Kim Hiang LIOW
Department of Real Estate
National University of Singapore
4 Architecture Drive
Singapore 117566
Tel: (65)65163420
Fax: (65)67748684
Email:
29 October 2009
: correlations, covariance, volatility, integration, Asian securitized real estate markets, stock markets, current global financial crisis
This research examines time-varying real estate-stock conditional correlation dynamics at the local, regional, and global levels as well as the general co-movements among the three types of correlations and their relative (real estate/stock) volatilities for a sample of eight Asian and two non-Asian securitized real estate markets during 1995-2008. We find real estate-stock correlations at the three levels do not display significant upward trending behavior. There is evidence of the existence and significance of some common factors influencing the real estate-stock correlation structures along the three integration paths. Our analysis is also extended to the current global financial crisis to assess the relative contribution of the correlation and volatility factors in influencing the respective covariance structures. The dynamic analyses of real estate-stock conditional correlations are crucial in identifying the optimal real estate-stock portfolio of investors across different economies with non-uniform degrees of real estate-stock integration at the local, regional and global levels.
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This research examines time-varying real estate-stock conditional correlation dynamics at the local, regional, and global levels as well as the general co-movements among the three types of correlations and their relative (real estate/stock) volatilities for a sample of eight Asian and two non-Asian securitized real estate markets during 1995-2008. We find real estate-stock correlations at the three levels do not display significant upward trending behavior. There is evidence of the existence and significance of some common factors influencing the real estate-stock correlation structures along the three integration paths. Our analysis is also extended to the current global financial crisis to assess the relative contribution of the correlation and volatility factors in influencing the respective covariance structures. The dynamic analyses of real estate-stock conditional correlations are crucial in identifying the optimal real estate-stock portfolio of investors across different economies with non-uniform degrees of real estate-stock integration at the local, regional and global levels.
With real estate as a major capital asset that contributes to both investor diversification and wealth creation in the world economy, the issue of real estate-stock market integration has increasingly attracted the attention of global investors and academics. This is because greater integration between national real estate markets and the global stock market may be enhanced through the globalization of financial markets, resulting in more real estate equity and debt investment instruments available for other international investors in international financial markets (Bardhan et al.
2008). Over the years, securitized real estate investment companies have become an increasingly important property investment vehicle in Asia and internationally, particularly through the success of
REITs (real estate investment trusts), listed property stocks, REOCs (real estate operating companies) in the US, Europe and Asia. Importantly, the level of securitized property in Asia is about 12% which is significantly above that of the mature markets (e.g. US: 6 percent; UK: 5 percent; France: 6 percent) and the global level (6 percent) (EPRA, 2008; Newell et al. forthcoming). With the growing economic importance of the Asia-Pacific region in recent years, greater real estate-stock market integration can occur at the local and regional levels because real estate is a key asset component of some national and regional economies. Regardless of which integration path that happens between real estate and stock markets, a highly integrated real estate-stock market indicates the two asset classes are similarly priced. Consequently, there is insignificant differential in risk premiums and the potential for cross-
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asset and cross-border diversification diminish for global investors and country funds. Similarly, market integration can also lead to market contagion as real estate investors incorporate into their trading decisions information about price changes in the stock markets .
On a broader front, market integration is an important aspect for global investors in understanding the level of informational efficiency in international financial architecture that focuses on the increased interdependence between public real estate securities and stock markets due to the influence of globalization and real estate asset securitization.
In pursuance to these developments, this study undertakes to quantify and evaluate the three paths of real estate–stock integration (i.e. global, regional and local) and their interrelationships across the securitized real estate and stock markets of a group of eight Asian and two non-Asian countries between January 1995 and December 2008. In line with the stock market literature, the co-movements of stock returns in two markets are measured through correlation. A greater degree of return comovement is interpreted to reflect greater correlation and market integration.
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Moreover, since the empirical literature generally agrees that market integration is a time-varying process, the evolution in the pattern and degree of real estate-stock integration will be characterized by dynamic changes in the correlation matrix across real estate-stock returns at the local, regional and global levels. With Asian stock markets accounting for 28% of global stock market capitalization (WFE, 2009) and Asian securitized real estate markets account for 48% of global property securities (Macquarie Securities,
2009), the significance and performance of Asian securitized real estate sector deserve global investors’ attention. We consider the possibility integration between the real estate and equity classes could occur first at the local level (local correlation). Next is the possible integration that could result between the local real estate indices and equity class at the regional level (regional correlation).
Finally we consider integration that could result directly between the local real estate indices and the
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We follow Bracker and Koch (1999)’ approach to focus market integration on the nature and extent of interdependence across asset returns for a pair of national equity markets. Henceforth, the terms
“integration”, “co-movement” and “correlation” are used interchangeably. Of course, another commonly accepted definition for integration is based on the law of one price and is essentially an asset pricing point of view.
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global equity benchmark (global correlation).
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In adding to the existing body of knowledge concerning real estate-local stock integration, our study highlights the increased need in more recent years to understand the evolution of real estate-stock integration at the regional and global levels; in particular the relative importance of the global integration component over the others as well as their changes over time will allow global investors to evaluate how has the ability to diversify within a country or within a region evolved over time in an international environment.
At the broader level, our research contributes to the ongoing scholarly work in globalization and financial market integration. Our specific contributions are in enhancing international investors with additional knowledge regarding the sources and trends of real estate-stock correlations at the three integration paths and add some insights as to what might be driving these trends for the real estate markets as well as some time-series dynamics which should be of interest to academics and practitioners. Moreover, a better understanding of the dynamic real estate-stock correlations is important, because these three correlation structures individually and jointly reflect the nature and extent of global market integration and the resulting impact on the risk-return performances of international real estate portfolios.
To-date, various models have been used to measure the correlation between assets returns.
The present study uses Asymmetric Dynamic Conditional Correlation (ADCC) model proposed by
Cappiello, Engle and Sheppard (2006), a specific class of multivariate GARCH models, to estimate pair-wise time-varying correlations between real estate and local stock, between real estate and regional stock and between real estate and global stock. The ADCC approach represents a significant methodological departure from those in the existing literature by displaying the time-dependent and asymmetric dynamics of correlations. Empirically, the ADCC methodology allows for the revision of correlation estimates based on immediate past conditional variances and the asymmetric effects and is thus capable of producing accurate estimates of correlations than the unconditional correlation counterpart. Another important feature of the ADCC methodology is the treatment of correlation as a
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Henceforth, unless otherwise stated, the terms are defined as: (a) local correlation (local integration): real estate-local stock correlation, (b) regional correlation (regional integration): real estate-regional stock correlation, and (c) global correlation (global integration): real estate-global stock correlation.
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variable with a time series structure/evolution. As such, the ADCC framework is able to combine with recent developments in time series econometrics such as trend analysis, factor analytic technique, unit root test as well as multiple regression analysis to provide a comprehensive understanding of the real estate-stock correlation dynamics at the local, regional and global levels. Accordingly, our four specific objectives are:
First, we explore and compare the dynamic co-movements represented by the time-varying conditional correlation coefficients estimated from an ADCC model, across the ten sample real estate markets and stock markets, three regional stock markets and a global stock market proxy for the three transmission components (local, regional and global). The relative importance of one correlation component over the others provides investors with a better understanding regarding the evolution of co-movements among the group of securitized real estate and stock markets examined. In addition, the
ADCC tests confirm prior expectation that the correlation between real estate and stock returns is dynamic across the three levels of integration and that some correlation series display asymmetry. We also assess whether the respective real estate–stock correlation series display trending behavior. This additional work helps shed some lights into the question of whether globalization has caused real estate-stock return correlations to increase; and at which level, over the full sample period.
Second, in light of current global financial crisis, we evaluate and compare the covariance, correlation and volatility dynamics in the three correlation levels during the “pre-crisis” period (Jan 05 to Dec 06: 104 weeks) and the “crisis” period (Jan 07 to Dec 08: 108 weeks). The correlation behavior of global investment markets during the current global financial crisis has received inadequate attention in the academic literature. Although our two sub-periods may appear arbitrary due to the constraint of data for the “crisis” period, we are able to decompose the covariance structure into correlation and variance in order to understand whether correlation plays the dominant role during the
“crisis” period and to determine whether the correlation/variance decomposition is significantly different during the “pre-crisis” and “crisis” periods. Consequently, any significant change in the local
/regional /global covariance structure can help explain why investors made changes to their optimal portfolios in response to the changing benefits of diversification across those markets affected.
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Third, we capture changes in the general co-movements of the local, regional and global correlations by reducing the dimensionality of the correlation innovations and extracting the principal components (PCA) that account for most of the variability in the pair-wise correlation innovations.
The summary of the conditional correlation dynamics in this manner will provide useful insights into the real estate-stock risk diversification and portfolio management at the local, regional and global levels and across the entire sample countries. This approach will also allow us detect any significant systematic co-variation of the three real estate-stock correlation paths for the entire sample.
Finally, to understand the factors that may influence the real estate-global stock correlation
(the highest level of integration), we examine the joint correlation and volatility dynamics (with oneperiod time lag) using stepwise regression technique, to identify the best fit global real estate-stock correlation models for each individual real estate markets and for the entire group. Liow et al. (2009) point out since global or regime shocks affect the markets’ volatilities and their correlations at the same time, any possible risk diversification benefits of international real estate investing may well be eliminated owing primarily to the strong and positive connection between the real estate securities market correlations and their conditional volatilities.
The volatility time lag (independent variable) represents the delay in transmission of volatility information to the global correlation (dependent variable).
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If this factor is found to be significant for some volatility components, then the information transmission speeds in different economies could be different for different transmission paths.
Although the area of correlation research is well established and has led to a large amount of published work especially in the stock market literature and to a lesser extent, the real estate literature
(see also section 2 below), to the best of our knowledge, this is the first study in the real estate literature that evaluates jointly the real estate-stock correlation along the three integration paths (i.e. local, regional and global) and their inter-relationships. The paper is organized as follows. The next section summarizes the important research findings in the areas of correlations and co-movements across real estate and stock markets. The following two sections describe the data to be examined and
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We only consider one lag because the sample period was not long enough.
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discuss the empirical methodology In relation to the research questions. Next, the empirical results are presented, while the last section concludes the study.
Correlations are important for international diversification. There is now extensive literature covering the dynamics of international stock market correlation structure and portfolio diversification
(see Bracker and Koch, 1999). The traditional way is to look at the estimation of pair-wise unconditional correlation coefficients between national stock prices. In most cases, low positive or negative correlations are a sign of potential benefits of international diversification through a reduction in portfolio systematic risk. For example, Bailey and Stulz (1990) find that Asia-Pacific equity markets are not highly correlated, and international diversification is thus possible. Moreover, the correlation of international stock market returns has varied substantially over time and across countries (Longin and Solnik, 1995) and that international correlation increases in periods of high market volatility (Solnik et al. 1996). Recently, Goetzmann et al. (2005) find the correlations among the major world equity markets vary considerable through time. Consequently, diversification benefits are also time-varying. Methodologically, an increasing number of studies have adopted the dynamic conditional correlation (DCC) from the multivariate GARCH model proposed by Engle (2002). The
DCC-GARCH model demonstrates a more direct indication of evolution of stock market correlation which is time dependent and is modeled together with those of the volatility of the returns. It can be estimated with two-stage procedures based on a likelihood function. For example, Yang (2005) examines international stock market correlations between Japan and four other Asian stock markets between 1990 and 2003 using the DCC methodology and find that it is necessary to consider the market condition when conducting international asset allocation. Wang and Moore (2008) investigate the extent to which the three emerging Central Eastern European stock markets have become integrated with the aggregate Eurozone market from 1994-2006 by using the DCC methodology. They find significant dynamic correlations for the emerging markets with the Eurozone market during the financial crises and a higher level of linkage in the aftermath of crises. Cappiello et al. (2006) extends
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the DCC model to the asymmetric dynamic conditional correlation (ADCC) model to permit conditional asymmetries in correlation dynamics and allow for the asymmetric volatility impact of an innovation to be explored using the “news impact surfaces” of Kroner and Ng (1998). More recent studies using the ADCC model includes Kearney and Poti (2006), Syriopoulos and Roumpis (2008) and Gupta and Mollik (2008).
On the contrary, less formal attention has been devoted to the issue of time-varying conditional correlation in international securitized real estate markets, and in particular, no study has jointly investigated the relationship between real estate-stock correlations at the local, regional and global integration levels and their inter-relationship; despite the ongoing trends in globalization and integration of financial markets. Some notable exceptions are Cotter and Stevenson (2006)’s multivariate VAR-GARCH model to examine the time-varying conditional volatilities and correlations in the daily US REIT and equity return series; Michayluk et al. (2006)’s asymmetric covariance model for examining the daily volatility spillover effect and time-varying correlation dynamics between the USA and UK securitized real estate markets; Liow et al. (2009)’s study on correlation and volatility dynamics of international securitized real estate markets using a DCC-GJR-
GARCH (1, 1) model. They find some significant variations and structural changes in the correlation structure happened within the sample period. There is also some evidence of a strong and positive connection between the real estate markets’ correlations and stock markets’ correlations at the local level. The latest study by Liow et al. (2009, forthcoming) develops a multivariate regime-dependent asymmetric dynamic covariance (MRDADC) model to detect the presence of significant meanvolatility linkages across five major securitized real estate markets under different volatility regimes.
Finally, Bardhan et al. (2008) point out the implied relationship between globalization and real estate markets is an under-explored area. Their study develop a multi-factor model to examine the impact of global economic and financial integration on securitized real estate’s excess returns in an international environment. Their findings suggest greater openness leads to more efficient markets in both financial and real terms and that excess real estate returns are reduced by international financial integration. However, they did not examine the pattern and degree of dynamic co-movements across
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international securitized real estate and stock markets at the three integration levels and their interrelationships over time. This is where the present study intends to contribute.
The data employed in this study consist of weekly returns of indices in the S&P/Citygroup
Global Property database over the period 6 January 1995 to 2 January 2009. 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. 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).
Based on the longest data availability criterion, eight Asian public real estate markets are covered in this study, namely Australia, Japan, Hong Kong, Singapore, China, Malaysia, Taiwan and the Philippines.
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In addition, the USA and UK markets, being the world’s two largest securitized real estate markets, are included to compare with the Asian results. Following literature, the eight Asian economies are broadly grouped according to their stages of economic development:
(a)
(b)
Asian developed economies: Australia, Japan, Hong Kong and Singapore
Asian emerging economies: China, Malaysia, Taiwan and the Philippines.
In addition, the 10 national stock indices, together with the three regional stock indices (Asia,
Europe and North-America) and the global stock index are extracted from the S&P BMI database.
Table 1 provides the usual descriptive statistics of weekly returns for all real estate and stock return series. In terms of average returns, the USA real estate market has the highest weekly average return of 0.15 percent, whereas Singapore has the lowest of -0.007 percent. For the national stock markets,
Australia has the highest weekly average returns of 0.183 percent, whereas Singapore again reports the lowest weekly average returns of -0.008 percent. Over the full period, the emerging real estate market of China is the most volatile with a weekly standard deviation at 5.83 percent followed by the
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Thailand and Indonesia are excluded from this study due to incomplete data for the full period.
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Philippines market with around 5.13 percent. The aggregate real estate market appears to have a much lower volatility of about 2.49 percent and is however higher than that of the aggregate stock market of about 2.26 percent. The volatilities for the three regional stock markets are, respectively, about 2.80 percent (Asian), 2.64 percent (Europe) and 2.54 percent (North America); compared with the 2.35 percent for the global stock volatility. The distribution of returns over time is negatively skewed for all national stock markets, three regional stock markets and the global sock market. In contrast, only two real estate markets’ returns are slightly positively skewed over time (Malaysia and China).
Additionally, all real estate and stock returns are characterized by a statistically significant kurtosis over time, suggesting that the underlying series are leptokurtic i.e. the return series have a fatter tail and a higher peak compared with a normal distribution. Table 2 reports test results of serial correlation and ARCH effects for all return series at 12 and 24 lags. The Ljung-Box Q statistics at 12 and 24 lags are computed for both the series and squared series. With some exceptions, the Q-statistics indicate the presence of significant linear and non-linear dependencies. The ARCH tests support the presence of autoregressive conditional heteroskedasticity in all return series at the one percent significance level.
(Tables 1 and 2 here)
The unconditional correlation is presented in Table 3, which shows that the real estate–local stock unconditional correlation of the sample markets for the full sample period ranges from 0.543
(China) to 0.927 (Hong Kong); the equivalent estimates for the regional and global counterparts are, respectively, between 0.283 (China) and 0.665 (Japan) and between 0.213 (Taiwan) and 0.577 (UK).
The highest level of real estate-stock correlation thus occurs at the local level for all sample markets.
In addition, the unconditional correlation estimates for the three shorter-sample periods (Jan 95-Dec
00; Jan 01-Dec 04 and Jan 05 – Dec 08) are reported. Except for the local correlations for the Asian markets which report a decrease in the second sub-periods, several other correlation estimates indicate a general increase over the three periods, implying that these securitized real estate markets are becoming more integrated with the regional and global stock markets over the last 14 years.
(Table 3 here)
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We use different methodologies in the empirical work. Following the specific objectives discussed above, our first research question asks to what extent are the securitized real estate markets correlated with the stock markets at the local, regional and global levels; what is the evolution and current level of local, regional and global integration? Are they progressing, at standstill, or even regressing? We will use the ADCC from the multivariate GJR-GARCH model to estimate the pairwise correlations between the ten real estate markets and stock markets at the local, regional and global levels. This specification allows for heteroskedasticity of the data and an asymmetric timevarying correlation in the conditional variance. The empirical foundation for the ADCC specification is Engle (2002)’s multivariate GARCH dynamic conditional correlation analysis. Using a multivariate
GARCH framework, Engle (2002) relaxes the assumption of constant correlations by allowing the correlation component to be time-varying and assuming a GARCH type specification. In the DCC-
MGARCH model, the conditional variance is: H t

D t
R t
D t
, where R t
is the time-varying correlation matrix and D t
is estimated from the univariate GARCH model.
Our DCC-GJR-GARCH model is estimated in a three-step procedure. Following Liow et al
(2009), first, a univariate GJR (1)-GARCH (1, 1) model is estimated for each time series. Then, the transformed residuals from the first stage are used to obtain a conditional correlation estimator in the second stage. The correlation structure is given as: R t

Q t

1
Q t
Q t

1
and the DCC covariance structure is specified by a GARCH process: Q t

( 1
 a
 b ) * Q
 a * (
 t 
1

' t

1
)
 b * Q t

1
, where
Q t is calculated as a weighted average of Q (the unconditional covariance of the standardized residuals),
 t

1

' t

1
(lagged function of the standardized residuals derived from the first stage univariate GARCH estimation, which is assumed to be i.i.d
.
with a mean zero and a variance R t
and
Q t

1
(past realization of the conditional covariance). In the DCC (1, 1) model, a and b are scalar parameters to capture the effects of previous (first lagged realization) standardized shocks and dynamic conditional correlations on current dynamic conditional correlations, respectively. The Q t
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expression will be mean-reverting when a + b < 1 . This specification reduces the number of parameters to be estimated and makes the estimation of time-varying correlation more tractable.
In the final stage, the DCC equation is enriched to allow for asymmetry in conditional correlation as follow:: q t

( 1
 a
 b )
  g
  a
 i , t

1
 j , t

1
 bq ij , t

1
 g
 t

1

' t

1
Where the parameter g introduces the asymmetric effects into the model;
 t

I (
 t

0 )
  t
( I [  ] is a function indicator that takes on value 1 if the residuals are negative and
0 otherwise;
 denotes the Hadamard product;
 
E [
 t
 t
] is the sample covariance matrix. Finally, the asymmetric response of correlation to volatility shocks can be examined through the “news impact surface” (Kroner and Ng, 1998).
From the ADCC estimation, we investigate whether the estimated correlation series display stochastic or deterministic trends by studying their autocorrelation structure and ADF unit root results.
We then assess whether the correlation series display trending behavior (due possibly to the process of globalization) relying on Vogelsang (1998) simple time trend test. The basic model is:
Correlatio n t
   
* 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.
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Our second research question asks about changes in the return covariances, return correlations and return volatilities during the ”pre-crisis” and “crisis” periods in the context of the current global financial crisis. Very briefly the current global financial crisis began in the US with the bursting of sub-prime mortgage market. This rapidly propagated across different asset classes and financial markets. In investigating how those return covariances changed near the crisis, we follow the
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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.
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argument put forward by Chakrabarti and Roll (2002) that understanding the covariance structure
(COV) requires decomposition into correlation (COR) and volatility (VOL) so that their relative importance will indicate whether correlation plays the dominant role during the “crisis” period and also whether the correlation/volatility decomposition is significantly different during the “pre-crisis” and “crisis” periods for the real estate-stock covariance series at the local, regional and global levels.
Mathematically, since
COV t

COR t
* VOL t
Then the log first difference between two periods (equivalent to continuous percentage changes) provides a decomposition of covariance change into its volatility and correlation components:
 log( COV )
  log( COR )
  log( VOL )
Next, assuming that correlation and volatility change in the same direction, the relative importance of correlation (
 cor
- a scalar bound between 0 and 1) can be measured by:
 cor
  log( COR ) /(
 log( COR )
  log( VOL )
Similarly, the relative importance of volatility (
 vol
) is measured by:
 vol
  log( VOL ) /(
 log( COR )
  log( VOL )
Our third research question is concerned with the general co-movements of the real estate– stock correlation at the local, regional and global levels. We use the multivariate method of factor analysis to capture the three real estate-stock correlation changes, with the mostly widely used approach, the principal component analysis (PCA)
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. Specifically, with the 10 correlation time series each for the local, regional and global estimation derived from the ADCC models, the factor structures of the three correlation types are separately estimated using the PCA. The main objective is to detect
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There are a number of approaches in factor analysis for factor extraction, such as maximum likelihood and image method of extraction. As in Hui (2005), we use the most widely used approach, the principal component analysis (PCA). The objective is to derive a reduced set of uncorrelated variables (“principal components” or “factors”) in terms of linear combinations of the original variables, so as to maximize the variance of these components. This is particularly useful in multivariate analysis when considering a large number of interrelated variables; a reduction in dimensionality may be useful to further studies.
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any significant systematic co-variation of the three correlation types. For each correlation type, say for example the global correlations, this process seeks to capture changes in the general co-movements of the 10 real estate-global stock’s bivariate correlations by representing each real estate market’s correlation as a linear combination of the “components” plus an error term. The first “factor” is the combination that accounts for the largest amount of variance in the sample. The second “factor” accounts for the next largest amount of variance and is uncorrelated with the first. Successive
“factors” explain progressively smaller portions of the total sample variance. To aid factor interpretation, the varimax method of orthogonal rotation is employed. The Kaiser criterion is used to decide on the “factors” that should be retained. As a common rule, those “factors” with an eigenvalue greater than or equal to one are retained. These eigenvalues measure the contributions of the corresponding factors to explain the cross-sectional variation of correlations across the sample real estate and stock markets.
Finally, our fourth question asks to what extent volatilities influence correlations. Although research in the area of portfolio management has looked into other factors (such as macroeconomic factors) which may drive the changes in the correlations over time, we focus on the volatility factors at the different integration levels (real estate volatility, local stock volatility, regional stock volatility and global stock volatility), specified through a multiple regression approach, to investigate the volatility factors that may cause the real estate–stock global correlation (regarded as the highest level of correlation) to vary over time. We conduct this analysis for the 10 individual real estate markets as well as for the full sample using the results from the PCA. The regression model is:
Correlatio n global , t
 f ( correl global , t

1
, correl regional , t
, correl local , t
, vol re , t vol local , t
, ( vol re vol local
) t

1
, vol re , t vol regional , t
, ( vol re vol regional
) t

1
, vol re , t vol global , t
, ( vol re vol global
) t

1
  t
)
In the above model, we test the joint influence of the correlations and volatilities on the real estate-global stock correlations. We use the ratio of volatilities to capture the respective relative volatility measure (e.g. ratio of the real estate market volatility to global stock market volatility – relative global volatility). Practitioners in the market frequently use the ratio as a measure of the relative volatility of the two markets. Furthermore, by reducing the number of the volatility variables
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in the model, this approach will help minimize the multicollinearity concern resulting from possible correlations of the independent volatility variables.
The ADCC–GJR (1)-GARCH (1, 1) is estimated using the quasi-maximum likelihood method to generate consistent standard errors that are robust to non-normality. Table 4 contains the estimates of the univariate GJR-GARCH model. As the numbers indicate most of the estimated
ARCH, GARCH and the asymmetry (GJR) parameters are statistically significant, which implies the
GJR (1)-GARCH (1, 1) model is sufficient to capture the temporal dependence and asymmetry of the real estate and stock markets under examination. Specifically, the ARCH and GARCH coefficients are positive and are, with some exceptions for the ARCH estimates, highly statistically significant, indicating “reactive” and “persisting” volatility dynamics.
(Table 4 here)
The estimates for the ADCC parameters (a, b) reported in Table 5 are mostly statistically significant. The assumption of constant conditional correlation is thus not supported empirically. The statistical significance of these two coefficients in each real estate-stock pair indicates the presence of dynamic (time-varying) real estate-stock correlation. The sum of a and b lies between 0.5852 (local correlation: Taiwan) and 0.9924 (regional correlation: UK), respectively. Since (a + b) is lower than unity, the dynamic correlations move around a constant level and the dynamic process appears to be mean-reverting. The assessment of asymmetric correlation responses to negative returns is examined through the parameter g. In all cases except Japan, Singapore, Philippines and the UK (local correlation) as well as Malaysia, Taiwan, Philippines and the UK (regional correlation) we find no evidence of asymmetries in other conditional correlation series. Moreover, except for Singapore’s
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local correlation
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, the asymmetric term, g, is found to be statistically positive at least at the ten percent level in seven cases and its value ranges from 0.0134 (UK: regional correlation) to 0.1518 (Taiwan: regional correlation), indicating some presence of asymmetric correlation. To visualize the impact of the significant asymmetries, Figure 1 plots the correlation news impact surfaces of Kroner and Ng
(1989). As can be observed, the correlation news impact surface is generally asymmetric, showing a larger response to shocks in the -/- than in the +/+ quadrant; i.e. it is more responsive to joint bad news than to joint good news of the same magnitude. These real estate and stock returns appear to have experienced an increase in their correlations during negative event periods. If this is the case, it implies that these pair-wise real estate and stock markets offer limited diversification benefits during bad market times. Table 5 also contains the descriptive statistics (mean, standard deviation, maximum and minimum) for the ADCC estimates for all 30 series. The conditional correlations are in the ranges of (0.515, 0.930), (0.227, 0.508) and (0.153, 0.517), respectively, for the local, regional and global correlation series. As in the case for the unconditional correlations, the average level of real estate– stock conditional correlations at the local level in all ten economies are all (significantly) higher than the corresponding regional and global conditional correlations. On a regional basis, while the four
Asian developed markets derive the highest average real estate-local stock correlation (average:
0.7044), the two non-Asian markets (US and UK) pick up the highest real estate-regional stock
(0.4960) and real estate-global stock (0.4717) correlations. For the four Asian emerging markets, their average real estate-stock correlations are, respectively, 0.5422 (local), 0.2746 (regional) and 0.2600
(global). Thus the results indicate for the Asian real estate markets, real estate-stock integration has evolved mainly at the local level. As real estate is a major asset component of many Asian economies, the high correlation between real estate and local stock is to be expected. Compared to two other groups, the lowest levels of regional and global correlation coefficients are evident for the Asian emerging markets which have been isolated from the regional and global stock markets; and are thus able to enhance portfolio diversification benefits regionally and internationally. This is especially the
7
Singapore’s local correlation series is associated with a significant negative asymmetry at the 1 per cent level.
16

case for China which has been driven by the significant economic growth and improved property market maturity in recent years. On the contrary, the average local, regional and global correlations for the two non-Asian’s real estate-stock markets are approximately in the same order of magnitude
(0.5422, 0.4960 and 0.4717) implying global and regional stock markets factors are influencing the real estate returns, in addition to the country factors.
Finally, the average correlation risk for the real estate-regional stock is the highest of about 10.31 per cent and is followed by the equivalents of 9.96 per cent and 7.08 per cent risk for the global and local correlations, respectively. A relevant question arises whether these correlation risks are priced by investors in the spirit of APT (arbitrage pricing model). We leave this question for future work.
(Figure 1 and Table 5 here)
Figure 2 plots the evolution of the three real estate-stock correlation types across the sample countries. Few 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 ADCC model. Second, there is generally a similar pattern of correlation observed around 1997-98 with the occurrence of the
Asian crisis, where the correlations tend to be significantly higher for some Asian economies such as
Japan, Hong Kong, Singapore, Malaysia and Philippines. Third, a similar pattern of correlation is observed around 2007-2008 with the occurrence of the current global financial crisis accompanied by higher level of correlations, and in several 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 Asian crisis and current global financial turmoil, the pattern of correlation seems to diverge among the economies. Figure 3 compares the three correlation types across the US, the UK, Asian (developed) and Asian (emerging) economies. It appears that the regional and global correlations co-move closer with each other in all cases than with their local correlation counterpart.
(Figures 2 and 3 here)
17

Before we assess whether the correlation series display trending behavior, we need to examine whether the estimated ADCC series display stochastic or deterministic trends. The autocorrelation results of up to 36-week lags reported in Table 6 indicates all 30 correlation series are substantially auto-correlated, with some small negative serial correlations for a number of lags for two correlation series. The level of persistence suggests that the correlation series can be predicted to some extent by their past values. Table 6 also reports the Augmented Dickey Fuller (ADF) test with intercept and with intercept and time trend. The results indicate the hypothesis of a unit root can be rejected for, respectively, 80 percent. 60 percent and 55 percent, of the local, regional and global correlation series, implying that these conditional correlation series are stationary and shocks only have a temporary impact.
(Table 6 here)
We then test for a deterministic linear time trend for all correlation series.
8
The results are reported in Table 7. Based on the t-ps and t-dan tests, we detect significant time trends for two correlation series only. For the Hong Kong economy, their real estate–global stock global correlation, which is stationary, has increased significantly by a total of about 9.65 per cent over the full study period. During the same period, the real estate-local stock correlation, which is stationary, for
Malaysia has nevertheless decreased significantly by a total of approximately 10.34 per cent. We do not detect either significant upward or downward time trends for other correlation series.
9
These findings imply the process of globalization over the past 14 years has not resulted in any significant increase in the co-movements over time between the real estate and stock markets at the local, regional and global levels. Accordingly, real estate markets have not become more integrated with the stock markets implying diversification potential across the two asset markets is still good. Of course, it
8
We determine the time trends for all correlations including those which are stationary, for completeness.
9
One other partial exception is that we derive a significant and positive t-dan trending coefficient for the real estate-local stock correlation, which is stationary, for China. However, its t-ps trending coefficient is insignificant.
18

is possible that significant deterministic trends are not detected because we have disregarded a structural break in the correlations.
10
(Table 7 here)
To understand the impact of the current global financial crisis on the dynamic correlations, we regress each conditional correlation series with its lagged correlation and a dummy variable, i.e. dummy one for 5 January 2007 onwards. The results of Table 8 indicate that the current global financial crisis has indeed imposed a statistically significant and positive impact on the real estatestock conditional correlations, confirming that, with some exceptions, these real estate and stock markets are vulnerable to the financial crisis resulting in differing degree of regime switch in the real estate-stock correlations. As for the local correlation dummy variable, the extent of the effect appears to be less than that of the regional and global correlations; only six markets’ dummies are statistically significant at the 10 per cent level for the local correlation series compared to eight significant dummies each for the regional and global correlation series respectively. Specifically for the USA, the dummy variable is found to be highly significantly positive at the one percent level for both local and regional correlations and five percent for the global correlation, respectively, which means that the three real estate-stock correlations are on average significantly higher than the normal level.
(Table 8 here)
Further results indicate the covariances, correlations and volatilities increased during the current global financial crisis in all local, regional and global correlation series. As the numbers in
Table 9 show, the increase in the average covariance is most striking, ranging between about 2000 percent and 4000 percent. Average real estate volatility is about 182 percent higher during the “crisis” period, and is nonetheless smaller than the increase of respectively, 219 percent, 240 percent and 334
10
Our preliminary test using Bai and Peron (2003)’s confirms at least a structural break exists in some correlation series. Since this issue is not our focus, we will not pursue further.
19

percent for the average volatility in local, regional and global stocks. Although correlation is also higher during the “crisis” period, the magnitude of increase is nevertheless much smaller, ranging between 6.54 percent (local correlation) and 26.74 percent (global correlation)
(Table 9 here)
 statistic, which measures the relative contribution of correlation to the change in covariance during the “pre-crisis“ and the “crisis” periods, is reported in Table 10. For the local covariance (Panel A), the
 cor statistics for the “crisis” period are, respectively, 8.5% (US), 7.43%
(UK), 5% (Asian developed average) and 11.18% (Asian emerging average). These contributions are all insignificantly smaller compared to the “pre-crisis” period of 11% (US), 9.22% (UK), 9.44 %
(Asian developed average) and 13.27% (Asian emerging average). It would thus appear that the volatility component (both real estate and stock) was somewhat more responsible than correlation in contributing to the covariance increase during the “crisis” period. However, there is little evidence to suggest that volatility has become significantly more important in influencing the covariance change during the “crisis” period as the
 vol
estimates for many markets are insignificantly different in magnitude across both sub-periods. The last column in Table 10 provides the average estimates for
“pre-crisis” and “crisis”
 cor and
 vol for the sample markets. For the local covariance, their average
 cor and
 vol
estimates are of the same order of magnitude for the “pre-crisis” and “crisis” periods. In contrast, a different picture emerges for the regional covariance measure. While the
 estimates cor indicate a decrease of 7.27 percent (from 30.09 to 22.82) in the “crisis” period; the
 vol estimates for the regional stock market show an increase of 6.65 percent (from 34.37 to 41.02). For the global covariance, the relative contribution of correlation to the change in covariance during the “pre-crisis“ and “crisis” periods is an decrease of only 3.76 percent (from 16.62 to 12.86); this is accompanied by a relatively significant decrease of real estate volatility contribution of 12.64% (from 35.4 to 22.76); but an increase of 5.41 percent (from 47.98 to 53.39) for the global stock’s
 vol
. Figures 4 to 6 depicts graphically the relative contribution of correlation and volatility to covariance change during the “pre-
20

crisis” and “crisis” periods for local integration (Figure 4), regional integration (Figure 5) and global integration (Figure 6). In summary, our relative contribution analysis produces little evidence to support correlation has played the dominant role during the “crisis” period; however the correlation/volatility decomposition is different at the local, regional and global integration levels during the “crisis” period. Finally, the correlation/volatility decomposition provides useful insights into the fundamental determinants of structural changes in the market covariance, which again we leave for future work.
(Table 10, Figures 4-6 here)
To capture the general real estate-stock co-movements at the three integration levels for the
10 economies, Tables 11 -13 presents the varimax rotated loadings results regarding the factor solution for the local, regional and global correlation series. The Kaiser-Myer-Okin (KMO) of sample adequacy for the three samples ranges from 0.632 (local correlation) to 0.740 (regional correlation).
The Barlett’s Test of Sphericity (BTS) is also statistically significant at the one percent level for the three correlation types. As the correlation series are standardized to have unit variance, the reported factor loadings represent both the sensitivity and the correlation of each variable with the factor. The relevant factor loadings, those along the diagonal, appear in bold. For all the three correlation types, the solution involves three factors which jointly accounts for 58.93 percent (local correlation), 65.98 percent (regional correlation) and 68.87 percent (global correlation), of the sample variance. In accordance with the weight of each correlation series in each factor (with factor loadings of 0.50 and above), the analysis reveals:
Factor 1: US /Japan /HK /China (local correlations); US /Australia /Japan /China
Factor 2:
(regional correlations); US/UK/Japan/China/Taiwan (global correlations)
Australia /Singapore /Malaysia /Philippines (local correlations); UK /HK
/Singapore (regional correlations); HK /Singapore /Malaysia (global correlations)
21

Factor 3: UK /Taiwan (local correlations); Malaysia /Philippines /Taiwan (regional correlations); Australia /Philippines (global correlations)
Therefore, the real estate-stock correlation structures of the US/Japan/China at the local
/regional/ global levels could be recovered from the first factor, which explains between 24.03% (local correlations) and 33.12% (global correlations) of the sample variance. Similarly, the real estate-stock correlation structures of Singapore at the three integration levels are linked to the second factor only, which explains up to 24.46% of the sample variance. The remaining six economies’ real estate-stock correlation structures are linked to the third factors. Our analyses have two important implications: first, they indicate the existence and significance of some common factors influencing the real estatestock correlation structures along the three integration paths. Although it can be difficult to interpret the resulting co-movement loadings on the common factors using factor analysis, the summary of correlation dynamics at the local/regional/global levels provides global investors with additional knowledge to consider a smaller set of real estate and stock markets for risk diversification thereby save search time and cost. Second, if the common factors are linked to economic factors, this outcome will motivate the need to further analyze potential economic determinants of the real estate-stock correlation structures at the local, regional and global levels. Again, we leave this issue for future research.
Table 14 reports the results of the 10 stepwise regression models.
11
Regression results show the regional correlation co-moves positively with the global correlation in all markets except
Malaysia. Similarly, the local correlation co-moves positively with the global correlation in seven markets, implying that the evolution of the real estate-global stock integration over time could be contributed from the local and regional integration. The regressions results suggest that the relative
(real estate/stock) volatility measures at the three integration levels are able to influence the real
11
As explained above, the combined correlation and volatility model use the global correlation as the dependent variable. In addition to the one-period lag of global correlation, regional correlation and local correlation, three measures of relative volatility (i.e. real estate/local stock, real estate/regional stock, real estate/global stock) and their respective one-period lagged variables are included as independent variables.
22

estate-global stock correlations in at least 8 markets, with either positive or negative influence. For example, the relative local (real estate/local stock) volatility has a significantly positive impact on the real estate-global stock correlation in the US, the UK, Japan and Philippines, implying the higher the real estate volatility relative to the local stock, the higher the real estate-global stock correlation; a significantly negative coefficient is derived for Australia, Hong Kong, Singapore, China and Taiwan, implying that the real estate-global stock correlation in these economies will be higher with higher relative local (real estate/stock) volatility. The detailed results for the relative (real estate/stock) at the regional and global levels are qualitatively similar; with the magnitude of influence differs for each economy. Additionally, the results show that the one-period lagged relative (real estate-stock) volatility variables are important and the direction as well as the speed of transmission of information is different in different markets (9, 7 and 5 significant coefficients, respectively, for relative (real estate/stock) volatility at the local, regional and global levels). Different directions and speeds in information transmission in different markets could be the results of different stage of integration of these real estate markets with the global stock market. Although our investigation is not new to international investors in so far as the relationship between correlation and volatility is concerned, our contribution provides the ability to estimate the diversification effects that are attributed to the different levels of real estate-stock correlations and their interactions with the corresponding relative
(real estate/stock) volatilities.
(Table 14 here)
To test the relationship between the real estate-global stock correlation, real estate-regional stock correlation, real estate-local stock correlation and the corresponding relative (real estate/stock) volatilities for the full sample, following the factor analysis, we capture changes in the general comovements of the correlation and relative market volatility variables with global integration. Table 15 reports the stepwise regression results for three models. In Models 1-3, the dependent variables are the real estate-global stock correlation’s three principal components (PCA1 for model 1, PCA2 for model
2 and PCA3 for model 3) derived from the factor analysis; the independent variables are the lagged one-period real estate-global stock correlations, three PCAs for the regional and local correlations,
23

three PCAs each for the relative volatility series (ratio of the real estate volatility to local stock volatility, ratio of the real estate volatility to regional stock volatility and ratio of real estate volatility to global stock volatility) as well as three one-period lagged relative volatility PCA series. In addition, we include the current global financial crisis dummy ( Dummy , with 1 for 5 Jan 07 onwards and crisis
0 otherwise) to assess whether the real estate-global stock correlation has increased during the “crisis“ time.
(Table 15 here)
The overall regression results are in agreement with the individual market results reported in
Table 14. The results on Table 15 indicate that real estate-global stock correlations are significantly positively related to real estate-regional stock correlations and real estate-local stock correlations, significantly negatively related to up to two relative (real estate/stock) volatilities as well as significantly positively related to up to two one-period lagged volatility measures. Moreover, two of the three “crisis” dummies are significantly positive, which means that the real estate-global stock correlation is on average significantly higher than the normal level, indicating that at least some of the real estate markets have become more synchronized with the global stock market when more contagion did exist during the current global financial crisis period. In agreement with those reported in Table 8, these results would definitely not constitute good news for the market participants.
In adding to the existing body of knowledge concerning Asian securitized real estate and stock markets, our analyses are beneficial for optimal portfolio consideration, especially to those international investors and portfolio managers who are keen to include Asian real estate equities and common stocks in their portfolios; while at the same time, are mindful about the time-varying linkages of their real estate assets with stocks along the three integration paths; as it would imply that the benefits arising from international portfolio diversification are not uniform and are changing over time, for two main reasons. First, while the correlations between real estate and national stock for an economy can be very high, the correlations between real estate and regional stock and between real
24

estate and global stock may be (much) lower. These dynamic real estate-stock conditional correlations are critical in identifying the optimal real estate-stock portfolio of investors across different economies with non-uniform degree of real estate-stock integration the local, regional and global levels. Second, the joint correlation and volatility analyses along the global correlation path (the highest hierarchy) provides a more complete picture regarding the interrelationship linking the three levels of integration and their respective volatility influence for the real estate markets concerned. This knowledge is important to determine if change in volatility (relating to real estate, local stock, regional stock and global stock) causes the global correlation to change over time; and their relative importance.
Understanding of this linkage may also be of interest to fund managers who seek to benefit from diversifying into Asian real estate and stock markets in the context of current global financial crisis.
This paper is a unique contribution to the literature in international correlation dynamics. The purpose was to evaluate and compare the behavior of time-varying real estate-stock correlations at the local, regional, and global levels as well as the general co-movements among the three types of correlations and their relative (real estate/stock) volatilities over a period beginning January 1995 and ending December 2008. Factor analysis was included to capture the general co-movements of the correlations and their interactions with the relative market volatility factors. Our analysis was also extended to the current global financial crisis to evaluate the relative contribution of the correlation and volatility factors in influencing the respective covariance structures. Our study complements and yet is different from Liow et al. (2009) who focus on the time-varying correlation and volatility links of major national and regional real estate securities markets as well as the corresponding stock markets.
The current study produces a number of valuable and statistically significant findings over the period of study. These findings enrich the thin body of literature on cross real estate-stock correlation dynamics, notably (a) Conditional real estate-stock correlations at the local, regional and global levels are mostly time-varying and asymmetric in some cases, findings that are consistent with those by
25

Longin and Solnik (1995) and Cappiello et al. (2006) for stock markets, (b) Real estate-stock correlations at the three levels do not display significant trending behavior. As such the globalization process has not resulted in any significant increase in the co-movements over time on the real estatestock correlations at the local, regional and global levels. (c) The current global financial crisis has imposed a statistically significant and positive impact on the real estate-stock conditional correlations, suggesting the majority of the real estate and stock markets are vulnerable to the financial crisis resulting in differing degree of regime switch in the real estate-stock integration at the local, regional and global levels, (d) The relative contribution analyses reveal correlation has not played the dominant role in causing the changes in covariance during the “crisis” period; however the correlation/volatility decomposition is different at the local, regional and global levels during the “crisis” period, (e) We detect the existence and significance of some common factors influencing the real estate-stock correlation structures along the three integration paths. The summary of correlation dynamics at the local/regional/global levels using the factor analytic technique provides global investors with additional knowledge to consider a smaller set of real estate and stock markets for risk diversification
– a finding analogous to Liow and Webb (2009) who examine common factors in international securitized real estate market returns in a different context and Tuluca and Zwick (2001) who investigate the effects of the Asian Crisis on global equity markets, (f) The three real estate-stock correlations co-move significantly positive with one another. Moreover, the relative (real estate/stock) volatility measures and their lagged one-period relative volatility measures at the three integration levels are able to influence the real estate-global stock correlations with either positive or negative influence.
26

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28

Mean S.D(%) Maximum Minimum Skewness kurtosis
Real   estate    markets
US
UK
Australia
Japan
Hong   Kong
Singapore
Malaysia
China
Philippines
Taiwan
All
0.00150
0.00112
0.00117
0.00016
0.00085
‐ 0.00007
‐ 0.00106
0.00139
‐ 0.00049
‐ 0.00175
0.00028
2.860
2.585
2.335
4.547
4.567
4.908
4.554
5.832
5.130
5.000
2.490
0.2153
0.1002
0.1169
0.1579
0.1986
0.3090
0.3501
0.2877
0.2284
0.1709
0.1121
‐ 0.2106
‐ 0.1489
‐ 0.2300
‐ 0.2257
‐ 0.2711
‐ 0.3979
‐ 0.1935
‐ 0.2473
‐ 0.2210
‐ 0.2421
‐ 0.1804
Stock   markets
US
UK
Australia
Japan
Hong   Kong
Singapore
Malaysia
China
Philippines
Taiwan
All
0.00137
0.00126
0.00183
‐ 0.00060
0.00115
‐ 0.00008
0.00017
0.00154
‐ 0.00036
‐ 0.00051
0.00058
2.519
2.337
1.931
2.814
3.485
3.451
3.487
4.767
3.659
3.711
2.256
0.1201
0.1243
0.0940
0.0793
0.1465
0.1440
0.2067
0.2051
0.1484
0.1907
0.0790
‐ 0.1982
‐ 0.2256
‐ 0.1754
‐ 0.2214
‐ 0.2052
‐ 0.2794
‐ 0.2079
‐ 0.2160
‐ 0.2246
‐ 0.1444
‐ 0.1792
Regional   /global   stock   markets
Asia ‐ 0.00008
Europe 0.00143
North   America 0.00140
Global 0.00111
2.795
2.639
2.538
2.350
0.0931
0.1344
0.1240
0.1160
Source: Derived from S&P/ Global Property Database
‐ 0.2012
‐ 0.2604
‐ 0.2037
‐ 0.2209
‐ 1.1342
‐ 1.0370
‐ 2.8769
‐ 0.1330
‐ 0.4543
‐ 0.5039
0.8371
0.0357
‐ 0.0667
‐ 0.2149
‐ 0.9957
‐ 0.9756
‐ 1.2079
‐ 1.4446
‐ 0.8380
‐ 0.6024
‐ 0.9921
‐ 0.0912
‐ 0.1850
‐ 0.6586
‐ 0.0395
‐ 1.1473
‐ 0.5222
‐ 1.6291
‐ 1.1018
‐ 1.4839
20.4103
8.5434
29.3509
4.3344
7.5808
13.4109
11.4526
5.2392
5.5093
5.2026
9.5197
10.7366
17.5857
14.4890
8.1959
6.9774
11.4948
9.1778
5.3829
7.4980
5.2790
10.2910
8.8481
19.2905
11.7242
16.3684
29

A R CH ‐ L M
Ser ies
Lj ung ‐ B ox   Q ‐ st at isti cs
Ser ies S quar ed   ser ies
12   lag s 24   lag s 12   lag s real   est ate   m ar kets
3 8. 03 ** * 23 .9 5* *
24   lag s 1 2   lag s 2 4   lag s
U S
UK
A ustr al ia
Japan
Hong   Kong
S in gap ore
C hin a
Malay sia
Phil ipp ines
Taiwan
7 4. 63 ** *
2 1. 67 ** *
4 3. 06 ** *
8.6 8 ** *
8.2 1 ** *
1 0. 88 ** *
3.4 6 ** *
6.9 0 ** *
3.6 0 ** *
4.1 2 ** *
1 1. 98 ** *
5 5. 53 ** *
4.6 9 ** *
4.5 8 ** *
6.1 0 ** *
3.0 9 ** *
3.7 5 ** *
3.1 3 ** *
2.2 2 ** *
24 .0 8* * 50 .5 7* * 4 76 .7 6* ** 4 99 .8 1* **
1 33 .6 1* ** 1 58 .6 1* ** 3 19 .2 4* ** 3 75 .7 6* **
21 .9 1* *
1 3. 91
42 .9 3* *
39 .7 8* *
1 58 .0 5* **
1 18 .5 8* **
1 72 .1 1* **
1 54 .7 4* **
2 9. 51 ** *
1 8. 22
18 .7 2*
1 4. 02
1 8. 11
37 .3 0* *
4 3 .11 ** *
3 2. 88
35 .6 2*
1 5. 79
2 3. 92
10 2 7.1 5 ** * 1 03 0. 43 ** *
1 53 .7 4* ** 1 71 .0 1* **
6 5 .18 * ** 1 36 .3 4* **
1 42 .9 0* ** 2 17 .3 4* **
6 6 .56 * ** 90 .9 0* **
7 1 .90 * ** 81 .9 7* **
U S
UK
A ustr al ia
Japan
Hong   Kong
S in gap ore
C hin a
Malay sia
Phil ipp ines
Taiwan
1 2. 93 ** *
1 0. 82 ** *
1 2. 18 ** *
3.9 8 ** *
6.1 2 ** *
5.7 7 ** *
4.7 4 ** *
1 3. 22 ** *
2.3 3 ** *
3.8 3 ** *
6.4 8 ** *
5.3 7 ** *
6.7 3 ** *
2.1 2 ** * stock   m ar ket s
3 8. 63 ** *
3 4. 79 ** *
1 2. 44
18 .7 4*
4 6 .37 ** *
4 7 .58 ** *
2 4. 84
34 .2 3*
3.8 0 ** *
3.4 0 ** *
2.9 0 ** *
7.8 8 ** *
2.8 2 ** *
1.9 4 ** *
1 6. 62
1 1. 46
1 2. 19
3 8. 49 ** *
22 .1 9* *
1 1. 19
35 .4 7*
2 2. 09
2 7. 02
5 1 .00 ** *
2 6. 85
2 5. 25 reg ion al/gl obal   stock   m ar ket s
2 10 .6 2* ** 2 12 .0 5* **
1 57 .2 1* ** 1 57 .8 0* **
1 71 .3 5* ** 1 76 .0 3* **
6 1 .95 * ** 62 .5 8* **
1 08 .2 8* ** 1 48 .2 5* **
1 01 .9 0* ** 1 24 .7 8* **
1 05 .6 6* ** 1 65 .3 0* **
3 18 .3 5* ** 4 68 .1 9* **
3 5 .80 * **
6 2 .52 * **
70 .6 5* **
68 .5 6* **
A sia
Eur op e
3.5 4 ** *
1 2. 29 ** *
No rt h   A m eri ca 1 2. 63 ** *
Glo bal 1 1. 29 ** *
2.2 9 ** *
6.0 5 ** *
6.2 9 ** *
5.5 0 ** *
1 6. 72
3 4. 39 ** *
3 9. 64 ** *
3 2. 07 ** *
36 .7 2* *
4 7 .50 ** *
4 6 .75 ** *
39 .4 1* *
6 0 .30 * ** 64 .9 9* **
2 27 .8 5* ** 2 28 .2 2* **
2 06 .7 4* ** 2 07 .7 9* **
2 05 .0 9* ** 2 05 .2 8* **
Note: Ljung Box Q-statistic tests the null hypothesis of no autocorrelation; ARCH-LM (Lagrange multiplier) tests the null hypothesis of conditional homoscedasticity. ***, **, * - indicates significance at the 1%, 5% and 10% level respectively.
30

RE   mkt
Sub   1
Local   stock
Sub   2 Sub   3 Full   period Sub   1
US
UK
Australia
Japan
Hong   Kong
Singapore
Average
China
Malaysia
Philipinnes
Taiwan
Average
0.404
0.456
0.604
0.682
0.920
0.861
0.767
0.461
0.846
0.865
0.640
0.703
Notes:
Sub 1: January 1995 - December 2000
Sub 2: January 2001 - December 2004
Sub 3: January 2005 - December 2008
Full: January 1995 – December 2008
0.501
0.637
0.512
0.763
0.927
0.810
0.753
0.453
0.764
0.822
0.507
0.637
0.778
0.734
0.719
0.794
0.944
0.883
0.835
0.749
0.758
0.875
0.715
0.774
0.589
0.633
0.650
0.741
0.927
0.853
0.793
0.543
0.825
0.859
0.611
0.710
0.402
0.335
0.249
0.592
0.455
0.421
0.429
0.135
0.281
0.330
0.097
0.211
Regional   stock
Sub   2 Sub   3 Full   period Sub   1
0.496
0.633
0.223
0.676
0.520
0.465
0.471
0.174
0.350
0.064
0.283
0.218
0.769
0.694
0.602
0.751
0.765
0.754
0.718
0.548
0.414
0.611
0.558
0.533
0.585
0.595
0.393
0.665
0.553
0.501
0.528
0.283
0.302
0.349
0.287
0.305
0.373
0.327
0.277
0.278
0.482
0.420
0.364
0.086
0.281
0.464
0.035
0.217
Global   stock
Sub   2 Sub   3 Full   period
0.499
0.624
0.276
0.324
0.593
0.547
0.435
0.218
0.350
0.086
0.169
0.206
0.706
0.690
0.650
0.657
0.725
0.738
0.693
0.440
0.347
0.596
0.442
0.456
0.575
0.577
0.481
0.435
0.570
0.508
0.499
0.254
0.269
0.404
0.213
0.285
31

Australia
Japan
Hong   Kong
Singapore
China
Malaysia
Taiwan
Philippines
UK
US return equations a0 a1 real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock regional   stock global   stock real   estate local   stock
0.0021***
0.0018*** regional   stock 0.0020*** global   stock 0.0018** real   estate local   stock
0.0029***
0.0022*** regional   stock 0.0023*** global   stock 0.0018**
0.0025***
0.0022***
0.00028
0.0018**
0.0012
‐ 0.00026
0.00028
0.0018**
0.0019
0.0022**
0.00028
0.0018**
0.0018
0.00037
0.00028
0.0018**
0.0016
0.0025*
0.00028
0.0018**
0.00025
0.00073
0.00028
0.0018**
‐ 0.0011
0.00043
0.00028
0.0018**
‐ 0.00085
‐ 0.00078
0.00028
0.0018** v0
( ‐ 0.0754*)
‐ 0.0178
‐ 0.0401
‐ 0.0187
( ‐ 0.0902**)
‐ 0.0315
‐ 0.0401
‐ 0.0187
0.0154
0.0259
‐ 0.0401
‐ 0.0187
0.0353
0.0694*
‐ 0.0401
‐ 0.0187
0.0316
0.0032
‐ 0.0401
‐ 0.0187
0.000029***
0.000037***
0.00013
0.000039***
0.1034*** 0.000019***
0.0904** 0.000009***
‐ 0.0401
‐ 0.0187
0.00013
0.000039***
0.0352
0.0126
‐ 0.0401
‐ 0.0187
‐ 0.0119
0.0256
‐ 0.0401
‐ 0.0187
0.00021***
0.000027***
0.00013
0.000039***
0.00019***
0.000024***
0.00013
0.000039***
0.000001
0.000036***
0.00013
0.000039***
0.000073**
0.00019***
0.00013
0.000039***
0.000047***
0.000029***
0.00013
0.000039***
0.000025***
0.000017***
0.00013
0.000039***
0.1384*** 0.000031***
‐ 0.0557
0.000017***
‐ 0.00098
‐ 0.0187
0.000073***
0.000039***
0.0618
0.000014**
( ‐ 0.0801**) 0.000026***
( ‐ 0.0873*)
‐ 0.0187
0.000029*
0.000039***
***,   **,*  ‐  Denotes   significance   at   the   1%,   5%   and   10%   levels
Variance equation v1(arch) v2(garch) c(GJR term)
0.0803***
0.0861***
0.0041
0.0241
0.0882***
0.0634***
0.0041
0.0241
0.1865***
0.0059
0.0041
0.0241
0.0343***
0.0561***
0.0041
0.0241
0.0552***
0.0176
0.0041
0.0241
0.0221
0.0744***
0.0041
0.0241
0.0332*
0.0349
0.0041
0.0241
0.0078
0.0196
0.0041
0.0241
0.0271
0.0155
0.0302
0.0241
0.1430***
0.0202
0.0226
0.0241
0.9076***
0.8733***
0.7125***
0.7937***
0.8834***
0.9104***
0.7125***
0.7937***
0.7712***
0.9323***
0.7125***
0.7937***
0.8648***
0.9227***
0.7125***
0.7937***
0.9396***
0.7764***
0.7125***
0.7937***
0.8929***
0.6743***
0.7125***
0.7937***
0.8715***
0.8676***
0.7125***
0.7937***
0.9264***
0.8981***
0.7125***
0.7937***
0.8244***
0.8594***
0.7124***
0.7937***
0.8192***
0.8173***
0.8058***
0.7937***
‐ 0.0233
0.0613*
0.2084
0.2705***
0.0438*
0.0404***
0.2084
0.2705***
( ‐ 0.0711*)
0.1043***
0.2084
0.2705***
0.1893***
0.0866***
0.2084
0.2705***
0.0214
0.2414***
0.2084
0.2705***
0.1015***
0.2676***
0.2084
0.2705***
0.1552***
0.1484***
0.2084
0.2705***
0.1033***
0.1389***
0.2084
0.2705***
0.1944***
0.2405***
0.3435**
0.2705***
0.0731*
0.2364***
0.2476**
0.2705***
32

This table summarizes the estimated coefficients produced by the ADCC model in a bivariate framework for real estate markets’ local correlations (Panel A), real estate markets’ regional correlations (Panel B) and real estate markets’ global correlations (Panel C). A number of descriptive statistics of the time-varying correlations are reported, including values of the mean, maximum, minimum and standard deviation values of the estimated correlations among the sample real estate market. ***, **, * - Denotes significance at the
1%, 5% and 10% levels.
Panel   A:   real   estate   market   and   local   stock   market
0.9521*** 0.0420
0.6198
0.0596
0.7704
0.4442
Australia
Japan
US
0.0319**
0.0162**
Hong   Kong 0.0505***
Singapore 0.0552**
China
Malaysia
0.0422**
0.0519***
Taiwan
Philippines
UK
0.0316
0.0007
0.0008
0.0566***
0.9646***
0.7931***
0.9104***
0.9457***
0.8447***
0.5536**
0.9744***
0.8682***
0.0312**
0.0127
( ‐ 0.0354*)
‐ 0.0115
0.0488
0.0992
0.0232*
0.0764**
0.7230
0.9298
0.8416
0.5562
0.7893
0.6203
0.8357
0.5690
0.1075
0.0135
0.0349
0.1191
0.0823
0.0580
0.0268
0.0792
0.9021*** ‐ 0.0078
0.5154
0.1275
Panel   B:   real   estate   market   and   regional   stock   market
0.8901
0.9657
0.9287
0.8053
0.9465
0.8774
0.9075
0.8476
0.8081
0.3416
0.8598
0.7073
0.1026
0.5386
0.3003
0.7817
0.4103
0.0542
Australia
Japan
Hong   Kong
Singapore
China
Malaysia
Taiwan
Philippines
UK
US
0.0124
0.0503**
0.0258*
0.0109
0.0188**
‐ 0.0291
0.0463*
0.0266
0.0073
0.0649***
0.9681***
0.9192***
0.9548***
0.9773***
0.9635***
0.8813***
0.7404***
0.8629***
0.9851***
‐ 0.0066
0.0070
0.0103
0.0180
‐ 0.0147
0.0648*
0.1518**
0.0722**
0.0134**
0.2752
0.6327
0.4864
0.4584
0.2423
0.3369
0.2267
0.2926
0.4816
0.0601
0.1141
0.1033
0.1041
0.1379
0.0545
0.1599
0.1226
0.0482
0.8742*** ‐ 0.0058
0.5084
0.1264
Panel   C:   real   estate   market   and   global   stock   market
0.5045
0.8654
0.7868
0.7660
0.5670
0.5341
0.5889
0.6906
0.6075
0.8015
0.1755
0.2436
0.1946
0.1996
‐ 0.1452
0.1450
‐ 0.2259
‐ 0.0215
0.3459
0.0239
Australia
Japan
Hong   Kong
Singapore
China
Malaysia
Taiwan
0.0034
0.0362***
0.0639**
0.0676***
0.0125*
0.0354
0.0287
0.9812***
0.9446***
0.8377***
0.8794***
0.9697***
0.8509***
0.9186***
0.0099
‐ 0.0103
0.0377
0.0171
‐ 0.0127
0.0038
0.0151
0.3158
0.3717
0.5169
0.4493
0.2081
0.3297
0.1534
0.0510
0.1693
0.1242
0.1588
0.1040
0.0639
0.1003
0.5082
0.7662
0.8391
0.8346
0.4230
0.5716
0.4600
0.2233
‐ 0.1305
0.0732
‐ 0.1749
0.0322
0.1266
‐ 0.1919
Philippines
UK
US
0.0125
0.0027
0.0666***
0.9612***
0.9676***
0.8385***
0.0104
0.0061
‐ 0.0265
0.3488
0.4778
0.4656
0.0824
0.0462
0.0963
0.5516
0.6008
0.6783
0.1914
0.3858
0.0865
33

US
UK
AU
JP
HK
SG
CH
MAL
PH
TW
US
UK
AU
JP
HK
SG
CH
MAL
PH
TW
US
UK
AU
JP
HK
SG
CH
MAL
PH
TW
1
0.923
0.989
0.981
0.96
0.966
0.981
0.992
0.87
0.917
0.856
0.942
0.91
0.969
0.989
0.834
0.938
0.97
0.903
0.981
0.633
0.888
0.986
0.974
0.984
0.882
0.93
0.996
0.876
0.975
0.942
4
Lag   (weeks)
8 16 24 32 36
Panel   A:   Local   correlations   (real   estate  ‐  local   stock)
0.777
0.675
0.884
0.953
0.513
0.802
0.893
0.678
0.615
0.4
0.791
0.905
0.29
0.545
0.802
0.458
0.398
0.183
0.668
0.82
0.031
0.382
0.657
0.24
0.269
0.087
0.585
0.727
0.114
0.305
0.571
0.189
0.263
0.12
0.525
0.605
0.136
0.291
0.508
0.066
0.281
0.095
0.523
0.556
0.134
0.305
0.47
0.024
0.924
0.122
0.85
‐ 0.008
0.753
‐ 0.029
0.697
0.064
0.65
0.053
0.627
‐ 0.007
Panel   B:   Regional   correlations   (real   estate  ‐  regional   stock)
0.71
0.512
0.282
0.171
0.201
0.231
0.904
0.935
0.62
0.769
0.984
0.56
0.896
0.791
0.956
0.924
0.855
0.862
0.926
0.97
0.59
0.663
0.542
0.913
0.856
0.746
0.747
0.854
0.943
0.359
0.413
0.314
0.822
0.754
0.569
0.571
0.717
0.883
0.124
0.253
0.153
0.741
0.671
0.44
0.442
0.599
0.816
0.087
0.193
0.018
0.645
0.591
0.313
0.351
0.475
0.744
0.083
0.132
0.069
0.621
0.945
Panel   C:   Global   correlations   (real   estate  ‐  global   stock)
0.371
0.892
0.147
0.805
0.11
0.74
0.131
0.679
0.173
0.651
0.594
0.553
0.29
0.322
0.418
0.705
0.065
0.148
0.078
0.822
0.871
0.341
0.576
0.969
0.334
0.812
0.607
0.72
0.754
‐ 0.04
0.339
0.932
0.117
0.726
0.373
0.643
0.665
‐ 0.215
0.256
0.892
0.096
0.658
0.202
0.552
0.59
‐ 0.155
0.238
0.848
‐ 0.051
0.575
0.197
0.504
0.557
‐ 0.031
0.271
0.826
‐ 0.033
0.562
0.213
Notes:
Critical   values   for   ADF   unit   root   tests:
Without   trend:  ‐ 3.44(1%);  ‐ 2.87(5%);  ‐
2.57(10%)
With   trend:  ‐ 3.97(1%);  ‐ 3.42
(5%);  ‐ 3.13
(10%)
ADF   Unit   Root   test
W/O   trend With   trend
‐ 6.49***
‐ 0.95
‐ 1.16
‐ 1.47
‐ 6.62***
‐ 4.92***
‐ 0.68
‐ 6.93***
‐ 2.57*
‐ 4.02***
‐ 5.27***
‐ 0.76
‐ 0.39
‐ 3.61***
‐ 2.69*
‐ 1.26
‐ 0.81
‐ 7.04***
‐ 5.24***
‐ 7.43***
‐ 4.46***
‐ 5.70***
‐ 3.23**
‐ 1.68
‐ 8.09***
‐ 4.78***
‐ 3.13**
‐ 6.06***
‐ 2.12
‐ 12.75***
‐ 6.76***
‐ 2.24
‐ 1.52
‐ 1.74
‐ 6.90***
‐ 5.51***
‐ 1.89
‐ 7.10***
‐ 2.64
‐ 4.65***
‐ 5.66***
‐ 1.63
‐ 0.90
‐ 3.76**
‐ 3.80**
‐ 2.23
‐ 1.92
‐ 7.18***
‐ 5.38***
‐ 7.89***
‐ 4.90***
‐ 5.92***
‐ 3.59**
‐ 1.88
‐ 8.09***
‐ 4.82***
‐ 4.06***
‐ 6.88***
‐ 2.00
‐ 12.74***
34

Correlatio n t
   
* TREND
  i
This table reports the estimated deterministic linear trends in the time series of (local, regional, global) correlations. For each correlation, we report the t-ps test from Vogelsang (1998) and the t-dan test from Brunzel and Vogelsang(2005). The 5% critical value (two-sided) for t-ps is 2.152 , and for t-dan is 2.052. The sample period is January 1995 to December 2008. **, * - denotes significance at the 5% and 10% level respectively.
Real estate market
Local correlations Regional correlations Global correlations
US
UK
Australia
Japan
Hong Kong
Singapore
China
Malaysia
Philippines
Taiwan t-ps test t-dan test t-ps test t-dan test t-ps test t-dan test
Coefficient t-statistic coefficient t- statistic coefficient t-statistic coefficient t- statistic coefficient t-statistic coefficient t- statistic
‐
8.98x10
4.04x10
‐ 1.38x10
6.45x10
‐ 9.73x10
‐ 6.28x10
2.33x10
‐ 1.50x10
‐ 8.85x10
3.35x10
‐ 5
‐ 5
‐ 4
‐ 5
‐ 6
‐ 5
‐ 4
‐ 4
‐ 5
‐ 5
‐
‐
‐
‐
‐
0.268
0.422
0.263
0.016
0.485
0.730
0.892
2.431
0.067
1.412
2.
18x10
‐ 4
8.56x10
‐ 1.04x10
1.22x10
‐ 2.26x10
‐ 5
‐ 4
‐ 4
‐ 6
‐ 2.54x10
‐ 5
‐ 4
3.23x10
‐ 1.33x10
‐ 4
‐ 5
‐ 5.22x10
9.63x10
‐ 7
‐
‐
‐
‐
1.856*
3.235**
‐
1.143
0.927
0.176
0.011
0.204
0.327
0.067
0.0382
7.82x10
‐ 5
7.93x10
‐ 5
‐ 4.62x10
‐ 5
6.84x10
‐ 5
2.23x10
‐ 4
1.94x10
‐ 4
2.06x10
‐ 4
‐ 6.27x10
‐ 5
‐ 4.68x10
‐ 5
2.35x10
‐ 5
‐
‐
‐
0.306
0.029
0.000
0.118
0.821
0.105
0.016
1.338
0.170
0.487
1.93x10
9.74x10
‐ 7.98x10
1.14x10
2.78x10
2.54x10
3.93x10
‐ 4
‐ 5
‐ 5
‐ 4
‐ 4
‐ 4
3.92x10
‐ 4.68x10
9.68x10
‐ 4
‐ 5
‐ 5
‐ 5
‐
‐
1.374
0.005
0.000
0.223
1.021
0.032
0.005
1.347
0.501
0.871
‐
6.48x10
‐ 5
1.01x10
‐ 4
4.49x10
‐ 8.65x10
1.40x10
‐ 5
‐ 2.45x10
1.18x10
‐ 5
‐ 4
2.02x10
‐ 4
2.12x10
‐ 4
3.74x10
‐ 5
‐ 5
‐ 4
‐
‐
0.445
0.053
0.001
0.003
1.916*
‐
0.856
0.000
0.629
0.026
0.700
1.15x10
1.32x10
‐ 5.65x10
1.88x10
1.46x10
2.99x10
3.19x10
6.38x10
3.93x10
1.92x10
‐ 4
‐ 4
‐ 5
‐ 4
‐ 4
‐ 4
‐ 4
‐ 5
‐ 5
‐ 4
‐
1.366
0.013
0.000
0.006
1.852*
1.156
0.000
1.493
0.003
1.097
35

Correlatio n t
 f ( Correlatio n t

1
, Dummy crisis )
)
  t
, where Dummy is the crisis dummy crisis with 1 for 5 Jan 07 onwards and 0 otherwise. ***, ** and * - indicates significant at the 1, 5 and 10 per cent
levels respectively.
Dummy crisis
0.0132*** 0.0150*** 0.0100**
0.0086* 0.0023*** 0.0026**
0.00097 0.0075*** 0.0101*
0.0026** 0.0062** 0.0212***
0.0089*** 0.0037* 0.0002
-0.0003 -0.0013 0.0075**
0.0010* 0.0155* 0.0048
0.0096* 0.0266** 0.0078*
Time series Local integration Regional integration Global integration
Covariance
Correlation
Volatility (real estate)
Volatility (local stock)
Volatility (regional stock)
Volatility (global stock)
1298.31
6.54
182.31
219.42
-
-
1707.66
24.84
182.31
-
240
-
3978.93
26.74
182.31
-
-
334.38
Notes:
Before crisis: Jan 7, 2005 - December 29, 2006
During Crisis: January, 05, 2007 - January 3, 2009
36

Before
During
Before
During
Before
During
Before
During
Before
During
Before
During
Before
During
Before
During
Before
During
US
10.99
8.5
38.11
46.62
50.89
44.88
US
12.76
9.89
49.33
47.39
37.91
42.72
US
14.73
13.81
35.66
42.79
49.61
43.4
UK
9.22
7.43
41.51
41.52
49.27
51.05
UK
3.54
1.36
51.67
50.48
44.79
48.16
UK
2.27
1.43
49.2
45.45
48.53
53.11
Australia
10.63
5.33
51.85
68.99
56.76
52.67
VOLATILITY% (LOCAL STOCK)
45.88
49.71
50.86
53.72
51.34
56.27
VOLATILITY% (REAL ESTATE)
48.95
54.38
37.51
25.48
34.62
42.46
50.17
46.89
34.66
40.26
39.24
38.72
37.95
37.8
Australia
Panel B: (Real estate-regional stock) -"regional" covariance
Japan Hong Kong Singapore Average China
15.58
7.94
45.32
55.23
39.1
36.83
Panel A: (Real estate- local stock) - "local" covariance
Japan Hong Kong Singapore Average China
8.61
5.06
CORRELATION (%)
3.95
3.4
14.58
6.02
9.44
5
13.1
7.82
22.3
10.89
CORRELATION (%)
16.08
9.5
17.78
9.42
17.94
9.44
47
45.73
VOLATILITY% (REGIONAL STOCK)
39.59
45.08
53.07
53.39
46.25
49.86
VOLATILITY% (REAL ESTATE)
30.7
43.37
44.33
45.41
29.14
37.19
35.82
40.7
15.68
7.18
43.24
52.4
41.08
40.43
Australia
7.86
5.2
57.59
64.78
34.54
30.02
Panel C: (Real estate- global stock) - "global" covariance
Japan Hong Kong Singapore Average China
22.62
8.61
CORRELATION (%)
25.28
17.79
32.79
16.7
22.14
12.08
VOLATILITY% (GLOBAL STOCK)
8.93
4.3
52..87
55.42
24.51
35.97
42.85
49.01
50.06
57.14
50.84
56.59
VOLATILITY% (REAL ESTATE)
31.87
33.2
17.15
26.16
27.02
31.34
54.42
62.02
36.65
33.68
Malaysia Taiwan Philippines Average
17.14
13.69
21.19
21.45
1.63
1.75
13.27
11.18
31.76
41.43
51.1
44.87
Malaysia Taiwan Philippines Average
20.46
16.12
38.4
44.09
41.14
39.78
44.95
51.58
34.22
31.41
25.58
25.27
53.23
53.28
51.32
40.23
20.08
28.6
28.59
31.17
36.92
43.22
37.93
34.82
43.71
33.67
54.65
64.57
32.91
27.76
35.76
38.98
31.34
33.26
55.63
56.72
32.8
35.13
37.5
38.69
49.23
50.13
30.09
22.82
34.37
41.02
35.54
36.16
Malaysia Taiwan Philippines Average
20.83
17.01
25.15
21.96
11.57
8.15
16.62
12.86
47.98
53.39
35.4
22.76
37

Local   correlations
US
UK
Australia
Japan
Hong   Kong
Singapore
China
Factor   1
0.728
0.289
0.306
0.863
0.467
0.378
0.736
Factor   2
0.062
‐ 0.072
0.632
0.097
0.345
0.630
‐ 0.302
Malaysia
Philippines
‐ 0.097
‐ 0.158
0.736
0.725
Taiwan   0.035
0.223
Kaiser ‐ Myer ‐ Olkin   measure   of   sampling   adequacy(KMO)   =   0.632
Barlett’s   test   of   Sphericity   (BTS):   Chi ‐ square   =   1747.72
(sig   0.000)
%   of   variance   explained   24.03
21.42
Factor   3
0.219
0.601
‐ 0.499
‐ 0.185
0.250
0.187
0.198
‐ 0.077
0.284
0.657
13.48
Cumulative   %   of   variance   explained
Eigen   value
24.03
45.45
58.93
2.688
1.952
1.252
Notes :   This   table   shows   the   factors   and   factor   loadings   of   the   system   of   10   local   (
‐
)   correlations   using   Principal   Component   Analysis   and   Varimax   Rotation   technique.
Bolded   loadings
represent   the   highest   loadings   for   that   factor.
Regional   correlations
US
UK
Australia
Japan
Hong   Kong
Singapore
China
Factor   1
0.743
0.062
0.801
0.660
0.244
0.180
0.777
Factor   2
0.031
0.760
0.242
0.011
0.790
0.871
0.393
Factor   3
0.214
‐ 0.284
0.033
0.021
0.290
0.254
‐ 0.102
Malaysia
Philippines
‐ 0.147
0.401
‐ 0.155
0.268
Taiwan   0.178
0.419
Kaiser ‐ Myer ‐ Olkin   measure   of   sampling   adequacy(KMO)   =   0.740
Barlett’s   test   of   Sphericity   (BTS):   Chi ‐ square   =   2984.21
(sig   0.000)
%   of   variance   explained   25.37
24.46
Cumulative   %   of   variance explained
25.37
49.82
0.765
0.618
0.602
16.16
65.98
Eigen   value   3.817
1.441
1.340
Notes :   This   table   shows   the   factors   and   factor   loadings   of   the   system   of   10   regional   (real   estate ‐  regional   stock)   correlations   using   Principal   Component   Analysis   and   Varimax   Rotation   technique.
Bolded   loadings   represent   the   highest   loadings   for   that   factor.
38

Regional   correlations
US
UK
Australia
Japan
Hong   Kong
Singapore
Factor   1
0.614
0.880
0.509
0.791
0.135
0.518
Factor   2
0.234
0.146
0.122
‐ 0.042
0.846
0.621
Factor   3
‐ 0.078
0.038
0.739
0.336
0.037
0.276
China
Malaysia
0.812
0.039
0.042
0.731
0.136
0.185
Philippines
Taiwan
‐ 0.020
0.571
0.240
0.325
Kaiser ‐ Myer ‐ Olkin   measure   of   sampling   adequacy(KMO)   =   0.738
Barlett’s   test   of   Sphericity   (BTS):   Chi ‐ square   =   3446.71(sig   0.000)
0.918
0.202
%   of   variance   explained
Cumulative   %   of   variance   explained
33.12
33.12
18.93
52.05
16.82
68.87
Eigen   value   4.350
1.464
1.073
Notes :   This   table   shows   the   factors   and   factor   loadings   of   the   system   of   10   global   (real   estate ‐  world   stock)   correlations   using   Principal   Component   Analysis   and   Varimax   Rotation   technique.
Bolded   loadings   represent   the   highest   loadings   for   that   factor.
39

Correlatio n
global , t
 f ( correl global , t

1
, correl regional , t
, correl local , t vol re , t
, vol local , t
, ( vol re vol local vol re , t
) t

1
, vol regional , t
, ( vol re vol regional vol re , t
) t

1
, vol global , t
, ( vol re vol global
) t

1
  t
)
Constant
Correlatio n global , t

1
Correlatio n regional , t
Correlatio n local , t
Vol re
0.0571
(10.36***)
0.4297
(19.76***)
1.070
(17.27***)
-06493
(-10.69***)
0.0779
(2.59***)
-0.0095
(-3.43***)
0.9575
(134.84***)
0.0218
(3.38***)
0.0324
(10.28***)
0.0018
(3.72***)
0.0023
(0.83)
0.9567
(83.18***)
0.0443
(4.85***)
-0.0078
(-6.18***)
-0.269
(-4.35***)
0.9599
(124.97***)
0.0773
(8.81***)
0.0046
(3.26***)
Kong
-0.5181
(-3.53***)
0.8258
(42.54***)
0.1050
(4.44***)
(3.73***)
-0.0664
(-4.37***)
-0.2559
(-4.97***)
0.8491
(48.85***)
0.0871
(3.74***)
(5.90***)
-0.0825
(-7.93***)
0.0037
(2.02**)
0.9786
(144.82***)
0.0202
(3.28***)
-0.0068
(-1.71*)
-0.0017
(-2.15**)
0.0353
(5.59***)
0.8654
(46.70***)
-0.0611
(-2.44**)
0.8873
(81.17**)
(12.26***)
(3.05***)
(8.06***)
-0.0525
(-4.50***)
0.8478
(66.89***)
(12.38***)
(4.44***)
(-3.23***) vol local
Vol
( vol re local
) t

1
-0.0562
(-1.87*) (5.53***0 (-2.25**)
0.0657
(4.29***)
0.0602
(5.69***)
0.0024
(3.09***)
0.0045
(2.14**)
-0.0193
(-8.80***)
0.0064
(4.06***)
Vol re vol regional
-0.0960
(-2.98***)
-0.0049
(-4.29***)
0.0051
(1.98**)
-0.0107
(-4.64***)
0.0096
(3.18***)
0.0010
(1.69*) (4.62***) (-2.16**)
-0.0042
(-4.98***)
(
Vol re vol regional
) t

1
0.0842
(2.62***)
0.0046
(3.97***)
-0.0053
(-1.84*)
0.0089
(3.90***)
-0.0067
(-2.21**) (-3.98***) (2.63***)
-
Vol re vol global
0.0131
(2.14**)
0.0020
(2.00**) (-2.52**) (-2.11**) (5.42***) (-4.06***)
-0.001
(-4.20***)
0.0019
(4.54***)
Vol
(
Vol re global
) t

1
Adjusted R2
Durbin Watson
-0.0222
(-3.84***)
0.906
1.12
-0.0029
(-3.00***)
0.984
2.01
0.970
2.11
0.978
1.97
0.800
1.94
0.887
2.02
(-6.70***)
0.994
2.05
(3.92***)
0.777
2.06
0.0012
(2.75***)
0.969
1.95
-
0.924
1.90
40

Following from the factor analysis, we capture changes in the general co-movements of the global, regional and local correlations as well as the influence of the volatility, as a factor, that may cause the correlations to change over time for the full sample. This table reports the stepwise regression results for the three models whose dependent variables are the real estate-global stock correlation (Global correlation)’ three principal components (i.e. PCA1 for Model 1; PCA2 for
Model 2 and PCA3 for Model 3) derived from the factor analysis; the independent variables are the lagged one-period real estate-global stock correlation,
Dummy (the crisis dummy with 1 for 5 Jan 07 onwards and 0 otherwise), three PCAs for the regional and local correlations, three PCAs for the relative crisis volatility series (ratio of the real estate volatility to local stock volatility, ratio of the real estate volatility to regional stock volatility and ratio of real estate volatility to global stock volatility) as well as three one-period lagged relative volatility PCA series.
Model Dependent variable Lagged correlation
Regional correlation
Local correlation
Independent variables crisis
Vol re vol local
(
Vol re vol local
) t

1
Vol vol re regional
(
Vol re vol regional
) t

1
Vol vol re global
(
Vol
Vol re global
) t

1 correlation I correlation
II
(67.16***)
II Global 0.9085
(57.70***)
III Global 0.9596 correlation (110.92***
III )
0.0336
(2.91***)
0.0386
(4.81***)
0.0522
(4.00***)
0.0711
(3.00***)
-0.0785
(-4.24***)
(-6.22***)
0.0591
(3.15***)
(5.54***)
-0.0176
(-2.22**)
-0.3253
(-6.66***)
0.3111
(6.24***)
(-2.06**)
Adj
R2
- 0.0923 0.0194
(3.81***) (2.45**)
41

42

43

44

US
.2
.1
.0
.7
.6
.5
.4
.3
.9
ADCC
.8
1996 1998 2000 2002 2004 2006 2008
USLOCAL USREGIONAL USWRD
.9
ADCC
.8
.7
.6
.5
.4
.3
UK
1996 1998 2000 2002 2004 2006 2008
UKLOCAL UKREGIONAL UKWRD
Australia
.3
.2
.1
.8
ADCC
.7
.6
.5
.4
1996 1998
AULOCAL
2000 2002 2004
AUREGIONAL
2006
AUWRD
2008
Japan
0.6
0.4
1.0
ADCC
0.8
0.2
0.0
-0.2
1996 1998
JPLOCAL
2000 2002 2004
JPREGIONAL
2006 2008
JPWRD
45

Hong Kong
1.0
ADCC
0.8
0.6
0.4
0.2
0.0
1996 1998 2000 2002 2004 2006 2008
HKLOCAL HKREGIONAL HKWRD
China
0.6
0.4
1.0
ADCC
0.8
0.2
0.0
-0.2
1996 1998 2000 2002 2004 2006 2008
CHLOCAL CHREGIONAL CHWRD
Singapore
0.6
0.4
1.0
ADCC
0.8
0.2
0.0
-0.2
1996 1998 2000 2002 2004 2006 2008
SGLOCAL SGREGIONAL SGWRD
Malaysia
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1.0
ADCC
0.9
1996 1998 2000 2002 2004 2006 2008
MALOCAL MAREGIONAL MAWRD
46

Philipinne
0.6
0.4
0.2
1.0
ADCC
0.8
0.0
-0.2
1996 1998 2000 2002 2004 2006 2008
PHLOCAL PHREGIONAL PHWRD
Asia developed
.7
.6
.5
.4
.3
.2
.1
.0
.9
ADDC
.8
1996 1998
LOCAL
2000 2002 2004 2006 2008
REGIONAL GLOBAL
Taiwan
-0.2
-0.4
0.2
0.0
0.6
0.4
1.0
ADDC
0.8
1996 1998 2000 2002 2004 2006 2008
TWLOCAL TWREGIONAL TWWRD
Asia developing
.6
.5
.4
.3
.2
.1
.0
.9
ADDC
.8
.7
1996 1998 2000 2002 2004 2006 2008
Local Regional Global
47

LOCAL
.7
.6
.5
.4
.3
.2
.1
.0
.9
ADCC
.8
1996 1998 2000
US
Asia developed
2002 2004 2006
UK
Asia developing
2008
REGIONAL
.3
.2
.1
.0
.9
ADDC
.8
.7
.6
.5
.4
1996 1998 2000
US
Asia developed
2002 2004 2006
UK
Asia developing
2008
GLOBAL
.3
.2
.1
.0
.6
.5
.4
.8
ADDC
.7
1996 1998 2000
US
Asia developed
2002 2004 2006
UK
Asia developing
2008
48

70
60
50
40
30
20
10
0
Correlation Local stock volatility Real estate volatility
49

FIGURE 5
Regional interdependence- contribution (%) of correlation, regional stock volatility and real estate volatility to covariance change - before (BEF) and during (DUR) the current global financial crisis
60
50
40
30
20
10
0
Correlation Regional stock volatility Real estate volatility
50

Figure 6
Global interdependence- contribution (%) of correlation, global stock volatility and real estate volatility to covariance change - before (BEF) and during (DUR) the current global financial crisis
70
60
50
40
30
20
10
0
Correlation global stock volatility Real estate volatility
51