Working Paper
INVESTMENT DYNAMICS OF GREATER CHINA SECURITIZED REAL
ESTATE MARKETS AND INTERNATIONAL LINKAGES
Kim Hiang LIOW*
Department of Real Estate
National University of Singapore
4 Architecture Drive
Singapore 117566
Tel: (65)65161152
Fax: (65)67748684
Email : rstlkh@nus.edu.sg
and
Graeme NEWELL
School of Economics and Finance
University of West Sydney
Email: G.newell@uws.edu.au
January 29, 2010
* Corresponding author
Abstract
This paper focuses on evidence on securitized real estate markets as it investigates the volatility
spillovers and correlation dynamics among the three real estate securities markets: China, Hong Kong
and Taiwan in Greater China (GC) as well as their international linkages with the US securitized real
estate markets over 1995-2009. Overall, results indicate the conditional volatility linkages and
correlations among the GC securitized real estate markets have outweighed those relationships with
the US, implying closer real estate market integration among the GC markets. Diversification potential
across the markets is still good due to lower cross-market volatility interactions and correlation.
Finally, there is also evidence supporting a higher level of conditional correlation and volatility
spillover index in the post Asian aw well as the global financial crisis periods.
INVESTMENT DYNAMICS OF GREATER CHINA SECURITIZED REAL
ESTATE MARKETS AND INTERNATIONAL LINKAGES
Abstract
This paper focuses on evidence on securitized real estate markets as it investigates the
volatility spillovers and correlation dynamics among the three real estate securities markets: China,
Hong Kong and Taiwan in Greater China (GC) as well as their international linkages with the US
securitized real estate markets over 1995-2009. Overall, results indicate the conditional volatility
linkages and correlations among the GC securitized real estate markets have outweighed those
relationships with the US, implying closer real estate market integration among the GC markets.
Diversification potential across the markets is still good due to lower cross-market volatility
interactions and correlation. Finally, there is also evidence supporting a higher level of conditional
correlation and volatility spillover index in the post Asian aw well as the global financial crisis periods.
1.
Introduction
This paper analyzes the investment dynamics of Greater China (GC) securitized real estate
markets (Mainland China, Hong Kong and Taiwan) and examines their volatility linkages and timevarying correlations with those of the United States (US), the world‘s largest and most transparent
securitized real estate market, over the last fifteen years (1995-2009). As economic growth continues
in the Mainland China, it is expected that the GC region is growing into an important player in the
global financial markets (Johansson and Ljungwall, 2009); with their real estate markets attracting the
interest of domestic and international investors. The longest study period adopted in this study is
particularly meaningful as it covers the post Asian financial crisis (AFC) and post global financial
crisis (GFC) periods and thus permit a comparative assessment of the market dynamics during the two
different crisis periods. We first study the time-series properties of real estate securities returns and
time-varying volatility using a univariate Threshold Autoregressive GARCH (TGARCH) specification.
The short-term linkage is then considered in the context of conditional return and volatility spillovers
across the four real estate markets and with the US as well as their time-varying co-movement via a
BEKK time-varying covariance model of Engle and Kroner (1995). Following literature, we interpret
more volatility spillovers and increased time-varying correlations to be indicative of closer
relationship among the four securitized real estate markets. Examining cross-market dynamics could
be important from a portfolio diversification perspective; for if the GC securitized real estate markets
were linked together, the gains from diversification across these markets would be reduced. Similarly,
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the US investors will be more interested in the region if there is little or insignificant cross-volatility
spillovers or correlations between the US and the real estate markets within the GC region.
At the broader level, our research contributes to the ongoing scholarly work in globalization
and financial market integration (Bardhan et al. 2008). Our specific contributions are in enhancing
international investors with additional knowledge regarding the volatility spillovers and time-varying
correlations of the three GC securitized real estate markets and their cross-market relationships with
those of the US. Whilst Groenewold et al. (2004) and Cheng and Glascock (2005) assessed the
dynamic interrelationships between the GC stock markets, fuller studies involving the dynamics of the
GC real estate securities markets and their international linkages have not been carried out. As such,
the dynamics of the GC real estate securities markets in an international context is the focus of this
paper. The main novelty in the current paper is the application of advanced time series technology, i.e.:
multivariate volatility and correlation analysis, volatility spillovers index and correlation time trend
analysis to the four securitized real estate markets. We also analyze the effects of the GFC and AFC
on our results regarding their impact on the cross-market relationships. Specifically, we evaluate and
compare the nature and magnitude of the volatility and correlation linkages as well as of the dynamic
responses due to the two crisis events. We find the conditional volatility linkages and correlations
among the GC securitized real estate markets have outweighed those relationships with the US,
implying closer real estate market integration among the GC markets. Diversification potential across
the markets is still good due to lower cross-market volatility interactions and co-movements. Finally,
there is also evidence supporting a higher level of conditional correlation and volatility spillover index
in the post Asian and global financial crisis periods.
The structure of the paper is as follows. Section two highlights the significance of the
securitized real estate markets within the GC region. Section 3 provides a concise review of the
relevant stock and real estate literature. Sections 4 and 5 describe the data and econometric
methodologies used in the present study. Section 6 presents the empirical results with the final section
concludes the study.
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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 in recent years
(Chin et al 2006; JLL, 2008). This sees 2010 GDP forecasts for China (10.1%), Hong Kong (4.0%)
and Taiwan (4.3%) ahead of the global growth forecast (2.6%) (JLL, 2009). These factors have seen
GC take on more significance amongst global real estate investors during the GFC, 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.
In particular, 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 stage, with REITs in Hong and Taiwan only established in
2005 and only accounting for 1.9% of REITs globally (Macquarie Securities, 2009) and REITS are yet
to be established in China.
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 at September
2009 (see Exhibit 1). In particular, a strong recovery from the global financial crisis is clearly evident;
significantly ahead of the mature US, UK and global markets.
(Exhibit 1 here)
3.
Selective Literature Review
The extent of price and volatility spillovers and correlation between international stock
markets has received much attention in the literature (e.g.: Hamos et al. 1990; Panayiotis et al. 1997).
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Similarly, much work has been conducted on the stock market integration in the Asian region (e.g.:
Chan, Gup and Pan, 1992; Chowdhury, 1994; Daratt and Zhong, 2002; Johnson and Soenen, 2002).
In particular, several studies have focused on the GC stock markets. Groenewold, Tang and
Wu (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.
Cheng and Glascock (2005) detect weak nonlinear relationships between the three stock
markets; however, these markets are not co-integrated with either US or Japan. Their results from the
innovation accounting analysis reveal 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.
Qiao, Chiang and Wong (2008) analyze the long-run equilibrium, short-term adjustment and
spillover effects across Chinese segmented stock markets (A-share and B-share) and Hong Kong stock
markets using a bivariate FIVECM-BEKK GARCH model. According to the authors, the FIVECM
(fractionally integrated vector error correction model) has two main appealing features. First it helps
investors observe long-run equilibrium relationships among co-integrated variables as well as shortterm adjustment. Second, it accounts for the possible long memory in the co-integration residual series.
Moreover, combining the FIVECM with a bivariate BEKK-GARCH formulation allows the analysis
of the first and second moment spillover effects across the stock markets and the time-varying
correlations between the markets simultaneously. Two main findings from their analyses are: (a) the
A-share markets are most influential in 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 markets.
4
In a different context, Fan, Lu and Wong (2009) investigate the dynamic linkages between the
China and the US, the UK, Japan and Hong Kong using a Markov-Switching Vector Error Correction
Model (MS-VECM) which considers the three regimes of depression, boom and speculation in the
market. In the short term, they find that the China stock market has been influenced by the four stock
international stock markets and the impacts vary under different regimes.
Finally, Tan and Zhang (2009) investigate the spillovers from the US to mainland China and
Hong Kong stock markets during the subprime crisis. With the univariate and multivariate GARCH
models, they find that China 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 HK’s equity
returns than China’s returns. The conditional correlation between China and HK is stronger than their
conditional correlations with the US, indicating increasing financial integration between China and
Hong Kong.
In the real estate literature, adequate attention has been given regarding the diversification
benefits from the US securitized real estate markets (e.g. Ling and Naranjo, 1999; Worzala and
Sirmans, 2003; Cotter and Stevenson, 2006; Michayluk et al. 2006). In contrast, empirical research
regarding the dynamics of the China commercial real estate markets is still limited. Newell et al (2005)
and Newell et al (2009) demonstrate the risk-adjusted performance, added value and portfolio
diversification benefits of China 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 China real estate securities in a pan-Asia real estate securities portfolio (e.g.: Liow
and Adair,2009; Liow and Sim,2006).Similarly, studies regarding listed real estate securities in
Taiwan have focused on their role in a pan-Asia portfolio (e.g. :Liow and Adair,2009; 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.: Chau et al 2001; He and Webb, 2000; Newell and Chau, 1996; Newell et
5
al 2004; Schwann and Chau, 2003; Tse and Webb, 2000), Hong Kong property company performance
(e.g.: Chau et al 2003; Newell et al 2007) and the role of Hong Kong property companies in a panAsia or global property company portfolio (e.g.: Liow, 2007, 2008; Liow and Alastair, 2009; Liow
and Sim, 2006; Wilson et al 2006); as well as the dynamics of the Hong Kong direct property market
(e.g.: Brown and Chau, 1997; Chau, 1997; Ganesan and Chiang, 1998; Newell et al 1996).
4.
Data
Data used in this paper are weekly real total return indexes for China, Taiwan and Hong Kong
and the US securitized real estate markets over the period January 6, 1995 to December 31, 2009,
making a total of 783 observations, the longest data period for which all four real estate indices are
available in an international study. Weekly data are preferred because they provide more accurate
estimates of the mean and volatility spillovers (Panayiotis et al, 1997) and avoid the problems caused
by non-synchronous trading associated with the use of daily data. The data are from S&P/Citygroup
Global Property database.1 Weekly real estate index returns are calculated as natural logarithm of the
total return index relative, measured in terms of the local dollars as investors in the four markets are
assumed to have different hedging abilities. Over this full period, two important events (AFC and
GFC) took place respectively in July 1997 and June 2007; this allows us to capture and assess possible
changes in the cross-market linkages in the respective post crisis periods. The AFC has reduced real
estate returns and increased real estate volatility and correlation with other asset classes (Kallberg et al,
2002).The GFC began in the US with the bursting of sub-prime mortgage market. This rapidly
propagated across different asset classes and financial markets. By end October 2008, stock prices in
China and Hong Kong had experienced the largest decline of 71 percent and 56 percent, respectively,
since their peak in October 2007 (compared with about 30 percent for the major economies) (Tao and
Zhang,2009). Consequently, the full study period (783 weeks) is divided into four shorter sample
periods and we focus on the AFC (Sub-period 2) and GFC (Sub-period 4).
(a)
Sub-period 1 (pre AFC): January 1995-June 1997 (130 weeks),
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(b)
Sub-period 2 (post AFC): July 1997-December 1999 (131 weeks)
(c)
Sub-period 3 (pre GFC): January 2000 to June 2007 (391 weeks)
(d)
Sub-period 4 (post GFC): July 2007 to December 2009 (131 weeks)
Exhibit 2 plots the four securitized real estate indices over the full study period. In addition,
Exhibit 3 presents the summary statistics for the weekly returns of the markets. Over the full study
period, the average weekly returns for China, Taiwan and Hong Kong and the US are, respectively,
0.192%, 0.154%, -0.069% and 0.176%; and the standard deviations are about 5.764%(China),
4.678%(Hong Kong), 5.007% (Taiwan) and 3.32% (US) respectively. Except for the US real estate
securities returns which over-performed the US stock market, all other average returns and risk
performances were inferior compared with the respective local stock markets Skewness and excess
kurtosis indicate that positive shocks are more common for China and negative shocks are more
common for Hong Kong, Taiwan and the USA. In general, the distribution properties of all four real
estate return series appear to be non-normal. There is sufficient evidence of linear and non-linear
dependence as revealed by the Portmanteau tests for serial correlation for the return and squared return
(proxy for volatility) series. Hence there is presence of conditional heteroskedasticity in the returns
and volatilities of all four real estate indices in the sample. These descriptive statistics are thus in favor
of a model that incorporates GARCH features. The numbers in Exhibit 4 indicate while all four real
estate markets derive a positive average return each for the 1st sub-period (0.679%-China; 0.546%-HK;
0.410%-TW and 0.365%-US), the returns were negative for them in the 2nd sub-period, due to the
adverse effect of the AFC. This period was also associated with higher investment risk for all the
markets. Finally, as expected, the 4th sub-period is characterized by lower returns and much higher
standard deviation in all four markets, reflecting the serious effect on the global financial market
consequent to the sub-prime turmoil that began in the summer of 2007 in the USA.
(Exhibits 2, 3 and 4 here)
Exhibit 5 contains the results of the unconditional correlations between the returns and
squared returns (a common proxy for risk) of the four securitized real estate markets. Over the full
study period, the return correlations among the GC markets (between 0.2938 and 0.3777) are higher
7
than those between the GC markets and the USA (between 0.1152 and 0.2584) indicating closer
relationships within the GC region. Nevertheless, these low correlation figures illustrate that
opportunities for diversification in the GC securitized real estate markets are many for both domestic
and international investors, at least before July 2007. In accordance with the literature, the 2nd subperiod (AFC period) and the 4th sub-period (GFC period) experienced significant increases in many
correlation series, with average correlation reach as high as 0.6526 for the CH-HK pair in the post
AFC period. This indicates that the crisis might have exerted a statistically significant and positive
impact on the unconditional correlation with a large structural break in cross-market relationship. We
will explore this issue again with the conditional correlation series generated from the BEKK model.
Finally, results for the squared return correlation are similar and weaker, with the strongest correlation
happened for the six market pairs in the post GFC period.
(Exhibit 5 here)
5.
Methodology
This study uses four different methodologies. First, a univariate TGARCH-in mean model is
investigated for the time-series behavior of real estate securities return and volatility. Second, we
model simultaneously the volatility spillovers and time-varying correlations among the four real estate
markets using a asymmetric VAR-BEKK-MGARCH-in mean model. Lastly, we measure volatility
spillovers and time trend in correlations using, respectively, a volatility spillover index and two time
trend tests. The empirical methodologies are briefly explained below.
(a)
Univariate AR (1)-TGARCH (1, 1)-in mean model
Following Chiang and Doong (2001), we estimate an AR (1)-TGARCH (1,1)-in mean model
to investigate the relationship between real estate securities returns and conditional volatility as well
as whether there is any asymmetric effect on each real estate market’s conditional variance. In the
conditional mean equation presented below, the real estate securities market return, R jt , is linearly
8
1/ 2
related to its first order autoregressive components and its conditional standard deviation h jt and the
innovation
 jt is conditional on the information set  t 1 with zero mean and variance h jt . Any
significant relationship between volatilities and real estate returns is captured by the estimated
coefficient  , with a significant and positive value implies that investors are compensated by higher
returns for bearing a higher level of risk. In the conditional variance equation, the conditional variance
is a linear function of four components: the previous variance ( ht 1 ), the square of the lagged shock
term (  t 1 ), the asymmetric indicator variable I t 1 that takes a value of 1 when the previous shock is
2
negative and zero otherwise, and the GFC dummy ( (D ) that takes a value of 1 for subperiod 4 and
zero otherwise. Note that the asymmetric effect is captured by the hypothesis   0 ; a positive
 implies that a negative innovation increases conditional volatility.
AR(1)  TGARCH (1, 1) -in mean model
R jt   0   1 R j ,t 1  h1j / 2   jt
 jt
 t 1 ~ N (0, h jt )
h jt   0   1 h j ,t 1  (  2  I t 1 ) 2j ,t 1
(b)
An asymmetric VAR (1)-BEKK-MGARCH (1, 1) – in mean model
Following stock market literature, we consider a multivariate asymmetric framework and use
the BEKK parameterization (Engle and Kroner, 1995) to model the conditional variance-covariance
matrix. The main advantage is that within this framework, it allows for a dynamic structure of
conditional correlation and empirical lead-lag relationship in the mean and volatility transmission in a
cross-market setting to be simultaneously estimated and captures potential asymmetries in the
volatility spillovers mechanism. In addition, the BEKK imposes restrictions on across and within
equations as well as guarantees a positive variance-covariance matrix. Hence it is preferred to
alternative multivariate models such as the VECH specification.2
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A BEKK model is specified by the following equations:
Ri ,t  0  1 Ri ,t 1   2 R j ,t 1   i ( H i ,t )   i ,t (1a)
R j ,t   0   1 R j ,t 1   2 Ri ,t 1   2 ( H j ,t )   j ,t (1b)
t
 t 1
 N (0, H t ) (2)
H t  C ' C  A'  t 1 t 1 A  B ' H t 1 B  G ' t 1 t 1G
(3)
Equations (1a) and (1b) show the first order moments expressed according to a bivariate
VAR-in mean model, where
 t  ( i ,t ,  j ,t ) is assumed to follow a normal bivariate distribution with
mean 0 and variance H t and  t 1 is the information set in t-1. Return linkages between a pair of
markets are ascertained by the VAR parameters. The conditional matrix of variance-covariance is
given by H t , and C, A, B, G are N x N parameters with C upper triangular (Equation 3). Volatility
spillovers effects are examined from the GARCH estimates ( a ij , bij ).The asymmetrical response of
the volatility to news of different signs is given by   Min(0,  t ) (Kroner and Ng, 1998). Hence
this specification allows us study volatility transmissions between the two markets as well as the signs
of the shocks, when the innovations are either positive or negative. Further examination using the
Akaike and Bayesian information criteria suggests a BEKK-GARCH (1, 1) specification is sufficient
for the conditional variance.
The parameters of the bivariate-BEKK system are estimated by maximizing the conditional
log-likelihood function of:
'
TN
1 T
L( )  
ln(2 )   (ln H t ( )   H t1 ( ) t )
2
2 t 1
t
10
Where T is the number of observations, N is the number of variables in the system and  is the vector
of all the parameters to be estimated. The estimation is carried out using the quasi maximumlikelihood estimation with the optimization algorithm of BFGS.
(c)
Volatility spillover index
We focus on variance decomposition which permits the aggregation of volatility spillovers
effect across the four securitized real estate markets and distills the information into a single volatility
spillover measure. Under the variance decomposition methodology, the decomposition of forecast
error variance determines explicitly how much of the conditional volatility movement in one market
can be explained by other markets in term of the percentage of the forecast variance of that market.
Following the procedures developed by Diebold and Yilmaz (2007), a volatility spillover index each is
constructed for the full period as well as for the AFC and GFC periods, thereby allowing the
measurement and comparison of the magnitude of volatility interactions among the four securitized
real estate markets
(d)
Time trend in time-varying correlation
From the BEKK estimation, we assess whether correlations display trending behavior by first
studying their autocorrelation structure and ADF unit root results. We then use Vogelsang’s (1998)
simple linear time trend test with the basic model specified as:
Correlationt     * TREND   i
We use the “t-PS” test in Vogelsang (1998) to test if
 =0. Additionally, we also conduct the
“t-dan” test developed by Bunzel and Vogelsang (2005) to check the consistency of the time-trend
results.3
6.
Results
11
First, the results of estimating the TAR-GARCH (1, 1)–in mean model of the four securitized
real estate markets are reported in Exhibit 6. We first consider the mean equation. No evidence of
positive serial correlation in the four weekly return series is detected. In addition, we are unable to
find any statistical significance by including a conditional standard deviation term in the weekly series.
With respect to the estimates of the variance equation, all the GARCH parameters are statistically
significant. Moreover, the estimated results show that the sum of estimated equations in the variance
equation is all more than 0.9; and is close to unity (0.9983) for China, implying the volatility shocks
may persist over a longer period of time. The hypothesis of no asymmetric effect is strongly rejected
for the US and Hong Kong weekly series at least at the five percent level of significance. The GFC
dummy estimates for all four markets are positive, and the estimated coefficient is statistically
significant for the US and Hong Kong markets, indicating that the AFC has created a significant
regime shift in volatilities. Finally, we examine the Ljung-Box (LB) statistics for diagnostic checking.
Results indicate the four estimated TGARCH models are generally well-specified with the removal of
serial dependence in the return and squared return series.
(Exhibit 6 here)
Second, Exhibit 7 reports the results of the multivariate return and volatility relationships
among the four securitized real estate markets using a asymmetric VAR-BEKK-MGARCH- M
model.4 The impact of a market’s own effects is represented by 11, 22, 33 and 44 for the market US,
China, Hong Kong and Taiwan. Cross-market effects are given by 21 and 12, 34 and 43…..etc. In the
conditional variance equation, the A coefficients represent ARCH effects, while the B coefficients are
GARCH effects, and the G coefficients indicate asymmetric effects.
The VAR examines the direction and magnitude of the return linkages. There are few
instances of significant cross-return transmission among the four markets; although the results are in
general less satisfactorily. Focusing on the volatility transmission, there is some strong evidence of
volatility spillovers between all markets considered. With respect to volatility innovations, the “own
“spillovers coefficients are significant in three cases. In addition, there are five significant crossmarket spillovers; between China and Taiwan (coefficient A24), Hong Kong and China (coefficient
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A32), Taiwan and US (coefficient A41), Taiwan and China (coefficient A42), Taiwan and Hong Kong
(coefficient A43), Hence we are able to verify that a bidirectional cross-market spillover transmission
only exists between the China and Taiwan markets. With regard to the effects of variance (GARCH),
all four “own” spillover coefficients are significant and of higher magnitude, results which are
reasonably expected. The schema of volatility transmission when the lagged variances are analyzed is
bidirectional between China and Taiwan (B24 and B42) and between Hong Kong and Taiwan (B34 and
B43), with unidirectional from the US to China (B12), US to Hong Kong (B13) and from China to Hong
Kong (B23). The results of the G asymmetries matrix indicate that the negative innovations of the
markets increase significantly the volatility in the same market in 3 cases (coefficients G11, G22 and
G33). In addition, the volatility transmission mechanism is asymmetric. Negative innovations in the
first market increase volatility in the second market considerably more than positive innovations do.
Nevertheless, this transmission is only unidirectional, from the US to Hong Kong (G13), US to Taiwan
(G14), China and Taiwan (G24) and Hong Kong and Taiwan (G34).
Based on this statistical evidence, significant and reciprocal lead/lag linkages emerge among
the three GC securitized real estate markets. There is also some weaker unidirectional volatility
interactions exist between the US and GC markets. The conditional volatility relationships among the
GC securitized real estate markets have outweighed their volatility linkages with the US, implying
closer real estate market integration among the GC markets.
(Exhibit 7 here)
To compare the impact of the AFC and GFC on the volatility spillover dynamics, we estimate
a volatility spillover index to measure the magnitude of volatility spillovers across the four securitized
real estate markets. The conditional volatility spillover tables for the full study period, the post AFC
and the post GFC periods are reported in Exhibit 8. The volatility spillover index is shown in the
lower right corner of each table. The spillover table provides an “input-output” decomposition of the
spillover index forecasted 12 weeks ahead. Over, the spillover indices are 0.467 (full period), 0.490
(post AFC period) and 0.635 (post GFC period). Therefore, the volatility spillover index registers its
highest in the post GFC period, a result which is consistent with expectation. Focusing on the post
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GFC period (Panel C), we find the US market is the strongest in term of volatility transmission to
other markets (173.55%). This is not surprising as, during this period, the subprime turmoil that began
in the summer of 2007 subsequently boiled over and set off dramatic changes not only in the US
financial markets, but also to the global markets. Consequently, none of the GC securitized real estate
markets is immune to the financial crisis, as evidenced by the volatility spillovers from the USA.
Finally, Hong Kong ranks 2nd second in transmitting volatility to other markets (40.6%) due to its
developed real estate market status.
(Exhibit 8 here)
The third section examines the implied time-varying correlations derived from the
multivariate asymmetric BEKK model. Descriptive statistics (mean, standard deviation, maximum and
minimum) for the weekly conditional correlations and volatilities series are provided in Exhibit 9.
Over the full study period, the conditional correlation among the three GC markets ranges between
0.1878 (CH/TW) and 0.3519 (CH/HK); while their conditional correlations with the US markets are
lower, between 0.0498 (US/TW) and 0.2079 (US/HK). The finding that the conditional correlations
between the GC markets have outweighed their conditional correlations with the Us market present
little surprise and support closer integration between the GC markets. Among the three GC markets,
US has the highest correlations with HK (0.2079) than with China (0.1238) and Taiwan (0.0498),
reflecting HK’s role as an international financial centre. Overall, the low correlation coefficients of the
GC markets with the US provide some diversification motivation to attract the US investors to invest
in the GC securitized real estate markets. Comparing with the pre-AFC period, the post AFC
experiences an increase (and in some cases, a significant increase) in correlation in all markets, with
the highest correlation reported for the CH/HK pair. During this period, the correlations are also less
stable reflecting turbulent investment climates. In accordance with the finance literature, this increase
in the mean and standard deviation of the market correlation indicates a “contagion” effect due to the
crisis. Focusing on the GFC, there appears to be a significant shift in the conditional correlation, as a
higher level of correlation for several market-pairs is observed in the post GFC period compared with
the earlier sample period. Comparing with the pre-GFC period, the correlations among the three GC
14
markets report an increase of about 49% (CH/HK), 88% (CH./HK) and 165%(HK/TW) respectively;
the increase in the correlation between the US and GC markets is 3% (US/HK), 100%(US/CH) and
131% (US/TW) respectively, highlighting that the securitized real estate markets are vulnerable to
external shocks with a large and positive shift in correlation. Together with significant increases in the
conditional volatilities (between 41% and 167%) of the four markets, any possible diversification
gains will quickly disappear in this volatile market environment.
(Exhibit 9 here)
To formally test the impact of the AFC and GFC events on dynamic correlations, we regress
the time-varying correlation (from the BEKK estimation) with its one-period lag and to crisis dummy
variables, dummy one for Jul97to Dec 99 (131 weeks) for the AFC and for Jul07 to Dec09 (131 weeks)
for the GFC, and zero otherwise. Due to the collinearity problem, the two dummies are included one
at a time, and the results are reported in Exhibit 10. It is clear with the exception for the US/HK series,
the GFC has exerted a statistically highly significant and positive impact on other five correlation
series implying there are significant structural break happened in the dynamic correlation. Thus the
five correlation series are on average significantly higher than the normal level. Results for the AFC
are weaker with only three cases of significant regime shift in correlation detected within the GC
markets. These results are in general consistent with the understanding that the AFC event was mainly
regional and did not impact significantly on the dynamic correlations between the US and GC
securitized real estate markets. Overall, these dummy regression results are in agreement with the
average correlation results reported in Exhibit 9.
(Exhibit 10 here)
Exhibit 11 plots six graphs regarding the evolution of the conditional correlations among the
three GC markets and with the US markets. Three observable trends are noted. First, the conditional
correlations deviate substantially from the point estimates of the unconditional correlations and in
most cases the unconditional correlations are different from the conditional correlations as estimated
by the time-varying covariance BEKK model. Second, there is generally a similar pattern of
correlation observed around 1997-98 with the occurrence of the AFC, where the correlations tend to
15
be significantly higher for China, Hong Kong and Taiwan; however, the AFC has exerted much lower
impact on the correlations between the US and GC markets. A similar pattern of correlation is
observed around 2007-2008 with the occurrence of the GFC and is accompanied by significantly
higher level of correlations, and in some cases, the correlations tends to peak. This is consistent with
the finance literature that documents international correlation increases when global factors dominate
domestic ones and affect all asset markets. Finally, other than periods of AFC and GFC, the pattern of
correlation seems to diverge among the economies due to some market-specific movements.
(Exhibit 11 here)
Finally, results for detecting a deterministic linear time trend for all six correlation series
appears in Exhibit 12. 5 Based on the t-dan tests, we detect one case of a significant time trend
coefficient (0.00014, significant at the 5% level) over the full study period. Specifically, the US/China
correlation, which is stationary, has increased significantly by a total of about 10.96 percent over the
full study period, implying increasing integration between the two securitized real estate markets. We
do not detect either significant upward or downward time trends for other correlation series. These
findings imply the process of globalization over the past 15 years has not resulted in any significant
increase in the co-movements over time among the GC real estate markets and with the US market.
Accordingly, these real estate markets have not become more integrated among themselves, implying
diversification potential across the markets is still good. Of course, it is possible that significant
deterministic trends are not detected because we have disregarded a structural break in the correlations
reported in Exhibit 10. Additional estimations indicate while there is no significant time trend
coefficients detected in the post AFC period, there are two cases of a significant coefficient estimates
for the CH/TW and HK/TW correlation series in the post GFC period, translating to an increase of
18.08 percent (CH/TW) and 30.52 percent (HK/TW) in dynamic correlation respectively.
(Exhibit 12 here)
7.
Conclusion
16
Although the dynamics of volatility spillovers and time-varying correlation in international
securitized real estate markets have received greater attention in the recent years, fuller studies
involving the dynamics of the GC securitized real estate markets and their international linkages have
not been carried out. These three markets have drawn great attention from investors all over the world
and it is likely they will form an even a stronger merged market in the future. In the full sample period
from beginning July 1995 and covers a 15-year history of GC securitized real estate markets up to end
December 2009, the main objective of this study was to provide a comprehensive study of their
investment dynamics and how the covariance and correlation structure have developed among them
and with the USA in the context of globalization and two major crisis events: AFC and GFC. We
estimate an asymmetric time-varying covariance multivariate BEKK model that takes into account the
asymmetric volatility phenomenon to simultaneously capture the lead-lag volatility spillovers and
time-varying correlation among the four markets. Using a volatility spillover index, we measure and
compare the magnitude of volatility spillovers across the four securitized real estate markets during
the post-AFC and post GFC periods. We also investigate whether the real estate securities markets
have become more closely linked by assessing whether the respective correlation series display
trending behavior. This in-depth analysis and understanding regarding the nature and degree of
volatility transmission and correlation relationship as well as their potential changes triggered by two
major crises is important for international investors in their portfolio management and risk
diversification effort that include real estate investing in the Greater China region. Similarly, policy
makers may find the time-varying market interdependence results useful in their crisis management
using macroeconomic tools.
Subject to the usual empirical limitations, this study produces several valuable and significant
findings, notably: (a) significant and reciprocal lead/lag linkages emerge among the three GC
securitized real estate markets exist, together with some weaker unidirectional volatility interactions
exist between the US and GC markets; (b) During the GFC period, the US market is the strongest in
term of volatility transmission to other markets and is followed by Hong Kong. None of the GC
securitized real estate markets is immune to the financial crisis, (c) A higher level of correlation is
17
observed in the post-GFC and post AFC periods, with a large regime shift in some correlation pairs; (d)
The conditional volatility interactions and dynamic correlations among the GC securitized real estate
markets have outweighed their equivalent relationships with the US, implying closer real estate
market integration among China, Hong Kong and Taiwan; and (e) Globalization and financial
integration over the past 15 years has not resulted in any significant increase in the co-movements
over time among the GC securitized real estate markets and between them and the US markets,
implying diversification potential across the markets is still good.
Some of the above findings motivate further research. For instance, it would be useful to
modify the BEKK specification (although a difficult task) to focus on the fundamental determinants of
volatility spillovers and time-varying covariance in the context of macro-economy. Little or no direct
evidence is available on the cross-market volatility and correlation dynamics between securitized real
estate markets and business cycles. Expanding this body of evidence in the GC securitized real estate
markets is a fruitful area for future research.
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21
Exhibit 1
Performance of Greater China real estate securities markets: Sept 2009
Average annual returns
3Y
5Y
1Y
China
10Y
101.1%
20.7%
35.0%
25.0%
Hong Kong
41.5%
9.8%
13.6%
9.9%
Taiwan
76.2%
9.0%
11.3%
1.1%
US
-28.3%
-13.1%
1.0%
9.6%
UK
-35.8%
-28.0%
-7.0%
3.1%
Global
-4.8%
-8.8%
4.8%
9.4%
Global stocks
1.5%
-2.4%
5.6%
3.3%
Source: S&P (2009)
Exhibit 2
Real estate securities total return index movement
800
700
600
500
400
300
200
100
0
95
96
97
98
99
00
01
China
Taiwan
02
03
04
05
06
07
08
09
Hong Kong
US
Source: S& P Property
22
Exhibit 3
Sample statistics of weekly securitized real estate returns: Jan95-Dec09
Statistic
Mean (%)
Std deviation (%)
Maximum (%)
Minimum (%)
CV (sd/mean)
Skewness
Ex Kurtosis
Jarque-Bara
Arch(10)
LB(10)
LB(50)
LB2(10)
LB2(50)
China
0.192
5.764
28.775
-24.731
30.021
0.0369
2.213***
159.97***
5.477***
18.367**
57.669
65.029***
207.720***
Hong Kong
0.154
4.678
19.855
-27.107
30.377
-0.3536***
3.8828***
508.19***
4.567***
10.083
59.911
59.061***
241.881***
Taiwan
-0.069
5.007
17.101
-24.206
-72.565
-0.1865**
2.1593***
156.66***
4.737***
20.729**
45.936
64.874***
112.509***
US
0.176
3.325
21.527
-21.056
18.892
-0.4234***
11.970***
4698.1***
53.992***
17.705*
123.005***
957.545***
1837.05***
Notes: The respective stock markets returns are: China (0.206%), Hong Kong (0.167%), Taiwan (0.027%),
US (0.154%); the standard deviations of the stock market returns are: China (4.699%), Hong Kong
(3.544%), Taiwan (3.688%) and US (2.605%). ***, **, * - indicates significant at the 1%, 5% and 10%
levels.
Exhibit 4
Statistic
Risk and return of weekly securitized real estate returns in different periods
China
Hong Kong
Taiwan
Panel A: Pre-Asian financial crisis (Jan95-June97: 130 weekly returns)
Average return (%)
0.679
0.546
0.41
Std deviation (%)
5.246
3.281
3.782
Panel B: Post Asian financial crisis (July 97 - Dec 99: 131 weekly returns)
Average return (%)
-1.071
-0.216
-1.139
Std deviation (%)
6.597
6.528
5.014
Panel C: Pre Global financial crisis (Jan00-Jun07: 391 weekly returns)
Average return (%)
0.586
0.155
0.078
Std deviation (%)
4.848
3.665
5.009
Panel A: Post global financial crisis (July 07 - Dec 09: 131 weekly returns)
Average return (%)
-0.200
0.137
0.087
Std deviation (%)
7.486
6.146
5.893
US
0.365
1.026
-0.086
2.018
0.352
2.069
-0.275
6.939
23
Exhibit 5
Unconditional correlations in returns and volatilities (squared returns)
among the securitized real estate markets
Full period
Jan95-Dec09
783 weeks
CH and HK
CH and TW
HK and TW
US and CH
US and HK
US and TW
0.3777***
0.2145***
0.2938***
0.1687***
0.2584***
0.1152***
CH and HK
CH and TW
HK and TW
US and CH
US and HK
US and TW
0.3087***
0.1131**
0.1128**
0.0604*
0.1866**
0.0651*
Subperiod 1
Subperiod 2
Jan95-Jun97
Jul97-Dec99
130 weeks
131 weeks
Panel A: return
0.1462*
0.3599***
0.0414
0.2429***
0.0083
0.2804***
0.0128
0.0591
0.3163***
0.1585***
0.0976
-0.0151
Panel B: squared returns
0.0199
0.3380***
-0.0789
0.0222
0.0088
-0.2001
0.0863
0.0849
0.2629***
0.1017
0.0166
0.0194
Subperiod 3
Jan00-Jun07
391 weeks
Subperiod 4
Jul07-Dec09
131 weeks
0.2358***
0.1066**
0.2097***
0.1690***
0.2291***
0.0478
0.6526***
0.4292***
0.5520***
0.2445***
0.3667***
0.2310***
0.0784
0.0344
0.1260**
0.0311
0.1178**
-0.0378
0.5535***
0.3520***
0.3729***
-0.0105
0.2976***
0.0817
Notes: China (CH), Hong Kong (HK), Taiwan (TW) and the US. ***, **, * - indicates statistical
significance at the 1%, 5% and 10% level
Exhibit 6
Estimates of univariate AR (1)-TARCH (1,1)-M model of weekly securitized
real estate returns with the impact of Global financial crisis (GFC): January
1995 – December 2009
Coefficient
China
Constant
AR(1)
In-mean
0.0027
0.0179
-0.4363
Constant
ARCH(1)
GARCH(1)
Leverage effect
Dummy (GFC)
Log-likehood
0.000033
0.0926***
0.9057***
-0.0035
0.000034
1207.41
Hong Kong
Taiwan
Conditional mean euqation
0.0038*
-0.0019
-0.0023
0.0318
-0.7788
-0.0738
Condtional variance equation
0.000023*
0.00025**
0.0159
0.2043***
0.9318***
0.7361***
0.0694**
-0.0682
0.00011**
0.00027
1385.65
1300.34
US
0.0028
0.0307
-0.1631
0.000008***
0.0398**
0.9086***
0.0667***
0.000076**
1887.37
Notes: (1) The Ljung Box (LB) statistics are computed for returns and squared returns to conduct
diagnostic checking. Results indicate that the four univariate T-GARCH-M models are generally well
specified with the removal of serial dependence in the square and square returns; (2)another four TGARCH-M models are estimated with Dummy (AFC-Asian financial crisis). Except for the HK market, the
other three markets’ AFC dummy estimates are statistically insignificant;(3) ***, **, * - indicate statistical
significance at the 1%, 5% and 10% level
24
Exhibit 7
Variable
Results of the Asymmetric VAR-BEKK-MGARCH-M model: January 1995
– December 2009 (full period)
US (i =1)
China (i=2)
HK (i=3)
Taiwan (i=4)
 i ,1
0.0111
Panel A: Conditional Mean
0.1183**
0.0844*
0.0722
 i,2
-0.0125
-0.0057
-0.0462*
-0.0563**
 i ,3
-0.0046
0.0194
0.0014
0.0794**
 i,4
0.0233*
-0.0178
0.0249
0.0210
i
-1.8587**
-4.3411**
-1.6996*
-3.7781
Ai ,1
0.2905***
Panel B: Conditional Variance
-0.0065
-0.0218
0.0383**
Ai , 2
0.0190
-0.0546
0.2579***
-0.1079***
Ai ,3
-0.0262
-0.0015
0.1616***
0.0650**
Ai , 4
-0.0023
-0.1437***
0.0404
0.1802***
Bi ,1
0.8950 ***
0.0146
-0.0187
0.0256
Bi , 2
0.1717**
-0.3531***
-0.0303
1.0943***
Bi ,3
-0.0667***
0.2076***
0.6853***
0.2923***
Bi , 4
0.0450
-0.7418***
-0.0801***
0.9506***
Gi ,1
0.4780***
-0.0252
-0.0932
-0.0192
Gi , 2
-0.0889
0.2092***
0.0353
0.0573
Gi , 3
0.0063***
0.1059
0.2874*
-0.0068
Gi , 4
0.1307***
0.0916*
0.1404*
-0.0185
Log likelihood
Skewness
Excess Kurtosis
ARCH (10)
LB(10)
LB(50)
LB2(10)
LB2(50)
5818.12
Panel C: Some Residual diagnostic
-0.047
0.264***
-0.0381
0.995***
1.096***
1.807***
0.803
1.632
0.426
1.931
13.751
9.850
45.43
41.826
55.200
8.166
16.393
4.313
45.60
101.982***
60.334
0.073
1.996***
1.759
11.206
38.375
11.028
71.218***
Note: The impact of a market’s own effects is represented by 11, 22, 33 and 44 for the market US, China,
Hong Kong and Taiwan respectively. Cross-market effects are given by 21 and 12, 34 and 43…..etc. In the
conditional variance equation, the A coefficients represent ARCH effects, while the B coefficients are
GARCH effects, and the G coefficients indicate asymmetric effects The respective coefficients are estimated
with heteroskedasticity/misspecification adjusted standard errors using WINRATS 7.0, with convergence of
the Asymmetric VAR-BEKK-MGARCH-M model reached at 235 iterations. The Ljung-Box level (LB),
statistics are for 10 and 50 lags for the residuals and the squared residuals. ***, **, * - indicate
significance at the 1%, 5% and 10% level
25
Exhibit 8
TO
TO
TO
Volatility spillovers index
Panel A: Full study period (Jan95 - Dec09)
FROM
0
Hong Kong
China
69.93
6.73
22.4
56.97
39.3
2.12
58.22
16.82
23.67
16.29
3
0.4
131.48
26.55
24.92
201.41
65.85
48.59
Taiwan
0.94
1.62
1.3
80.31
3.86
84.17
Contribution from others
30.07
60.71
76.34
19.69
186.81
spillovers index =0.4670
US
Hong Kong
China
Taiwan
Contribution to others
Contribution including own
Panel B: Post Asian Financial crisis period (Jul97 -Dec99)
FROM
US
Hong Kong
China
69.41
12.76
14.71
58.58
39.7
1.07
61.84
23.67
13.06
10.1
2.99
4.97
130.52
39.42
20.75
199.93
79.12
33.81
Taiwan
3.11
0.65
1.42
81.94
5.18
87.12
Contribution from others
30.58
60.3
86.93
18.06
195.87
spillovers index = 0.4897
US
Hong Kong
China
Taiwan
Contribution to others
Contribution including own
Panel C: Post Global financial crisis period (Jul07-Dec 09)
FROM
US
Hong Kong
China
72.49
5.94
13.23
63.63
30.73
0.9
53.1
16.11
21.52
56.82
18.55
3.32
173.55
40.6
17.45
246.04
71.33
38.97
Taiwan
8.32
4.74
9.26
21.31
22.32
43.63
Contribution from others
27.49
69.27
78.47
78.69
253.92
spillovers index = 0.6348
US
Hong Kong
China
Taiwan
Contribution to others
Contribution including own
Notes: The ijth entry in the table is the estimated contribution to the forecast error variance of market i, coming from innovations from country j. Therefore the
“contribution to others” (off-diagonal column sums) or “contribution from others” (off-diagonal row sums), when total across markets, give the numerator of
the spillover index. The denominator of the spillover index is given by the column sums or row sums (including diagonals) and totaled across markets.
26
Exhibit 9
Summary statistics of weekly conditional correlation and volatility estimates
CH and HK
Mean
Std deviation
Maximum
Minimum
CH and TW Mean
Std deviation
Maximum
Minimum
HK and TW Mean
Std deviation
Maximum
Minimum
US and CH Mean
Std deviation
Maximum
Minimum
US and HK Mean
Std deviation
Maximum
Minimum
US and TW Mean
Std deviation
Maximum
Minimum
China
Hong Kong
Taiwan
US
Mean
Std deviation
Mean
Std deviation
Mean
Std deviation
Mean
Std deviation
Full period
Subperiod1
Subperiod 2 Subperiod3
Subperiod 4
Jan95-Dec09 Jan95-Jun97 Jul97-Dec99 Jan00-Jun07 Jul07-Dec09
783 returns
130 returns
131 returns
391 returns
131 returns
Panel A: Weekly conditional correlations
0.3519
0.2690
0.4162
0.3177
0.4720
0.1394
0.1067
0.1432
0.1141
0.1300
0.7362
0.4785
0.7362
0.6236
0.7322
-0.0917
-0.0849
0.0190
-0.0917
0.0805
0.1878
0.1186
0.2484
0.1555
0.2924
0.1431
0.1161
0.1634
0.1162
0.1411
0.6582
0.4248
0.5845
0.5855
0.6582
-0.2028
-0.2028
-0.1840
-0.1996
-0.1452
0.2124
0.0307
0.3451
0.1587
0.4201
0.2034
0.1577
0.1918
0.1430
0.1513
0.6923
0.3832
0.6923
0.5689
0.6900
-0.3702
-0.3702
-0.1443
-0.2694
0.0511
0.1238
0.0753
0.1268
0.1080
0.2161
0.1509
0.1383
0.1377
0.1225
0.2060
0.6528
0.3366
0.4796
0.5082
0.6528
-0.3115
-0.2820
-0.2424
-0.3115
-0.3000
0.2079
0.2153
0.2087
0.2041
0.2109
0.1648
0.1683
0.1678
0.1358
0.2272
0.6863
0.6495
0.6234
0.5848
0.6863
-0.2157
-0.1758
-0.1508
-0.1507
-0.2157
0.0498
0.0137
0.0716
0.0402
0.0928
0.1488
0.1453
0.1548
0.1248
0.1942
0.6024
0.3363
0.6024
0.3540
0.5192
-0.3967
-0.3706
-0.2180
-0.3865
-0.3967
Panel B: Weekly conditional volatilities
0.0033
0.0025
0.0045
0.0029
0.0041
0.0014
0.0005
0.0013
0.0011
0.0015
0.0023
0.0010
0.0041
0.0015
0.0040
0.0020
0.0004
0.0027
0.0008
0.0022
0.0028
0.0019
0.0037
0.0022
0.0033
0.0012
0.0003
0.0012
0.0009
0.0012
0.0011
0.0002
0.0005
0.0005
0.0045
0.0025
0.0002
0.0005
0.0005
0.0050
Source: Derived from the Asymmetric VAR-BEKK-MGARCH-M model
27
Exhibit 10
Impact of AFC and GFC on the conditional correlations
We examine the impact of the Asian financial crisis (AFC) and Global financial crisis (GFC) on the
dynamic correlation by regressing the time-varying correlation (from the BEKK estimation) with its oneperiod lag and dummy variables, dummy one for Jul97to Dec 99 (131 weeks) for the AFC and for Jul07 to
Dec09 (131 weeks) for the GFC, and zero otherwise. Due to the collinearity problem, the two dummies are
included one at a time, and the results are reported in this Exhibit
Correlation
China-Hong Kong
AFC
(July1997-Dec1999)
0.0588**
(0.0188)
(0.0208)
China-Taiwan
0.0744***
0.1383***
(0.0251)
(0.0263)
Hong Kong-Taiwan
0.0431***
0.0817***
(0.0138)
(0.0157)
0.0029
0.0961***
US-China
GFC
(July 2007-Dec2009)
0.1219***
(0.0163)
(0.0271)
US-Hong Kong
0.00036
-0.0021
(0.0081)
(0.0097)
US=Taiwan
0.0182
0.0374**
(0.0194)
(0.0169)
28
Exhibit 11
Conditional correlations and volatilities graphs
Panel A:
Among the Greater China securitized real estate markets
Correlation: Hong Kong and China
.8
.4
.0
-.4
1996
1998
2000
2002
2004
2006
2008
Correlation: China and Taiwan
.8
.4
.0
-.4
1996
1998
2000
2002
2004
2006
2008
Correlation: Hong Kong and Taiwan
.8
.4
.0
-.4
1996
1998
2000
2002
2004
2006
2008
29
Panel B: Between the US and Greater China securitized real estate markets
Correlation: US and China
.8
.4
.0
-.4
1996
1998
2000
2002
2004
2006
2008
Correlation: US and Hong Kong
.8
.4
.0
-.4
1996
1998
2000
2002
2004
2006
2008
Correlation: US and Taiwan
.8
.4
.0
-.4
-.8
1996
1998
2000
2002
2004
2006
2008
30
Panel C:
Conditional volatility
China
.010
.005
.000
95
96
97
98
99
00
01
02
03
04
05
06
07
08
09
04
05
06
07
08
09
03
04
05
06
07
08
09
03
04
05
06
07
08
09
Hong Kong
.02
.01
.00
95
96
97
98
99
00
01
02
03
Taiwan
.010
.005
.000
95
96
97
98
99
00
01
02
US
.03
.02
.01
.00
95
96
97
98
99
00
01
02
Source: Derived from the Asymmetric- VAR (1, 1)-Mean-BEKK-MGARCH (1,1) model
31
Exhibit 12
Deterministic time trend in correlation, allowing for serial correlation
This table reports the estimated deterministic linear trends in the time series of correlations:
( Correlationt     * TREND   i ) among the securitized real estate markets of China (CH),
Hong Kong (HK), Taiwan (TW) and the US for the full sample period, post Asian financial crisis (subperiod 2) and post global financial crisis (sub-period 4). For each correlation series, we report the t-ps test
from Vogelsang (1998) and the t-dan test from Brunzel and Vogelsang (2005). The 5% critical value (twosided) for t-tps is 2.152, and for t-dan, is 2.052. % increase/decrease in the time trend is estimated where
either the t-ps or t-dan coefficient is significant (% increase/decrease = coefficient x no of weeks over the
period). * - denotes significance at the 5% level
Correlation
CH - HK
CH - TW
HK - TW
US - CH
US - HK
US - TW
CH - HK
CH - TW
HK - TW
US - CH
US - HK
US - TW
CH - HK
CH - TW
HK - TW
US - CH
US - HK
US - TW
t-dan test
coefficient
t-stat
Full period (Jan 95 - Dec 09)
0.00002
0.128
0.00012
1.202
0.00000
0.028
0.00010
1.309
0.00005
0.097
0.00023
0.427
0.00009
1.284
0.00014*
2.332
-0.00002
-0.241
-0.00004
-0.415
0.00003
0.561
0.00004
0.711
Subperiod 2 (Jul97-Dec99) - Post Asian financial crisis
-0.00147
-1.032
-0.00157
-2.22800
-0.00153
-1.125
-0.00161
-1.76900
-0.00244
-0.349
-0.00274
-0.58200
-0.00098
-2.105
-0.00083
-1.06500
-0.00190
-0.215
-0.00170
-0.29700
-0.00139
-0.899
-0.00151
-1.43000
Subperiod 4 (Jul07-Dec09) - Post global financial crisis
0.00167
1.241
0.00116
1.838
0.00187
1.598
0.00138*
2.762
0.00287
1.762
0.00233*
2.446
0.00107
0.329
0.00008
0.048
-0.00051
-0.008
-0.00157
-0.006
0.00003
0.006
-0.00072
-0.437
coefficient
t-ps test
t-stat
% increase/decrease
na
na
na
10.96
na
na
na
na
na
na
na
na
na
18.08
30.52
na
na
na
\
32
Endnotes
1
This S&P/Citygroup 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).
2
Stock market studies uding the BEKK methodology include Arago-Manzana and Fernandez-Izquierdo
(2007) (asymmetric bivariate BEKK), Caporale, Pitttis and Spagnolo (2006) (bivariate BEKK), Bhar and
Nikolova (2009) (bivariate BEKK), Skintzi and Refenes (2006) (bivariate BEKK), Qiao, Chiang and Wong
(2008) (bivariate BEKK) and Antoniou and Pescetto (2007) (trivariate BEKK). In the real estate literature,
only one published study by Cotter and Stevenson (2006) use a bivariate symmetric VAR-BEKK-GARCH
model.
3
Brunzel and Vogelsang (2005) develop a time trend test that retains the good size properties of the “t-ps”
test and also has better power (both asymptotically and in finite samples). It is called the “t-dan” test as it
uses a “Daniel Kernel” to non-parametrically estimate the error variance needed in the test.
4
We follow the stock market literature that suggests a GARCH (1,1)-BEKK specification is appropriate for
the conditional variance lag length.
5
Before we assess whether the correlation series display trending behavior, we need to examine whether
the estimated correlation series display stochastic or deterministic trends. Results from the autocorrelation
and ADF unit root tests indicate that the conditional correlation series are stationary and shocks only have a
temporary impact.
33