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