Diversification Gains and Systematic Risk Exposure in International Public Real Estate Markets
Marielle Apisara Chuangdomrongsomsuk and Colin Lizieri (*)
Department of Land Economy University of Cambridge
19 Silver Street Cambridge CB25 9AD
Paper for the European Real Estate Society Conference, Vienna
Version of 26 June 2013
Please contact authors for latest version
(*) contact author: [email protected]
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Diversification Gains and Systematic Risk Exposure in International Public Real Estate Markets
Abstract We test whether the diversification benefits of international real estate securities depend on how integrated or independent the firms and countries are at global or regional level. Applying a variety of techniques, we separate property securities into cointegrated and independent portfolios and test the performance and factor sensitivities of these groups, first at national level and then separately at individual sector level and for firms with high exposure to global cities. The results confirm the importance of cointegration, but also demonstrate that there are sharp differences across sectors. Investors should fine tune their portfolio selection strategies to account for these differences. Keywords: International Real Estate Securities, Portfolio Risk Management, Regional
Cointegration, Sector Effects. 1. Diversification and Cointegration in Real Estate Markets: An Introduction
In this paper, we re‐examine the benefits of holding a portfolio of international real estate securities in the light of evidence of growing co‐movement of securitised asset returns across markets. Do diversification benefits depend on how integrated or independent the firms and countries are at global or regional level? Specifically, can more risk reduction be achieved through holding international diversified investments in markets that are less dominated by global real estate factors? If so, is it possible to identify the extent of this effect, hence informing global investor strategy, particularly in the light of a growing integration within global securities markets? This task is given further significance by the events of the global financial crisis, where correlation between markets (and asset classes) appeared to increase rapidly precisely when diversification would have been most valuable. The paper adopts a long run focus and develops studies such as those of Wilson & Zurbruegg (2003b), Gerlach et al. (2006) and Gallo & Zhang (2010) in focussing on the cointegration between markets. Utilising data from GPR’s international real estate company database, we extend that work in seeking to identify the sources of difference and in investigating the impact of cointegration on the sensitivity of asset returns to factor risks. We aggregate individual company returns to produce value weighted national indices and then disaggregate the data to focus on individual sectors and on companies that have a high exposure to international gateway cities and financial centres to test whether such firms are more influenced by global capital market factors. We capture cointegration using a variety of techniques, taking into account the possibility of structural breaks in the data. From these tests, we produce two portfolios of “cointegrated” and “independent” indices and assess whether they differ in terms of risk‐adjusted return. We examine Sharpe ratios and sensitivity to systematic risk, using a range of multi‐factor models, and decompose portfolio risk using a Fama‐Macbeth approach testing for differences in risk sensitivity and volatility. The results indicate substantial differences in factor sensitivity and risk between the cointegrated and independent portfolios although benefits from risk sensitivity may be offset by lower aggregate performance. We re‐examine
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the results for different time periods and for sector‐specific company indices and, finally, examine the results for companies focussed in global financial centres. Differences in the results shed light on the sources of integration and systematic risk. We begin with a brief review of the literature on the long run integration of real estate securities. We then outline our modelling approach and set out the data series used. We report results, first in aggregate and then separately by sector and for companies with high exposure to global cities. Finally, we discuss our results and point to policy implications. 2. International Real Estate Diversification: A Review of the Literature Since the seminal papers of Grubel (1968) and Solnik (1974), academic research provides evidence of diversification gains from the creation of international equity investment portfolios. Correlation analysis between different international asset classes became an important tool for making inferences about the presence of diversification benefits. However, subsequent studies pointed to the limitations of correlation coefficients since the degree of correlation between any two markets can be associated with either country or industry factors. Accordingly, researchers developed methods to decompose international equity returns to examine the most important factors. Earlier papers generally found that country effects had greater impacts on returns than industry effects. Heston and Rouwenhorst (1994) implemented a multi‐factor approach to isolate and measure country and industry effects, and found country effects to be more important drivers of return volatility. They thus suggest that more risk reduction can be achieved through investing across different countries. These findings were supported by Griffin and Karolyi (1998), who extend the HR model to include a weighting for the relative market value of equity for a country and industry. Since the late 1990s, however, a number of studies report the industry‐sector factors to have overtaken the country‐region factors in explaining many major equity returns. Van Dijk and Keijzer (2004) decomposed region, industry‐sector, size and value or growth factors for global equities, and argued that region and industry‐sector factors more important than other factors between 1987 and 2002, They also found evidence of the increasing importance of industry‐sector effects relative to country effects, especially in the second half of the sample period.1 However, these results have recently been disputed by, for example, Bekaert et al. (2009), who find country factors still dominate industry factors, using the APT and a Fama and French (1998)‐type model to examine time‐varying correlations across country and industry portfolio returns. They also suggest that, although there is some evidence of globalisation effects, international diversification benefits are still obtainable since they find no significant correlation trend for country returns in North America and Asia. They conclude that US and European investors can benefit more from investments in the Asian region relative to investing in each other’s regions. Public listed real estate markets are generally perceived to be somewhat segmented from other financial asset classes. Many studies have reported that property securities provide
1 Similar findings have also been reported in, for example, Baca et al. (2000); Cavaglia et al. (2000).
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diversification benefits to global mixed‐asset portfolios.2 These studies seem to suggest that the extent of such diversification gains is likely to decrease due to increased integration of real estate and equity markets at national level. Furthermore, a substantial body of research has reported that, within real estate‐only portfolios there is evidence of global property diversification benefits at country and regional levels.3 The consensus emerging from these papers is that, because property markets tend to converge regionally, integrating relationships are much stronger between national markets within one economic or geographic region than between national markets located in different regions.4 These results are appealing as they suggest dissimilarities in drivers of property returns across regions. Therefore investors can achieve diversification benefits from broadening their investment from domestic or continental‐focused to other regional markets. The magnitude of such gains, though, is still unclear because different statistical methodologies generate conflicting outcomes (Wilson and Zurbruegg, 2003a). This evidence of country and regional factors needs to be set in the context of studies pointing to growing international integration and evidence of a global real estate factor5. As in equity market research, early studies on real estate often relied upon modern portfolio theory (MPT) based on correlation coefficients between different asset classes or international markets and on mean‐variance analyses. Using these methods to examine the inclusion of US securitised property in Canadian property portfolios, Hudson‐Wilson and Stimpson (1996) found that Canadian investors would have achieved diversification benefits by including US real estate in their portfolios in 1980‐94. Asabere et al. (1991) investigated the role of listed property vehicles within a mixed‐asset portfolio in 1980‐99, finding low positive correlations between US REITs and other international property securities. They therefore concluded that more benefits could be achieved from diversifying across international real estate assets than equity and bond markets. These results were supported by Eichholtz (1996), who found correlations between international property markets to be lower than equities and bonds. Gordon and Canter (1999) investigated the correlations between property and equity markets in relation to type of investment vehicle and the international nature of property companies. They generally found mixed results – some markets exhibiting return convergence, while others having divergent returns. Both of these latter studies reported unstable correlation matrices between international real estate assets, casting further doubts on the application of the correlation coefficient and the mean‐variance framework for assessing long‐term diversification benefits. Later studies suggest that temporal covariance instability is attributable to differences in return volatility that can understate global real estate diversification gains (e.g., Forbes and Rigobon, 2002). Accordingly, researchers shifted their focus to study longer‐run cointegrating relationships in international real estate markets. Cointegration methods are
2 e.g., Okunev and Wilson (1997); Chaudhry et al. (1999); Liow and Yang (2005); Bond and Glascock (2006).
3 e.g., Giliberto (1990); Liu and Mei (1992, 1994, 1998); Mei and Liu (1994); Newell and Webb (1996); Quan and Titman (1997); Karolyi and Sanders (1998); Stevenson (2000); Conover et al. (2002); Worzala and Sirmans (2003); Bond et al. (2003).
4 e.g., Eichholtz et al. (1993); Worzala and Bernasek, (1996); Myer et al. (1997); Wilson and Zurbruegg (2001); Kleimann et al. (2002).
5 e.g. Ling and Naranjo (2002); Bond et al. (2003); Lizieri et al. (2003); Hamelink and Hoesli (2004);
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regarded as more reliable measurement tools of diversification gains than other approaches because, not only do they account for time‐varying long‐term integration processes, but they can also detect patterns or stages of market integration (Gallo and Zhang, 2010). Cointegration tests have been implemented by Wilson and Zurbruegg (2003b) amongst others. Specifically, from an Australian perspective they used a number of different methods to decompose the factors driving listed property security performance into permanent and transitory components, accounting for structural breaks. They then applied various restrictions on the long‐run cointegration matrix in order to identify variables that may be considered as drivers of real estate markets. They found that six international indirect property markets were interlinked, with large, major economies (i.e. Japan and the US) having a significant influence on smaller markets. However, the results on international property market diversification benefits were mixed. A recent paper, Gallo and Zhang (2010) investigates regional and country real estate market diversification benefits for US investors over the period 1992‐2007. They find that markets are integrated by common trends that erode the diversification benefits of assembling a portfolio of international real estate securities. Specifically, a portfolio consisting of cointegrated property markets consistently underperforms a portfolio encompassing of “independent” property markets. They conclude that independent real estate markets account for the majority of global property diversification gains, but cointegrated markets, particularly from North America and Asia‐Pacific, still retain some ability to enhance portfolio risk reduction. Consistent with earlier studies, they find evidence of cointegration relationships within a region, but limited evidence between regional indices. The majority of real estate studies focus their attention on national REIT or property securities indices or, if dealing with individual firms, treat companies as in some sense homogenous. Yet REITs and property companies typically have a sector focus and very often have a specific geographical focus (e.g. investing in a particular city or region within a country). Given the equity market evidence of the importance of industry factors and the evidence from private real estate markets of the importance of sector factors, this suggests that a more disaggregated approach is necessary to ensure that diversification benefits are maximised. This need is emphasised by the concentration of investment at city level, particularly in office markets. As Lizieri (2009) has suggested, the office markets of major global cities and international financial centres may be more closely linked across space than to their domestic markets, given functional specialisation in the occupier market and globalisation in the investment and financing markets. If larger REITs and property companies – the type that are likely to be heavily weighted in global property securities funds – are similarly concentrated in those markets then this might affect delivered diversification. It is these issues that this paper addresses. 3. Research Methods The underlying principle of international diversification is that the average covariance in an international portfolio is lower than for a domestic portfolio (and, hence, potential exposure to specific risk is also lower) since priced national risk factors are less than perfectly correlated across countries. However, reliance on contemporaneous measures of risk, return and correlation may be misleading as they may disguise lags in transmission of shocks
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across countries or may be artefacts of differing institutional structures or information processes. Hence, it seems preferable to examine the long‐run co‐movement of asset returns to assess diversification potential. Following the general approach in Gallo and Zhang (2010), we use cointegration, the linear combination of non‐stationary variables, to separate country indices by the nature of their cointegration. To test stationarity in data series, four unit root tests are applied: the Augmented Dickey & Fuller (1981); the Phillips & Perron (1988) test; the approach of Kwiatkowski et al. (KPSS) (1992) and Zivot and Andrews (1992), which allows for structural breaks. Cointegration rank and exclusion tests are subsequently warranted if any vector indices have a unit root representation in price level (nonstationarity).6 We then perform cointegration tests on property market index returns to test for the presence long run co‐movement of property returns. Non‐stationary national and regional indices in our sample are subject to cointegration rank tests to establish the number of cointegrating vectors (CIVs) as an indication of long‐run equilibrium relationships. 7 Initially, we utilise the standard Johansen cointegration test (Johansen 1988; Johansen and Juselius 1990) which computes the statistic for the null hypothesis of at most CIVs as in (1).
1 . 1
where is an eigenvalue. Cointegrated indices are identified by significant statistics, with others assumed independent. To test for common linear trends among any of the indices, specification tests are run which, conditional on CIVs, calculate the G(r) statistics, asymptotically distributed as (Johansen 1994; Johansen et al. 2000; Gallo & Zhang, 2010):
ln 1 ln 1 . 2
where and are the vector autoregressive (VAR) eigenvalues for CIVs with and without linear trends, respectively. Market independence would be indicated by non‐significant exclusion test results.8 We test for the presence of structural breaks in the vectors using likelihood‐ratio (L‐R) tests and, where necessary, control for large shocks with dummies. 6 Previous literature (e.g. Chen et al. 2002) provides consistent evidence that real estate index series are non‐stationary.
7 We convert the indices to natural logarithms: this makes the first differences of index returns, the percentage change or return, conceptually more meaningful in the vector autoregressive representation than the absolute changes of index returns. 8 If property indices exhibit non‐linear dependencies, the linear autoregressive Johansen results can be questioned. We transform all data series to logarithm (linear) to mitigate any non‐linearity in the data series, include structural break and linear trend components in the model.
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The presence of cointegration implies a common movement between assets which may have an impact on diversification benefits (Kleiman et al. 2002). Accordingly, we create portfolios separated based on their cointegration results and test for differences in performance. We differ from Gallo & Zhang (2010) who characterise their portfolios as simply “cointegrated” and “independent”. Given the presence of regional integration, our results and, consequently, the description of the portfolios, is more nuanced. We discuss this further below. Relative performance of the portfolios is measured using standard methods. First, we compute Sharpe ratios to measure total risk‐adjusted portfolio performance:
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where, for portfolio , is the average monthly style portfolio return, is the standard deviation of monthly returns, and is the monthly average monthly Treasury bill yield.
We then run a series of factor models. The first, again following Gallo and Zhang, is a three factor model incorporating global property returns, a size factor and a momentum factor:
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where is the monthly portfolio excess return9, is the value‐weighted global
property index excess return. GSMB is a global property size risk factor, created by subtracting the returns of the large cap property market proxy from those of the small cap property market index (Gallo et al. 2006) and GMOM is a momentum factor (Stevenson 2002; Chui et al. 2003; Marcato and Key 2005) estimated as the return on a portfolio that is long on the prior year’s upper quartile property indices and short on the previous year’s lower quartile indices by return performance. The GSMB and GMOM factors are rebalanced annually. Fama and French (1998) identified a global valuation factor, HML, in international stock returns. We computed a global HML by differencing the average returns of two high book‐to market ration (BE/ME) stock indices and those of two low‐BE/ME stock indices. These factors are then used to measure portfolio systematic risk‐adjusted performance. Returns for the Fama‐French factors were obtained from the Kenneth French data library.
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Finally, we combine equations 3 and 4 to provide a measure of systematic risk‐adjusted portfolio performance with a four‐factor model: 9 Excess returns are measured relative to the US one month Treasury bill rate, since the analysis reported here is in US$.
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The slope parameter, , , and measure the portfolio market risk, size and
momentum effects, respectively, while the intercept, , measures the incremental risk adjusted performance relative to the global property index (GPI) benchmark. Equations 4 6 examines performance with the hypothesis : 0 versus : 0. Finally, portfolio risk is separated using the Fama and MacBeth (1973) two‐pass methodology. Using a rolling five year window, we run the factor models and retain the coefficients and the mean square error (MSE), the latter representing unsystematic or non‐factor risk. The subsequent time series of MSEs and other risk metrics for the two portfolios can be tested for statistical difference using standard procedures. This allows us to assess the impact of cointegration on the diversification benefits of international real estate portfolios. Initially, these tests are run for all our sample property companies, aggregating company performance to national level irrespective of sector or geographical focus at firm level. We then identify those companies that do have a specific sector focus and re‐run the tests within sector (for example, focussing only on companies that predominantly acquire office buildings). Finally, we retest our results using only companies that have a strong exposure to globally integrated cities (or international financial centres). This allows us to test the extent to which investors need to consider company focus in order that they obtain the global diversification they seek. 4. Data Employed The empirical research for this paper uses company data from Global Property Research (GPR). Monthly total return series from 1994 to 2011 are utilised with returns calculated in log difference form. Value weighted indices of the performance of nineteen countries are created and then aggregated to regional level (North America, Asia, Oceania and Europe with, after examination of correlation structures and mindful of the size of the market, the UK considered as a separate quasi‐region. We also utilise the GPR and EPRA‐FTSE‐NAREIT global indices as benchmarks. In this paper, we report US$ denominated returns; separate analyses using domestic currency have also been run but are not reported for reasons of space. At sector level, SNL Financial is used to help identify indirect property companies that have a significant proportion of their investments in a particular property sector (i.e. more than 50% of total property asset portfolio is invested in office, retail, residential or industrial). Similarly, at city level, SNL Financial is used to find companies that focus their investments in one of the global cities (i.e. more than 50% of total real estate investment is invested in Tokyo, Singapore, Hong Kong, London, New York, Sydney, Paris, Frankfurt or Zurich). For example, SL Green Realty Corporation invests approximately 81% in office properties and about 68% of those offices are located in New York. Thus, SL Green Realty is considered as a company that has significant exposure to New York office markets. Such firms are then
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aggregated at national and regional level to produce value‐weighed sector‐specific or global city exposure indices. Although performance in the public real estate securities market is easier to measure because such information is more readily available than private property market data, there are some issues with indirect property data. First, using a benchmark currency raises concerns regarding currency effects, e.g., markets may appear to be integrated because of co‐movements in exchange rates. Also, currency risk can have an impact on the diversification potential of international real estate investment (Liu and Mei, 1998). Second, many REITs and property companies have relatively small market capitalisation and larger bid‐ask spreads than large cap stocks, suggesting that significant illiquidity problems may occur in difficult market conditions. Third, care must be taken in using sector indices because very dissimilar types of firms may be included. Fourth, there are issues of market regulation and composition changes, survivorship bias and the changing nature of the listed market (for example the introduction of a REIT regime in a country altering the tax status of listed firms or other changes to REIT regulation and taxation). Lastly, small sample size of data, especially at sector and city levels, may affect some of the cointegration analysis. Nonetheless, the data permit a quantitative analysis of the risk‐return characteristics of international real estate and the factors influencing diversification potential. Table 1 shows descriptive statistics for the aggregated national indices over the full 216 month analysis period. We show the mean and standard deviation as measures of risk and return, the Sharpe ratio (based on the US three month Treasury Bill rate) and the beta from a single index model regression of the country index on the global real estate index. In all cases, the betas are positively signed and statistically significant, other than those of Canada and Japan10. In terms of risk‐adjusted return, the best performing region is Europe (ex‐UK) while Asian markets underperformed relative to the world index. Pairwise Pearson correlations of the property market indices range from 0.29 (Asia and United Kingdom) to 0.93 (North America and Oceania) at the regional level, and from ‐0.19 (Hong Kong to Switzerland and to Germany) to 0.98 (United States and Netherlands) at the country level. Although these low pairwise correlations imply strong property market diversification potential, inter‐temporal instability and lagging effects may distort and deflate the correlations, misrepresenting which markets produce the strongest diversification gains. <Table 1 about here> 5. Real Estate Cointegration: Empirical Results 5.1 Aggregate Regional and National Results Table 2 shows the results of unit root tests on the aggregate regional and country property indices. Almost all Augmented Dickey‐Fuller (ADF), Philips‐Perron (PP) and Zivot and Andrews (ZA) test statistics, at both the regional and country levels, are statistically insignificant. Combined with consistently significant Kwiatkowski, Phillips, Schmidt, Shin (KPSS) test statistics, non‐stationarity and unit root representation is indicated in each regional and country property index. Unit root tests merit the implementation of the 10 The former result is somewhat surprising, given the high correlation between the US and Canadian returns.
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cointegration methodology to detect long‐run equilibrium among the non‐stationary indices. <Table 2 about here> The procedure continues with vector rank tests conducted initially on the regional indices and secondly, on the country indices within each region. The rank test results for the regions and the countries are shown in Table 3. Regional cointegration tests results indicate at least two cointegration relations among the regional indices. Asia, Europe, North America, Oceania and United Kingdom are, therefore, inter‐regionally dependent. Intra‐regional tests also indicate cointegrating relationships within each region. Specifically, one cointegrating relationship is implied within the North America, Oceania and Asia regions, while either one or, more strongly by the G(r) criterion, three cointegrating relations are found among the European markets. We conduct a cointegration test for the twelve countries with the largest market capitalisation and the results suggest at least four cointegration relations. These results, then, corroborate regional patterns in global property returns (Eichholtz et al. 1993; Worzala and Bernasek 1996; Eichholtz et al. 1998; Bond et al. 2003) but suggest that Europe, even excluding the UK, is less coherent than the other world regions. <Table 3 about here> The intra‐regional country market exclusion tests, presented in Table 4, identify cointegrated and independent markets in each region. Countries with significant (insignificant) likelihood‐ratio (L‐R) test statistics are cointegrated (independent) regionally. Results indicate New Zealand shares a cointegrating relationship within Oceania, while Australia, is independent (perhaps more linked to global or Asian markets). In Europe, cointegrating relations are found among the Austrian, Finnish, Norwegian, Spanish, Swedish and Swiss markets. However, France, Germany and the Netherlands markets appear to be independent of European cointegration. Within the Asia region, Hong Kong, Malaysia and Philippines share a cointegrating relationship, while Japan and Singapore are independent. The United States and Canada are cointegrated in North America, and all the regions are generally cointegrated globally. Amongst the 12 countries with the largest market capitalisation, only Singapore is found to be an independent market. In sum, there are there are 12 cointegrated country markets and six country markets independent of cointegrating relations. As shown in Table 1, the cointegrated (independent) countries account for 78 (22) percent of the market value weighted global property index. Interestingly, the cointegration and MPT procedures produce similar country portfolio allocations. The mean pairwise correlation of independent markets selected by the cointegration test (ρ = 0.461) is, as expected, lower than the mean of the cointegrated markets (ρ = 0.576). <Table 4 about here> Tables 5 and 6 reports portfolio performance tests with separate results shown for the independent (INDE, n = 6) and cointegrated (COINT, n = 12) market portfolios. Table 5 summarises the Sharpe performance of the portfolios. The independent markets outperform both the cointegrated markets and the global property index during the sample period. The three‐factor and four‐factor models performance results are shown in Table 6. The sample period performance could be an artefact of a particular period exerting undue
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influence of a particular period exerting undue influence. Therefore, we examine three‐factor and four‐factor performance in equal, 1994‐2002 and 2002‐2011. <Table 5 about here> <Table 6 about here> There is some evidence of superior performance by the cointegrated portfolio as measured by alpha: although α coefficients are only significant in the second sub‐sample for the three‐factor and four‐factor models. In all the models, the beta coefficients are positive and significant, indicating that the markets are driven by a global real estate market factor. The betas are stronger and more significant for the independent portfolio. The three‐factor model highlights the significance of momentum coefficients for both portfolios (positive for the independent portfolio, negative for the cointegrated portfolio), although the effect is confined to the second half of the sample period. The Fama‐French three factor model identifies the positive significance of a global valuation factor, HML, in the cointegrated market portfolio. The overall results from the three‐factor and four‐factor models suggest that the independent markets, which represented by major economies (i.e. Australia, France, Germany, Netherlands, Japan and Singapore) of various regions, appear to be more statistically indistinguishable from market benchmark characteristics, and therefore, less attractive portfolio candidates in terms of diversification – that is that they are regionally independent but more influenced by global factors. Table 7 confirms this result, employing the two‐pass tests of Fama and MacBeth (1973) to decompose the portfolio risk. Using 60 month rolling regressions of the portfolios against the three‐ and four factor models, a cross‐sectional time series of intercepts, market, size, momentum and valuation slope coefficients is created along with mean square errors and standard deviations. The table shows clearly that there are statistical differences in the behaviour of the regionally cointegrated and independent portfolios. The independent portfolio has a significantly higher beta than the (regionally) cointegrated portfolio and is positively sensitive to global momentum effects. The average mean square error is smaller for the independent portfolio than the cointegrated portfolio which suggests that overall performance is explained more by global real estate factors – although the independent portfolio has a lower aggregate average standard deviation. These results suggest that regional effects work against global real estate factors and provide additional diversifying properties. <Table 7 about here> Summarising the results for the aggregate analysis, there is evidence of regional cointegration in listed real estate securities. However, within each region, there are country indices which appear to be somewhat independent of that regional convergence. Many of these countries are larger countries that are driven more by global real estate factors than local or regional factors. A portfolio strategy that focussed on countries with a stronger regional dimension would thus offer some additional diversification benefits in protecting investors from global market movements and momentum effects – the relevance of this being most evident in the second half of the sample period, incorporating the global financial crisis, when the regionally cointegrated portfolio offers statistically superior alpha. Key to our study, however, is whether those results apply over all real estate sectors. Thus
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we reanalyse performance for reconstructed indices that separate out companies that specialise in particular sectors or have significant exposure to global cities. 5.2 Disaggregated Sector Results The analyses in the preceding section are repeated for specially constructed country indices comprised of firms specialising in particular sectors (retail, residential, office, industrial) along with a separate analysis of diversified firms and firms with a high exposure to global cities and international financial centres. In focussing on sector specialists, it is inevitable that the number of firms in any one country can be small and, hence, the country level indices may be subject to specific risk. We do not report the results for residential or industrial sector specialists given the low number of country indices that can be analysed. Space precludes full reporting of all results in this paper11. Rather, we focus on the main differences observed between the aggregate and the disaggregated analyses. For retail specialist firms12, the number of countries that can be analysed falls from 19 to 13. By contrast to the aggregate results, the best performing region by Sharpe ratio is North America and the best performing country is Canada. Fewer country indices have significant betas with respect to the global index and the magnitudes are generally lower. However average inter‐country correlation is slightly higher at 0.679. The unit root tests suggest that cointegration analysis is appropriate (although Germany’s KPSS score is marginally non‐significant). There is inter‐regional cointegration; within regions, one cointegrating relationship is favoured for North America and Asia while Europe appears to have three (possibly two) relationships. Within Europe, France and Germany appear to be independent. Splitting the results into independent and cointegrated portfolios (the latter containing Canada, the US, Finland, Norway, Switzerland and the Netherlands) the results differ from those of the aggregated analysis. The cointegrated group has a higher Sharpe ratio and a lower standard deviation but a negative alpha for the first half of the analysis period; market beta is positive and significant and the momentum factor is positive in some models. In the Fama‐Macbeth tests, the independent group have a significantly higher alpha while the cointegrated group have a significantly higher and positive beta. By implication, the cointegrated group is more driven by a common set of factors, confirmed in that the independent group has a larger mean square error. For office markets13, eleven country indices may be analysed. Best performing markets are Oceania as a region and France as a country. There appears to be a strong general office factor with a clear of the countries and regions exhibiting significant betas (with Japan and Switzerland exceptions). As with retail, the office correlations are on average higher than for the aggregate analysis, emphasising the importance of sector in understanding performance. The unit root tests are well behaved, permitting cointegration analysis. At a regional level, there is evidence of cointegration, in particular locking Europe and North America together. Within regions, there are generally single cointegrating relationships. Within Europe, Switzerland stands as independent with France and Sweden having independent characteristics. Analysing only the larger markets, the results are less clear but
11 Full results for all sectors may be obtained from the authors. 12 Results are shown in Appendix 1. 13 Results are shown in Appendix 2.
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there seem to be a number of relations. Assuming a single relationship, Japan and Switzerland appear most independent; Swiss independence persists as the number of relationships analysed increases. In the models for cointegrated and independent portfolios (with France, Sweden and Switzerland forming the latter), the independent group exhibits a larger Sharpe ratio; the cointegrated group has positive betas on the market index in all models with the coefficients being close to one and substantially larger than for the independent portfolio; the R‐squared values for the cointegrated group are consistently larger than for the independent group. It seems that although there are regional factors, there is a strong common movement reflected in the cointegrated portfolio. The independent group exhibits higher relative risk, whether measured by total risk (standard deviation) or unsystematic risk (mean square error from the factor models) and lower market risk than the cointegrated group. Evidence of a separate portfolio risk factor analysis (not reported here for reasons of space) suggest that the cointegrated group are much more strongly influenced by risk premia, term structure and institutional capital flows, consistent with the idea that the these offices markets are more generally influenced by the capital markets. For the diversified group of indices, we can analyse 16 countries. As might be expected, the average correlation between countries is lower, at 0.522; betas with the global index are lower too. Best performing region is Oceania, with Canada again the best performing country. The unit root tests are satisfactory (although the results for Austria and Malaysia are marginal). Again, we see regional cointegration and, within regions a single cointegrating vector in Asia, North America and Oceania and a more complex situation in Europe with three or four relationships detected. Countries that appear to be independent are Japan, Malaysia and Switzerland: given the small size of the independent group, the portfolio results may be less robust. The cointegrated group is more strongly influenced by market returns, with higher R2 and larger betas: there is some evidence of sensitivity to momentum in the second half of the time series. However, these results may be an artefact of the size of the cointegrated group and, hence, the weight of these firms in the global index. Finally, we examine those firms with a high exposure to global cities and international financial centres14: given the definition of cities and the localised focus of most firms, this restricted analysis to just eight countries. The best performing region was North America; the best performing country was Sweden. The beta coefficients from the single index market model were significant but of varying size – with that of Japan being significantly negative. Mean correlations between countries were lower than for the aggregate index, with much of that difference attributable to low Australian correlations with other markets. The KPSS tests were more mixed than for other analyses with some question about the status of Australia and Singapore. Once again, there is evidence of cointegration across regions. Within regions, North America and Asia exhibited a single cointegrating relationship, while an analysis of all the country indices together suggested three or four relationships. The results pointed to separation of Switzerland, Hong Kong and Singapore as the independent market portfolio, Sweden, Japan, US, UK and Australia as the cointegrated group. The cointegrated group outperformed the 14 Results are shown in Appendix 3
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independent group in terms of Sharpe ratios; further, they had higher betas in the factor models than the independent group (which had non‐significant betas in the first half of the time series). Evidence for a common global financial influence on the cointegrated group is provided by the separate portfolio risk factor analysis which shows sensitivity to risk premium, term structure and institutional investment flows. Summing up, the disaggregated results for the sector specialists and firms exposed to global cities presents a rather different picture to the aggregate analysis. In the aggregate analysis, cointegration implied regional cointegration with the independent portfolio showing more sensitivity to market factors. By contrast, in the sector analyses, while cointegration is defined regionally, it is the cointegrated firm indices that show most sensitivity to general risk factors and converging more to their sectoral benchmark. This implies that a detailed understanding of these sectoral relationships is critical in assembling a global portfolio of real estate securities that optimises risk diversification. 6. Summary and Conclusions The literature on international real estate investment strategies has emphasised the importance of analysing long run relationships between returns in different countries in devising optimal risk diversification strategies. In general, research has shown that real estate is influenced by global, regional and local factors; and that constructing portfolios that have different exposure to regional and country level factors provides superior risk adjusted returns. Applying such strategies to listed real estate securities provides a liquid and relatively low cost route to global property market exposure. The majority of such analyses, however, have focussed their attention on national aggregate indices of property company performance. While these are investible, they are composed of individual firms that have specialised focus on particular sectors or physical markets and locations. That a national index is independent from, or cointegrated with, its regional market or the global market is no guarantee that the firm’s performance is similarly independent or cointegrated. The contribution in this paper is to disaggregate the analysis to sector level, and to separately analyse those firms with high exposure to global cities, to see if differences emerge below the national level of analysis. We use cointegration analysis and a variety of factor models to test this proposition, using real estate securities data from 1994‐2011. The results demonstrate clearly that the relationships that are observed at national aggregate level do not hold at sector level. Regional and national cointegration relationships differ across sectors; countries that are regionally cointegrated at aggregate level may be independent at a sector level. Further, the results suggest that the impact of regional location in determining diversification benefits varies by sector. At aggregate level, regional cointegration seems to offer some diversification from common global factors: it is the “independent” portfolio that is most exposed to overall property market risk, with higher market betas and R2s in factor models. At sector level, this seems not to be the case. The cointegrated office, retail and international financial centre portfolios are more driven by the common global factors – and the level of inter‐regional cointegration is stronger.
14
One of the key benefits, then, of breaking the data down to different sectors and cities is that international investors can gain a better understanding of the interrelationships within real estate markets, helping them to optimally construct their portfolios. Our analysis also provides valued‐adding information for international investors with a mandate to invest in particular countries as part of their portfolio. From our findings, investors/managers would realise that, although the performance of that country may be globally driven at a broad level, particular sectors may be independent while other sectors or cities may be more cointegrated with other countries and converge with the global market benchmark. This knowledge can assist investors and fund managers in optimising their portfolios to maximise risk diversification.
15
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Table 1 Regional/Country Descriptive Statistics, Aggregate Indices Index Returns (%) SD (%) Sharpe ratio bpi WGT (%)
United States 0.63% 7.12% 0.052 0.189*** 36.18% Canada 2.85% 32.65% 0.079 0.079 5.33% Great Britain 0.24% 6.33% -0.004 0.314*** 5.70% Australia 1.34% 10.55% 0.102 0.766*** 8.59% New Zealand 0.68% 6.03% 0.070 0.164*** 0.32% Austria -0.11% 8.25% -0.046 0.231*** 0.96% Finland 0.80% 9.91% 0.054 0.225*** 0.29% France 1.40% 8.86% 0.128 0.232*** 5.39% Germany -0.32% 11.80% -0.050 0.229*** 1.26% Norway 0.97% 8.38% 0.084 0.099* 0.26% Spain -1.80% 15.10% -0.137 0.217** 0.09% Sweden 0.71% 8.73% 0.051 0.155** 1.52% Switzerland 0.49% 5.15% 0.044 0.107*** 3.82% Netherlands 0.56% 5.93% 0.050 0.224*** 1.10% Hong Kong 0.45% 9.98% 0.019 0.320*** 10.61% Japan 0.53% 11.54% 0.023 -0.014 12.74% Malaysia -0.42% 14.25% -0.048 0.248** 0.75% Philippines 0.65% 9.44% 0.040 0.181*** 0.67% Singapore -0.14% 11.62% -0.035 0.290*** 4.43% North America 0.69% 6.76% 0.063 0.190*** 41.51% United Kingdom 0.24% 6.33% -0.004 0.314*** 5.70% Oceania 1.34% 10.65% 0.101 0.778*** 8.91% Europe 1.48% 9.67% 0.126 0.254*** 14.68% Asia 0.23% 13.46% -0.002 0.470*** 29.20% World property index 1.16% 9.54% 0.094 - 100.00% 3-month T-bill 0.26% 0.17% 0.000 - -
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Table 2 Unit Root Tests: Aggregate Indices Region ADF PP KPSS (mu) ZA (Break) North America -0.867 -0.275 1.630*** -3.152 (11/2008) United Kingdom -1.649 -1.446 1.120*** -3.245 (05/2008) Oceania -2.876 -2.26 1.550*** -3.299 (04/2004) Europe -0.125 -0.053 1.680*** -3.097 (11/2001) Asia -0.915 -1.635 1.120*** -5.090** (11/1997) Country United States -1.024 -0.455 1.620*** -3.272 (11/2008) Canada -1.315 -2.592 1.520*** -4.856 (08/1999) Great Britain -1.649 -1.446 1.120*** -3.245 (05/2008) Australia -2.888 -2.28 1.550*** -3.507 (04/2008) New Zealand -0.824 -0.801 1.470*** -3.547 (10/2002) Austria -2.035 -2.079 1.185*** -6.898*** (10/2008) Finland -1.251 -1.128 1.520*** -2.925 (10/2003) France -0.147 -0.119 1.730*** -4.525 (01/2006) Germany -1.388 -1.485 1.280*** -3.884 (04/2008) Norway -0.981 -0.82 1.560*** -3.020 (11/2003) Spain -1.087 -0.919 1.213*** -3.806 (12/2005) Sweden -0.464 -0.373 1.320*** -3.331 (04/1997) Switzerland 0.146 0.191 1.640*** -5.088** (11/1999) Netherlands -0.871 -0.934 1.570*** -3.043 (10/2003) Hong Kong -0.461 -0.859 1.390*** -4.795 (11/1997) Japan -1.122 -1.566 0.836*** -3.531 (09/2003) Malaysia -2.582 -2.761 0.282*** -5.318** (09/1997) Philippines -1.005 -1.066 0.739*** -3.841 (02/2000) Singapore -1.872 -1.932 1.292*** -3.369 (09/1997)
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Table 3 Cointegration Rank Tests: Aggregate IndicesI(1) - Analysis G(r) p-r r Eigen Value Trace Regional (5 regions) - 5 0 0.131 77.165***
0.945*** 4 1 0.095 47.494** 11.291*** 3 2 0.067 26.436
North America (2 countries) - 2 0 0.055 14.385*** 0.023*** 1 1 0.012 2.478
Oceania (2 countries) - 2 0 0.042 11.418*** 0.472*** 1 1 0.010 2.163
Europe (9 countries) - 9 0 0.274 260.867 1.929*** 8 1 0.238 194.312 5.327*** 7 2 0.182 137.824***
23.687*** 6 3 0.130 96.009** Asia (5 countries) - 5 0 0.088 52.280***
2.129*** 4 1 0.070 33.120 Largest Market Cap (12 countries) - 12 0 0.381 411.462***
0.016*** 11 1 0.260 310.332 5.180*** 10 2 0.256 246.874
16.870*** 9 3 0.182 184.501 35.610*** 8 4 0.138 142.061
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Table 4 Cointegration Exclusion Tests: Aggregate Indices Regional (n=5) r North America United Kingdom Oceania Europe Asia L-R statistic 1 14.000 17.000 14.000 5.800 9.100 p-value 0.000*** 0.000*** 0.000*** 0.016** 0.003*** L-R statistic 2 18.000 19.000 14.000 6.100 12.000 p-value 0.000*** 0.000*** 0.001*** 0.048** 0.002*** North America (n=2 countries) r United States Canada L-R statistic 1 10.000 6.100 p-value 0.001*** 0.014** Oceania (n=2) r Australia New Zealand L-R statistic 1 0.660 17.000 p-value 0.417 0.000*** Europe (n=9) r Austria Finland France Germany Norway Spain Sweden Switzerland Netherlands L-R statistic 1 11.000 7.200 0.240 0.670 3.300 3.100 0.024 7.300 0.006 p-value 0.001*** 0.007*** 0.626 0.413 0.067* 0.078* 0.877 0.007*** 0.940 L-R statistic 2 26.000 16.000 1.300 1.700 17.000 5.200 4.100 7.700 1.200 p-value 0.000*** 0.000*** 0.515 0.429 0.000*** 0.074* 0.127 0.022** 0.559 L-R statistic 3 30.000 18.000 4.500 1.700 17.000 10.000 7.900 7.700 1.200 p-value 0.000*** 0.000*** 0.211 0.638 0.001*** 0.018** 0.048** 0.054** 0.762 Asia (n=5) r Hong Kong Japan Malaysia Philippines Singapore L-R statistic 1 11.000 1.300 6.800 5.700 1.500 p-value 0.001*** 0.257 0.009*** 0.017** 0.220 The largest market-cap (n=12) r United States Canada Great Britain Australia France Germany Sweden Switzerland Netherlands Hong Kong Japan Singapore L-R statistic 1 2.900 21.000 0.420 0.690 6.300 7.100 0.770 1.900 0.400 0.120 3.400 1.000 p-value 0.088* 0.000*** 0.519 0.406 0.012** 0.008*** 0.380 0.168 0.530 0.734 0.067* 0.317 L-R statistic 2 13.000 42.000 22.000 11.000 10.000 11.000 16.000 6.000 24.000 20.000 23.000 3.600 p-value 0.002*** 0.000*** 0.000*** 0.005*** 0.006*** 0.004*** 0.000*** 0.049** 0.000*** 0.000*** 0.000*** 0.168 L-R statistic 3 13.000 46.000 26.000 12.000 17.000 14.000 22.000 11.000 25.000 21.000 29.000 5.400 p-value 0.006*** 0.000*** 0.000*** 0.008*** 0.001*** 0.003*** 0.000*** 0.010*** 0.000*** 0.000*** 0.000*** 0.145 L-R statistic 4 21.000 49.000 35.000 18.000 24.000 14.000 30.000 12.000 30.000 21.000 37.000 6.500 p-value 0.000*** 0.000*** 0.000*** 0.001*** 0.000*** 0.009*** 0.000*** 0.015** 0.000*** 0.000*** 0.000*** 0.166
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Table 5 Property Portfolio Performance Summary Portfolio Returns (%) SD (%) Sharpe ratio Global property index 0.50% 10.61% 0.022 INDE 0.56% 10.05% 0.030 COINT 0.47% 10.87% 0.019 3-month T-bill 0.26% 0.17% - Table 6 Property Portfolio Performance Portfolio αp αp (t-stat) βp1 βp1 (t-stat) γp2 γp2 (t-stat) λp3 λp3 (t-stat) ζp4 ζp4 (t-stat) R2 Panel 1A Three-factor performance model INDE -0.015 -1.270 1.451 29.420*** 0.003 0.400 0.152 1.790* 0.805 COINT 0.019 1.590 0.398 8.110*** 0.003 0.450 -0.171 -2.030* 0.267 Panel 1B Three-factor performance model 1994-2002 INDE -0.033 -0.980 1.605 18.010*** -0.025 -0.920 0.383 1.320 0.763 COINT 0.009 0.390 0.466 7.520*** 0.025 1.310 -0.150 -0.740 0.366 Panel 1C Three-factor performance model 2003-2011 INDE -0.014 -1.910* 1.275 37.030*** 0.003 0.980 0.091 1.940* 0.933 COINT 0.036 2.140** 0.309 3.980*** 0.001 0.190 -0.250 -2.370** 0.219 Panel 2A Fama-French three-factor performance model INDE -0.005 1.140 1.442 29.640*** -0.246 -1.740* -0.189 0.118 0.806 COINT -0.004 -0.940 0.400 8.330*** 0.197 1.410 0.324 2.790*** 0.219 Panel 2B Fama-French three-factor performance model 1994-2002 INDE -0.010 1.160 1.614 18.300*** -0.296 -1.320 -0.265 -1.320 0.764 COINT -0.008 -1.310 0.466 7.680*** 0.188 1.220 0.294 2.120** 0.382 Panel 2C Fama-French three-factor performance model 2003-2011 INDE -0.001 -0.200 1.257 36.960*** -0.210 -1.540 -0.007 -0.070 0.932 COINT -0.001 -0.190 0.331 4.350*** 0.150 0.490 0.413 1.880* 0.214 Panel 3A Four-factor performance model INDE -0.015 -1.260 1.458 29.740*** -2.543 -1.800* 0.156 1.870* -0.185 -1.570 0.809 COINT 0.017 1.480 0.385 7.980*** 0.206 1.480 -0.167 -2.020** 0.320 2.770*** 0.297 Panel 3B Four-factor performance model 1994-2002 INDE -0.036 -1.090 1.599 18.100*** -0.325 -1.450 0.413 1.430 -0.300 -1.480 0.769 COINT 0.012 0.500 0.472 7.720*** 0.200 1.290 -0.172 -0.860 0.308 2.210** 0.387 Panel 3C Four-factor performance model 2003-2011 INDE -0.014 -1.950 1.276 36.710*** -0.221 -1.640 0.097 2.070** 0.008 0.080* 0.934 COINT 0.032 1.970* 0.284 3.670*** 0.175 0.580 -0.236 -2.270** 0.375 1.740* 0.251
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Table 7 Portfolio Risk Decompositions Coefficient INDE COINT H0 t-stat Mean Mean Three-factor performance model Intercept -0.051 0.042 αINDE=αCOINT 17.329*** Rmt 1.516 0.330 βINDE=βCOINT 31.713*** SMB -0.022 0.037 γINDE=γCOINT 71.837*** GMOM 0.417 -0.345 λINDE=λCOINT 15.021*** MSE 0.004 0.004 MSEINDE=MSECOINT 11.896*** SD 0.053 0.060 SDINDE=SDCOINT 50.949*** Fama-French three-factor performance model Intercept -0.001 -0.001 αINDE=αCOINT 39.944*** Rmt 1.523 0.310 βINDE=βCOINT 28.203*** SMB -0.230 0.146 γINDE=γCOINT 20.914*** HML -0.205 0.404 ζINDE=ζCOINT 69.401*** MSE 0.004 0.004 MSEINDE=MSECOINT 75.939*** SD 0.054 0.061 SDINDE=SDCOINT 32.919*** Four-factor performance model Intercept -0.056 0.042 αINDE=αCOINT 19.772*** Rmt 1.519 0.297 βINDE=βCOINT 28.560*** SMB -0.244 0.157 γINDE=γCOINT 30.379*** GMOM 0.467 -0.365 λINDE=λCOINT 21.527*** HML -0.218 0.390 ζINDE=ζCOINT 74.946*** MSE 0.003 0.004 MSEINDE=MSECOINT 71.705*** SD 0.053 0.060 SDINDE=SDCOINT 31.609***
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Appendix A1: Retail Specialist Firms Table A1.1 Retail Property Regional/Country Descriptive StatisticsIndex Returns (%) SD (%) Sharpe ratio bpi WGT (%) United States 0.87% 6.85% 0.093 0.068 44.29% Canada 2.46% 11.22% 0.198 0.209*** 6.47% Great Britain 0.32% 9.33% 0.009 0.212*** 7.86% Australia 1.12% 9.37% 0.094 0.794*** 17.31% New Zealand 0.42% 6.72% 0.028 0.137*** 0.03% Finland 0.76% 9.58% 0.055 0.056 0.38% France 1.48% 8.64% 0.144 0.107* 11.39% Germany -0.11% 9.12% -0.038 0.062 0.93% Norway 1.10% 7.83% 0.111 0.114* 0.62% Switzerland 0.50% 8.01% 0.033 -0.017 0.95% Netherlands 0.71% 6.47% 0.074 0.167*** 2.75% Hong Kong 0.00% 13.87% -0.017 0.042 5.17% Philippines 0.41% 9.43% 0.018 0.160** 1.87% North America 1.00% 6.70% 0.115 0.078** 50.75% United Kingdom 0.32% 9.33% 0.009 0.212*** 7.86% Oceania 1.12% 9.38% 0.094 0.795*** 17.33% Europe 1.76% 9.44% 0.161 0.104 17.02% Asia 0.24% 14.83% 0.000 0.058 7.03% World property index 1.10% 10.91% 0.080 - 100.00% 3-month T-bill 0.23% 0.17% 0.000 - -
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Table A1.2 Retail: Unit root tests Region ADF PP KPSS (mu) ZA (Break) North America -0.684 -0.105 1.370*** -3.216 (11/2008) United Kingdom -1.319 -1.533 0.776*** -3.679 (09/2008) Oceania -2.225 -2.237 1.340*** -4.014 (08/2004) Europe -1.046 -0.802 1.460*** -4.263 (01/2006) Asia -0.688 -0.993 1.040*** -4.011 (08/2003) Country United States -0.753 -0.310 1.360*** -3.136 (11/2008) Canada -0.561 -0.488 1.390*** -3.443 (12/2003) Great Britain -1.319 -1.533 0.776*** -3.679 (09/2008) Australia -2.228 -2.238 1.340*** -4.012 (08/2004) New Zealand -1.462 -1.311 0.906*** -3.789 (11/2002) Finland -0.834 -0.833 1.180*** -2.739 (06/2008) France -0.771 -0.681 1.450*** -4.178 (01/2006) Germany -1.869 -2.042 0.231 -6.795*** (01/2001) Norway -0.238 -0.584 1.320*** -3.041 (06/2004) Switzerland -0.064 -0.227 1.170*** -4.164 (04/1999) Netherlands -1.061 -1.041 1.380*** -2.777 (07/2008) Hong Kong -0.873 -1.387 0.920*** -3.699 (08/2003) Philippines -0.0425 -0.614 0.890*** -3.829 (02/2000) Table A1.3 Retail: Cointegration rank tests I(1) Analysis G(r) p-r r Eigen Value Trace Regional (5 regions) - 5 0 0.139 71.784***
3.212*** 4 1 0.114 45.173
North America (2 countries) - 2 0 0.079 21.969*** 1.873*** 1 1 0.042 7.529**
Europe (6 countries) - 6 0 0.309 144.311 2.547*** 5 1 0.142 81.117***
15.065*** 4 2 0.111 54.866 33.324 3 3 0.094 34.663**
Asia (2 countries) - 2 0 0.033 6.913*** 5.377*** 1 1 0.005 0.865
The largest market-cap (9 countries) - 9 0 0.389 338.929 0.995*** 8 1 0.306 254.242
14.704*** 7 2 0.247 191.474 30.658*** 6 3 0.222 142.752
60.855 5 4 0.178 99.525 93.007 4 5 0.155 65.890
134.897 3 6 0.097 36.864*** 191.625 2 7 0.085 19.306**
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Table A1.4 Retail: Cointegration Exclusion Tests Regional (n=5) r North America United Kingdom Oceania Europe Asia L-R statistic 1 3.300 4.100 1.600 11.000 4.400 p-value 0.068* 0.044** 0.201 0.001*** 0.037** North America (n=2 countries) r United States Canada L-R statistic 1 12.000 13.000 p-value 0.001*** 0.000*** Europe (n=6) r Finland France Germany Norway Switzerland Netherlands L-R statistic 1 37.000 0.520 0.650 19.000 7.500 23.000 p-value 0.000*** 0.473 0.420 0.000*** 0.006*** 0.000*** L-R statistic 2 47.000 2.600 2.700 22.000 9.100 24.000 p-value 0.000*** 0.277 0.266 0.000*** 0.011** 0.000*** L-R statistic 3 48.000 3.900 4.100 23.000 9.200 24.000 p-value 0.000*** 0.274 0.249 0.000*** 0.027** 0.000*** Asia (n=2) r Hong Kong Philippines L-R statistic 1 1.900 0.530 p-value 0.170 0.468 The largest market-cap (n=9) r United States Canada Great Britain Australia France Germany Switzerland Netherlands Hong Kong L-R statistic 1 2.500 15.000 11.000 22.000 17.000 18.000 25.000 1.600 0.700 p-value 0.987 0.000*** 0.001*** 0.000*** 0.000*** 0.000*** 0.000*** 0.211 0.403 L-R statistic 2 5.800 24.000 16.000 30.000 19.000 22.000 29.000 10.000 5.400 p-value 0.056* 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.005*** 0.069* L-R statistic 3 17.000 29.000 29.000 43.000 30.000 26.000 36.000 13.000 20.000 p-value 0.001*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.005*** 0.000*** L-R statistic 4 26.000 37.000 38.000 47.000 33.000 36.000 38.000 21.000 23.000 p-value 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** L-R statistic 5 28.000 38.000 40.000 47.000 34.000 38.000 40.000 21.000 25.000 p-value 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.001*** 0.000***
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Table A1.5 Retail Property portfolio performance summary Portfolio Returns (%) SD (%) Sharpe ratio Z-stat (INDE vs. COINT) Global property index 1.105% 10.913% 0.080 INDE 0.44% 10.26% 0.020 4.206** COINT 0.98% 8.58% 0.087 3-month T-bill 0.23% 0.17% - Table A1.6 Retail Property portfolio performance Portfolio αp αp (t-stat) βp1 βp1 (t-stat) γp2 γp2 (t-stat) λp3 λp3 (t-stat) ζp4 ζp4 (t-stat) R2 Panel 1A Three-factor performance model INDE 0.042 2.050** 0.087 1.180 0.001 0.070 -0.192 -1.360 0.221 COINT -0.004 -2.220** 1.022 14.090*** 0.000 -0.230 0.027 2.010** 0.902 Panel 1B Intertemporal three-factor performance model 1997-2004 INDE -0.028 -0.980 -0.047 -0.450 -0.005 -2.000* 0.414 0.670 0.153 COINT -0.004 -0.580 1.025 18.490*** 0.000 0.530 0.018 0.340 0.906 Panel 1C Intertemporal three-factor performance model 2004-2011 INDE 0.047 1.560 0.236 1.600 0.118 2.090 -0.250 -1.200** 0.117 COINT -0.005 -0.940 1.017 14.760*** -0.014 -1.540 0.031 0.830 0.910 Panel 2A Fama-French three-factor performance model INDE 0.016 1.940* 0.103 1.410 0.126 0.540 0.198 1.050 0.019 COINT -0.001 -0.810 1.020 15.060*** 0.004 0.020 -0.032 -1.820* 0.902 Panel 2B Intertemporal Fama-French three-factor performance model 1997-2004 INDE 0.022 0.043** -0.049 -0.460 0.055 0.220 0.078 0.410 0.146 COINT -0.001 -2.010** 1.025 18.790 0.015 0.730 0.005 0.280 0.906 Panel 2C Intertemporal Fama-French three-factor performance model 2004-2011 INDE 0.010 0.840 0.231 1.680* 0.193 0.400 0.266 0.730 0.167 COINT 0.010 0.010 1.020 10.780*** 0.020 0.350 -0.089 -1.980* 0.909 Panel 3A Four-factor performance model INDE 0.040 1.960* 0.085 1.150 0.128 0.550* -0.185 -1.310 0.186 0.980 0.029 COINT -0.004 -2.090** 1.023 14.950*** 0.002 0.010 0.025 1.930* -0.031 -1.740* 0.902 Panel 3B Intertemporal Four-factor performance model 1997-2004 INDE -0.023 -0.320 -0.045 -0.430 0.060 0.240 0.371 0.600 0.064 0.320 0.129 COINT -0.004 -0.660 1.025 17.420*** 0.015 0.740 0.020 0.390 0.004 0.230 0.906 Panel 3C Intertemporal Four-factor performance model 2004-2011 INDE 0.038 1.020 0.201 1.560 0.243 0.510 -0.188 -0.710 0.224 0.580 0.181 COINT -0.003 -0.530 1.023 14.080*** 0.014 0.260 0.021 0.460** -0.084 -1.790* 0.910
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Table A1.7 Retail Portfolio risk decompositions Coefficient INDE COINT H0 t-stat MeanINDE MeanCOINT Three-factor performance model Intercept 0.072 -0.006 αINDE=αCOINT 21.099*** Rmt 0.114 1.019 βINDE=βCOINT 2.150*** SMB 0.040 -0.004 γINDE=γCOINT 48.826*** GMOM -0.455 0.045 λINDE=λCOINT 20.208*** MSE 0.009 0.000 MSEINDE=MSECOINT 35.720*** SD 0.096 0.008 SDINDE=SDCOINT 1.380*** Fama-French three-factor performance model Intercept 0.016 0.001 αINDE=αCOINT 52.004*** Rmt 0.090 1.023 βINDE=βCOINT 2.800*** SMB 0.208 0.018 γINDE=γCOINT 39.518*** HML 0.275 -0.041 ζINDE=ζCOINT 80.540*** MSE 0.010 0.000 MSEINDE=MSECOINT 31.364*** SD 0.098 0.008 SDINDE=SDCOINT 1.150*** Four-factor performance model Intercept 0.070 -0.005 αINDE=αCOINT 17.951*** Rmt 0.069 1.026 βINDE=βCOINT 1.910*** SMB 0.226 0.015 γINDE=γCOINT 29.690*** GMOM -0.467 0.042 λINDE=λCOINT 15.125*** HML 0.236 -0.040 ζINDE=ζCOINT 67.094*** MSE 0.010 0.000 MSEINDE=MSECOINT 34.725*** SD 0.097 0.008 SDINDE=SDCOINT 1.360***
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Appendix A2: Office Specialist Firms Table A2.1 Office Property Regional/Country Descriptive StatisticsIndex Returns (%) SD (%) Sharpe ratio bpi WGT (%) United States 0.93% 9.78% 0.068 0.662*** 40.88% Canada 1.25% 17.15% 0.057 0.333*** 7.73% Great Britain 0.37% 8.38% 0.013 0.542*** 4.59% Australia 1.19% 11.95% 0.077 0.237*** 4.46% Austria -0.16% 8.79% -0.048 0.435*** 0.62% France 1.27% 10.53% 0.096 0.401*** 4.22% Germany -0.39% 13.11% -0.050 0.623*** 1.45% Spain -1.95% 14.81% -0.149 0.298** 0.50% Sweden 0.48% 12.17% 0.017 0.503** 0.87% Switzerland 0.69% 30.12% 0.014 0.252 2.76% Japan 0.27% 11.18% 0.000 0.073 31.90% North America 0.95% 9.89% 0.070 0.670*** 48.62% United Kingdom 0.37% 8.38% 0.013 0.542*** 4.59% Oceania 1.19% 11.95% 0.077 0.237*** 4.46% Europe 0.66% 9.69% 0.041 0.416*** 10.43% Asia 0.27% 11.18% 0.000 0.073 31.90% World property index 0.98% 10.73% 0.066 - 100.00% 3-month T-bill 0.26% 0.17% 0.000 - -
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Table A2.2 Office: Unit root tests Region ADF PP KPSS (mu) ZA (Break) North America -0.703 -1.57 1.750*** -3.671 (06/2004) United Kingdom -2.105 -2.541 0.949*** -3.676 (05/2008) Oceania -3.069 -2.562 1.410*** -3.783 (11/2008) Europe -0.835 -1.149 1.710*** -3.894 (05/2003) Asia -1.805 -2.306 0.739*** -4.585 (09/2005) Country United States -0.753 -1.327 1.760*** -3.568 (06/2004) Canada -1.241 -0.546 1.620*** -3.972 (11/1996) Great Britain -2.105 -2.541 0.949*** -3.676 (05/2008) Australia -3.069 -2.562 1.410*** -3.783 (11/2008) Austria -2.501 -2.312 1.970*** -6.428*** (07/2008) France -0.912 -1.04 1.810*** -3.474 (05/2003) Germany -1.202 -1.389 1.400*** -3.737 (01/2006) Spain -1.108 -1.067 1.160*** -3.431 (12/2005) Sweden -0.866 -0.818 1.420*** -3.487 (05/2003) Switzerland -1.483 -1.716 0.895*** -7.874*** (04/2000) Japan -1.805 -2.306 0.739*** -4.585 (09/2005) Table A2.3 Office: Cointegration rank tests I(1) Analysis G(r) p-r r Eigen Value Trace Regional (5 regions) - 5 0 0.112 69.592***
3.985*** 4 1 0.078 42.861 North America (2 countries) - 2 0 0.039 10.964***
8.781*** 1 1 0.009 2.117 Europe (6 countries) - 6 0 0.109 82.462***
1.562*** 5 1 0.097 56.386 The largest market-cap (9 countries) - 9 0 0.000 270.453
43.129 8 1 0.000 194.050 22.089*** 7 2 0.000 135.660*** 7.517*** 6 3 0.000 95.754**
0.530*** 5 4 0.000 62.082
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Table A2.4 Office: Cointegration Exclusion Tests Regional (n=5) r North America United Kingdom Oceania Europe Asia Regional (n=5) L-R statistic 1 4.800 1.500 0.220 7.600 0.760 L-R statistic p-value 0.028** 0.227 0.642 0.006** 0.382 p-value North America (n=2 countries) r United States Canada L-R statistic 1 5.000 4.800 p-value 0.026** 0.028** Europe (n=6) r Austria France Germany Spain Sweden Switzerland L-R statistic 1 30.000 5.000 15.000 12.000 3.800 1.700 p-value 0.000*** 0.025** 0.000*** 0.000*** 0.050** 0.186 The largest market-cap (n=9) r United States Canada Great Britain Australia France Germany Sweden Switzerland Japan L-R statistic 1 19.000 13.000 3.600 3.300 3.300 15.000 11.000 0.610 1.700 p-value 0.000*** 0.000*** 0.059* 0.068* 0.070* 0.000*** 0.001*** 0.433 0.190 L-R statistic 2 33.000 17.000 16.000 9.800 4.900 24.000 18.000 2.100 16.000 p-value 0.000*** 0.000*** 0.000*** 0.007*** 0.086* 0.000*** 0.000*** 0.354 0.000*** L-R statistic 3 43.000 33.000 33.000 10.000 8.800 40.000 19.000 15.000 29.000 p-value 0.000*** 0.000*** 0.000*** 0.016*** 0.032*** 0.000*** 0.000*** 0.002*** 0.000*** L-R statistic 4 49.000 41.000 37.000 14.000 9.900 40.000 21.000 20.000 33.000 p-value 0.000*** 0.000*** 0.000*** 0.009*** 0.042*** 0.000*** 0.000*** 0.001** 0.000***
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Table A1.5 Retail Property portfolio performance summary Portfolio Returns (%) SD (%) Sharpe ratio Z-stat (INDE vs. COINT) Global property index 0.975% 10.727% 0.066 INDE 0.81% 17.61% 0.031 2.371** COINT 0.18% 12.00% -0.007 3-month T-bill 0.26% 0.17% - Table A2.6 Retail Property portfolio performance Portfolio αp αp (t-stat) βp1 βp1 (t-stat) γp2 γp2 (t-stat) λp3 λp3 (t-stat) ζp4 ζp4 (t-stat) R2 Panel 1A Three-factor performance model INDE 0.015 0.810 0.442 6.580*** -0.010 -0.970 -0.080 -0.610 0.166 COINT 0.000 0.020 1.024 29.690*** 0.000 0.730 -0.009 -0.130 0.907 Panel 1B Three-factor performance model 1992-2002 INDE -0.028 -0.730 0.159 1.320 0.028 0.446 0.250 0.780 0.034 COINT 0.007 0.600 1.009 29.810*** 0.000 0.400 -0.005 -0.530 0.909 Panel 1C Three-factor performance model 2002-2011 INDE 0.025 1.170 0.577 7.450*** -0.012 -1.280 -0.116 -0.840 0.356 COINT -0.001 -0.550 1.033 18.860*** 0.000 0.500 0.005 0.500 0.907 Panel 2A Fama-French three-factor performance model INDE 0.005 0.670 0.444 6.610*** 0.069 0.310 -0.099 -0.530 0.162 COINT 0.000 -0.320 1.023 29.150*** -0.011 -0.940 0.192 1.990** 0.907 Panel 2B Fama-French three-factor performance model 1992-2002 INDE 0.002 0.170 0.190 1.610 -0.038 -0.120 0.054 0.200 0.024 COINT 0.000 0.430 1.009 30.480*** -0.010 -1.160 0.003 0.360 0.909 Panel 2C Fama-French three-factor performance model 2002-2011 INDE 0.009 0.950 0.605 7.960*** 0.658 1.730* -0.547 -1.970* 0.370 COINT 0.000 -0.240 1.030 18.640*** -0.040 -1.450 0.048 2.420** 0.907 Panel 3A Four-factor performance model INDE 0.017 0.920 0.439 6.510*** 0.076 0.340 -0.094 -0.710 -0.101 -0.540 0.164 COINT 0.000 -0.130 1.023 29.220*** -0.011 -0.940 0.007 0.010 0.019 1.990** 0.907 Panel 3B Four-factor performance model 1992-2002 INDE -0.030 -0.760 0.174 1.450 -0.070 -0.230 0.273 0.840 0.022 0.080 0.030 COINT 0.001 0.550 1.009 30.500*** -0.009 -1.090 -0.004 -0.450 0.003 0.420** 0.909 Panel 3C Four-factor performance model 2002-2011 INDE 0.032 1.500 0.589 7.650*** 0.680 1.790* -0.164 0.231 -0.577 -2.080** 0.378 COINT -0.001 -0.820 1.031 18.610*** -0.041 -1.480 0.008 0.800 0.050 2.420** 0.907
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Table A2.7 Office Portfolio risk decompositions Coefficient INDE COINT H0 t-stat Mean Mean Three-factor performance model Intercept -0.007 0.000 αINDE=αCOINT 51.295*** Rmt 0.293 1.021 βINDE=βCOINT 4.080*** SMB 0.004 -0.001 γINDE=γCOINT 11.301*** GMOM 0.166 0.002 λINDE=λCOINT 96.703*** MSE 0.013 0.000 MSEINDE=MSECOINT 39.310*** SD 0.110 0.005 SDINDE=SDCOINT 12.590*** Fama-French three-factor performance model Intercept 0.011 0.000 αINDE=αCOINT 69.489*** Rmt 0.336 1.019 βINDE=βCOINT 5.770*** SMB 0.151 -0.023 γINDE=γCOINT 17.880*** HML -0.250 0.016 ζINDE=ζCOINT 79.610*** MSE 0.013 0.000 MSEINDE=MSECOINT 40.105*** SD 0.110 0.005 SDINDE=SDCOINT 12.608*** Four-factor performance model Intercept -0.006 -0.001 αINDE=αCOINT 63.373*** Rmt 0.308 1.020 βINDE=βCOINT 4.900*** SMB 0.139 -0.023 γINDE=γCOINT 17.132*** GMOM 0.158 0.003 λINDE=λCOINT 17.359*** HML -0.300 0.018 ζINDE=ζCOINT 12.511*** MSE 0.013 0.000 MSEINDE=MSECOINT 38.084*** SD 0.110 0.005 SDINDE=SDCOINT 12.830***
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Appendix A3: Firms with Exposure to International Financial Centres Table A3.1 IFC Property Regional/Country Descriptive Statistics Index Returns (%) SD (%) Sharpe ratio bpi WGT (%) United States 0.88% 13.57% 0.048 0.688*** 3.65% Great Britain 0.20% 7.43% -0.003 0.587*** 5.05% Australia -0.27% 10.74% -0.046 0.430*** 14.00% Sweden 0.77% 8.11% 0.067 0.308** 2.04% Switzerland 0.38% 5.06% 0.031 0.150*** 6.44% Hong Kong 0.60% 11.32% 0.033 0.187** 8.15% Japan 0.12% 10.99% -0.009 -0.166** 47.81% Singapore -0.40% 11.60% -0.054 0.210** 12.85% North America 0.88% 13.57% 0.048 0.688*** 3.65% United Kingdom 0.20% 7.43% -0.003 0.587*** 5.05% Oceania -0.27% 10.74% -0.046 0.430*** 14.00% Europe 0.42% 4.97% 0.040 0.138*** 8.48% Asia 0.37% 16.91% 0.009 0.472*** 68.81% World property index 0.29% 9.81% 0.007 - 100.00% 3-month T-bill 0.23% 0.17% 0.000 - -
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Table A3.2 IFC: Unit root tests Region ADF PP KPSS (mu) ZA (Break) North America -1.895 -1.637 0.960*** -5.281** (10/2008) United Kingdom -2.154 -2.050 0.489** -4.290 (05/2008) Oceania -1.761 -1.865 0.241 -4.794 (07/2008) Europe -0.701 -0.551 0.522** -5.308** (09/2000) Asia -0.987 -1.623 1.010*** -4.113 (03/2002) Country United States -1.895 -1.637 0.960*** -5.281** (10/2008) Great Britain -2.154 -2.05 0.489** -4.290 (05/2008) Australia -1.761 -1.865 0.241 -4.794 (07/2008) Sweden -0.546 -0.281 1.270*** -3.397 (09/2008) Switzerland -0.832 -0.800 0.464** -5.432** (09/2000) Hong Kong -1.122 -0.969 1.220*** -4.890 (07/2008) Japan -1.57 -1.747 0.776*** -4.265 (09/2005) Singapore -1.847 -3.875 0.153 -4.275 (08/2002) Table A3.3 IFC: Cointegration rank tests I(1) Analysis G(r) p-r r Eigen Value Trace Regional (5 regions) - 5 0 0.233 84.710 1.410*** 4 1 0.107 41.641*** 10.827*** 3 2 0.067 23.250 Europe (2 countries) - 2 0 0.038 8.947*** 1.698*** 1 1 0.016 2.642 Asia (3 countries) - 5 0 0.088 24.441*** 1.210*** 4 1 0.034 8.857 The largest market-cap (8 countries) - 8 0 0.324 209.242 2.338*** 7 1 0.237 145.089 11.357*** 6 2 0.186 100.814*** 24.351*** 5 3 0.137 66.982 37.968*** 4 4 0.100 42.768
36
Table A3.4 IFC: Cointegration Exclusion Tests Regional (n=5) r North America United Kingdom Oceania Europe Asia L-R statistic 1 9.700 9.300 1.600 9.300 9.100 p-value 0.002*** 0.002*** 0.210 0.002** 0.003*** L-R statistic 2 14.000 15.000 4.300 20.000 18.000 p-value 0.001*** 0.000*** 0.119 0.000** 0.000*** Europe (n=2) r Sweden Switzerland L-R statistic 1 15.000 0.820 p-value 0.000*** 0.364 Asia (n=3) r Hong Kong Japan Singapore L-R statistic 1 0.070 2.900 2.400 p-value 0.791 0.087* 0.118 The largest market-cap (n=8) r United States Great Britain Australia Sweden Switzerland Hong Kong Japan Singapore L-R statistic 1 0.710 13.000 13.000 6.400 4.600 4.400 14.000 14.000 p-value 0.399 0.000*** 0.000*** 0.011** 0.033** 0.036** 0.000*** 0.000*** L-R statistic 2 14.000 38.000 36.000 30.000 11.000 16.000 21.000 22.000 p-value 0.001*** 0.000*** 0.000*** 0.000*** 0.004*** 0.000*** 0.000*** 0.000*** L-R statistic 3 16.000 45.000 43.000 30.000 16.000 17.000 23.000 24.000 p-value 0.001*** 0.000*** 0.000*** 0.000*** 0.001*** 0.001*** 0.000*** 0.000*** L-R statistic 4 24.000 52.000 50.000 35.000 23.000 24.000 29.000 30.000 p-value 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***
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Table A3.5 IFC Property portfolio performance summary Portfolio Returns (%) SD (%) Sharpe ratio Z-stat (INDE vs. COINT) Glboal property index 0.291% 9.812% 0.007 INDE 0.19% 9.32% -0.003 2.762** COINT 0.34% 10.17% 0.011 3-month T-bill 0.23% 0.17% - Table A1.6 IFC Property portfolio performance Portfolio αp αp (t-stat) βp1 βp1 (t-stat) γp2 γp2 (t-stat) λp3 λp3 (t-stat) ζp4 ζp4 (t-stat) R2 Panel 1A Three-factor performance model INDE 0.002 0.090 0.211 2.760*** -0.008 -0.830 -0.019 -0.150* 0.048 COINT -0.002 -0.560 1.128 9.580*** 0.001 0.560 0.015 0.720 0.900 Panel 1B Three-factor performance model 1997-2004 INDE -0.102 -1.960 -0.097 -0.850*** -0.008 -0.930 0.782 1.850 0.050 COINT -0.008 -1.100 1.152 7.110*** 0.001 0.500 0.073 1.220 0.905 Panel 1C Three-factor performance model 2004-2011 INDE 0.012 0.540 0.425 4.270*** -0.040 -0.860 -0.031 -0.220 0.200 COINT -0.001 -0.200 1.107 5.840*** 0.001 0.130 0.004 0.160 0.907 Panel 2A Fama-French three-factor performance model INDE 0.002 0.230 0.188 2.390** 0.251 1.150 0.124 0.690 0.053 COINT 0.000 0.350 1.128 8.010*** -0.050 -1.380 0.003 0.100 0.900 Panel 2B Fama-French three-factor performance model 1997-2004 INDE -0.011 -1.090 -0.144 -1.240 0.405 1.590 0.441 1.930* 0.061 COINT 0.001 0.760 1.162 7.170*** -0.062 -1.730* -0.039 -1.200 0.905 Panel 2C Fama-French three-factor performance model 2004-2011 INDE 0.007 0.680 0.455 4.500*** 0.386 0.890 -0.344 -1.120 0.206 COINT 0.000 -0.030 1.100 5.060*** -0.092 -1.090 0.076 1.290 0.908 Panel 3A Four-factor performance model INDE 0.002 0.130 0.186 2.320** 0.252 1.150 -0.031 -0.240 0.124 0.680 0.054 COINT -0.002 -0.600 1.130 8.090*** -0.050 -1.390 0.017 0.810 0.003 0.110 0.900 Panel 3B Four-factor performance model 1997-2004 INDE -0.098 -1.920* -0.166 -1.440 0.419 1.660* 0.716 1.740* 0.426 1.890* 0.095 COINT -0.008 -1.130 1.159 7.890*** -0.061 -1.700* 0.076 1.310 -0.040 -1.250 0.905 Panel 3C Four-factor performance model 2004-2011 INDE 0.018 0.780 0.442 4.250*** 0.406 0.930 -0.072 -0.530 -0.352 -1.150 0.209 COINT -0.001 -0.290 1.101 5.430*** -0.095 -1.110 0.008 0.310 0.077 1.300 0.908
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Table A3.7 IFC Portfolio risk decompositions Coefficient INDE COINT H0 t-stat MeanINDE MeanCOINT Three-factor performance model Intercept -0.006 -0.006 αINDE=αCOINT 21.452*** Rmt 0.154 1.130 βINDE=βCOINT 5.770*** SMB -0.011 0.000 γINDE=γCOINT 39.828*** GMOM 0.077 0.044 λINDE=λCOINT 15.532*** MSE 0.007 0.000 MSEINDE=MSECOINT 26.580*** SD 0.085 0.011 SDINDE=SDCOINT 1.080*** Fama-French three-factor performance model Intercept 0.002 0.000 αINDE=αCOINT 5.458*** Rmt 0.177 1.129 βINDE=βCOINT 3.980*** SMB 0.178 -0.056 γINDE=γCOINT 12.892*** HML -0.019 0.006 ζINDE=ζCOINT 12.068*** MSE 0.007 0.000 MSEINDE=MSECOINT 27.590*** SD 0.086 0.011 SDINDE=SDCOINT 1.110*** Four-factor performance model Intercept 0.003 -0.006 αINDE=αCOINT 21.422*** Rmt 0.151 1.131 βINDE=βCOINT 4.980*** SMB 0.166 -0.057 γINDE=γCOINT 15.160*** GMOM -0.002 0.053 λINDE=λCOINT 15.123*** HML -0.060 0.007 ζINDE=ζCOINT 3.433*** MSE 0.007 0.000 MSEINDE=MSECOINT 25.677*** SD 0.085 0.011 SDINDE=SDCOINT 11.020***
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