Cyclical impact of business cycle co-movements on public property market
correlations: an empirical exploration
This draft: 20 April 2013
By: Liow Kim Hiang, Department of Real Estate, NUS, paper to be presented at the 2013 Asian Real Estate Society
Conference, June 28 – July 1, Kyoto, Japan; email: rstlkh@nus.edus.g
Abstract
Globalization and the emergence of China in the world economy have been two major events in the
international economic literature over the last two decades. The key research question addressed in this paper
is whether and to what extent the co-movement of business-cycle fluctuations affects public property market
correlations and integration within the three GC economies and with their five regional and global partners
over the period 1995 Q1 to 2012Q3. Since public property market is a component part of domestic stock
market, we control for the influence of stock market cycle correlations. In order to capture dynamic comovement, our main methodology appeals to an alternative measure of business cycle synchronization –
dynamic correlation proposed by Croux et al. (2001). We find the cyclical co-movement and interdependence
dynamics between public property market correlations, business-cycle correlations and stock market
correlations have been quite different at three time horizons (long-run frequencies, traditional business-cycle
frequencies and short-run frequencies) although the pro-cyclical links between the respective correlation-types
are generally statistically significant at the aggregate frequency level. We also found evidence that the detected
pro-cyclical co-movement dynamics are different within and across the GC regions. Finally, our results using
the GC dataset should be regarded as indicative and implies that the cyclical variations in quarterly public
property market cycle correlations could be highly dependent on the business-cycle co-movement
dynamics at different time horizons and corresponding stock market cycle correlations, and is hence more
complex than what is normally expected.
Keywords:
1.
co-movement, dynamic correlation, business-cycles, public property market cycles, stock
market cycles, Greater China
Introduction
An important general measure of a country’s economic states that is widely analyzed in the
economic literature is the business cycle. Motivated by Ragunathan et al. (1999) who argue that the extent
to which financial and economic integration are related indicate that business cycles require careful study
as a potential driver for shift in the degree of capital market integration, we study the cyclical link
between business cycle co-movements and public property market correlations within three Greater China
(GC) economies (Mainland China, Hong Kong and Taiwan), as well across between each of the GC
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economies and their selected international and regional partners over the period 1995Q1-2012Q3. This
underlying motivation of the research is further supported by the move towards globalization of financial
markets which could result in cross-market linkages being affected by the co-movements of business
cycles, and could either happen internationally or in a regional setting. However, Kizys and Pierdzioch
(2006) have noted that whether the co-movement of business cycles is a factor that explain changes in
international stock market correlations has remained as an unsettled question in the international
macroeconomic and finance literature. Our research hopes to alternative evidence.
Specifically, as an increasingly influential emerging country, China is aspiring to become one of
the largest economies in the world. China's impressive economic success so far is attracting the attention
of academics, practitioners, and policy makers worldwide. The capital market is set to play a crucial role
in China's development since the efficient allocation of financial resources is a key determinant of
economic growth. Moreover, an informal economic region that embraces Mainland China, Hong Kong
and Taiwan is rapidly emerging as a new epicenter for industrial, commerce and Finance. Increasing
globalization of financial and economic activities is expected to impact real estate market which is a
significant asset component in the three GC economies. As real estate companies invest in real properties
and are themselves real estate vehicles for other investors in international capital markets, an in-depth
study of cross-market correlations and how they are altered by business cycle changes will provide fresh
insights into the nature and extent of interdependence between business-cycle correlations and public
property market integration or segmentation within and across the GC context.
On this issue of business cycle and public property cycle integration, we note that whilst authors
such as Longin and Solnik (1995) as well as Erb, Campbell and Viskanta 1994) have indicated that
business cycle fluctuations tend to influence international stock market correlations; others like Karolyi
and Sultz (1996); King, Sentana and Wadhwani (1994); Ammer and Mai (1996) as well as Kizys and
Pierdzioch (20006) have documented a negligible relationship or found no relationship between
international stock market correlations and business cycle co-movement. Since real estate securities
2
market is a component part of the domestic stock market, we are interested to find out the nature and
extent of relationship between property equity correlations and business cycle correlations as there is no
a-priori reasons to expect that public property markets should behave similar to general stock markets
with regard to the link with business cycle correlations. This is also an increasing important topic for four
main practical reasons: (a) Asia-Pacific is the most dynamic region and its regional integration, as well as
its global integration (i.e. vis-à-vis rest of world) deserve critical attention from international investors
and policy makers; (b) the emerging discussion on convertibility of Renminbi (Chinese Yuan), and an
Asian zone implies a need for more research on contagion, market linkages, capital mobility and business
cycle synchronization between and among countries of the region; (c) the increasing “tradability” of the
ultimate non-tradable asset – real estate, through its complex linkages with the financial world via the
development of a global real estate capital market; and (d) as the economic activities among Mainland
China, Hong Kong and Taiwan have increased over the years, the significance of “common business
cycles” among these GC economies may have increased. With real estate assets sharing the same trend,
the multilateral economic activities could have contributed a great deal to the correlation relationships
among the three GC public property markets. Similarly, closer economic integration has raised the
question of whether the business cycles in the three GC economies have become more synchronized over
time as well as its consequent impact on the degree of capital market (stock and public property)
integration.
In adding to the existing body of knowledge concerning global financial market integration, this study
makes important contribution. It utilizes recent advances in time series econometrics for a relatively long
period of time (1995Q1 to 2012Q3) to investigate the link between business cycle co-movement, public
property market cycle correlation and stock market cycle correlation from the cyclical perspective among the
three GC economies, as well their international relationships. To our knowledge, whilst previous studies
have sought to explain business cycle correlations in a cross-country context, less is known regarding the
3
link between public property market correlations and business cycle co-movements. This is where our
study hopes to contribute. Specifically, our empirical contributions are the following:

We estimate a Hodrick-Prescott (HP) cycle for each business cycle, real estate and stock time
series. The cyclical co-movements between the HP business cycles, between the HP public
property price cycles and between the HP stock market price cycles for the eight markets are then
examined for their contemporaneous correlations and semi-correlations. A simple Spearman rank
order correlation analysis is conducted to ascertain whether the magnitude of the stock /public
property cycle correlations is correlated to the magnitudes of business cycle correlations.

We derive an alternative measure of business cycle, property market cycle and stock market cycle
co-movements - dynamic correlation. Following Croux et al (2001), dynamic correlations can be
interpreted as a decomposition of aggregate correlation into co-movement at particular
frequencies. This approach further allow us estimate the degree of co-movement of the
correlations at three different time horizons: business cycle frequencies, long-run frequencies and
short-run frequencies.

We undertake a regression-based analysis in order to formally assess whether the co-movements
of business cycle fluctuations are significantly linked to the property market cycle correlations
after controlling for the stock market cycle correlation effect at long-run frequencies, business
cycle frequencies and short-run frequencies. To increase the power of estimation, we estimate
regression models for panel-data using feasible generalized least square (FGLS), random effect
GLS and dynamic GMM techniques in order to check the robustness of the results.
The plan of this paper is as follows. Section 2 provides a brief update of the relevant literature.
Section 3 contains the description of the data used while the explanation of the methodologies that are
utilized in the paper is included in Section 4. Section 5 provides a discussion of the results. Section 6
concludes the paper.
4
2.
Literature review
Duarte and Holden (2003) have noted that during the last two decades, there has been an
increased interest in the study of business cycles within and between countries. In Europe, the term
“synchronicity” is associated with the concept of symmetry to indicate the convergence and greater
harmonization of the business cycles. Gerlach (1988) studied the monthly industrial production index and
found evidence of a world business cycle. Other studies on business cycle relationship include Baxter and
Stockman (1989), Artis and Zhang (1997), Duarte and Holden (2003), Sato and Zhang (2006), Jarko and
Ivana (2008) and Koopman and Azevedo (2008).
Second, Barras (1994) has documented the interaction of property market with the four- to fiveyear business cycle, giving rise to an office development cycle of eight to ten years duration. The RICS
(1994) has also produced comprehensive reports on the causes, dynamics and implications of commercial
real estate cycles. An underlying theme is that an improved understanding of real estate cycles is vital in
the property investment process. The RICS report covers the UK real property returns for a period 1962 to
1992, with real estate cycle duration of four to five years. Its main objectives are to prove that property
cycles exists, that it is systematically related to movements in the economy, and that most of the
relationships can be quantified through statistical analysis. The report defines property cycles as “a
recurrent but irregular fluctuations in the rate of all-property total returns, which are also apparent in
many other indicators of property activity, but with varying leads and lags against the all-property cycle”.
The report further demonstrates that commercial property cycles are linked to the economic cycle and
some internal mechanisms within the property industry. Since stock markets and public property markets
are part of the domestic economy, they are expected to be influenced by the business cycle fluctuations.
McGough and Tsolacos (1995) use the HP-filter method to detect pro-cyclicality between GDP,
manufacturing and business output in the office and industrial sectors in the United Kingdom. A procyclical pattern was also established for demand side variables such as GDP, consumer expenditure and
5
no-food retail sales in relation to the retail property cycle. Employing the same methodology, Brooks,
Tsocalos and Lee (2000) fit a long-term trend to the various economic time series in the UK, and to derive
short-term cycles of each series. They find that the cycles of consumer expenditure, total consumption per
capita, the dividend yield and the long-term bond yield were moderately correlated, and mainly
coincident, with the property stock price cycle. The approach in the study is largely based on the long
tradition of research on the stylized facts of business cycles. In another study by Witkiewicz (2002), the
author uses HP-filter technique to identify cycles in the real estate market and to construct real estate
cycle indicators. His findings suggest that the HP-filter offers a means for constructing such indicators
and for correctly identifying the turning points that are largely dependent on the real business cycles.
With increasing globalization of global financial markets, there are considerable studies
investigating the correlation structure across GC and international stock markets (e.g. Cheng and
Glascock, 2005; Caporale et al. 2006; Qiao et al, 2008 and Johansson and Ljungwall, 2009), as well as
among international public property markets (e.g. Liow et al, 2009; Liow, 2012; Liow and Newell, 2012).
In these studies, increased time-varying correlations imply higher stock/ real estate stock market
integration. Liow and Newell (2012) investigate simultaneously the effects of volatility spillover and
time-varying conditional correlation on the cross-market relationships between the three GC securitized
real estate markets, as well as their international links with the US securitized real estate markets. They
find that the conditional correlations between the GC securitized real estate markets have outweighed
their conditional correlations with the US market, supporting closer integration between the GC markets
due to geographical proximity and closer economic links.
3.
Data
We use data on real GDP to measure business cycle fluctuations. The quarterly data covers from
1995Q1 to 2012Q3 in eight countries, and comes from the International Financial Statistics issued by the
IMF. In addition, we include Standard & Poor’s quarterly closing public property indexes and stock
market indexes for three GC {China (CH), Hong Kong (HK), Taiwan (TW)} and five non-GC economies
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{Australia (AU), Japan (JP), Singapore (SG), the US and the UK} over the same period. The US, UK, JP
and AU have well-developed financial markets and open capital accounts. In particular, the US and JP are
the main investors and trading partners with the GC economies. SG, due to its geographical proximity and
cultural similarity, has had close economic ties with the three GC economies. Exhibit 1 provides the
mean, standard deviation, maximum and minimum of the real GDP growth, public property market
returns and stock market returns (for local dollars) over the full study period.
(Exhibit 1 here)
4.
Research methodology
We use two complementary approaches in our analysis. First we apply the Hodrick-Prescott
(1980) (HP) filter to determine the cyclical component of the GDP, property and stock series. The HP
filter is a de-trending technique that is widely used in the economic literature. Second, an alternative
measure of business cycle synchronization is dynamic correlation, as was proposed by Croux et al.
(2001). We use the methodology to estimate the dynamic correlation measure for the business cycle,
property market cycle and stock market cycle series at three different time horizons: business cycle
frequencies, long-run frequencies and short-run frequencies. Panel regressions are conducted to determine
whether business cycle co-movements significantly affect property market cycle correlations after
controlling for the stock market cycle correlations under different time horizons. The main points of each
of the methods are explained below
4.1
Hodrick-Prescott (HP) Filter
Bilateral correlations in business cycles, property market cycles and stock market cycles are
computed on the basis of the cyclical components of quarterly real GDP; real estate and stock market time
series indexes, isolated using the bandpass filter introduced by Hodrick and Prescott (1980) (HP) which
7
has been used extensively in the literature.1 The HP methodology consists of determining the cyclical
component of the data in analysing the degree of synchronization /correlation between any two series.
Further, the HP-filter is a linear filter that decomposes a time series into a cyclical component and a
growth component. Its basic assumption is that the trend growth rate is smooth, and the cyclical
components sum to zero over time. The Lagrangian equation set up by this technique is expressed as
follows, where the square of the cyclical part of a variable is minimised, subject to a smooth growth rate
(Hodrick and Prescott, 1980, 1997)
T
T
t 1
t 1
Min { ct2    [( g t  g t 1 )  ( g t 1  g t  2 )]2 } {g t }T t  1
where ct and gt represent the cyclical and trend component of variable Yt, respectively, with
Yt  ct  g t . For quarterly time series,  =1600.
After the HP filter was used to fit a smooth trend to all time series, the cyclical component of
each series could be derived. The cycle of each series is defined as the deviations of the actual values
from the HP trend fitted to the series (i.e. cycle = actual series – HP trend). Following this procedure, the
cyclical co-movements between the HP real GDP cycles, between the HP public property price cycles and
between the HP stock market price cycles for the eight economies are examined for their
contemporaneous correlations and semi-correlations.2 Finally, a simple non-parametric Spearman rank
order correlation analysis is conducted to determine whether higher magnitudes of the unconditional stock
1
For example, the HP Filter has been applied by the European Commission to estimate output gaps, which is the
difference between actual and potential GDP in Europe.
2
For the purpose of this study, we focus on 18 synchronization/ correlation pairs; (a) within the GC areas (between
CH-HK, CH-TW, HK-TW); (b) China and international (between CH-JP, CH-SG, CH-AU, CH-US, CH-UK); (c)
Hong Kong and international (between HK-JP, HK-SG, HK-AU, HK-US, HK-UK) and (d) Taiwan and
international (between TW-JP, TW-SG, TW-AU, TW-US and TW-UK). Groups (b), (c) and (d) together are also
labeled as across the GC regions.
8
/property stock correlations/semi-correlations are associated with higher magnitudes of business cycle
correlations / semi-correlation; or vice-versa.
4.2
Dynamic correlation
Although the classical correlation and semi-correlation analyses are two popular approaches for
describing co-movements between economic variables, they are basically static analyses that fail to
capture any dynamics in the co-movements of business cycles/capital market cycles. We use an
alternative measure of synchronization, dynamic correlation, which can be decomposed by frequency and
frequency band in order to study business cycle synchronization, property market cycle correlation and
stock market cycle correlation at different time horizons. Following Croux et al (2001), if S x ( ) and
S y ( ) are the spectral density estimates of x and y and C xy ( ) is the co-spectrum, which are defined for
all frequencies       , then the dynamic correlation is
 xy ( ) 
C xy ( )
S x ( ) S y ( )
……….(1)
The dynamic correlation coefficient is defined to be between -1 and +1. In addition, the average
value of dynamic correlation over all frequencies is approximately equal to the static correlation. It can be
interpreted as a decomposition of the aggregate correlation into co-movements at particular frequencies.
Croux et al. (2001) further point out that the interpretation of coherence (or squared coherency) is quite
similar to dynamic correlation. To motivate the dynamic correlation measure, we perform co-spectral
analyses to obtain the three component estimates in (1) for the 18 economy pairs’ GDP, property and
stock market cycles at each frequency. Co-spectral analysis is a means if revealing variance and
covariance, but in the frequency domain. On the basis of NBER studies, we look at three components of
the aggregate dynamic correlations. First, the long-run movements (over 8 years) correspond to the low
frequency band below  /16. Second, the traditional business cycles (lasting between 1.5 and 8 years)
belong to the medium part of the figure and are between  /16 and  /3. Finally, the short-run
9
movements are defined by frequencies over  /3. We examine the average dynamic correlation of the
GDP/public property/stock cycles at these three time horizons and search for preliminary evidence
regarding the co-movements between the dynamic correlations of business cycles, public property market
cycles and stock market cycles in our data set using Spearman rank correlation analysis..
Finally, we formally test the cyclical co-movements between property market correlations (RE),
business cycle correlations (GDP) and stock market correlation (ST) for the 18 economy pairs using the
dynamic correlation estimates. In this way, we are interested to find out whether and to what extent
business cycle co-movements could affect property market cycle correlation after controlling for the stock
market cycle correlation at the long-run, business cycle and short-run frequencies. By undertaking an
orthogonalization in Model II, we hope to extract the co-movements in the public property market in
excess of the general stock market correlations, so that any residual relationship between the detected
market correlation and business cycle co-movement can be reasonably attributed to the public property
markets per se.
We run the following two models:
RE freqwency  f (GDPfrequency) ……………………Model I
RE frequency  f (GDPfrequency, ST frequency) ………………………Model II
The two models are specified for the 18 economy-pairs. The regression series are estimated in
three different ways. First, all 18 equations are estimated as a pooled cross-sectional model to minimize
unobserved panel-level effects, thereby constraining all regression coefficients, except the intercepts, to
be identical across all 18 equations. As the Hausman test indicates the GLS random effect estimator is
better than a within estimator for fixed effect models, we estimate the two models with a GLS estimator
for random effects model. Second, we estimate the two models using feasible generalized least square
(FGLS) to account for possible cross-sectional correlation dependence and heteroskedasticity across
panels (i.e. the variance for each of the panels differs). Finally, we use a consistent generalized method of
10
moment (GMM) estimator (Arellano and Bond, 1991) to re-estimate the two models. This dynamic panel
GMM model is implemented to render the unobserved panel effects orthogonal to the one-quarter lagged
correlation variable, as well serves a robustness check on the pooled sample specification.
5.
Results
Applying the HP filter on the time series, Exhibit 2 shows average business-cycle correlation,
public property market cycle correlation and stock market cycle correlation for the 18 economy-pairs. We
observe that the highest business-cycle (GDP cycle) correlation is found between Hong Kong and
Singapore (0.7985). This highest business cycle correlation corresponds with the highest property cycle
correlation (0.8750), as well as the stock market cycle correlation (0.9099) between Hong Kong and
Singapore. Additional Spearman rank correlation estimates for the full sample are 0.472 (average rank
coefficient between GDP cycles and stock market cycle correlations), 0.412 (average rank coefficient
between GDP cycles and property market cycle correlation) and 0.474 (average rank coefficient between
stock market cycles and property market cycle correlation), indicating for the entire sample, higher
business-cycle correlations are moderately associated with higher real estate market cycle correlations and
higher stock market cycle correlations over the entire study period.
(Exhibit 2 here)
In order to test whether the link between the property market cycle correlations, stock market
cycle correlations and the co-movements of business cycle fluctuations exists, we define phases of
economic booms and recessions. Using the quarterly real GDP growth rate as a measure of economic
performance, we define “common-up” when a pair of economies experienced a boom (positive real GDP
growth) at the same quarter. Similarly, we define “common-down” when a pair of economies experienced
a recession (negative real GDP growth) at the same quarter. Finally, we define a “mixed market” when
the business cycles of a pair of economies were out-of-phase. The semi-correlation results are reported in
Exhibit 3.1.
11
(Exhibit 3.1 here)
It is unclear from the numbers that the magnitudes of HP property cycles /HP stock market cycles
semi-correlations are systematically associated with the magnitudes of the HP business cycles’ semicorrelations. For the full sample, additional Spearman rank correlation analysis (Exhibit 3.2) reveals that
whilst the rank correlation coefficient between the business-cycle correlations and property market cycle
correlations are higher in “common-down” market condition (0.627) than in “common up” market
condition (0.539), the reverse is true between the business-cycle correlations and stock market cycle
correlations during “common-down” (0.455) and during “common-up” (0.549) market periods. It is
therefore interesting to examine more formally by means of dynamic correlation analysis any conclusive
link between property market cycle correlations, stock market cycle correlations and the co-movements of
business cycle fluctuations.
(Exhibits 3.2 here)
Exhibit 4 compares average dynamic correlations at the business cycle, short-run and long-run
frequencies for the 18 country-pairs and 4 regional-pairs. It must be cautioned that the long-run results
should be interpreted with great caution due to the small number of observations available for each
economy pairs 3 . Within the three GC economies, the business cycles display low levels of dynamic
correlation (between 0.219 and 0.405), especially for the short-run frequencies. Similarly, there are
between low and moderate correlations of property cycles between CH, HK and TW, implying that three
are diversification opportunities in the GC public property markets, especially for the short-run
frequencies. At the business-cycle frequencies, the three GC economies (as a group) display the highest
average GDP cycle correlation (0.555), property market cycle correlation (0.457) and stock market cycle
3
For each economy pair, the co-spectral results have 3 observations for the long run frequencies, 12 observations for
the business cycle frequencies and 24 observations for the short run frequencies.
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correlation (0.755). Across the GC areas, the numbers indicate the average dynamic correlation of the
GDP, property and stock market cycles are the highest for the business cycle frequencies for Chinainternational (0.428, 0.403 and 0.622) , Hong-Kong-international (0.570, 0.583 and 0.765), as well as
Taiwan-international (0.520, 0.444 and 0.658). These results imply that the interdependences between the
economic and capital market (stock /public property markets) development in the three GC economies
and in developed economies are becoming similar for the business cycle development and more
permanent shocks should influence the business cycle frequencies. Next whilst all dynamic correlation
estimates are positive for the business frequencies, there are several instances of negative correlations for
the long-run and short-run frequencies. For example, the correlations of business cycles are negative
between CH and the US, between CH and the UK, between HK and the US, as well between HK and AU
for the long run frequencies. In addition, CH’s stock market cycle is negatively correlated with those of
US and UK. For the short-run frequencies, negative correlations of business cycle are found between HK
and the UK, HK and AU, as well as between TW and US. In addition, the property market cycle of TW is
negatively correlated with those of the US and AU, albeit at the very low negative levels (-0.033 and 0.070 respectively). Overall, the correlations of business cycle, property cycle and stock market cycle
between each of the three GC economies and two Asian partners (JP and SG) are stronger than those of
non-Asian partners (The US, the UK and AU) probably because of geographical proximity and some
similarities in their economic development, especially with SG.
Finally, additional Spearman rank
correlation analyses for the full sample reveal that the co-movement between the business cycle
correlations and property market cycle correlation (0.521), as well as between the business cycle
correlations and stock market cycle correlations (0.684) are the highest and statistically significant at least
at the 5% level for the long-run frequencies compared to those for the business cycle frequencies and
short-run frequencies.
(Exhibit 4 here)
13
Exhibits 5.1 to 5.4 present dynamic correlation profile of business-cycles, property market cycles
and stock market cycles within and across the CH-international, HK-international and TW-international
groups, separated by the long-run, business cycle and short-run frequencies One key observation from
the graphs is that while there are pro-cyclical co-movements across the three dynamic correlation types
(GDP, property and stock) for most of the long run and certain business cycle frequencies for some
economy-pairs, the co-movements between the correlations of business cycles, property cycles and stock
market cycles fluctuate in different patterns especially at various short-run frequencies. The general
pattern of co-movement across the three correlation-types over the short-run cycles is not as clear as
might have been expected. In general, the cyclical co-movements are weaker (and in some cases negative)
although there is ad-hoc evidence of high positive co-movements between the dynamic correlations of
business cycles, public property and stock market cycles at intermediate short-run frequencies.
(Exhibit 5 here)
Within the GC region, Exhibit 6 shows the average co-movement (represented by the Spearman
rank coefficient) between the dynamic correlations of business cycles and property cycles is positive
(0.333) for long run frequencies; but is negative for business cycle (-0.117) and short-run (-0.009)
frequencies. In contrast, the relevant average co-movement is all positive for all three time horizons
between each of the three GC economies and their global partners. Of the 18 economy-pairs, there are,
respectively, 3 cases and 11 cases of negative cyclical co-movement between the business-cycle
correlations and property market cycle correlations for the traditional business cycle and short run
frequencies. Similarly, we find another 4 cases (business cycle frequencies) and 9 cases (short-run
frequencies) of negative cyclical co-movements between the business cycle correlations and stock market
cycle correlations over the same period.4
(Exhibit 6 here)
4
Due to lack of data for long run frequencies, we were not able to estimate the relevant Spearman correlation
coefficients for each economy-pair.
14
Finally, Exhibit 7 reports the results of estimating Model 1 and Model II as a pooled crosssectional and time series regression using (a) a generalized least square (GLS) estimator for random effect
model; (b) feasible GLS method; and (c) dynamic panel GMM estimation. The dependent variable is the
Fisher-transformed dynamic correlation coefficient since the raw estimates are not normally distributed.
At an aggregate frequency level (Panel A), we find, except for the China-international group, there are
significant pro-cyclical movements between property market cycle correlations and business cycle
correlations for other five groups, a finding that is reasonably expected. However, when the effect of the
underlying stock market correlation is controlled, the link between the property market cycles and
business-cycles becomes somewhat weaker since significant pro-cyclical co-movements between the
dynamic correlations of property cycles and business cycles are only detected for four groups.
Nevertheless, these findings imply that the co-movement of business cycle fluctuations is a potential
driver in influencing the degree of public property integration and is in agreement with the impact of the
economic activity level on stock returns. Panel B shows in the long run, the link between the correlations
of property cycles and business cycles is significantly positive within the GC region, Hong Kong and
international, as well as for the Taiwan and international groups. In contrast, the correlations of property
cycles are not linked to the stock market cycles for CH-International, HK-international and TWinternational groups, implying that there is little integration of public property markets with their stock
markets between each of the three GC markets and their global partners. It can be argued that these results
indicate public property cycles are not aligned with stock market cycles in the long run, a finding which is
in agreement with the understanding that the long-run performance of public property is tied to the
performance of the underlying direct property assets; but not the general stock market. Finally, it is
evident from the results that the long-run co-movement dynamic between the correlations of business
cycles and property markets cycles is different within and across the GC groups.
(Exhibit 7 here)
15
For the business cycle frequencies, results of Panel C reveal that except for the within GC group,
the correlations of business cycle fluctuations affect significantly the correlations of property market
cycles even after the effect of the stock market cycle correlations are controlled for the full sample, GCinternational, CH-international, HK-international and TW-international groups, Similarly, with one
exception (Taiwan-international) there are significant pro-cyclical co-movements between the dynamic
correlations of property and stock market cycles for other five groups. Based on these results, we are
inclined to conclude that the cyclical variations in quarterly public property correlations are reasonably
linked to the co-movements of business cycle fluctuations and stock market correlations at the traditional
business frequency horizon.
Finally, the short-run link (Panel D) between public property cycle correlations between
business-cycle correlations is found to be very weak and negative for most of the panels examined.
However, there are strong cyclical links between the public property correlations and stock market
correlations for all groups examined. The results are generally robust with the different estimation
methods.
Based on the regression results generated in our analyses, we are inclined to conclude the
cyclical co-movement dynamics between public property market correlations, business-cycle correlations
and stock market correlations have been different at the three time horizons. In the long run, public
property cycle correlations are significantly pro-cyclical to business-cycle co-movements but are less
aligned with the stock market cycle correlations. Between 1.5 years and 8 years (the traditional businesscycle frequencies), the positive links between the co-movements of property cycle correlations and
business-cycle correlations, and between property cycle correlations and stock market cycle correlations
are generally stronger. In the short run, however while there is generally insignificantly and negative link
between the cyclical co-movement of public property market correlations and business-cycle correlations,
the cyclical link between the public property market correlations and stock market correlations emerge to
be the strongest in many cases. These results are generally in agreement with the requirements of
16
economic theory and imply that assessing and understanding the links between business-cycle
correlations, public property market cycle correlations and stock market cycle correlations can be more
complicated than generally expected. Second, it appears that the above co-movement of the two groups:
i.e. within the GC region and GC-international are largely different. For example, while the property
market cycle correlations are not linked to the business-cycle correlations within the three GC economies,
this link is statistically significant across the GC-international group. This can be probably attributed to
the fact that the three GC economies (especially China) and their global developed partners are quite
different in terms of their macroeconomic conditions, degree of market openness, informational
transparency, legal system, size and maturity level, as well as levels of government intervention on the
real estate market.
6.
Conclusions
Globalization and the emergence of China in the world economy have been two major events in
the international economic literature over the last two decades. The key research question addressed in
this paper is whether and to what extent business cycle co-movements affect correlations and integration
of public property market cycles within the GC and across the GC areas over the study period from
1995Q1 to 2012Q4. In this paper we have used the Hodrick-Prescott filter to decompose real GDP (a
common proxy for business cycle), public property market index and stock market index into cyclical and
trend components. The resulting series of cyclical components were then examined for across
relationships using correlation and semi-correlation measures. Generally, higher (semi-) correlations of
business cycles are moderately associated with higher (semi-) correlations of public property market
cycles and higher (semi-) correlations of stock market cycles over the entire study period. Further, we
derive dynamic correlation, which can be decomposed by frequency and frequency band to study business
cycle co-movement, public property market cycle correlations and stock market cycle correlations at
different time horizons using descriptive statistics and panel data regressions. In short, based on the
results using our data, we find the cyclical co-movement dynamics between public property market
17
correlations, business-cycle correlations and stock market correlations are different at the three time
horizons, although the pro-cyclical links between the respective correlation-types are generally
statistically significant at the aggregate frequency level.. We also find evidence to indicate that the
detected pro-cyclical co-movement dynamics are different between the within and across the GC regions
Despite our broad findings, some of results remain inconclusive. As such, our results on the GC
dataset should be regarded as indicative and implies that the cyclical co-movement variations in quarterly
property market cycle’s dynamic correlations could be highly dependent on the business-cycle comovement dynamics at different time horizons and corresponding stock market cycle correlations, and is
hence more complex than what is normally expected. Notwithstanding this shortcoming the central point
of our analysis still holds true: co-movement of business-cycle fluctuations impact upon public property
cycle and stock market correlations. In future research the impact of business-cycle changes on capital
market integrations warrants deeper analysis using a combination of time and frequency domain
approaches to testing for their significant relationship using different international datasets.
18
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20
Exhibit 1
Average and standard deviation of quarterly real GDP growth, public property market returns and stock
market returns: 1995Q1 to 2012Q3 (Local dollars)
Real GDP growth rate
Real estate securities market return
Stock market return
Mean (%)
Standard
deviation (%)
Mean (%)
Standard
deviation (%)
Mean (%)
Standard
deviation (%)
China
1.956
21.749
0.900
23.589
1.557
16.956
Hong Kong
0.824
5.777
0.063
16.378
1.431
13.678
Taiwan
0.994
5.865
-1.287
18.167
-0.225
14.655
US
0.584
0.668
0.554
12.577
1.731
9.237
UK
0.533
0.695
-0.026
11.889
0.983
7.957
Australia
0.818
0.588
-0.214
9.132
0.102
14.060
Japan
0.200
1.073
-0.403
15.003
1.332
7.316
Singapore
1.313
2.748
-0.943
17.906
-1.023
10.468
Source: Derived from IMF statistics, S& P Global BMI, based on local currency.
21
Exhibit 2
Unconditional correlations: HP real GDP cycles, HP public property market cycles and HP stock market cycles:
1995Q1 to 2012Q3
Pairs
Correlation of real
GDP growth
CH and HK
CH and TW
HK and TW
0.7395
0.8788
0.8484
CH and US
CH and UK
CH and AU
CH and JP
CH and SG
0.1663
-0.0304
0.0453
0.1052
0.3395
HK and US
HK and UK
HK and AU
HK and JP
HK and SG
0.2105
0.0989
0.0529
0.1825
0.5633
TW and US
TW and UK
TW and AU
TW and JP
TW and SG
0.2683
0.1266
0.0832
0.1930
0.5298
Correlations of real GDP
cycles
Within Greater China (GC)
0.3177
0.2084
0.5950
China and International
0.2060
0.3423
0.2911
0.3559
0.4331
Hong Kong and International
0.5729
0.6142
0.0626
0.7322
0.7985
Taiwan and international
0.6733
0.6795
0.3411
0.6130
0.7483
Correlations of stock
market cycles
Correlations of real estate
market cycles
0.6930
0.6170
0.7668
0.5112
0.1875
0.3765
0.4831
0.4798
0.6569
0.4159
0.6370
0.3918
0.3489
0.3467
0.4817
0.4320
0.7260
0.6968
0.7542
0.7742
0.9099
0.4528
0.4806
0.4032
0.6533
0.8750
0.7274
0.6760
0.6399
0.6041
0.6629
0.5044
0.4815
0.4149
0.3995
0.2818
22
Exhibit 3.1
Unconditional semi-correlations: HP GDP cycles, HP real estate securities market (RE) cycles and HP stock
market (ST) cycles: 1995Q1 to 2012Q3
Common up Markets Correlation
Market Pair
CH
HK
CH
TW
CH
US
CH
UK
CH
AU
CH
JP
CH
SG
HK
TW
HK
US
HK
UK
HK
AU
HK
JP
HK
SG
TW
US
TW
UK
TW
AU
TW
JP
TW
SG
US
UK
US
AU
US
JP
US
SG
UK
AU
UK
JP
UK
SG
AU
JP
AU
SG
JP
SG
Common down markets Correlation
Mix markets correlation
# of observations for each market state
GDP cyclesRE Cycles ST Cycles GDP growthGDP
ratecyclesRE Cycles ST Cycles GDP growthGDP
ratecyclesRE Cycles ST Cycles GDP growthUprate Down Mixed
0.1085
0.4933
0.6899 -0.5707
0.5315
0.4902
0.4634
0.3829
0.8857
0.2571
0.6571 -0.2571
47
18
6
0.1128
0.1411
0.6837
0.4286 -0.0939
0.1744
0.6347 -0.7110
na
na
na
na
51
18
2
0.0882
0.2565
0.3708
0.1435 -0.5429
0.6571
0.8286 -0.4286
0.3423
0.4012
0.1423 -0.6766
34
6
31
0.0891
0.2188
0.3177 -0.1909
0.2028
0.6224
0.3007
0.0000
0.5382
0.5824
0.6353 -0.5471
43
12
16
0.2005
0.1212
0.5593
0.1494
na
na
na
na
0.1386
0.1895
0.4526 -0.2439
51
1
19
0.2589
0.4507
0.3205
0.1406
na
na
na
na
0.3053
0.3105
0.3246 -0.5912
49
3
19
0.4219
0.3481
0.5679
0.0258
na
na
na
na
0.4773
0.3765
0.3953 -0.7006
46
2
23
0.5499
0.3245
0.7865
0.0181
0.4912
0.6070
0.7000
0.1825
0.7714
0.2571
0.6571 -0.5429
46
19
6
0.6206
0.4790
0.7206 -0.0770
0.3000
0.5500
0.7000
0.5333
0.5633
0.2714
0.5972 -0.7484
31
9
31
0.5068
0.5073
0.6490
0.2167
0.6893
0.6500
0.6036
0.4536
0.8882
0.3000
0.8529 -0.6912
40
15
16
0.0523
0.2779
0.7449
0.0524
na
na
na
na
0.1200
0.2515
0.6508 -0.2108
45
1
25
0.7930
0.6092
0.7387 -0.1047
na
na
na
na
0.6600
0.4846
0.8146 -0.5646
43
3
25
0.7091
0.8704
0.9026 -0.0335
na
na
na
na
0.8877
0.8146
0.8477 -0.5831
42
4
25
0.5027
0.5244
0.5882
0.0769
0.7857
0.8571
0.8571 -0.1071
0.4589
0.4677
0.5343 -0.6645
33
7
31
0.5017
0.4246
0.6093
0.3067
0.4813
0.7626
0.6835
0.0242
0.6747
0.1516
0.5165 -0.6659
43
14
14
0.3470
0.3696
0.6280
0.2040
na
na
na
na
0.3246
0.4088
0.5298 -0.2140
50
2
19
0.3570
0.3152
0.5467 -0.0027
na
na
na
na
0.3140
0.3053
0.4579 -0.4930
48
4
19
0.6510
0.1348
0.6909
0.1672
na
na
na
na
0.6077
0.4753
0.5682 -0.5415
45
3
23
0.7219
0.8811
0.9377
0.0811
0.6970
0.9152
0.9879
0.7576
0.7387
0.8339
0.8388 -0.6996
34
10
27
0.5150
0.7335
0.7411
0.0773
na
na
na
na
0.4043
0.7243
0.5487 -0.0217
45
2
24
0.4339
0.6909
0.7303 -0.2431
na
na
na
na
0.5652
0.6148
0.5296 -0.3313
43
4
24
0.4850
0.5214
0.6394
0.0471
0.8286
0.6000
0.9429
0.6571
0.5618
0.1892
0.4737 -0.4557
43
6
22
0.4096
0.8578
0.8227 -0.0475
na
na
na
na
0.4249
0.8291
0.9239 -0.2638
47
1
23
0.4279
0.7249
0.7092
0.1185
na
na
na
na
0.7831
0.7455
0.6143 -0.3727
46
4
21
0.4939
0.5759
0.6195
0.2627
na
na
na
na
0.6700
0.4822
0.5455 -0.5296
44
4
23
0.1399
0.5875
0.6705
0.1802
na
na
na
na
0.3810
0.4286
0.7857 -0.5714
62
1
8
0.2018
0.4154
0.6006
0.3038
na
na
na
na
-0.2485
0.4909
0.8788 -0.5636
60
1
10
0.6644
0.7004
0.8082
0.3634
na
na
na
na
0.6667 -0.2857
0.4762 -0.6190
59
4
8
23
Exhibit 3.2
Spearman rank correlation coefficient: 1995Q1 – 2012Q3
“Common-up”
markets
“Common-down”
markets
“Mixed” markets
Between HP GDP cycle
correlations and HP real estate
cycle correlations
0.539
0.627
0.085
Between HP GDP cycle
correlations and HP stock
market cycle correlations
0.549
0.455
0.347
Between HP real estate market
cycle correlations and HP
stock market cycle correlations
0.602
0.465
0.463
Between real GDP growth
correlations and HP real estate
market cycle correlations
-0.178
0.072
0.075
Between real GDP growth
correlations and HP stock
market cycle correlations
-0.194
0.333
0.033
24
Exhibit 4
2012Q3
Country
Pair
CH-HK
CH-TW
HK-TW
Within GC
CH-US
CH-UK
CH-AU
CH-JP
CH-SG
CH-INT
HK-US
HK-UK
HK-AU
HK-JP
HK-SG
HK-INT
TW-US
TW-UK
TW-AU
TW-JP
TW-SG
TW-INT
GC-INT
Average dynamic correlations of business cycles, stock market cycles and public property market cycles: 1995Q1-
ALL
frequency
0.302
0.219
0.405
0.309
0.158
0.165
0.214
0.236
0.399
0.234
0.204
0.113
-0.012
0.316
0.560
0.236
0.228
0.340
0.167
0.258
0.443
0.287
0.253
Business cycle
Long
Business
run
cycle
0.707
0.411
0.182
0.528
0.382
0.728
0.424
0.555
-0.319
0.278
-0.221
0.596
-0.144
0.301
0.317
0.399
0.675
0.567
0.062
0.428
-0.060
0.619
0.070
0.578
-0.204
0.131
0.632
0.701
0.827
0.824
0.253
0.570
0.844
0.655
0.725
0.504
0.449
0.503
0.741
0.507
0.836
0.644
0.719
0.562
0.345
0.520
Short
run
0.211
0.108
0.286
0.202
0.173
0.051
0.226
0.165
0.302
0.183
0.081
-0.057
-0.042
0.132
0.428
0.109
-0.056
0.175
0.020
0.045
0.270
0.091
0.128
ALL
frequency
0.373
0.258
0.196
0.275
0.236
0.183
0.132
0.303
0.280
0.227
0.395
0.315
0.357
0.434
0.809
0.462
0.141
0.172
0.091
0.253
0.012
0.134
0.274
Property cycle
Long
Business
run
cycle
0.453
0.643
0.154
0.309
0.628
0.417
0.412
0.457
0.226
0.268
0.223
0.347
0.140
0.368
0.515
0.496
0.512
0.534
0.323
0.403
0.501
0.416
0.417
0.483
0.186
0.457
0.788
0.648
0.931
0.910
0.565
0.583
0.509
0.429
0.536
0.247
0.528
0.072
0.391
0.436
0.259
0.545
0.444
0.346
0.444
0.444
Short
run
0.261
0.251
0.058
0.190
0.225
0.116
0.043
0.204
0.156
0.149
0.374
0.239
0.341
0.309
0.756
0.404
-0.033
0.026
-0.070
0.178
-0.147
-0.009
0.181
Stock market cycle
ALL
Long
Business
frequency
run
cycle
0.600
0.361
0.786
0.364
0.293
0.666
0.585
0.687
0.815
0.516
0.447
0.755
0.414
-0.268
0.550
0.405
-0.082
0.610
0.565
0.428
0.713
0.393
0.427
0.484
0.594
0.535
0.754
0.474
0.208
0.622
0.743
0.550
0.742
0.718
0.684
0.722
0.759
0.599
0.789
0.655
0.826
0.693
0.827
0.858
0.927
0.741
0.703
0.775
0.555
0.769
0.678
0.504
0.737
0.779
0.544
0.667
0.515
0.481
0.667
0.542
0.510
0.735
0.366
0.519
0.715
0.576
0.578
0.542
0.658
Short
run
0.561
0.259
0.485
0.435
0.449
0.390
0.527
0.355
0.541
0.452
0.768
0.721
0.767
0.620
0.786
0.732
0.459
0.382
0.501
0.403
0.444
0.438
0.541
25
Exhibit 5.1
Co-movements between business cycle correlations, public property market cycle
correlations and stock market cycle correlations: within GC region
1.0
China and Hong Kong
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Long
run
.00
Business cycle
.04
.08
.12
Short run
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
.48
.52
FREQUENCY
1.0
China and Taiwan
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
Long
run
.00
Short run
Business cycle
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
FREQUENCY
1.2
Hong Kong and Taiwan
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
0.8
0.4
0.0
-0.4
-0.8
Long
run
.00
Business cycle
.04
.08
.12
Short run
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
26
Exhibit 5.2
Co-movements between business cycle correlations, public property market cycle correlations and stock market cycle
correlations: China and International
1.0
.8
Long
run
.6
China and UK
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
China and US
0.8
0.6
.4
0.4
.2
0.2
.0
0.0
-.2
-0.2
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
Business cycle
-.4
Short run
-0.4
-0.6
-.6
.00
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
.48
Long
run
.00
.52
Business cycle
.04
.08
.12
Short run
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
FREQUENCY
1.0
1.0
China and Japan
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
China and Australia
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.0
0.2
-0.2
0.0
-0.4
-0.6
-0.8
Long
run
.00
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
Business cycle
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
-0.2
Short run
.48
-0.4
.52
Long
run
.00
Business cycle
.04
.08
.12
Short run
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
FREQUENCY
1.0
China and Singapore
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
Long
run
.00
Business cycle
.04
.08
.12
Business cycle correlation
Real estate securities mkt correlation
Stock market cycle correlation
Short run
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
27
Exhibit 5.3
Co-movements between business cycle correlations, public property market cycle correlations and stock market cycle
correlations: Hong Kong and International
1.0
1.0
Hong Kong and US
Hong Kong and UK
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.0
0.2
-0.2
0.0
-0.2
Long
run
Business cycle
-0.4
.00
.04
.08
.12
-0.4
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market correlation
.16
.20
.24
.28
.32
.36
.40
-0.6
Short run
-0.8
.44
.48
.52
Long
run
.00
Short run
Business cycle
.04
.08
.12
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market correlation
.16
.20
FREQUENCY
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
1.0
1.0
Hong Kong and Australia
Hong Kong and Japan
0.8
0.8
0.6
0.6
0.4
0.2
0.4
0.0
0.2
-0.2
0.0
-0.4
-0.6
-0.8
Long
run
.00
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
Business cycle
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
-0.2
Short run
.48
-0.4
.52
FREQUENCY
Long
run
.00
Business cycle
.04
.08
.12
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market correlation
Short run
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
1.0
Hong Kong and Singapore
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Long
run
.00
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market correlation
Short run
Business cycle
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
28
Exhibit 5.4 Co-movements between business cycle correlations, public property market cycle correlations and stock market cycle
correlations: Taiwan and International
1.00
1.0
Taiwan and US
Taiwan and UK
0.8
0.75
0.6
0.50
0.4
0.25
0.2
0.00
0.0
-0.25
-0.2
-0.50
-0.75
-1.00
Long
run
.00
-0.4
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
Short run
Business cycle
-0.6
Long
run
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market correlation
Business cycle
-0.8
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
.00
.04
.08
.12
.16
FREQUENCY
1.0
.20
.24
.28
.32
.36
.40
Short run
.44
.48
.52
.48
.52
FREQUENCY
Taiwan and Japan
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market correlation
1.0
Taiwan and Australia
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.0
0.2
-0.2
0.0
-0.4
-0.6
-0.8
Long
run
.00
Business cycle correlation
Real estate securities mkt cycle correlation
Stock market cycle correlation
Business cycle
-0.2
Short run
-0.4
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
Long
run
.00
Business cycle
.04
.08
.12
Short run
.16
.20
.24
.28
.32
.36
.40
.44
FREQUENCY
1.0
Taiwan and Singapore
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
Long
run
.00
Business cycle correlation
Real estate securities mkt cycle corrleation
Stock market correlation
Short run
Business cycle
.04
.08
.12
.16
.20
.24
.28
.32
.36
.40
.44
.48
.52
FREQUENCY
29
Exhibit 6
CH-HK
CH-TW
HK-TW
Within GC
CH-US
CH-UK
CH-AU
CH-JP
CH-SG
CH-INT
HK-US
HK-UK
HK-AU
HK-JP
HK-SG
HK-INT
TW-US
TW-UK
TW-AU
TW-JP
TW-SG
TW-INT
Spearman rank correlation coefficient results: 1995Q1 to 2012Q3
Between business- cycles and real estate
securities market cycles
Long –run
Business
Short-run
cycle
frequency
frequency
0.667
0.078
frequency
0.250
-0.105
-0.267
0.007
0.333
-0.117
-0.009
0.750
0.062
0.400
0.119
-0.100
0.264
0.417
-0.090
0.267
-0.358
0.882
0.364
0.031
0.133
0.009
-0.067
0.605
0.583
-0.561
0.850
-0.059
0,517
0.380
0.639
0.732
0.315
0.500
-0.128
0.417
-0.165
0.033
-0.411
0.667
-0.094
0.700
-0.196
0.739
0.299
-0.241
Between business- cycles and stock
market cycles
Long –run
Business
Short-run
cycle
frequency
frequency
0.417
-0.014
frequency
0.733
0.399
0.050
0.394
-0.467
0.685
0.212
0.850
0.621
0.750
0.027
0.333
0.099
-0.550
-0.317
0.417
-0.292
0.214
0.377
0.093
0.517
-0.035
-0.267
-0.012
0.467
0.029
0.850
0.036
0.467
0.249
0.792
0.524
0.181
-0.500
-0.262
-0.900
-0.435
0.083
-0.152
0.217
0.100
0.783
-0.268
0.229
0.267
-0.234
Between real estate securities market
cycles and stock market cycles
Long –run
Business
Short-run
cycle
frequency
frequency
0.417
0.328
frequency
0.183
0.171
0.767
0.430
0.600
0.255
0.249
0.850
0.508
0.417
0.328
0.367
0.140
0.417
0.604
0.433
0.664
0.268
0.397
0.389
0.750
0.450
0.900
0.250
0.683
0.588
0.950
0.383
0.750
0.573
0.679
0.730
0.537
-0.033
0.393
-0.167
0.104
0.517
0.419
0.817
0.768
0.300
-0.123
0.150
0.338
0.337
Notes: “-“: indicates that results are not available due to insufficient time series data for long run frequency for every country-pair (only 3 points) . For each
country pair, the number of time series data is: 9 (business cycle frequencies); 24 (short-run frequencies). Within GC (pooled CHHK; CHTW and HKTW); CH-INT
(pooled CHUS, CHUK, CHAU, CHJP and CHSG); HK-INT (pooled HKUS, HKUK, HKAU, HKJP and HKSG); TW-INT (pooled TWUS, TWUK, TWAU, TWJP and TWSG)
30
Exhibit 7
Panel data regression results: links between dynamic correlations of business cycles, public property market
cycles and stock market cycles
RE freqwency  f (GDPfrequency) ……………………Model I
RE frequency  f (GDPfrequency, ST frequency) ………………………Model II
Panel A: All-frequencies
Panel random
effect GLS
GDP
Full sample
Within GC
Across GC
China &
International
Hong Kong &
International
Taiwan &
International
Model 1
Panel FGLS
Panel random effect GLS
Model 2
Panel FGLS
GDP
Dynamic panel
GMM
GDP
Dynamic panel GMM
GDP
ST
GDP
ST
GDP
ST
0.315***
(z=5.10)
0.603**
(z=2.57)
0.274***
(z=8.22)
0.305***
(z=4.01)
0.160***
(z=4.42)
0.293***
(z=3.35)
0.161***
(z=3.22)
-0.161
(z=-0-.64)
0.626***
(z=9.67)
0.813***
(z=6.12)
0.207***
(z=7.15)
0.073
(z=1.09)
0.581***
(z=18.68)
0.674***
(z=10.86)
0.086**
(z=2.44)
-0.133
(z=1.56)
0.451***
(z=9.14)
0.784***
(z=11.70)
0.287***
(z=4.40)_
0.084
(z=1.06)
0.252***
(z=7.01)
0.076
(z=1.48)
0.135***
(z=4.26)
0.035
(z=0.58)
0.187***
(z=3.88)
-0.037
(z=-0.34)
0.576***
(z=9.06)
0.499***
(z=3.71)
0.225***
(z=7.16)
-0.018
(z=-0.35)
0.557***
(z=15.32)
0.450***
(z=8.10)
0.097***
(z=3.44)
-0.027
(z=-0.50)
0.403***
(z=10.36)
0.380***
(z=6.19)
0.280*
(z=1.68)
0.260***
(z=4.80)
0.178***
(z=3.87)
0.292***
(z=1.65)
0.475***
(z=5.83)
0.273***
(z=5.81)
0.478***
(z=7.61)
0.193***
(z=4.73)
0.337***
(z=5.60)
0.396***
(z=8.18)
0.193***
(z=4.30)
0.181***
(z=3.52)
0.242***
(z=4.85)
0.637***
(z=15.03)
0.160***
(z=3.18)
0.310***
(z=4.40)
0.109**
(z=2.31)
0.429***
(z=6.32)
31
Panel B: Long-run frequencies
Panel random
effect GLS
GDP
Model 1
Panel FGLS
GDP
Dynamic panel
GMM
GDP
GDP
ST
GDP
ST
GDP
ST
1.067***
(z=4.09)
1.309***
(z=14.57)
-1.183
(z=-1.51)
0.298**
(z=2.21)
1.957***
(z=3.18)
1.306***
(z=13.76)
NA
NA
NA
NA
NA
0.150
(Z=0.94)
0.693***
(z=3.02)
-0.604
(Z=-0.97)
1.543***
(Z=2.65)
NA
NA
0.473*
(z=1.67)
NA
0.370
(z=1.10)
0.046
(z=0.16)
0.398
(z=1.18)
0.207
(z=0.64)
NA
NA
0.942***
(z=21.01)
0.931***
(z=8.31)
NA
1.009***
(z=3.83)
-0.234
(z=-0.47)
1.175***
(z=6.82)
-0.413
(z=-1.08)
NA
NA
1.171***
(Z=3.23)
1.107***
(Z=8.44)
NA
0.996**
(Z=2.28)
0.241
(Z=0.69)
0.838**
(Z=2.56)
0.304
(Z=0.95)
NA
NA
0.968***
(z=4.18)
0.483
(z=1.52)
NA
Across GC
0.625***
(z=4.68)
0.894***
(Z=3.50)
China &
International
Hong Kong &
International
Taiwan &
International
0.394***
(z=2.87)
Within GC
Dynamic panel GMM
-0.041
(z=-0.21)
0.274***
(z=3.69)
0.636***
(z=4.42)
0.811
(z=1.26)
Full sample
Panel random effect GLS
Model 2
Panel FGLS
NA
Panel C: Business cycle frequencies
Panel random
effect GLS
GDP
Full sample
Within GC
Across GC
China &
International
Hong Kong &
International
Taiwan &
International
Model 1
Panel FGLS
GDP
Dynamic panel
GMM
GDP
0.494***
(z=8.45)
0.167
(Z=0.55)
0.345***
(z=14.69)
0.179
(z=1.50)
0.532***
(z=9.28)
0.458***
(z=4.22)
Panel random effect GLS
Model 2
Panel FGLS
Dynamic panel GMM
GDP
ST
GDP
ST
GDP
ST
0.537***
(z=7.53)
0.155
(Z=1.10)
0.348***
(z=4.20)
-0.357
(z=-0.90)
0.358***
(z=3.62)
0.713***
(z=5.26)
0.150***
(z=8.12)
-0.080
(z=-0.60)
0.421***
(z=15.91)
0.399***
(z=4.78)
0.482***
(z=7.50)
0.040
(Z=0.27)
0.372***
(z=4.20)
0.439**
(Z=2.24)
0.372***
(z=18.34)
0.241***
(z=5.73)
0.561***
(z=7.17)
0.408***
(z=3.17)
0.407***
(z=5.47)
0.285*
(z=1.91)
0.307***
(z=2.91)
0.374***
(z=3.34)
0.183***
(z=10.26)
0.122***
(z=2.77)
0.413***
(z=18.25)
0.273***
(z=3.44)
0.506***
(z=7.24)
0.336***
(z=2.85)
0.391***
(z=4.03)
0.620***
(z=2.98)
0.514***
(z=8.03)
0.558***
(z=11.64)
0.502***
(z=6.28)
0.346***
(z=4.45)
0.393**
(z=2.40)
0.321***
(4.85)
0.508***
(z=8.09)
0.396***
(z=4.83)
0.232**
(z=2.47)
0.500***
(z=3.60)
0.288***
(z=3.29)
0.643***
(z=4.44)
0.369**
(z=2.23)
0.147
(z-0.75)
0.278***
(z=2.99)
0.082
(z=0.49)
0.650***
(z=4.81)
0.255
(z=1.51)
32
Panel D
Panel random
effect GLS
GDP
Full sample
Within GC
Across GC
China &
International
Hong Kong &
International
Taiwan &
International
Model 1
Panel FGLS
Short-run frequencies
Panel random effect GLS
Model 2
Panel FGLS
GDP
Dynamic panel
GMM
GDP
Dynamic panel GMM
GDP
ST
GDP
ST
GDP
ST
0.005
(z=0.04)
0.481*
(z=1.88)
0.008
(z=0.70)
0.270***
(Z=2.84)
-0.087**
(z=-2.13)
0.124*
(z=1.69)
-0.002
(z=-0.03)
-0.163
(Z=-0.98)
0.657***
(z=8.84)
0.790***
(z=10.45)
-0.006
(z=-0.50)
0.078
(Z=0.93)
0.516***
(z=30.53)
0.612***
(Z=8.11)
-0.105***
(z=-2.94)
-0.138
(-1.55)
0.388***
(z=10.22)
0.551***
(z=8.51)
-0.139
(z=-1.56)
-0.319**
(z=-2.30)
-0.022
(z=-0.95)
-0.159**
(z=-2.18)
-0.155***
(z=-3.40)
-0.145*
(z=-1.65)
-0.021
(z=-0.27)
-0.267**
(z=-2.02)
0.583***
(z=6.25)
0.518***
(z=2.82)
-0.025
(z=-1.23)
-0.154**
(z=-2.26)
0.452***
(z=15.96)
0.523***
(z=6.17)
-0.105**
(z=-2.49)
-0.088
(z=-1.07)
0.349***
(z=7.37)
0.323***
(z=4.39)
0.011
(z=0.06)
0.022
(z=0.42)
-0.088
(z=-1.43)
0.151
(z=0.84)
0.460**
(z=2.53)
0.087
(z=1.57)
0.291***
(z=3.94)
-0.022
(z=-0.36)
0.261***
(z=3.80)
-0.075
(z=-0.60)
0.090
(z=1.45)
-0.171**
(z=-2.35)
-0.019
(z=-0.19)
0.627***
(z=7.01)
0.042
(z=0.68)
0.298***
(z=3.72)
-0.111
(z=-1.52)
0.293***
(z=4.30)
Notes: ***, **, * - indicates two-tailed significance at the 1, 5 and 10% level respectively
33