correlations are below 0.30. Based on the results reported, we are inclined to conclude that the three
GC securitized real estate markets are correlated among themselves and with major international developed
public property markets in both first and second moments. However, securitized real estate market integration
within and across the GC areas was mostly weak during the entire period, implying portfolio diversification
opportunities within the GC securitized property sector are available.
(Exhibit 3 here)
5.2
Evidence of long-run price equilibrium
We appeal to Johansen multivariate co-integration test (1988, 1991) to determine whether there is
long-run price equilibrium on seven groups of markets. These groups are constructed so that we can test for cointegration among three GC markets, between the GC markets and the US market, between the GC markets
and the UK market, between the GC markets and AU market, between the GC markets and the JP market,
between the GC markets and SG market, and among the regional GC index (Panda) and AP, EU and NM.5
Results are summarized in Exhibit 4.
(Exhibit 4 here)
Based on the computed values of
  trace and   max , all seven co-integration tests fail to reject
the null hypothesis of zero co-integrating vector, implying the securitized real estate market indices are not cointegrated over the entire period; i.e. the three GC markets are not co-integrated themselves, the three GC
markets are not co-integrated with the US or UK or Australia or Japanese or Singapore markets, and the
regional GC Panda index is not co-integrated with other three regional indices. Hence, although these markets
9
are correlated, they are still segmented in the long-run. Although with increased transparency and openness in
their (public) property markets, the mainland CH and, to a lesser extent, the TW markets, are basically less
influenced by international developed public property markets. Additionally, relative to all other markets that
already had an established REIT structure, CH has yet to introduce and enact REIT legislation. The CH, and to
a lesser degree, TW real estate and public property markets are in the midst of development and maturing at
different stages and require some more time to synchronize with international developed property markets. In
summary, one important implication from the rejection of co-integration is that international investors may be
able to enhance diversification benefits by spreading some resources across the GC public property markets
since there is lack of a common long-price equilibrium relationship between the GC and international public
property markets.
.
In contrast, Exhibit 5 reveals that during the AFC period from July 1997 to December 2009, there is
one significant (at the five-percent level) co-integrating vector between the GC markets and the US market,
and between the GC markets and the JP markets. During the recent GFC period spanning from July 2007 to
December 2009, there is one co-integrating vector for all seven market groups. These results imply that the
GFC, and to a lesser extent, the AFC have probably altered the strength of market interdependence, in terms of
the coverage of co-integrating relationship, within and across the GC areas over time. The presence of cointegration also indicates some degree of convergence among the GC and international developed public
property markets in the long run. This finding is in broad agreement with the extant stock market literature that
global stock markets have become more interdependent following some market turbulence because of
contagion effect. The results further imply that during the crisis period, any reduction in portfolio risk through
diversification might not be significant or even absent. Hence, it is prudent for portfolio managers to regularly
review their international diversifications models and strategies with respect to GC securitized property
portfolios because of possible changes in market interdependence triggered by a major crisis. Although these
results are not new, they serve as a warning to investors about the limit of diversification due to crisis.
(Exhibit 5 here)
5.3
Evidence of nonlinear relationship
10
To gain deeper insight as whether the securitized property markets could be nonlinearly related, we
implement a non-linear bivariate partial co-integration test developed by Okunev and Wilson (1997). The
model is specified as:
Log
yt 1
yt
  0  1 Log
Our focus is on the coefficient
xt 1
xt
  2 Log ( yt )   3 Log ( xt )   t
 1 in the above equation. Specifically, if  1 falls between zero and
one, there is a nonlinear relationship between y t (index 2) and x t (index 1) and the values of the coefficient
indicate the strength of this nonlinear relationship. From Exhibit 6,  1 is significant for all pairs of securitized
public property indices at the 1% level. The highest
 1 value is found for the CH/AU pair (0.6392), followed
by the HK/SG pair (0.6238), and CH/HK pair (0.5839). In contrast, the smallest value of
 1 is found for the
CH/JP pair (0.2371), followed by the TW/US pair (0.2719) and the HK /TW (0.2857), implying that the nonlinear relationship between these markets are weak.
In summary, although we find no evidence of long-run price equilibrium within and across the GC
areas during the full study period, we find bivariate weak to moderate nonlinear relationships between the
securitized public property markets. One important implication from this analysis is that failure to detect
possible non-linear relationships between the securitized public property markets is likely to overestimate the
diversification benefits from portfolio allocation that includes the GC securitized real estate markets.
5.4
VAR-VECH-GARCH model
We extend the analysis to multivariate GARCH models, which have been widely used to investigate
volatility and correlation transmission and spillover effects. In the present context, we employ multivariate
VAR-VECH-GARCH methodology to filter the securitized real estate market returns. Vector auto-regression
(VAR) is a dynamic model that has been popular to analyze the multivariate return relationships among
financial time series. Additionally, we add in a VECH-GARCH component to capture conditional
11
heteroskedasticity in the system of residual variances.
The VECH-GARCH model was introduced by
Bollerslev, Engle and Woodbridge (1988) with the following specification:
VECH ( H t )  W  A.VECH (t 1  ' t 1 )  B.VECH ( H t 1 ), t / t 1 ~ N (0, H t )
Where  t is a nx1 disturbance vector, W is a nx1 parameter vector, A is the coefficient matrix for the ARCH
term and B is the coefficient matrix for the GARCH term, VECH (.) for the operator that stacks the upper
triangular portion of a symmetrical matrix.
To ensure that the specification of the VECH model is reasonably parsimonious and has sufficient
flexibility, a common form, Diagonal VECH model, to restrict ARCH and GARCH coefficients to be diagonal
(i.e. they are all specified as rank one matrices) is used. One important advantage of this diagonal specification
is that it reduces the number of parameters estimated and guarantee that the conditional covariance matrix is
positive semi-definite. Moreover, this specification allows us to identify the own-volatility spillover effect as
well as the cross-volatility spillover effect for implementing volatility spillover methodology analysis 6. We
follow a Student’s t-distribution to model the thick tail in the residuals.
The estimated coefficients and significance for variance-covariance of equations in the VECHGARCH model are presented in Exhibit 7. These coefficients quantify the effects of the lagged own and cross
innovation, as well as lagged own and cross volatility persistence on the current own and cross volatility of the
eight securitized real estate markets. As the numbers indicate, own-volatility spillovers (ARCH) in all eight
markets are significant and ranges between 1.69% and 4.09%. All cross-volatility effects are also significant
and ranges between 1.99% and 3.98%, In the GARCH sets of parameters, all estimated own lagged volatility
persistence and cross-volatility persistence coefficients are highly significant.
(Exhibit 7 here)
Conditional correlation analysis (Exhibit 8) shows that the average correlation among the three GC
markets (0.1717) is higher than the average correlation between the GC markets and international markets
(0.14076), implying closer integration among the three GC securitized real estate markets due to geographical
12
proximity and closer economic links. These findings are thus in agreement with the work of Liow and Newell
(2012). In addition, the correlation between CH and HK (0.3015) has outweighed correlations with her five
international partners (between 0.0553 and 0.2546), echoing increasing real estate market integration between
CH and HK. This finding is supported by the time trend results that indicate over the full period, the
correlation between CH and HK has experienced, after controlling for the effects of AFC and GFC, a
significant increase of about 2.54%. Similarly the average conditional correlations within and across the GC
areas have, respectively, registered a significant and yet smaller increase of 1.63% and 0.57%. The graphs in
Exhibit 9 (within GC) and Exhibit 10 (across GC) support a trend toward greater integration among the three
GC markets within the Panda region and their international linkages. Overall, we are inclined to conclude that
there is an increasing trend of real estate market integration within and across the GC areas over the full
period, with CH and HK takes the lead in this race. Finally, a (much) higher level of correlation for many
market-pairs is observed during the GFC period. Average conditional correlations during the GFC period
report an increase (relative to the full period) of about 107.9% (GC), 175.9% (China and international:
CH/INT), 63.3% (HK/IT), 96.9% (TW/INT) and 100.1% (PANDA/INT)7 implying these correlations could be
subject to regime switching.
(Exhibits 8 -10 here)
5.5
Interdependence of securitized real estate markets’ conditional volatilities
The graphs for the conditional variance series presented in Exhibit 11 clearly reveal the phenomenon
of time-varying volatility in the GC and international markets. Additionally, Exhibit 12 presents a summary of
some statistical properties of the fitted conditional variances series. Notably, the Ljung-Box Q statistics for the
conditional variances (Q12 and Q24) are highly significant at the 1% level for all markets, indicating the
presence of serial correlations. As expected, the mean conditional variances show an increase during the GFC
period.
(Exhibits 11 and 12 here)
13
Exhibit 13 shows the correlation matric of the conditional variance series. Among the three GC
markets, the strongest correlation is again observed between CH and HK (0.6544); whereas across the GC
areas, the strongest correlation is observed between HK and SG (0.7683). Moreover, volatility correlations
have increased significantly during the GFC period. Overall, we can conclude that conditional volatilities of
the sample GC and international markets are interdependent and that variance interrelations are stronger than
the relationships among returns, within and across the GC areas. TW is the least influenced public property
market as its conditional variance is not correlated with those of AU, UK and US markets.
(Exhibit 13 here)
5.6
Conditional volatility spillover and interdependence
We include the fitted eight conditional volatility series to a generalized VAR framework (Diebold and
Yilmz, 2012). This generalized VAR model allows us to examine the decomposition of forecast error variances
through analyzing the total and directional volatility spillovers across all markets, whilst at the same time the
results are invariant to the variable ordering.8 Our dynamic spillover analysis covers two main aspects; (1) an
average aggregate conditional volatility spillover index which measures what proportion of the volatility
forecast error variances comes from rolling spillovers; (2) gross and net directional volatility spillover indices
to indicate which of the markets are gross volatility importer, gross volatility exporter and net volatility
exporter. In the present context, the net directional volatility spillover analysis will confirm whether the GC
real estate securities markets have more influence to or have been more influenced by other international
markets during the full sample and GFC periods.
To implement rolling analysis, we estimate dynamic volatility spillovers using 52-week rolling
window, into a rolling spillover plot in order to assess the extent and nature of the total volatility spillover
(non-directional) variation over time, due to financial market evolution and financial turbulences (e.g. GFC).
In addition, we analyze the net directional spillover which is the difference between two measures of gross
directional spillover, i.e. the difference between “contribution from” column sum and the “contribution to” row
sum. The main results are summarized below:
14
(a)
Exhibit 14 graphs time series variations of the aggregate and net directional volatility spillovers
among the three GC public property markets. Similarly, Exhibit 15 graphs time series variations of the
aggregate conditional spillovers between the three GC markets and international markets (CH/INT; HK/INT;
TW/INT; and PANDA/INT). The plots indicate highly fluctuating nature of volatility spillovers with no clear
patterns observed. During the entire period, on average, among the GC markets, 36.67% of the volatility
forecast error variance in the three GC public property markets comes from spillover. The remaining 63.23%
of the total forecast error variance is explained by own shocks rather than spillover of shocks across the three
GC markets. In addition, across the GC areas, average aggregate volatility spillover indices are, respectively,
60.04% (CN/INT), 60.71% (HK/INT), 60.19% (TW/INT) and 60.73% (PANDA/INT). Additionally, the time
trend analyses reveal that after controlling for the effects of AFC and GFC, the estimated aggregate volatility
spillover indices have experienced a significant increase of 2.81% (GC), 2.12% (CH/INT), 2.25% (HK/INT),
1.72% (TW/INT) and 2.14% (PANDA/INT) over the entire study period,9 implying there is an increasing trend
of volatility interaction and thus securitized real estate market integration within and across the GC areas.
Finally the average aggregate GC volatility index around the GFC period is 45.48% which is a 8.8% increase
in volatility interaction over the full period. Similarly, the volatility spillover index increase during the GFC
period is 11.0% (CH/INT), 11.0% (HK/INT), 7.2% (TW/INT) and 10.4% (PANDA/INT). These results
confirm to the contagion hypothesis.
(Exhibits 14 and 15 here)
(b)
Within the GC areas, the three GC markets have influenced on and have been influenced by other
markets moderately with the average gross directional spillover index ranges from 29.09% to 41.57% during
the entire period.10 Our interest is the “average net directional spillover index” that will reveal which of the
three GC securitized real estate market is the most dominant (i.e. influential) in exporting volatilities to the
other two GC markets. With an average net directional volatility spillover index of -11.73%(CH), 3.79%(HK)
and 7.94%(Taiwan), TW appears to have larger influence than CH and HK on the GC public property markets,
with the index fluctuated between -100% and 198.7% during the full study period. Additional results (not
reported for brevity reason) indicate that among the three GC markets, CH has increased her volatility
15
influence on HK over time. Finally, with an average net directional volatility spillover index of 4.66% (50.2745.61), Hong Kong emerges as the “volatility leader” in transmitting more conditional volatilities to other two
GC markets, with the time series net volatility spillover index fluctuated between -79.4% and 190.6% during
the GFC period. This result is not surprising.
Across the GC areas, Exhibit 16 presents four rolling net directional volatility spillover plots
(CH/INT, HK/INT, TW/INT and PANDA/INT). According to the estimated results, all three GC markets are
moderately influenced by volatility impacts from the international markets (average gross directional volatility
spillover index ranges from 54.3% to 59.2%). At the same time, each of the three GC markets also influence
other international markets in different degree, with HK ranks the top (gross directional index is 73.83%),
followed by CH (63.09%) and TW (48.52%). Of further interest is HK public property market, which is the
“volatility leader”, was exerting the greatest net volatility influence on other international markets during the
full sample period. Her average net directional volatility spillover index is 15.67% (73.83- 58.16) and
fluctuated between -98.3% and 291.8 over the full sample period. Moreover, HK is again the “volatility
leader” around the GFC period, with its average net directional volatility spillover index reaches a high of
37.2%. Finally, the time trend results reveal that after controlling for the effects of AFC and GFC, the
estimated average net directional volatility spillover indices across the GC groups have largely remained
relatively stable over the full period, implying that it may take longer time for the GC markets to have greater
volatility influence on other international markets.
(Exhibit 16 here)
Based on the results reported, we are inclined to conclude that the extent of conditional volatility
interdependence within and across the GC areas is moderate. Over time, the three GC securitized real estate
markets have become more integrated by themselves and with other international developed public property
markets from the volatility transmission perspective. Moreover, each of the three GC public property markets
has influenced and has been influenced by other GC and international markets with similar magnitudes,
thereby resulting in relatively low net directional volatility spillover index estimates obtained. In consistent
with expectation, HK appears to be the “volatility leader” since she was exerting the greatest net volatility
16
influence on other international property markets the full sample period. However, within the GC areas, CH
appears to transmit increased volatility influence on HK over time. Finally, the GC markets have experienced
an increase in the volatility integration within themselves and with the selected international developed public
property markets, as well as become more open around the GFC period. With favorable political and economic
developments happening in the three GC economies, we expect volatility interactions and connections among
the GC markets, as well as between the GC and international markets would be intensified over time. Overall,
our cross impact analysis of GC real estate equities’ conditional volatilities, based on the volatility spillover
methodology, helps validate the evidence for a stronger interdependence among the conditional volatilities of
the GC and selected international developed public property markets in response to globalization and financial
crisis, thereby contributes supplementary and significant findings to the extant literature.
6
Conclusions
Although there are considerable studies investigating global securitized real estate market correlation
and integration, our research is one of very few that specifically focuses on the three Greater China (GC) and
selected international developed public property markets with regard to their dynamic integration from the
long run and short term perspective. It contributes to the literature by: (a) evaluating and comparing the extent
of linear long-run relationships among the securitized property groups over a relatively long period of time and
around the global financial crisis (GFC) period; (b) employing a bivariate partial co-integration model
developed by Okunev and Wilson (1997) to detect possible nonlinear relationship between the public property
markets over time; (c) utilizing recent advances in multivariate GARCH methodologies and Diebold and
Yilmaz (2012)’s generalized volatility spillover methodology (including rolling, gross and net volatility
spillovers) to examine whether the three GC securitized real estate markets have become more integrated by
themselves and/or with other markets, as well as analyze whether the three GC markets have influenced or
have been more influenced by other markets, from the conditional volatility perspective.
Based on weekly real estate stock total return index data provided by S&P, the main results can be
summarized as follows. There is no evidence of long-run relationship among the different securitized property
groups over 1995-2012; however the GC markets are co-integrated with all five international markets during
17
the GFC period. Results of the non-linear co-integration analysis reveals that the sample securitized real estate
markets share moderate but significant non-linear relationships, although they are not co-integrated. And
lastly, results from volatility spillover index methodology indicate that the extent of conditional volatility
interdependence within and across the GC areas is moderate. Over time, the three GC securitized real estate
markets have become more integrated by themselves and with other regional/international developed public
property markets from the volatility transmission perspective. Additionally, each of the three GC public
property markets has influenced and has been influenced by other GC and international markets with similar
magnitudes, thereby resulting in relatively low average net directional volatility spillover index estimates
obtained. Moreover, Hong Kong public property market appears to be the “volatility leader” within and across
the GC context.
Whilst the conclusions are not novel, the results from this study confirm previous findings and enrich
the thin body of literature on GC securitized property market integration. Results also serve as warning to
investors and portfolio managers about the limits of diversification due to increase securitized real estate
market integration within and across the GC areas. Our cross impact analysis of GC securitized real estate
conditional volatilities, based on the volatility spillover methodology, help validate the evidence for a stronger
interdependence among the conditional volatilities of the GC and selected international developed public
property markets in response to globalization and financial crisis, and thus contributes supplementary and
significant findings to the extant volatility literature.
18
References
Brown, G. and K.W. Chau, Excess returns in the Hong Kong commercial real estate market, Journal of Real Estate
Research, 1997, 14, 91-105.
Chau, K.W., S.K. Wong and G. Newell, Performance of property companies in Hong Kong: a style analysis
approach, Journal of Real Estate Portfolio Management, 2003, 9, 29-44.
Cheng, H. and J. Glascock, Dynamic linkages between the Greater China Economic Area stock markets – Mainland
China, Hong Kong and Taiwan, Review of Quantitative Finance and Accounting 2005, 24, 343-,357.
Chin, W., P. Dent and C. Roberts, An exploratory analysis of barriers to investment and market maturity in
Southeast Asian cities, Journal of Real Estate Portfolio Management, 2006, 12, 49-58.
Diebold, F. and K. Yilmaz, Measuring financial asset return and volatility spillovers, with application to global
equity markets, Economic Journal 2009, 120, 506, F334-F356
Diebold, F. and K. Yilmaz, Better to give them to receive: predictive directional measurement of volatility
spillovers, International Journal of Forecasting, 2012, 28, 57-66
Engle, R.F., Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional
heteroskedasticity models, Journal of Business and Economic Statistics 2002, 20, 339-350.
Fan, K., Z. Lu and S. Wang, Dynamic linkages between the China and International stock markets, Asia-Pacific
Financial Markets 2009, 16, 211-230.
Fung, H., A. Huang, W. Liu and M. Shen, The development of the real estate industry in China, The Chinese
Economy 2006, 39, 84-102.
Groenewold. N., S. Tang and Y. Wu, The dynamic interrelationships between the Greater China share markets,
China Economic Review 2004, 15, 45-62.
Johansson, A. and C. Lijunwall, Spillover effects among the Greater China stock markets, World Development 2009,
37, 839-851.
Ke, Q., Are state-owned companies underperforming? A case study of Chinese listed property companies, Journal
of Real Estate Literature 2008, 16, 183-200.
Kim, Y. and Shin, J. Interactions among Chinese related stocks, Asia-Pacific Financial Markets 2000, 7, 97-115
Liow, K.H., The dynamics of return volatility and systematic risk in international real estate security markets,
Journal of Property Research 2007, 24, 1-29.
Liow, K.H. and Adair, A., Do Asian real estate companies add value to investment portfolios, Journal of Property
Investment and Finance 2009, 27, 42-64.
Liow, K.H. and M.C. Sim, The risk and return profile of Asian real estate stocks, Pacific Rim Property Research
Journal 2006, 12, 283-310.
Liow, K.H., D. Ho, M. Ibrahim and Z. Chen, Correlation and volatility dynamics in international real estate
securities markets, Journal of Real Estate Finance and Economics 2009, 39, 202-223.
19
Liow, K.H. and Newell, G, Investment dynamics of Greater China securitized real estate market, Journal of Real
Estate Research 2012, 34(3), 399-428
Macquarie Securities, Global Property Securities Analytics: June 2009, Macquarie Securities.
Newell, G. and M. Razali, The impact of the global financial crisis on commercial property investments in Asia,
Pacific Rim Property Research Journal 2009, 15, 430-452
Newell, G., K.W. Chau and S.K. Wong, The level of direct property in Hong Kong property company performance,
Journal of Property Investment and Finance 2004, 22, 512-532.
Newell, G., K.W. Chau and S.K. Wong, The significance of Chinese commercial property in an Asian property
portfolio, Journal of Property Investment and Finance 2009, 27, 102-119
Qiao, Z., C. Chiang, and W. Wong, Long-run equilibrium, short-term adjustment, and spillover effects across
Chinese segmented stock markets and the Hong Kong stock market, Int. Fin. Markets., Inst. & Money 2008, 18,
425-437
Tao, S. and X. Zhang, Spillovers of the US subprime financial turmoil to Mainland China and Hong Kong SAR:
evidence from the stock markets, IMF working paper 2009: monetary and capital market, Chinese Academy of
Social Sciences
World Federation of Exchanges, Focus: July 2009, WFE.
Yilmaz, K, Return and volatility spillovers among the East Asian equity markets, Journal of Asian Economics 2010,
21, 304-313
Zhu, H., Z. Lu and S. Wang, Causal linkages among Shanghai, Shenzhen, and Hong Kong stock markets,
International Journal of Theoretical and Applied Finance 2004, 7, 135-149.
.
20
Exhibit 1
Securitized real estate market weekly indices (in US dollar returns)
800
700
CH
HK
TW
US
UK
AU
JP
SG
600
500
400
300
200
100
0
1996
1998
2000
2002
21
2004
2006
2008
2010
Exhibit 2
Mean
Descriptive statistics of weekly real estate securities market returns: 4/1/19995 – 25/4/2012 (US Dollar returns)
China
Hong
Kong
Taiwan
US
UK
Australia
Japan
Singapore
Panda
AsiaPacific
Europe
N. Americas
0.0008
0.0006
0.0001
-0.0001
0.0004
0.0004
0.0007
0.0007
0.0006
0.0006
0.0006
-0.0001
Maximum
0.0962
0.1431
0.0698
0.1014
0.0893
0.0626
0.1753
0.1185
0.1388
0.0904
0.0618
0.0958
Minimum
-0.1065
-0.1111
-0.2534
-0.1432
-0.1092
-0.1125
-0.0910
-0.0821
-0.1080
-0.0775
-0.0694
-0.1398
Std. Dev.
0.0256
0.0191
0.0226
0.0163
0.0153
0.0151
0.0213
0.0191
0.0189
0.0144
0.0122
0.0158
Skewness
-0.1018
0.2556
-1.4716
-1.2660
-0.3198
-1.0520
1.1172
0.5341
0.1804
0.1332
-0.4748
-1.3600
Kurtosis
5.7885
10.1419
20.3183
23.4074
10.4953
10.9702
10.6873
7.6052
9.5420
9.0041
9.7457
23.8062
Jarque-Bera
295.4141
1937.5010
11662.0000
15981.1100
2138.5520
2567.9440
2421.9700
844.6192
1616.9630
1360.5260
1747.9510
16584.5300
Probability
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Exhibit 3
China
Hong Kong
Taiwan
US
UK
Australia
Japan
Singapore
Unconditional correlations in returns and variances in real estate securities markets: 1995-2012 (USD)
China
Hong Kong
Taiwan
US
UK
Australia
Japan
Singapore
1.0000
0.3643
0.1780
-0.0160
0.1255
0.0798
0.2146
0.0588
0.1665
1.0000
0.1396
0.1631
0.0680
0.0931
-0.0045
0.1132
0.1596
0.0962
0.3039
0.2470
0.3998
0.1498
0.0486
0.3248
0.1291
0.3323
0.1538
-0.0163
0.0987
0.3211
1.0000
0.2903
0.5855
0.1454
0.1797
0.2235
0.3842
0.3191
0.4197
1.0000
0.0218
0.0822
0.0450
0.3029
0.1239
0.4039
1.0000
-0.0035
-0.0111
0.0158
0.0191
0.0146
1.0000
1.0000
0.3304
0.1726
0.0678
0.1148
0.3157
0.0172
0.0770
1.0000
0.4735
0.5371
Notes: Figures above the diagonal are return correlations; figures (in bold) below the diagonal are correlations in variances
22
Johansen’s co-integration test results (USD)
Exhibit 4
Model 1
(CH, HK, TW)
r =0
r <=1
r <=2
r <=3
Model 2
(CH, HK, TW,
US)
Model 3
(CH, HK, TW,
UK)
Model 4
(CH, HK, TW,
AU)
Model 5
(CH, HK, TW,
JP)
Model 6
(CH, HK, TW,
SG)
Model 7
(PDA, ASP,
EUR, NAMR)
trace
max
trace
max
trace
max
trace
max
trace
max
trace
max
trace
max
25.36
6.38
2.38
-
18.98
3.99
2.38
-
31.56
12.14
6.29
2.78
19.41
5.85
3.51
2.78
30.07
10.82
4.35
1.62
19.25
6.48
2.73
1.62
30.25
8.59
4.56
1.69
21.67
4.03
2.87
1.69
38.16
17.76
5.97
2.38
20.40
11.79
3.59
2.37
42.28
19.17
6.56
2.87
23.11
12.61
3.70
2.87
34.57
19.88
8.33
2.46
14.69
11.56
5.86
2.46
Notes
This table summarizes the co-integration test results based on Johansen’s (1988) multivariate approach from 1995-2011 for China (CH), Hong Kong (HK),
Taiwan (TW), US, UK, Australia (AU), Japan (JP) and Singapore, as well as for Panda (PDA), Asia-Pacific (ASP), Europe (EUR) and North Americas (NAMR)
regions. The lag length in the VAR system is selected according to AIC and additionally, a trend term is included in the VAR system. r denotes the number of cointegrating vectors in the null hypothesis. We report the maximum trace ( trace ) and maximum eigen (  max ) statistics and compare with 5% and 1% critical
values. Both tests consistently indicate no co-integration at both 5% and 1% levels.
23
Exhibit 5
Johansen co-integration tests: No of co-integrating vectors (at the 5% level) during the
Asian financial crisis (AFC) and Global financial crisis (GFC) periods
Model
VAR system
Number of co-integrating vectors
AFC
GFC
1
CH, HK, TW
0
1
2
CH, HK, TW, US
1
1
3
CH, HK, TW,UK
0
1
4
CH, HK, TW, AU
0
1
5
CH, HK, TW, JP
1
1
6
CH, HK, TW, SG
0
1
7
Panda, Asia-Pacific,
Europe, North Americas
0
1
24
Exhibit 6
Nonlinear tests (bivariate) between real estate securities markets: 1995-2011
Markets
CH /HK
HK/TW
CH/TW
CH/US
CH/UK
CH/AU
CH/JP
CH/SG
HK/US
HK/UK
HK/AU
HK/JP
HK/SG
TW/US
TW/UK
TW/AU
TW/JP
TW/SG
Coefficients
Adjusted R2
0
1
2
3
-0.0395
(-1.37)
0.0457
(1.48)
0.0285*
(1.74)
-0.0090
(-0.21)
-0.0122
(-0.49)
0.0044
(0.10)
-0.0360
(-1.42)
-0.0075
(-0.34)
0.0452
(1.40)
0.040
(1.14)
0.0232
(0.63)
0.037
(1.21)
0.0459***
(2.66)
-0.0028
(-0.08)
0.0056
(0.21)
0.0043
(0.14)
-0.020
(-0.84)
-0.0188
(-0.98)
0.5839***
(8.69)
0.2857***
(6.74)
0.3128***
(5.47)
0.3842***
(7.07)
0.4671***
(8.46)
-0.0062*
(-1.73)
-0.0093
(-1.37)
-0.0038
(-1.46)
-0.0043
(-1.05)
-0.0027
(-1.00)
-0.0021
(-0.71)
-0.0068**
(-2.12)
-0.0042
(-1.14)
-0.0115*
(-1.65)
-0.0090
(-1.45)
-0.0077
(-1.14)
-0.0270***
(-3.51)
-0.0259***
(-4.00)
-0.0031
(-1.40)
-0.0033
(-1.42)
-0.0033
(-1.51)
-0.0036
(-.1.59)
-0.0044*
(-1.77)
0.0134*
(1.77)
0.0013
(0.44)
-0.0002
(-0.91)
0.0061
(0.57)
0.0048
(1.01)
0.0014
(0.16)
0.0144**
(2.31)
0.0055
(1.04)
0.0032
(0.64)
0.0014
(0.51)
0.0035
(0.75)
0.0218***
(3.83)
0.0172***
(3.69)
0.0024
(0.38)
0.0007
(0.17)
0.0010
(0.18)
0.0064
(1.23)
0.0061
(1.49)
0.6392***
(9.36)
0.2371***
(3.46)
0.4055***
(4.77)
0.4394***
(11.36)
0.4428***
(9.40)
0.5318***
(15.59)
0.3291***
(6.30)
0.6238***
(20.89)
0.2719***
(5.22)
0.3299***
(5.36)
0.4403***
(7.00)
0.2992***
(5.71)
0.3472***
(6.43)
0.185
0.123
0.081
0.047
0.087
0.139
0.035
0.112
0.128
0.158
0.188
0.136
0.492
0.029
0.053
0.080
0.066
0.102
Notes: The tests are conducted using US dollar returns and based on the following non-linear model developed by
Okunev and Wilson (1997):
Log
yt 1
yt
  0  1 Log
xt 1
xt
  2 Log ( yt )   3 Log ( xt )   t
Specifically, if  1 falls between zero and one, then there exists a nonlinear relationship between y t and x t , and the
value of the coefficient indicates the strength of this nonlinear relationship. Each cell in the table reports the
coefficient estimate and the associated t-statistic (in parenthesis). ***, **, * - indicates two-tailed significance at
1%, 5% and 10% levels respectively
25
Exhibit 7
Estimated coefficients for variance-covariance equations in real estate securities markets
CH (i=1)
HK(i=2)
TW(i=3)
US (i=4)
UK(i=5)
AU(i=6)
JP(i=7)
SG(i=8)
0.0000034***
0.00000032
0.0000093***
0.0000004***
0.0000018***
0.0000016***
0.0000080***
0.0000011***
0.0409***
0.0317***
0.0263***
0.0398***
0.0355***
0.0377***
0.0310***
0.0386***
0.0317***
0.0245***
0.0203***
0.0308***
0.0275***
0.0261***
0.0240***
0.0299***
0.0263***
0.0203***
0.0169***
0.0256***
0.0228***
0.0261***
0.0199***
0.0248***
0.0398***
0.0308***
0.0256***
0.0387***
0.0345***
0.0328***
0.0302***
0.0375***
0.0355***
0.0275***
0.0228***
0.0345***
0.0308***
0.0293***
0.0269***
0.0335***
0.0377***
0.0261***
0.0261***
0.0328***
0.0293***
0.0278***
0.0269***
0.0318***
0.0310***
0.0240***
0.0199***
0.0302***
0.0269***
0.0256***
0.0235***
0.0293***
0.0386***
0.0299***
0.0248***
0.0375***
0.0335***
0.0318***
0.0293***
0.0364***
0.9575***
0.9652***
0.9620***
0.9603***0.9591***0.9621***
0.9579***
0.9592***
0.9652***
0.9731***
0.9698***
0.9681***
0.9669***
0.9699***
0.9657***
0.9670***
0.9620***
0.9698***
0.9666***
0.9649***
0.9636***
0.9666***
0.9625***
0.9638***
0.9603***
0.9681***
0.9649***
0.9631***
0.9619***
0.9649***
0.9607***
0.9620***
0.9591***
0.9669***
0.9636***
0.9619***
0.9607***
0.9637***
0.9595***
0.9608***
0.9621***
0.9699***
0.9666***
0.9649***
0.9637***
0.9666***
0.9625***
0.9638***
0.9579***
0.9657***
0.9625***
0.9607***
0.9595***
0.9625***
0.9584***
0.9596***
0.9592***
0.9670***
0.9638***
0.9620***
0.9608***
0.9638***
0.9596***
0.9609***
Notes: Derived from using an eight-market VAR-VECH model that restricts A & B to be diagonals. All regressions follow the GARCH (1, 1) model and are estimated by
maximum likelihood using the Berndt-Hall-Hall-Hausman (BHHH) maximization algorithm. *** - indicates two-tailed significance at the 1% level.
M (1,1)
M (2,2)
M (3,3)
M (4,4)
M (5,5)
M (6,6)
M (7,7)
M (8,8)
A (i, 1)
A (i, 2)
A (i, 3)
A (i, 4)
A (i, 5)
A (i, 6)
A (i, 7)
A (i, 8)
B (i, 1)
B (i, 2)
B (i, 3)
B (i, 4)
B (i, 5)
B (i, 6)
B (i, 7)
B (i, 8)
26
Exhibit 8
Average conditional correlations in real estate securities markets
Notes: Based on a multivariate VAR-VECH-GARCH estimation, this table reports the weekly average value,
standard deviation, maximum value, minimum value and trend of real estate securities markets’ conditional
correlations (CC) for the full period (Jan 1995-April 2012), as well as the average value and standard deviation of
the CC series during the global financial crisis (GFC) period (Jul 2007- Dec2009). The weekly trend is estimated
via the following equation: RCt  f (CCt 1. , AFC t , GFCt , trend t , const ) (AFC: Asian financial crisis period:
Jul 1997- Dec 1999) with Newey-West HAC Standard errors and covariance adjustment. ***, **, * - indicates that
the estimated weekly time trend is statistically significant at the 1%, 5% and 10% level respectively.
Full period
GFC
Mean
0.3015
0.0830
0.1306
Std dev
0.2568
0.1198
0.1028
Max
0.8479
0.3738
0.5348
Min
-0.1430
-0.1536
-0.1108
Trend (%)
0.0028***
0.0013***
0.0010*
Mean
0.7181
0.1949
0.1578
Std dev
0.0755
0.1244
0.1188
Within GC average
CH-JP
CH-SG
CH-AU
CH-US
CH-UK
0.1717
0.0619
0.2546
0.1701
0.0533
0.0693
0.1355
0.1571
0.2135
0.1625
0.1152
0.1436
0.4739
0.5350
0.7766
0.6873
0.4267
0.4400
-0.0688
-0.2839
-0.1817
-0.1105
-0.2331
-0.3283
0.0018***
0.0016***
0.0019***
0.00037
0.00005
0.0012**
0.3569
0.3321
0.6234
0.4318
0.1068
0.1858
0.0805
0.1064
0.0743
0.1743
0.1066
0.1234
CH-INT(average)
HK-JP
HK-SG
HK-AU
HK-US
HK-UK
0.1218
0.2032
0.5066
0.2977
0.0908
0.1264
0.1258
0.1279
0.1358
0.1543
0.1112
0.1180
0.5126
0.5383
0.8296
0.6297
0.3883
0.4583
-0.0986
-0.0878
0.1536
-0.0602
-0.1612
-0.3443
0.0011**
0.00088**
0.00090**
0.00091**
-0.00015
0.00063
0.3360
0.3981
0.7262
0.5251
0.1506
0.2003
0.0861
0.0824
0.0501
0.0643
0.1275
0.1157
HK-INT(average)
TW-JP
TW-SG
TW-AU
TW-US
TW-UK
0.2450
0.0757
0.0935
0.0813
-0.0248
0.0502
0.0991
0.0748
0.1408
0.0844
0.0653
0.0729
0.5296
0.3068
0.4088
0.3639
0.1168
0.2676
0.0596
-0.1541
-0.4042
-0.1235
-0.2379
-0.1451
0.00085**
-0.00002
0.00072**
0.00087**
0.00012
0.00034
0.4001
0.1218
0.1967
0.1901
-0.0406
0.0754
0.0617
0.0961
0.1417
0.0820
0.0874
0.0479
TW-INT(average)
0.0552
0.0615
0.2505
-0.0689
0.00036*
0.1087
0.0727
GC-INT(average)
0.1407
0.0856
0.4117
-0.0290
0.00063**
0.2816
0.0503
CH-HK
CH-TW
HK-TW
27
Exhibit 9
Time series conditional correlations: GC markets
1.0
.4
China - Hong kong
China - Taiw an
0.8
.3
0.6
.2
0.4
.1
0.2
.0
0.0
-.1
-0.2
-.2
96
98
00
02
04
06
08
10
12
.6
96
98
00
02
04
06
08
10
12
08
10
12
.5
Greater China Average
Hong Kong - Taiw an
.4
.4
.3
.2
.2
.1
.0
.0
-.2
-.1
96
98
00
02
04
06
08
10
12
96
Notes: Derived from multivariate VAR-VECH-GARCH estimation, in US dollar returns
28
98
00
02
04
06
Exhibit 10
Time series conditional correlation graphs: averages of GC and international
.6
.6
China - International
Hong Kong - International
.5
.4
.4
.2
.3
.2
.0
.1
-.2
.0
96
98
00
02
04
06
08
10
12
96
.3
98
00
02
04
06
08
10
12
10
12
.5
Taiw an - International
Greater China - International
.4
.2
.3
.1
.2
.1
.0
.0
-.1
-.1
96
98
00
02
04
06
08
10
12
96
Notes: Derived from multivariate VAR-VECH-GARCH estimation, in US dollar returns
29
98
00
02
04
06
08
Exhibit 11
Time series of conditional variances in real estate securities markets (USD)
.0025
.0012
.0016
Hong Kong
China
Taiwan
.0010
.0020
.0012
.0008
.0015
.0006
.0008
.0010
.0004
.0004
.0005
.0002
.0000
.0000
96
98
00
.0016
02
04
06
08
10
12
.0000
96
98
00
02
.004
Panda
04
06
08
10
12
96
.003
.0012
.0008
.002
.0008
.0004
.001
.0004
.000
96
98
00
.0012
02
04
06
08
10
12
02
04
06
08
10
12
04
06
08
10
12
06
08
10
12
UK
.0000
96
98
00
02
.0016
Auatralia
00
.0016
US
.0012
.0000
98
04
06
08
10
12
96
98
00
.0025
Japan
.0010
02
Singapore
.0020
.0012
.0008
.0015
.0006
.0008
.0010
.0004
.0004
.0005
.0002
.0000
.0000
96
98
00
02
04
06
08
10
12
.0000
96
98
00
02
Notes: Derived from the VAR-VECH-GARCH model (in US dollar returns)
30
04
06
08
10
12
96
98
00
02
04
Exhibit 12
Summary statistics for conditional variances in real estate securities markets
CH
HK
TW
US
UK
FULL PERIOD
AU
JP
SG
Panda
Mea n(x103)
0.7020
0.3550
0.5270
0.2790
0.2190
0.2280
0.4400
0.3720
0.3770
Std dev (x103)
0.4500
0.2260
0.1640
0.5520
0.2190
0.2110
0.1860
0.3350
0.2510
Skewnes s
0.7838
1.3779
2.1011
3.2532
2.3360
2.5098
2.1602
2.3693
1.4329
8.3264
8.9653
Kurtos i s
2.6852
4.6014
11.7006
13.4173
Q(12)
9546***
9541***
7364***
9930***
Q(24)
16913*** 16251*** 10648*** 17169*** 18568*** 18570*** 12752*** 17961*** 15993***
10121*** 10109***
9.5425
8.8381
4.6882
8297***
10002***
9454***
GLOBAL FINANIAL CRISIS PERIOD (GFC)
Mea n(x103)
1.1270
Std dev (x103)
0.3940
Skewnes s
0.2459
Kurtos i s
2.3232
Q(12)
Q(24)
0.4680
0.4940
1.2230
0.6200
0.6090
0.5180
0.4790
0.5330
0.1480
0.0683
0.9480
0.2820
0.3050
0.1400
0.1510
0.1790
-0.7740
-0.0594
0.4946
0.3885
0.1619
0.5066
0.2840
-0.5046
3.2167
1.3813
1.7338
1.8358
1.5096
2.4338
1.6877
2.7582
969***
838***
1163***
1256***
1190***
1207***
959***
847***
897***
1116***
929***
1622***
1749***
1802***
1813***
1073***
982***
1035***
Notes: CH (China), HK (Hong Kong), TW (Taiwan), US, UK, AU (Australia), JP (Japan), SG (Singapore), Panda
(Greater China region). **** - indicates statistical significance at the 1% level. The full sample data span the
period January 4, 1995 to April 25, 2012. The Q statistic is the Ljung-Box statistic applied on the conditional
variance series, which tests for autocorrelation up to 12 and 24 lags. The GFC period covers from July 4, 2007 to
December 30, 2009. The conditional variances are derived from the VAR-VECH model (in US dollar returns).
31
Exhibit 13
Correlation structure on the conditional variances in real estate securities markets
CH
HK
TW
US
UK
AU
JP
SG
Panda
CH
HK
TW
US
UK
AU
JP
SG
Panda
1.0000
0.6544
0.1053
0.3943
0.3059
0.4582
0.6162
0.6251
0.6740
0.8815
1.0000
0.2095
0.2162
0.1133
0.2900
0.7006
0.7683
0.9962
0.5660
0.6249
1.0000
-0.0258
-0.0675
0.0066
0.3777
0.3225
0.1810
0.7423
0.7794
0.7667
1.0000
0.8978
0.9588
0.1993
0.1467
0.2742
0.5110
0.6081
0.9092
0.8053
1.0000
0.9247
0.0768
0.0827
0.1736
0.7985
0.8164
0.8227
0.9466
0.8518
1.0000
0.2796
0.2359
0.3488
0.8785
0.8146
0.3378
0.7020
0.3006
0.6926
1.0000
0.8247
0.6869
0.7159
0.7691
0.7661
0.8803
0.8278
0.8912
0.5900
1.0000
0.7494
0.9306
0.9909
0.6574
0.8026
0.6275
0.8497
0.8365
0.7931
1.0000
Notes: Figures above the diagonal are correlations for the full period; figures below the diagonal are correlations
for the GFC period. CH (China), HK (Hong Kong), TW (Taiwan), US, UK, AU (Australia), JP (Japan), SG
(Singapore), Panda (Greater China region). The full sample data span the period January 4, 1995 to April 25,
2012. The GFC period covers from July 4, 2007 to December 30, 2009. The conditional variances are derived from
the VAR-VECH model (in US dollar returns)
32
Exhibit 14
70
Rolling conditional aggregate and net directional volatility plots: GC securitized real estate markets
100
Aggre gate volatility spillove r index (%): GC marke ts
Ne t directional volatility spillover inde x(%): China
60
50
50
0
40
30
-50
20
-100
10
average index value (full period:: 36.67%
average index value (GFC): 45.48%
0
1996
200
1998
2000
2002
2004
2006
GFC
2008
average index value (full period): -11.73%
average index value (GFC): -5.88%
-150
2010
1996
300
Ne t dire ctional volatility spillove r inde x (%): Hong Kong
1998
2000
2002
2004
2006
GFC
2008
2010
Ne t dire ctioonal volatility spillove r inde x (%): Taiwan
160
200
120
80
100
40
0
0
-40
-100
-80
average index value (full period): 3.79%
average index value (GFC): 4.66%
-120
1996
1998
2000
2002
2004
2006
GFC
2008
average index value (full period): 7.94%
average index value (GFC): 1.22%
GFC
-200
2010
1996
1998
2000
2002
2004
2006
2008
2010
Notes: Derived from Diebold and Yilmaz (2012)’s volatility spillover index methodology, estimated using US dollar returns. The procedures are implemented
based on 52-week rolling window and 24-step horizon. Each point in the three net directional graphs is the difference between “contribution from” column sum
and the “contribution to” row sum (i.e. = contribution to – contribution from);” Full period” – (Jan 10, 1996 to Apr 25, 2012- 851 weeks);”GFC” –global
financial crisis (July 4, 2007 – Dec 30, 2009- 131 weeks)
33
Exhibit 15
90
Rolling conditional aggregate conditional volatility spillover plots: GC and international
90
China and international markets
80
80
70
70
60
60
50
50
40
average index value (full period): 60.04%
average index value (GFC): 71.05%
30
1996
90
1998
2000
2002
2004
2006
Hong Kong and international markets
40
GFC
2008
average index value (full period): 60.71%
average index value (GFC): 71.68%
30
2010
1996
90
Taiwan and international markets
1998
2000
2002
2004
2006
GFC
2008
2010
Greater China & international markets
80
80
70
70
60
60
50
50
40
average index value (full period): 60.19%
average index value (GFC): 67.91%
40
1996
1998
2000
2002
2004
2006
average index value (full period): 60.73%
average index value (GFC): 71.13%
GFC
30
2008
2010
1996
1998
2000
2002
2004
2006
GFC
2008
2010
Notes: Derived from Diebold and Yilmaz (2012)’s volatility spillover index methodology, estimated using US dollar returns. The procedures are implemented
based on 52-week rolling window and 24-step horizon.” Full period” – (Jan 10, 1996 to Apr 25, 2012- 851 weeks);”GFC” –global financial crisis (July 4, 2007
– Dec 30, 2009- 131 weeks)
34
Exhibit 16
300
Rolling conditional net directional conditional volatility spillover plots: GC and international
300
Net directional spillover: China & international
200
200
100
100
0
0
-100
-100
average index value (full period): 8.79%
average index value (GFC): 28.64%
-200
1996
500
Net directional spillover: HK & international
1998
2000
2002
2004
2006
average index value (full period): 15.67%
average index value (GFC): 37.24%
GFC
2008
-200
2010
1996
300
Net directional spillover: Taiwan & international
400
1998
2000
2002
2004
2006
GFC
2008
2010
Net directional spillover: Panda & international
200
300
100
200
100
0
0
-100
-100
average index value (full period): -10.90%
average index value (GFC): 8.74%
-200
1996
1998
2000
2002
2004
2006
average index value (full period): 12.26%
average index value (GFC): 44.08%
GFC
2008
-200
1996
2010
1998
2000
2002
2004
2006
GFC
2008
2010
Notes: Derived from Diebold and Yilmaz (2012)’s volatility spillover index methodology, estimated using US dollar returns. The procedures are implemented
based on 52-week rolling window and 24-step horizon. Each point in the graphs is the difference between “contribution from” column sum and the “contribution
to” row sum (i.e. = contribution to – contribution from);” Full period” – (Jan 10, 1996 to Apr 25, 2012- 851 weeks);”GFC” –global financial crisis (July 4,
2007 – Dec 30, 2009- 131 weeks)
35
1
In recent years, China stock exchanges have expanded rapidly in terms of capitalization, turnovers and the number
of listed firms since their establishment; with the result being China’ stock market becoming the second largest in
Asia, behind only Japan (Groenewold ta al. 2004).
2
In this study, the terms: securitized real estate markets, public property markets and real estate securities markets
are used interchangeably.
3
Henceforth, these market abbreviations (instead of full names) will be used throughout the report where
appropriate
4
The concept of co-integration, introduced by Granger (1981, 1986), further developed by Engle and Granger
(1987) and extended by Johansen (1988, 1991), incorporates the presence of non-stationarity, long-term
relationships and short-run dynamics in the modeling process. A financial time series is said to be integrated of order
one, I(1), if it becomes stationary after differencing once. If two series are integrated of order one, they may have a
linear combination which is stationary without requiring differencing and, if they do so, they are said to be cointegrated. Since a lengthy, detailed description of co-integration and Johansen co-integration method can be found
in many standard econometric texts, readers are encouraged to consult them if necessary. The details are not
included in this paper for brevity.
5
Before testing whether the price indices are co-integrated, we first check that each univariate series is nonstationary, or I(1). The tests are necessary, as the finding of a unit root in any of the series indicates non-stationarity,
which has implication for modeling the relationship between any of the two series (bivariate) and all the series in the
system (multivariate). Two standard procedures, the augmented Dickey Fuller (ADF) test and the Phillips-Perron
(PP) test, are utilized to check the non-stationarity of each individual series. The unit-root test results (not included
in order to conserve space) show that there is a unit-root in each of the real estate securities series; but no unit-root
in their first logarithm differences at the five-percent significance level.
6
Liow and Newell (2012) use a Diagonal-BEKK model which is identical to our Diagonal-VECH model (Engle,
1982 and Bollerslev 1986).
7
The five securitized property sub-groups are: (a) Greater China (GC): CH, HK and TW; (b) China and
international (CH/INT): CH, US, UK, AU, JP and SG; (c) Hong Kong and international (HK/INT): HK, US, UK,
AU, JP and SG; (d) Taiwan and international (TW/INT): TW, US, UK, AU, JP and SG; and (e) Panda and
international (PANDA/INT): Panda (regional), US, UK, AU, JP and SG. Henceforth, the abbreviations, GC,
CH/INT, HK/INT, TW/INT and PANDA/INT will be used to replace the full names where appropriate.
8
According to Diebold and Yilmaz (2012), the Cholsesky factorization method is able to achieve orthogonality; but
the variance decompositions depends on the ordering of the variables. Instead, the generalized VAR framework of
Pesaran and Sim (1998) produces variance decompositions which are invariant to the ordering by allowing
correlated shocks and using the historically observed distribution of the errors to account for the shocks.
9
Detailed time trend analyses and results are not presented in order to conserve space.
10
“Gross” directional volatility spillover plots are not presented and the associated results are not presented in order
to conserve space.
36