Dynamics of Economic Globalization and Capital Market Integration:
Evidence from Greater China and International Linkages
By: Kim Hiang LIOW (rstlkh@nus.edu.sg)1 and Qing YE (yeqing@nus.edu.sg)2
Department of Real Estate, National University of Singapore
1
Professor of real estate, Department of Real Estate, National University of Singapore, email:
rstlkh@nus.edu.sg
2
PhD student, Department of Real Estate, National University of Singapore, email: yeqing@nus.edu.sg
Dynamics of Economic Globalization and Capital Market Integration:
Evidence from Greater China and International Linkages
Abstract
In this paper, the dynamics and current status of economic globalization and capital market
correlation, as well as their interdependence, among the three Greater China (GC) economies
(mainland China, Hong Kong and Taiwan) and across the GC areas with their regional and
international partners (Japan, Singapore, the US and the UK) is assessed, using monthly data on onemonth interbank rates, exchanges rates, inflation and realized correlation. Despite the limited extent of
economic globalization and inconclusive time-trend evidence, the unit root and mean reversion results
imply that real interest rates, uncovered interest rates and relative purchasing power in the GC context
tend to converge and therefore the three parity conditions are likely to hold as a long-run equilibrium
condition, respectively. The increased realized cross-real estate securities market correlations and
realized cross-stock market correlations imply that international capital markets have become
increasingly integrated over the study period. Finally, the integration spillover index and plot
investigations have detected some evidence of interdependence between economic globalization and
capital market integration. Our results should be regarded as preliminary, but indicative. They suggest
that economic globalization could be one of the key driving forces of capital market integration.
Further studies are definitely required to order to confirm whether the GC results reported in this
paper could be generalizable to e developed and emerging economies.
Keywords: economic globalization, capital market integration, realized correlation, international
parity, integration spillover index
1.
Introduction
With the trend of economic globalization since 1980’s, China has become one of the fastest
growing economies in the world, an informal economic region that embraces Mainland China, Hong Kong
and Taiwan is rapidly emerging as a new epicenter for industrial, commerce and Finance. The accession of
China to the WTO in November 2001 further marks a distinctive break in China’s economic relation with
the rest of the world. The economic globalization between the Mainland China and Hong Kong has been
enhanced since Hong Kong returned to China in 1997. In addition, China is the largest recipient of
Taiwan’s overseas investment and Taiwan is China’s third largest source of direct investment. The
mainland China, Hong Kong and Taiwan, which are often identified as the Greater China (GC) region, has
emerged as one of the most dynamic economic regions in the world, and contributes 23.70% of world GDP
in 20103. The export in the GC region accounts for 13.9% of world’s total export while the import value
3
Data source: World Bank and National Statistics (Taiwan).
accounts for 12.08% in 20094. The foreign exchange reserve in these economies is also among the largest
in the world.
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. Economic literature has pointed
out that in addition to positively impact trade activities and investment flows, global integration is capable
of influencing real and financial markets through the pricing of and investment in local real estate markets
as well as in international capital markets (Bardhan et al. 2008). Specifically, with continuing strong
economic growth, massive urbanization and the growth of private real estate ownership in China, the scope
for Hong Kong REITs to provide more pure-play property investment opportunities in China, as well as
Taiwan’s growing economic ties with China, the GC region is expected to grow into an important player in
the global capital markets, with their direct real estate and securitized real estate markets attracting the
interest of domestic and international investors; although Fung et al (2006) have noted that the three GC
real estate securities markets are quite different in terms of their macroeconomic conditions, degree of
market openness and transparency, legal system, size and maturity level, as well as levels of government
intervention on the real estate market. As real estate companies invest in real properties and are themselves
real estate vehicles for other investors in international capital markets, it is timely to investigate the nature
and extent of interdependence between economic globalization and capital market integration as literature
has suggested international capital markets are closely correlated in today’s global economy. Following
literature, increased capital market correlations imply, respectively, higher across-stock and higher acrossreal estate stock integration.
The core objective of this paper is to assess the real interest parity (RIP), uncovered interest parity
(UIP) and deviations from relative purchasing power parity (RPPP) hypotheses within the GC economies,
as well as across the GC areas with their selected regional and world partners (the US, the UK, Japan and
Singapore); to assess whether the corresponding capital markets (i.e. stock and real estate securities
markets) have become increasingly correlated; and to examine whether there is significant interdependence
between economic globalization and capital market integration, as well their dynamic changes over time.
4
Data source: International Trade Statistics 2009 of WTO.
The US, UK and Japan have well-developed financial markets and open capital accounts. In particular, the
US and Japan are the main investors and trading partners with the GC economies. Singapore, due to its
geographical proximity and cultural similarity, has had close economic ties with the GC economy. We
consider RIP, UIP and RPPP as the basis of economic globalization because these three parity conditions
are popularly used to evaluate the degree of economic globalization in international finance (Cheung et al.
2003). We use realized correlation measure as an alternative to the dynamic conditional correlation (DCC)
of Engle (2002), to estimate the time-varying cross-stock market correlation and cross-real estate securities
market correlation. Finally, we hypothesize that higher capital market integration is positively linked to the
level of economic globalization (as measured by the three parity conditions) for the markets under
examination. Finding a positive relationship between (say), RIP and capital market integration within the
GC region implies that capital mobility can be enhanced through facilitating and financial and commodity
arbitrage that will in turn eliminate the differentials between these economies’ rate of return on investment
among the three GC economies.
Although there is a wealth of literature on economic globalization and capital market integration5
and that our work is similar in spirit to that of Bracker et al (1999) that links some macroeconomic factors
to stock market co-movements, to our knowledge this present work is probably one of very few to explore
the joint dynamics of economic globalization and capital market integration within and across the GC
context, using advanced time series and econometric methodologies. Specifically, our study is able to
contribute significantly in at least three different aspects. First, previous economic globalization studies
mainly focused on countries in Europe and other developed countries. Our study using a different dataset is
thus an addition to the already large body of literature on economic globalization on an extended period
(including era of global financial crisis). Results from this study are expected to enrich the economic
globalization literature on RIP, UIP and RPPP within and across the GC areas. Second, instead of relying
on parametric procedures (e.g. GARCH) to estimate variance and correlation, we appeal to the realized
correlation methodology to estimates ex-post realized correlations across the sample stock and securitized
real estate markets. In consistent to Anderson et al. (2003), correlations so constructed are model free and
5
See Section 2 for a brief literature review.
contain little measurement error as the sampling frequency of the returns approaches infinity. These
econometric merits motivate our use of this approach which has received less formal attention in the real
estate literature. Finally, our panel regression and integration spillover index methodologies link economic
globalization (as measured by the three international parity differentials) to capital market integration (as
measured by the cross-real estate realized correlations and cross-stock realized correlations) and is
consistent with the economic convergence and market integration literature. Our contribution is based on
the belief that the more the economies of two countries are globalized, the more interdependent their real
estate securities markets and stock markets will be. In addition to the conventional panel investigation, we
motivate the gerneralized volatility spillover index methodology of Diebold and Yilmaz (2012) and
develop an “integration spillover index” to investigate whether there is increasing connection (from the
spillover perspective) between economic globalization and capital market integration in the GC context.
Our study can thus be regarded as a good supplement to, as well as provides a new perspective on the
economic globalization and market integration literature within and across the GC regions.
The plan of the paper is as follows. The next section introduces briefly the international parity
relations framework and related literature, as well as explains briefly how the RIP, UIP and RPPP can be
applied to assess economic globalization. Section 3 explains the data sample and econometric procedures of
the research. This is followed by Section 4 which provides report and discussion on evidence of economic
globalization and stock/real estate securities market correlations, as well as the interdependence between
economic globalization and capital market integration from panel regression and integration spillover
analysis. Section 5 concludes the paper.
2.
International Parity Relations Framework and relevant literature
The relevant theoretical framework underpinning the concept of economic globalization in this
study is the international parity relations that provide an intuitive framework explaining the relationship
among exchange rates, inflation and interest rates. Following literature, the RIP, UIP and RPPP conditions
are adopted to assess the parity relations within and across the GC areas. According to Cheung et al.
(2003), the RIP condition depends on whether capital flows equalize real interest rates across economies,
UIP involves financial arbitrage between money and foreign exchange markets and RPPP entails arbitrage
in goods and services. Mathematically, the RIP condition is derived by requiring that UIP, RPPP and the
ex-ante Fisher equation in both the domestic and foreign currency to hold. In this way, the RIP condition
encompasses elements of both real and financial integration. This is further explained below:
(a)
Given
and
are the expected j period RI, nominal interest rate and inflation rate in the
first economy respectively and “*” indicates the second economy, then:
The real interest parity (RIP), which is the ex-ante RIP differential between two economies, is:
(1)
(b)
Annualized expected depreciation
is defined as:
(2)
Where
is the expected nominal foreign exchange rate in logarithm form between two
countries at time
(c)
Assume further
while
and
is the nominal foreign exchange rate in logarithm at time
are respectively the price in logarithm expected at time t+1 and the
price at t.
Annualized expected inflation rate at time t is given by:
(3)
Finally, the term in the first square bracket on the right side of equation (1) is the expected UIP
differential while the term in the second square bracket is the expected RPPP differential. Since we do not
have observations on market expectations, we will employ an operational version based on ex post
differentials6. Hence equation 4 is the ex post international parity relations that implies: RIP = UIP + RPPP:
6
Under the rational expectation hypothesis, the ex post realizations should be unbiased predictors of the ex ante
counterparts.
(4)
Empirically, this international parity relations framework and the three separate components (i.e.
RIP, UIP and RPPP) have attracted a great deal of attention and has been explored extensively in the
international finance literature using recent advances in the field of time series econometrics. Empirical
studies include Goodwin and Grennes (1994); Chinn and Frankel (1995); Moosa and Bhatti (1996); Wu
and Chen (1998); Fountas and Wu (1999); Holmes and Maghrebi (2004); Cheung, Chinn and Fujii (2003,
2005); Holmes and Wang (2008); Serletis and Gogas (2010) and Cuestas and Harrison (2010).
Similarly, with increasing globalization of the world 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 securitized real estate markets (e.g. Liow et al, 2009; Liow, 2012; Liow and Newell,
2012, forthcoming). In these studies, increased time-varying correlations imply higher stock/ real estate
stock market integration. Specifically, Liow and Newell (2012, forthcoming) 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 and Methodology
The research agenda is captured in the form of three research questions. Our first research question
asks to what extent the RIP, UIP and RPP parity conditions will hold in the long run. We use monthly
observations on one-month interbank interest rates, exchange rates and consumer price indices on seven
economies, namely, China (CN), Hong Kong (HK), Taiwan (TW), Japan (JP), Singapore (SG), the United
Kingdom (UK) and the United States (US). The interbank interest rate is regarded as the most flexibly
determined interest rate available. The sample period is from February 1996 to June 2011, the longest time
series data that is available for each country since China’s one-month interbank rate was only available
only from February 19967. For each of the 15 pairs8, the ex post RI differential (
differential (
) and ex post RPP differential {
), ex post UI
) are constructed. An
assessment of the mean, standard deviation and range of the differential series will allow us make useful
preliminary inferences regarding their respective parity characteristics.
We then evaluate the parity conditions via two perspectives. First, we test for the presence of zero
mean reversion characteristics in these three differential series. Second, we examine whether the deviations
from the three parity conditions are shrinking over time or not. For the mean reversion property, we employ
a modified Dickey Fuller test known as the ADF-GLS test (Elliot, Rothenburg and Stock, 1986), a panelbased unit-root test provided by Im, Pesaran and Shih (IPS, 1995) 9 and the variance ratio test. The common
argument underlying all the tests is that if the deviations from ex post parity are transitory and stationary,
then even though the condition does not hold in the short run, deviations from parity are stationary. In
contrast, if the deviations from parity are not stationary, there is permanent disequilibrium resulting from
shocks and consequently there is no guarantee to restore the parity condition in the long run. For example,
if the non-stationary null is rejected for the RI differential series, this implies that there is a tendency for the
said series to be mean reverting which is consistent with the implication of RIP. Finally, the variance ratio
test has been regarded as more powerful to detect the presence of the stationary component in any random
series. For the purpose of this test, the three differential series are decomposed into permanent and
transitory components to examine their stationary properties at different frequencies.
For the time-trend test, we use a cross-dispersion approach to assess if differential convergence
(i.e. decreasing trend) or divergence (i.e. increasing trend) exists over the full period for the RI, UI and RPP
7
A unified national interbank market was only established in January 1996; prior to that the interbank market in
the mainland China was substantially controlled (Cheung et al, 2005).
8
This is restricted to three within GC pairs (CN/HK, CN/TW and HK/TW), four CN and international pairs (i.e.
CN/US, CN/UK, CN/JP and CN/SG), four HK and international pairs, as well as four TW and international
pairs.
9
Panel unit root tests have been widely applied in the empirical literature, especially in the RPPP literature. For
example, the Im, Pesaran and Shih (IPS) W-statistic (with trend) tests the null hypothesis of joint non-stationary
on a pooled cross-sectional dataset and can provide “dramatic improvement in statistical power” (Im, Pesaran
and Shih, 1995)
series. The cross-dispersion is the standard deviation of the various differential series relative to three group
average series {overall, within GC, across GC (GC/international)}. The Hodrick-Prescott technique then
follows to estimate the long-term trend component of the differential series. For each series, the final level
(as at 2011M06) is compared with its initial level (as at 1996M02) and its average time trend over the study
period is estimated. For a particular series, a statistically negative time trend imply that the differentials are
narrowing (converging) during the sample period and is an indication of increasing economic globalization.
Our second research question asks to what extent the real estate securities markets of the three GC
economies are correlated, as well between each of the three GC economies and their regional/international
partners. We also examine the correlations among the corresponding stock markets. Our data are the daily
common stock and real estate stock returns of indices for the seven economies derived from the Broad
Market Index (for common stocks) and Global Property (for real estate stocks) sections of the Standard and
Poor (S&P) database. Daily stock returns are computed as the natural logarithmic of the total return indices
relative, I, in successive days, over February 1996 to June 2011. Next, to obtain a consistent estimate of
correlation at monthly frequency, we appeal to the concept of realized moments to compute a monthly
estimate of the cross-market correlation10. The construction process is briefly explained here. Define the
daily
stock
return
at
country
i
as
Ri ,t ,d  ln( I i ,t ,d I i ,t ,d 1 ) x100 and
country
j
as
R j ,t ,d  ln( I j ,t ,d I j ,t ,d 1 ) where I are the daily stock (real estate stock) price indices, the realized
variances is given by:
Dt
Dt
d 1
d 1
 t2,i   ( Ri ,t ,d ) 2 and  t2, j   ( R j ,t ,d ) 2
, where d = (d= 1,….D), D
t
is the
total business day in the month t. Then, the realized covariance between cross-country stock (real estate
stock) returns is measured as  ij ,t
Dt
  ( Ri ,t ,d xR j ,t ,d ) . Finally, the realized correlation  ij,t measure
d 1
between the cross-country returns is obtained as
10
 ij ,t 
 ij ,t
 i2,t x 2j ,t
. The correlation series are then
This is an alternative approach to the use of parametric models such as the GARCH or the multivariate
GARCH models. See Andersen et al. (2003), Kim et al. (2006) and Cappiello et al. (2006).
examined for their descriptive statistics, mean reversion property and trending behavior over the full
sample period. Our main objective is to confirm that the sample stock markets and real estate securities
markets) have become increasing correlated among themselves over the sample period, as causal
observation has suggested.
Finally, our third research question asks whether there is a significant positive link between capital
market integration (represented by cross-real estate and cross-stock realized correlations, respectively) and
economic globalization (as defined by RIP, UIP and RPPP). Given that economic globalization has greatly
enhanced the significance and performance of the global real estate securities investment and management
over the last two decades (Bardhan et al, 2008), we are motivated to examine scientifically, whether
economic globalization and capital market integration (in particular, real estate securities market
coerrelation) are positively linked within and across the GC areas, as well as understand the evolution of
their interdependence over time. In addition to the usual panel regression approach, we appeal to the return
spillover index methodology proposed by Diebold and Yilmaz (2009, 2012), which is based on
decomposition of return forecast error variances obtained from a vector auto-regression (VAR)11. In our
context, we model the three economic globalization differential series (RI, UI and RPP) and two market
integration series (cross-stock and cross-real estate realized correlation: CORR
S
and CORR
RE)
as a five-
factor VAR. Then we conduct a variance decomposition analysis to 36-month long rolling windows of the
five variables. For each factor i we add the shares of its return forecast error variance due to shocks come
from four other factors j. Then we sum across all i = 1, 2, 3, 4, 5 to obtain the spillover index. In other
words, the “integration spillover index”, as we wish to label, is the sum of all non- diagonal in the return
forecast error variance matrix. Our integration spillover analysis covers two aspects; (1) an aggregate
integration spillover index which measures what proportion of the return forecast error variance comes
11
Diebold and Yilmaz (2012) introduced a generalized VAR methodology and the concept of directional
spillovers in volatility transmission research. Their approaches represent significant improvement over the
traditional Cholesky-factor identification of VAR. According to Diebold and Yilmaz (2012), the Cholsesky
factorization method is able to achieve orthogonality; but the variance decompositions depend on the ordering of
the variables. Instead, the generalized VAR framework of Pesaran and Sim (1998) produces variance
decompositions which are invariant to the ordering by allowing correlated shocks and using the historically
observed distribution of the errors to account for the shocks.
from spillovers; and (2) integration spillover plots which are constructed from the rolling-samples of the
spillover indices to assess the extent and nature of the integration spillover variations over time.
4.
Results
4.1
Evidence of economic globalization
The three differential series (RI, UI and RPP) for the 15 economy pairs are first adjusted for the
effect of Asian financial crisis (AFC) and Global financial crisis (GFC) and are expressed in annualized
percentages. They are graphed in Figures 1-3. In addition, Table 1 reports their mean, standard deviation
and range (maximum-minimum) over the full period. Some observations are documented. First, within the
GC areas, the summary statistics indicate of the nine average differential series, only three series (two RI
and one UI) are statistically significant at least at the 5% level. However, an evaluation of the band of the
differential series reveals very large range, spanning 33 (CN/HK, UI) or more percentages points. For the
GC/international group, the average differentials are significantly different from zero in 7 (RI), 3(UI) and 2
(RPP) of the 12 comparisons. The significant RI differential averages imply the existence of persistent
opportunities for arbitrage activities and may thus provide evidence against financial and real integration
between some GC and non-GC economies.
(Figures 1-3 and Table 1 here)
The ADF-GLS unit root test results (Table 2, Panel A) support stationary in every RI, UI and RPP
case, although the degree of support for stationary differentials in two cases of RI (HK/UK and
HK/international) and one case of UI (China/US) is not so strong as that offered to other pairs in that the
test indicates stationary only at the 10% level of significance. Similarly, the IPS unit root test results (Table
2, Panel B) indicate the null hypothesis that the three differential series are joint non-stationary can be
rejected consistently in all groups (GC, China/international, HK/international and TW/international).
Additional variance ratio test results (Table 3) indicate at up to 16th lags, the estimated values of the
variance ratio are smaller than 1 at the 5% significance level in the majority of the cases, indicating quite
strong mean reversion in the data under examination.
(Tables 2 and 3 here)
Finally, Figure 5 (Panel A and Panel B) graphs the Hodrick-Prescott filtered cross-dispersion,
classified by two broad groups (GC, GC/international). In addition, Table 5 estimates the average annual
trend over the full sample period. The results are mixed. They indicate that, on average, the magnitude of
the deviation from the RIP condition is declining at between 1.43% and 2.96% per annum. In contrast, the
magnitude of the deviation from RPPP is increasing at between 1.31% and 3.29% per annum.
(Figure 5 and Table 5 here)
Based on the results reported, we are inclined to conclude that that the three parity conditions hold
in the long run within and across the GC areas. The unit root and variance ratio null are rejected for all the
series/groups and, thus, the deviations from the three parity conditions are stationary. Despite the limited
extent of economic globalization and inconclusive time-trend results, the mean reversion results indicate
that the RI, UI and RPP in the GC context tend to converge and therefore the three parity conditions tend to
hold in the long run. Thus, there is some evidence of economic globalization, in particular, real and
financial integration within the three GC economies and across the GC areas regionally and globally.
However, there is very little evidence of short-run equilibrium detected in the three differential series which
are characterized by the existence of profitable arbitrage opportunities.
4.2
Capital market integration
Table 5 provides the cross-market realized correlation estimates of real estate securities market
and stock markets for the 15-economy pairs. Figure 5 graphs the average time series variations over the
study period for the two groups (GC, GC/international). As the numbers in Panel A indicates, average
cross-real estate market securities correlations range between 0.028 (Taiwan/UK) and 0.361 (Hong
Kong/Singapore). Only one pair (HK/Singapore) is above 0.3. The average correlation among the three GC
economies is 0.152, which is about, respectively, 43% and 103% higher than the averages between
China/international (0.106) and between Taiwan/international (0.075); yet about 36% lower than the
average Hong Kong/international correlations (0.207). The corresponding cross-stock realized correlations
are consistently higher than their real estate securities counterpart (between 0.135-TW/UK and 0.469-
CN/HK). Except for the TW/UK real estate securities market correlation, all other correlation estimates are
statistically significant at the 1% level. Our results are thus in agreement with those reported by Liow et al.
(2009) that indicates (significantly) lower cross-real estate securities market correlation (and hence implies
lower market integration) than the corresponding cross-stock market correlation in international developed
countries. The unit root test (Panels A and B) and variance ratio test (Panel C) results consistently reject the
null of non-stationary in every case, and offer strong evidence in support of stationarity in capital market
correlation whereby cross-real estate securities correlations and cross-stock market correlations do not
persistently diverge from one another in the long run. Finally, the Hodrick-Prescott filtered cross-market
correlation dispersion (Figure 6 and Panel D, Table 5) indicates that the sample real estate securities
markets have become increasingly correlated, at the rate of between 3.85% and 4.50% per year; the
magnitude of increase in stock market correlations within and across the GC areas; however; is on the
lower end, ranging between 0.58% and 0.67% per annum.
(Table 5, Figure 5 and Figure 6 here)
4.3
Link between economic globalization and capital market integration
We formally investigate whether there is a significant link between economic globalization and
capital market integration for our dataset. Based on the usual perception, we expect a negative relationship
between the two sets of indicators because higher correlations should be associated with declining
RI/UI/RPP differentials (which implies economic globalization). This outcome will in turn imply that
economic globalization and capital market integration are positively linked, and is consistent with practical
observation. We adopt two formal empirical procedures. First, based on Model (A) and Model (B), Table 6
presents six panel regression results (full sample, GC, GC/international, CN/International, HK/International
and TW/international) over the full study period. This means that in each panel, the influence of each
explanatory variable is constrained to be identical across all constituent equations while the intercepts are
allowed to vary across all equations. The two models are:
Model (A):
CORR RE- t = f (RID t /UID t /RPPD t)
Model (B):
CORR RE- t = f (RID t /UID t /RPPD t , CORR ST-t )
In the above, Model (A) tests the relationship between real estate securities market realized
correlation (CORR RE- t) and three economic globalization parity differentials (i.e. RID, UID and RPPD). To
avoid the influence of multicollinearity only one of the three parity factors is included in the regression at a
time. Of the 18 regressions, the results reported in Table 6 confirm the expected significant negative
relation between CORR RE- t and three cases of RID, four cases of UID and four cases of RPPD. Moreover,
since international correlation of real estate securities and the broader stock market are synchronized with
each other (Liow et al, 2009), the inclusion of the stock correlation factor in Model B will serve to control
for any stock market effect on the real estate securities markets’ correlations, so that any residual
relationship between the detected real estate securities market correlation and economic globalization can
be reasonably attributed to the real estate securities markets per se. As the numbers indicate, when the
effect of the underlying stock market correlation factor is controlled, there are only four regressions (i.e.
two RID, one UID and one RPPD) that display a significantly negative relationship each between real
estate securities market correlation and economic globalization The stock market correlation factor is
always significantly positively linked to real estate securities market correlation factor (p<0.01). This result
is expected since real estate securities market is part of wider stock market. Consequently, we are inclined
to infer from these results that the underlying positive relationship implied between real estate securities
market integration and economic globalization of our sample could be largely attributed to the underlying
stock market effect of the two economies concerned. However, as real estate securities market correlations
do not completely synchronized with stock market correlations, any additional link observed between
economic globalization and cross-real estate securities market integration should be orthogonal from the
underlying stock markets’ influences, as our results have implied.
(Table 6 here)
To understand the multilateral interactions among the three economic globalization and two
capital market integration factors, Table 7 is the five-factor” integration spillover table” for the full sample.
Following Diebold and Yilmaz (2012), this table is constructed using the row normalization method with
the sum of the return forecast error variances in a row equals 100%.12. The i th entry is the estimated
contribution to the forecast return variance of factor i, resulting from innovations to factor j. The sum of the
return forecast error variance in a row (column), excluding the diagonal returns, indicates the impact on the
return forecast error variance of other factors in the VAR system. The aggregate “integration spillover
index” is computed as the sum of all return forecast error variances in the 5 x 5 matrix minus the sum of the
diagonal return forecast error variances. In our case with a six-month ahead return forecast, on average,
across the full sample, about 36.2% of the return forecast error variance comes from integration spillover
among the three economic globalization and two capital market integration factors. The remaining 63.8%
of the total forecast error variance is explained by own shocks rather than spillover of shocks across the
five factors. Similarly, the integration spillover indices are, respectively, 36.6% (within GC), 38.3% (GC/
international),
33%
(China/international),
38.3%
(Hong
Kong
/
international)
and
32.5%
(Taiwan/international). Based on these results, we are inclined to conclude that the extent of
interdependence between economic globalization and capital market integration within and across the GC
areas is at best moderate. Compared with the GC group, the GC/international group has a slightly higher
total integration spillover index. We interpret this result to reflect that the GC region has become one of
most dynamic economic regions in the global environment since the last decade. Especially with China
joining the WTO since November 2011, its international economic and capital market linkages have grown
in tandem with its open economic policies.
(Table 7 here)
Given that the full sample integration spillover index can only provide an indication of the
“average” integration spillover behavior, we estimate integration spillover indices over rolling 36-month
sub-sample windows. Figure 5 presents a spillover plot each for six groups (all, GC, GC/international,
CN/international, HK/international and TW/international). One general observation is that the six
integration spillover plots display different fluctuating patterns over the different sub-sample windows,
implying that the time-varying integration spillover indices might not share a common trend. Table 8
12
Alternatively, the column normalization method makes the sum of the forecast error variance in each column
equals to 100%.
provides the mean, standard deviation and maximum-minimum values of the six integration spillover index
series to aid easier interpretation.
(Figure 5 and Table 8 here)
Within the GC areas, the relationship between economic globalization and capital market
integration has experienced frequent fluctuations between 39.6% and 54.2%, resulting in at least three
peaks and two troughs. The integration spillover plot started at about 48.5% in March 1999 and
subsequently dropped to 43.5% within a period of eight months (December 1999) in response to the Asian
/Russian/Brazilian financial crisis during this period. The index subsequently recovered to its highest peak
of about 54.2% in January 2001, implying that economic globalization has enhanced the across-stock/real
estate securities market correlations within the GC areas, in line with the growth of global real estate
securities markets during this period. From this peak level, the spillover index headed south and fluctuated
between 43% and 48% for over two years, till reached the first trough in May 2004 where the index was at
40.5% level. Within a period of slightly more than a year, the index rose to the 2nd highest peak of 53.8% in
August 2005 which coincided with two reforms to the Chinese financial markets initiated in 2005. In
particular, the policy effects were significant and had noticeable effect on the Chinese capital markets
(Zhou et al. 2005). In response to the global financial crisis, the index started to slide from mid2008 onward
and reached its lowest level in December 2008 (39.6%), resulting from the USA subprime crisis in
February 2007 and subsequent Lehman Brothers’ bankruptcy in September 2. Although the stock and real
estate securities correlations between the three GC economies during this crisis period are significantly
higher than the normal level (Liow and Newell, forthcoming), these higher market correlations have little to
do with economic globalization; rather than due to regime and contagion effects associated with the crisis
(Forbes and Ribogon, 2002). Finally, the index recovered to its third highest level within a period of about
2 1/2 year (52.6% in December 2010) in line with the improved world market condition after the global
financial crisis. As of June 2011, the return spillover index was about 47.8% which was slightly lower than
its initial level (i.e. March 1999 - 48.5%).
For the GC/international group, starting at a higher value of about 51.2% in the first window, the
total integration spillover index climbed to its first peak of 54.6% in July 2001. Then the index went
through few smaller cycles before hitting its first bottom of 42.4% in just then below four years, in May
2005. In September 2007, the spillover recorded a significant upward improvement of 15.7% to reach its
highest level of 58.1%. Thereafter this cycle lasted till the end of the global financial crisis in October 2009
when the index dropped to its lowest of 39.3%. Compared to the spillover behavior within the GC group,
we observe that although the two spillover indices are very weakly correlated (correlation coefficient is 8.2%), it appears that the movement of the GC/international spillover index always lag behind that of the
GC spillover index. Additional analysis indicates the maximum correlation between the two indices
happens at 26 months lag, with a correlation coefficient of about 0.441.
Based on the reported results, we are inclined to conclude that despite the frequent and dynamic
integration spillover fluctuations, the extent of interdependence between economic globalization and capital
market integration over the study period is at best moderate. The maximum integration spillover intensity
for the six groups ranges between 48% (TW/international) and 58.1% (GC/international). Moreover, the
global financial crisis has impacted negatively the integration spillover between economic globalization
and capital market integration implying economic fundamentals have little influence on capital market
correlation for the markets under examination during the crisis periods.
Finally, Table 9 provides an estimate of the “integration spillover index” between each of three
economic globalization factors and two capital market integration measures. Thus, we have three subsystems for each identified group: [RID, CORRR
CORRR
RE,
CORR
ST].
RE,
CORR ST], [UID, CORRR
RE,
CORR ST] and [RPPD,
Results of Table 9 reveal that all 18 integration spillover indices are below 30%
(between 18.4% and 28.8%), with the GC/international group reports a higher integration index each in all
three economic globalization measures (between 0.6% and 2.7% higher) than within the GC group.
Additional evidence indicates among the three GC economies, Hong Kong has a much higher international
interdependence consistently reflected by all three integration spillover indices (between 26.9% and 28.8%).
Finding a stronger international connection between economic globalization and capital market integration
for Hong Kong is consistent with market belief that Hong Kong, as a relatively developed global economy,
has contributed significantly to the increasing globalization of the world financial markets. In contrast,
Taiwan has the lowest integration spillover index (in all three measures) with their regional/international
partners, implying that increasing global integration of financial and economic activities might not have
increased their international capital market linkage significantly. However, we expect this linkage to
increase significantly with Taiwan’s growing economic ties with China in the near future.
(Table 9 here)
5.
Conclusion
In the context of increasing globalization and financial integration, the main contribution of this
paper is to investigate the dynamics of economic globalization and capital market integration, as well as
their possible link, among the three GC economies and across the GC areas with their regional and
international partners during the period from 1996 February to 2011 June. Our study is particularly
meaningful as the GC region has emerged as one of the most dynamic economic regions in the world since
the last decade. The first part involves an evaluation of the three parity conditions (i.e. RIP, UIP and RPPP)
using descriptive statistics, mean reversion procedures and time-trend test. Using estimates of realized
correlation in the second part, we assess the extent of integration between the real estate securities markets,
as well as between the sample stock markets. Consequently in the third part whether economic
globalization and capital market integrated are significantly linked using the panel regression approach and
Diebold and Yilmaz (2012)’ generalized VAR procedure and spillover index methodology on our dataset.
The economic globalization results reveal in spite of the limited extent of economic globalization
and inconclusive time-trend results, the real interest rates, uncovered interest rates and relative purchasing
power within and across the GC areas tend to converge and therefore the three parity conditions hold in the
long run. Consequently, there is some evidence of economic globalization, in particular, real and financial
integration within the three GC economies and across the GC areas regionally and globally. The increased
realized cross-real estate securities market correlations and realized cross-stock market correlations imply
that international capital markets have become increasingly integrated over the study period. Finally, the
integration spillover index and plot investigations have detected some evidence of interdependence
between economic globalization and capital market integration. However, the extent of spillover between
economic globalization and capital market integration within and across the GC areas is at best moderate
over the study period.
Our results should be regarded as preliminary, but indicative. They suggest that economic
globalization could be one of the key driving forces of capital market integration. Further studies are
definitely required to order to confirm whether the GC results reported in this paper could be generalizable
to e developed and emerging economies.
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Table 1
Descriptive Statistics: 1996M02 to 2011M06
Real interest differentials
Std
Mean
Range
dev.
CN/HK
CN/TW
HK/TW
GC(ALL)
CN/US
CN/UK
CN/JP
CN/SG
CN /ALL
HK/US
HK/UK
HK/JP
HK/SG
HK /ALL
TW/US
TW/UK
TW/JP
TW/SG
TW/ALL
Uncovered interest
differentials
Deviations from relative
purchasing power parity
Std
Mean
Range
dev.
Mean
Range
Std dev.
0.793
1.164**
64.614
39.325
10.578
7.578
1.693***
-0.186
33.009
220.361
4.914
21.522
0.900
-1.350
81.437
231.564
9.994
23.632
2.455***
53.938
8.287
0.110
223.134
21.549
-0.261
224.819
22.874
1.471***
27.985
5.258
0.539
146.698
14.488
-0.237
154.557
15.866
1.622***
0.813
2.356***
52.549
58.503
61.976
8.413
8.680
9.088
1.386***
-3.789
0.279
33.527
291.161
522.389
4.629
33.303
50.771
-0.236
-4.602*
-2.077
60.869
292.361
533.452
8.597
34.155
50.511
2.503***
44.253
8.468
0.488
163.177
21.604
-2.015
171.038
21.702
1.823***
49.478
8.262
-0.409
152.678
19.182
-2.232
155.821
19.618
0.829
0.020
1.563***
53.267
46.767
50.046
7.181
6.927
7.543
-0.316
-5.505**
-1.382
15.405
289.023
540.962
1.916
33.255
51.243
-1.145
-5.525**
-2.945
51.213
299.887
570.701
7.204
34.147
52.894
1.710***
47.897
7.977
-1.225
163.149
21.673
-2.935
172.691
22.923
1.030**
48.206
6.941
-2.107
160.112
19.257
-2.017**
160.112
19.257
0.458
-0.351
1.339***
38.706
39.434
46.297
5.277
5.836
6.360
-0.729
-5.158*
-0.803
223.937
378.273
323.481
21.720
38.678
44.681
-1.187
-4.807
-1.995
234.760
388.209
331.299
22.595
39.670
44.730
1.192***
44.010
6.004
-0.917
180.010
19.827
-2.256
185.296
20.651
0.659**
23.268
3.665
-1.902
233.276
22.357
-2.561
244.241
23.042
Notes: The real interest differentials, uncovered interest differentials and deviations from relative purchasing
power parity series between the three Greater China (GC) economies i.e. Mainland China (CN), Hong Kong (HK),
Taiwan (TW), as well as between each of the GC economies and US, UK, Japan (JP) and Singapore (SG) have been
adjusted for the effects of the July 1997 Asian financial crisis (AFC) and July 2007 Global financial crisis
(GFC). They are all annualized and measured in percentage terms. “Range” is the difference between maximum
and minimum values for the series. “***”, “**” and “*” indicate that the sample mean is significantly different
from zero at the 1, 5 and 10% levels, respectively.
Table 2
Results of Unit Root Tests: 1996M02 to 2011M06
Real interest differential
Uncovered interest
Relative purchasing
(RID)
differential (UID)
power differential (RPPD)
Panel A: Elliot-Rothenberg-Stock (ERS) DF-GLS test statistic
CN/HK
CN/TW
HK/TW
GC(ALL)
CN/US
CN/UK
CN/JP
CN/SG
CN /ALL
HK/US
HK/UK
HK/JP
HK/SG
HK /ALL
TW/US
TW/UK
TW/JP
TW/SG
TW /ALL
-3.195**
-9.601***
-3.927***
-9.271***
-5.191***
-4.017***
-10.383***
-6.004***
-4.497***
-3.007**
-2.829*
-3.149**
-7.620***
-2.823*
-2.974**
-4.307***
-19.040***
-4.363***
-16.106***
Within GC
CN with non-GC
HK with non-GC
TW with non-GC
-12.560***
-13.550***
-18.547***
-16.532***
-18.578***
-27.941***
-21.999***
-23.946***
-29.075***
-25123***
-29.066***
-29.671***
-3.426**
-11.628***
-11.743***
-11.576***
-2.733*
-12.607***
-7.537***
-13.717***
-12.666***
-12.119***
-12.340***
-13.791***
-13.075***
-12.571***
-11.886***
-13.250***
-14.413***
-13.513***
-13.868***
-6.868***
-11.712***
-12.720***
-11.583***
-9.828***
-12.542***
-7.783***
-14.284***
-12.921***
-3.131**
-12.809***
-13.715***
-13.680***
-13.352***
-11.972***
-13.340***
-14.340***
-13.726***
-14.101***
Panel B: IPS Panel unit root test W-statistic
Notes: The economic-pairs are labeled in the first column. Panel A gives the results of individual Elliot-RothenbergStock (ELS) DF – GLS unit-root t- statistic (
- with trend) with the lag parameters selected by the Schwarz
criterion (the null has a unit root). Panel B provides the panel unit root test results of Im, Pesaran and Shih (IPS) Wstatistic (with trend) that tests the null hypothesis of joint non-stationary. The IPS test assumes individual unit root
processes and is asymptotically normal with 1%, 5% and 10% critical values of -2.33, -1.64 and -1.28 respectively.
“1”, “2” and “3” –indicates the null is rejected at the 1%, 5% and 10% levels respectively.
Table 3
Results of Variance Ratio Test for Random Walk: 1996M02 to 2011M06
Lag
1
2
4
8
16
32
64
RID
GC
UID
CN/international
UID
RPPD
RPPD
RID
1.000
0.589***
0.319***
0.154**
0.108**
0.054*
1.000
0.584***
0.326***
0.153**
0.078*
0.043*
1.000
0.566***
0.333***
0.147**
0.080*
0.044*
1.000
0.543***
0.288***
0.153***
0.104**
0.048*
1.000
0.476***
0.259***
0.124**
0.078*
0.038*
1.000
0.484***
0.48***
0.123**
0.079**
0.038*
1.000
0.417***
0.190***
0.111**
0.070**
0.040*
1.000
0.519***
0.270***
0.127**
0.077**
0.037*
1.000
0.485***
0.260***
0.119**
0.075**
0.038*
1.000
0.444***
0.184***
0.108***
0.063**
0.031*
1.000
0.553***
0.284**
0.126**
0.072*
0.032*
1.000
0.552***
0.265***
0.125**
0.074*
0.032*
0.039*
0.023*
0.024*
0.036*
0.026*
0.025*
0.023*
0.026*
0.024*
0.018*
0.022*
0.021*
RID
HK/international
UID
RPPD
RID
TW/international
UID
RPPD
Notes: RID (real interest differentials); UID (uncovered interest differentials) and RPPD (relative purchasing power differentials). Test probabilities are computed using
wild bootstrap. “***”, “**” and “” – significantly less than 1 at the 1, 5 and 10% levels, respectively
Table 4
Hodrick-Prescott filtered cross-pair dispersion – simple trend analysis
Initial level (as at
1996M02)
Final level (as at
2011M06)
Total change (%)
Average annual
change (%)
RI D
GC
UID
RPPD
RID
GC /International
UID
RPPD
5.4570
4.3971
5.0916
3.6391
9.3222
9.9493
2.9631
4.2316
7.6678
2.8433
10.6151
11.9461
-45.70
-2.96
-3.76
-0.24
50.06
3.29
-21.87
-1.43
13.87
0.90
20.07
1.31
Note: Average annual change (%) = (Total change (%) /185 months)*12
Table 5
Realized Correlation Results: 1996M02 to 2011M06
Panel A
Descriptive statistics and individual unit root test
Real estate market
correlation
Mean
Range Std dev.
CN/HK
CN/TW
HK/TW
GC(ALL)
CN/US
CN/UK
CN/JP
CN/SG
CN /ALL
HK/US
HK/UK
HK/JP
HK/SG
HK /ALL
TW/US
TW/UK
TW/JP
TW/SG
TW /ALL
Stock market correlation
Mean
Range Std dev.
Unit root test
RE
Stock
0.226***
0.092***
0.137***
0.152***
0.112***
0.057***
0.084***
0.17***
0.106***
0.176***
0.102***
0.190***
0.361***
0.207***
0.034**
0.028*
0.116***
0.123***
1.400
1.316
1.461
1.164
1.217
1.115
1.196
1.349
0.913
1.208
1.161
1.250
1.868
1.113
1.114
1.044
1.227
1.296
0.303
0.262
0.259
0.223
0.223
0.231
0.246
0.291
0.181
0.246
0.235
0.248
0.300
0.194
0.228
0.211
0.258
0.248
0.469***
0.214***
0.294***
0.326***
0.203***
0.138***
0.223***
0.298***
0.215***
0.324***
0.197***
0.327***
0.421***
0.318***
0.201***
0.135***
0.250***
0.279***
1.198
1.233
1.260
1.156
1.424
1.396
1.265
1.104
0.919
1.321
1.184
1.284
1.580
0.995
0.986
1.174
1.217
1.413
0.353
0.294
0.304
0.286
0.270
0.242
0.270
0.271
0.207
0.273
0.236
0.279
0.324
0.231
0.226
0.228
0.275
0.287
-5.681***
-11.576***
-10.283***
-9.049***
-9.770***
-12.134***
-5.295***
-3.565***
-2.806*
-9.676***
-13.263***
-9.770***
-4.829***
-4.423***
-12.882***
-12.882***
-5.389***
-5.345***
-3.406**
-5.480***
-4.694***
-4.084***
-10.653***
-11.173***
-4.691***
-2.846*
-4.716***
-2.929*
-6.650***
2.928*
-4.530***
-3.088**
-4.090***
-7.487***
-4.933***
-3.555***
0.075***
1.015
0.165
0.216***
0.937
0.209
-10.483***
-3.586***
Notes: The results of individual Elliot-Rothenberg-Stock (ELS) DF – GLS unit-root t- statistic (
with trend) are reported (the null has a unit root). The lag parameters are selected by the Schwarz criterion.
Panel B
Correlation series
Real estate
Stock
-
IPS Panel Unit Root Test W-statistic
Within GC
-16.759***
-15.291***
China with non-GC
-20.827***
-25.350***
Hong Kong with
non-GC
-25.229***
-27.353***
Taiwan with non-GC
-27.062***
-28.780***
Notes: The panel unit root test results of Im, Pesaran and Shih (IPS) W-statistic (with trend) that tests the null
hypothesis of joint non-stationary are reported. The IPS test assumes individual unit root processes and is
asymptotically normal with 1%, 5% and 10% critical values of -2.33, -1.64 and -1.28 respectively. “***” –
indicates the null is rejected at the 1% level.
Panel C
Lag
Variance Ratio test for random walk
Within GC
China with non-GC
Cross- real
estate
correlation
Crossstock
correlation
Cross- real
estate
correlation
Crossstock
correlation
1
0.600***
0.332***
0.194***
0.117**
0.068*
0.043
1
0.679***
0.445***
0.272***
0.209**
0.164*
0.098*
1
0.563***
0.273***
0.153***
0.119**
0.062*
0.037*
1
0.554***
0.333***
0.204***
0.175**
0.114*
0.046*
1
2
4
8
16
32
64
Hong Kong with nonGC
Cross- real
Crossestate
stock
correlation correlation
1
0.565***
0.297***
0.183***
0.130**
0.075**
0.049*
1
0.608***
0.330***
0.235***
0.209**
0.130**
0.081*
Taiwan with non-GC
Cross- real
estate
correlation
Crossstock
correlation
1
0.604***
0.292***
0.179***
0.088**
0.056*
0.029
1
0.540***
0.338***
0.199***
0.130**
0.094*
0.062*
Notes: Test probabilities are computed using wild bootstrap. “***”, “**” and “” – significantly less than 1 at
the 1, 5 and 10% levels, respectively
Panel D
Hodrick-Prescott filtered cross-pair dispersion – simple trend analysis
Initial level (as at 1996M02)
Final level (as at 2011M06)
Total change (%)
Average annual change (%)
Within GC
Real estate
Stock
0.1065
0.1453
0.1802
0.1605
69.29
10.42
4.50
0.67
Note: Average annual change (%) = (Total change (%) /185 months)*12
GC and international
Real estate
Stock
0.1485
0.2366
0.1954
0.2129
59.37
8.9
3.85
0.58
Table 6
Panel regression results on the link between real estate securities market
integration and economic integration: 1996M02 to 2011M06
Based on Model I and II, the dependent variable is the monthly realized correlations in real estate
securities markets adjusted for the effects of Asian financial crisis (AFC) and Global financial crisis
(GFC), from 1996M02 to 2011M06. This table presents results of estimating the two models as a panel
constraining the influence of each explanatory factor to be identical across all equations concerned , while
allowing the intercept to vary (cross-section fixed effects), adjusted for White period standard errors and
covariance and auto-correlated residual errors where appropriate (up to 2nd orders) The explanatory
factors are : (1) real interest differentials (RID) / uncovered interest differentials (UID) /deviations from
relative purchasing power parity (RPPD), adjusted for effects of AFC and GFC; (b) across stock market
realized correlations (adjusted for AFC and GFC). ***, **, * - indicates statistical significance at the 1%,
5% and 10% level respectively. Legends: W/GC (China, Hong Kong and Taiwan – 3 pairs), GC/INT
(China with US, UK, Japan and Singapore, Hong Kong with US, UK, Japan and Singapore, Taiwan with
US, UK, Japan and Singapore – 12 pairs); CH/INT (China with US, UK, Japan and Singapore, 4 pairs);
HK/INT(Hong Kong with US, UK, Japan and Singapore – 4 pairs); TW/INT (Taiwan with US, UK, Japan
and Singapore - 4 pairs).
Model I: Real estate realized correlation = f (RID /UID/RPPD)
Model II: Real estate realized correlation = f (RID /UID/RPPD, stock market realized correlation)
Dependent variable:
Cross-real estate market realized correlation
M odel I
Differentials
Adj R2
Cross section
Differentials
Fixed F
M odel II
Stock
Adj R2
correlation
Cross section
Fixed F
Panel A: Real interest differentials (RID)
ALL
W/GC
GC/INT
CH/INT
HK/INT
TW/INT
-0.00272
0.00157
-0.00392*
-0.00752**
0.00436
-0.01121***
ALL
W/GC
GC/INT
CH/INT
HK/INT
TW/INT
-0.001161***
-0.00447***
-0.00090**
-0.00125***
-0.000437
-0.00066
ALL
W/GC
GC/INT
CH/INT
HK/INT
TW/INT
0.296
0.302
0.293
0.242
0.357
0.091
12.99***
7.00***
16.96***
4.62***
28.19***
11.09***
-0.00022
0.00274
-0.00134
-0.00375**
0.00097
-0.00535*
0.5691***
0.6044***
0.5638***
0.5111***
0.6437***
0.4920**
0.537
0.574
0.519
0.438
0.628
0.325
10.39***
2.63*
12.21***
13.62***
4.08***
0.535
0.568
0.519
0.433
0.626
0.324
12.93***
3.40**
14.49***
17.09***
3.63**
Panel C: Deviations from relative purchasing power parity (RPPD)
-0.00086**
0.297
12.08***
0.00006
0.5722***
0.535
-0.00342***
0.307
6.21***
-0.00061
0.6072***
0.568
-0.00064*
0.292
16.76***
0.00011
0.5567***
0.519
-0.00099***
0.239
4.49***
-0.00074***
0.5319***
0.433
-0.00005
0.337
27.83***
0.00056
0.6479***
0.626
-0.00053
0.084
10.24***
0.00032
0.4968***
0.326
12.93***
3.46***
14.57***
17.19***
3.64***
Panel B: Uncovered interest differentials (UID)
0.297
0.309
0.293
0.241
0.337
0.084
12.57***
5.69***
16.69***
4.49***
27.71***
10.23***
-0.000001
-0.00051
0.00005
-0.00075***
0.00038
0.00031
0.5721***
0.6071***
0.5565***
0.5319***
0.6476***
0.4969***
Table 7
Total integration spillover table: economic globalization and capital
market integration: 1996M02 to 2011 M06
Full sample
RID
86.7
1
0.4
0.1
0.3
UID
4.7
50.8
44.7
1
1.8
RPPD
4.8
43.7
51.9
0.7
1.1
RECOR
2.6
1
0.6
61.5
28.5
STCOR
1.1
3.4
2.5
36.7
68.3
From others
13
49
48
39
32
Contribution
to others
2
52
50
33
44
181
Contribution
including
owns
88
103
102
94
112
Total integration
spillover index:
36.2%
RID
89.9
0.6
2
1
1.5
UID
0.5
51.2
42.5
1.4
2.1
RPPD
2.7
43.6
50.3
1.1
1.6
RECOR
2.9
1.7
1.5
57.8
26.9
STCOR
4
2.9
3.7
38.8
67.8
From others
10
49
50
42
32
5
46
49
33
49
183
95
98
99
91
117
Total integration
spillover index:
36.6%
RID
89.8
0.4
0.8
1.1
1.5
UID
1.1
49.5
45.4
2.2
1.9
RPPD
2.5
44.7
50.4
1.6
0.9
RECOR
3.2
1.8
1.1
55
32.1
STCOR
3.3
3.6
2.3
40.1
63.5
From others
10
50
50
45
36
4
51
50
38
49
192
113
Total integration
spillover index:
38.3%
RID
UID
RPPD
RECOR
STCOR
Within GC areas
RID
UID
RPPD
RECOR
STCOR
Contribution
to others
Contribution
including
owns
GC/international
RID
UID
RPPD
RECOR
STCOR
Contribution
to others
Contribution
including
owns
94
100
100
93
China/international
RID
91
0.1
0.2
2.1
1.8
UID
1.8
54.2
41.3
1.6
2.4
RPPD
3
40.3
56.1
1.1
0.9
RECOR
3.5
1.9
0.9
63.5
24.9
STCOR
0.7
3.5
1.4
31.7
70
From others
9
46
44
37
30
Contribution
to others
4
47
45
31
37
165
Contribution
including
owns
95
101
101
95
107
Total integration
spillover index: 33%
RID
90.5
1.4
3.6
0.1
0.1
UID
1.4
50.2
42.6
2.8
1.2
RPPD
5.3
42.9
49.8
1.8
0.9
RECOR
0.8
2.3
1.4
53.2
32.9
STCOR
2
3.2
2.5
42.2
64.9
From others
9
50
50
47
35
5
48
51
37
50
191
RID
UID
RPPD
RECOR
STCOR
Hong Kong /International
RID
UID
RPPD
RECOR
STCOR
Contribution
to others
Contribution
including
owns
98
101
91
115
Total integration
spillover index:
38.3%
RID
94.7
0.4
0.6
2.4
1.3
UID
0.8
50.9
47.8
3.1
0.6
RPPD
1.1
47.8
50.8
3.3
0.5
RECOR
2.5
0.2
0.2
66
22.7
STCOR
0.8
0.6
0.5
25.2
74.9
From others
5
49
49
34
25
5
52
53
26
27
163
102
Total integration
spillover index:
32.5%
96
Taiwan / international
RID
UID
RPPD
RECOR
STCOR
Contribution
to others
Contribution
including
owns
99
103
104
92
Notes: The underlying variance decomposition is based upon a monthly VAR of order 2, identified using a
generalized VAR spillover framework proposed by Diebold and Yilmaz (2012) (forecast error variance
decompositions are invariant to variable ordering). The (i , j)th value is the estimated contribution to the
variance of the six-month ahead integration return forecast error variance of factor i coming from innovations to
the return of factor j The five factors in the VAR system are RID (absolute real interest differential), UID
(absolute uncovered interest differential), RPPD (absolute relative purchasing power differential), RECOR
(realized correlations – real estate securities markets) and STCOR (realized correlations- stock markets)
Table 8
Mean
Std. Dev
Maximum
Minimum
Descriptive statistics on time-varying integration spillover
plots: 1996M02 to 2011M06
ALL
45.6
3.41
54.33
37.17
GC
46.71
3.1
54.2
39.57
GCINT
47.49
3.42
58.09
39.33
CNINT
46.14
3.84
55.73
38.83
HKINT
47.09
4.68
57.46
36.97
TWINT
41.51
2.8
47.98
34.37
Notes: All numbers are expressed in percentage term. ALL(full sample), GC (within the GC areas), GCINT
(GC/international), CNINT (China/international), HKINT (Hong Kong / international), TWINT
(Taiwan/international)
Table 9
ALL
GC
GCINT
CNINT
HKINT
TWINT
Total integration spillover index: by group and sub-system:
1996M02 to 2011M06
System 1
RID, RECOR, STCOR
23.6%
25.7%
27.3%
21.9%
26.9%
18.7%
System 2
UID, RECOR, STCOR
25.4%
26.0%
28.7%
24.0%
29.9%
18.6%
System 3
RPPD, RECOR, STCOR
24.3%
26.3%
27.4%
21.6%
28.8%
18.4%
Figure 1
Real Interest Differentials (RID): 1996M02 to 2011M06
40
50
China/Hong Kong
China/Taiwan
Hong Kong/Taiwan
30
China/US
China/UK
China/Japan
China/Singapore
40
30
20
20
10
10
0
0
-10
-10
-20
-20
-30
-30
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
30
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
10
11
30
20
Taiwan/US
Taiwan/UK
Taiwan/Japan
Taiwan/Singapore
20
10
10
0
0
-10
Hong
Hong
Hong
Hong
-20
Kong/US
Kong/UK
Kong/Japan
Kong/Singapore
-10
-30
-20
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
96
97
98
99
00
01
02
03
04
05
06
07
08
09
Figure 2
Uncovered Interest Differentials (UID): 1996M02 to 2011M06
80
100
40
0
0
-100
-40
-200
-80
China/US
China/UK
China/Japan
China/Singapore
-300
China/Hong Kong
China/Taiwan
Hong Kong /Taiwan
-120
-400
-160
-500
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
100
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
07
08
09
10
11
100
0
0
-100
-200
-100
-300
Hong
Hong
Hong
Hong
-400
Kong/US
Kong/UK
Kong/Japan
Kong/Singapore
Taiwan/US
Taiwan/UK
Taiwan/Japan
Taiwan/Singapore
-200
-500
-300
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
96
97
98
99
00
01
02
03
04
05
06
Figure 3
Deviations from Relative Purchasing Power Parity (RPPD): 1996M02 to 2011M06
80
100
40
0
0
-100
-40
-200
-80
China/US
China/UK
China/Japan
China/Singapore
-300
China/Hong Kong
China/Taiwan
Hong Kong/Taiwan
-120
-400
-160
-500
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
100
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
07
08
09
10
11
100
0
0
-100
-100
-200
Hong
Hong
Hong
Hong
-300
Kong/US
Kong/UK
Kong/Japan
Kong/Singapore
-200
Taiwan/US
Taiwan/UK
Taiwan/Japan
Taiwan/Singapore
-300
-400
-500
-400
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
96
97
98
99
00
01
02
03
04
05
06
Figure 4
Realized Correlations: 1996M02 to 2011M06
1.2
.8
Average Real Estate Market Correlations: GC with Non-GC
Real Estate Market Correlations Within Greater China (GC)
.6
0.8
.4
0.4
.2
.0
0.0
-.2
-0.4
China/Hong Kong
China/Taiwan
Hong Kong/Taiwan
-0.8
96
1.0
97
98
99
00
01
02
03
04
05
06
07
Average: China with Non-GC
Average: Hong Kong with Non-GC
Average: Taiwan with Non-GC
-.4
-.6
08
09
10
11
96
97
.8
Stock Market Correlations Within Greater China (GC)
0.8
98
99
00
01
02
03
04
05
06
07
08
09
10
11
10
11
Average Stock Market Correlations: GC with Non-GC
.6
0.6
.4
0.4
.2
0.2
.0
0.0
-0.4
96
97
98
99
00
01
02
03
04
05
06
07
Average: China with Non-GC
Average: Hong Kong with Non-GC
Average: Taiwan with Non-GC
-.2
Chiina/Hong Kong
China/Taiwan
Hong Kong/Taiwan
-0.2
-.4
08
09
10
11
96
97
98
99
00
01
02
03
04
05
06
07
08
09
Figure 5
Hodrick-Prescott Filtered Cross-dispersion: 1996M02 to 2011M06
Panel A.
Average within Greater China (GC)
10
9
Deviations from RPP paprity
8
7
6
UI differentials
5
4
RI differentials
3
2
96
Panel B
97
98
99
00
01
02
03
04
05
06
07
08
09
10
Average between GC and Non-GC
40
35
RI differentials
UI differentiuals
Deviations from RPP parity
30
25
20
15
10
5
0
25
50
75
100
125
150
Notes: The three series are Real Interest differential(RID), Uncovered Interest differential(UID) and
Deviations from Relative Purchasing Power (RPPD) parity
Source: Authors’ estimates
175
11
Figure 6
Hodrick-Prescott Filtered Cross-dispersion in Realized Correlation:
1996M02 to 2011M06
Panel A
Average within Greater China (GC)
.24
.20
.16
.12
.08
Average real estate market correlations
Average stock market correlations
.04
25
Panel B
50
75
100
125
150
175
150
175
Average between GC and Non-GC
.24
Average s tock mark et correlations
.20
.16
Average real es tate mark et correlations
.12
.08
.04
25
Source: Authors’ estimates
50
75
100
125
Figure 7
Integration spillover plots
ALL
GC
56
56
52
52
48
48
44
44
40
40
36
36
99
00
01
02
03
04
05
06
07
08
09
10 11
99
00
01
02
03
GCINT
04
05
06
07
08
09
10 11
06
07
08
09
10 11
06
07
08
09
10 11
CNINT
60
56
55
52
50
48
45
44
40
40
35
36
99
00
01
02
03
04
05
06
07
08
09
10 11
99
00
01
02
03
HKINT
04
05
TWINT
60
52
55
48
50
44
45
40
40
36
35
32
99
00
01
02
03
04
05
06
07
08
09
10 11
Notes: Based on 36-month window, 6-step horizon
99
00
01
02
03
04
05