24 July 2004
STOCK RETURNS AND VOLATILITY IN SECURITISED REAL ESTATE – SOME INTERNATIONAL
EVIDENCE
Liow, Kim Hiang and Yantao Gong, Department of Real Estate, National University of Singapore
Abstract
With an increased emphasis on international real estate investment and an improved economic
outlook for Asia, Asian real estate companies potentially provide an important real estate investment
opportunity for the USA and international fund managers. Using four major securitized real estate
markets, namely, Hong Kong, Singapore, the USA and the UK as case evidence, there is a negative
relation between firm stock excess returns and future volatility change. We also detect a similar
negative relation between firm stock excess returns and future volatility. On the contrary, a significantly
positive contemporaneous relation exists between firm stock excess returns and volatility. Furthermore,
although the variations of the return-volatility relations with firms’ debt/equity ratios, size and
market-to-book valuation ratios are generally significant, the importance of the three firm-specific
characteristics varies across the mature and developing securitized real estate markets as well as
across the individual securitized real estate markets.
1.
INTRODUCTION
There are two common types of indirect or securitized real estate investment vehicles available
to investors. The first type is the Real Estate Investment Trusts (REITs) in the United States. The
second type of securitized real estate investment, popularly known in countries such as the UK, Hong
Kong and Singapore, consists of shares of real estate companies quoted on a stock market. Whilst the
USA has a history of more than 40 years history of REIT operations, listed real estate companies have
become an increasing important property investment vehicle in Asia and internationally (Steinert and
Crowe, 2001).
In this paper, we investigate the relations between stock excess returns and changes in excess
return volatility at the firm level for four major securitized real estate markets, namely, Hong Kong,
Singapore, the USA and the UK. In addition, the links between contemporaneous firm stock excess
returns and excess return volatility, as well as the relation between firm stock excess returns and future
excess return volatility are analyzed. We further examine whether the three return-volatility relations are
influenced by three popularly documented firm-specific characteristics, namely, firm size (MV), financial
1
leverage (D/E) and market-to-book valuation ratio (MV/BV) in the context of a multi-factor specification
for stock returns. Finally, we compare the significance of various relations across the four securitized
real estate markets.
The current study is primarily motivated by two important reasons. Firstly, most research
examines the return and volatility relations on different national stock markets and at individual common
stock levels, with little attention paid to securitized real estate markets. One key concern is that the
research results from the stock markets may not be automatically extended to the securitized real estate
markets because the underlying risk-return performance of real estate stocks / REITs, being real estate
backed securities, are likely to be significantly different from those of general stock market in the short-,
medium and long run. Secondly, recent studies such as Conover et al. (2002) and Steinert and Crowe
(2001) have highlighted the diversification benefits of including international real estate in mixed asset
portfolios, particularly through the success of REITs in the USA and Listed Property Trusts (LPTs) in
Australia, the recent establishment of equivalent REIT vehicles in Japan, Korea and Singapore, and the
long established track record of listed real estate companies in Asia (Chau et al, 2001). Consequently,
attention has been given to examining various aspects of securitized real estate asset performance in
Asia-Pacific markets such as Singapore, Hong Kong and Australia.1 A body of empirical knowledge in
return-volatility dynamics of securitized real estate will provide comparative evidence from the stock
markets and expand the international real estate literature.
Using the Duffee (1995)’s methodology, we find that real estate firms’ excess returns and future
changes in excess return volatility are significantly negatively related. The relation between firm stock
excess returns and one-month ahead volatility is also significantly negative but weaker. A significantly
positive contemporaneous relation between firm stock excess returns and volatility is also reported.
2
Furthermore, our multiple-factor regression models that incorporate three firm-specific characteristics,
namely MV, D/E and MV/BV (not examined by Duffee, 1995) reveals that variations over time in real
estate firms’ financial characteristics could explain at least part of the correlations between stock excess
returns and current volatility and between stock excess returns and future changes in volatility. However,
these noted relationships are not uniform and depend upon the individual securitized real estate
markets.
The rest of this paper is organized as follows. Section 2 presents a brief review of related
literature. Section 3 describes the data set used and Section 4 explains the methodology adopted in this
paper. Section 5 presents the empirical results for a range of tests. Section 6 concludes the paper.
2.
RELATED LITERATURE
A number of empirical studies have observed that stock return volatility falls (rises) after stock
prices increase (decrease). Accordingly to Black (1976), leverage can induce future stock return
volatility to vary inversely with stock prices, a fall in a firm’s stock value relative to the market value of its
debt raises the firm’s D/E ratio (financial leverage) and results in an increase in the volatility of equity.
Black (1976), Christie (1982), Nelson (1991), Cheung and Ng (1992) and Duffee (1995) use individual
firms’ stocks to provide empirical evidence consistent with the leverage effect explanation. Following
from the work of Black and Christie, Duffee (1995) finds that the negative relation between current stock
returns and changes in future stock return volatility at the firm level is the result of a positive
contemporaneous relation between stock returns and return volatility, and this positive relation is
strongest for both small firms and firms with little financial leverage. Using the exponential GARCH
(EGARCH) specification of Nelson (1991), Cheung and Ng (1992) find that a negative relation exists
3
between current stock return and one-period ahead return volatility, and this negative relation is stronger
for larger firms. On the other hand, some other researchers such as Pindyck (1984) and French,
Schwert, and Stambaugh (1987) rely on time-varying risk premia explanation to argue that a forecasted
increase in return volatility results in an increase in expected stock returns and therefore an immediate
stock price decline. In addition, asymmetry in the volatility of macroeconomic variables has also been
used by Schwert (1989) to account for the negative relation. For example, a forecast of an unanticipated
lowered industrial production output (INDP) growth may result in an immediate fall in stock prices and
followed by higher stock return volatility in the period of low INDP growth.
In a separate vein, previous research such as Fama and French (1992), Daniel and Titman
(1997), Daniel et al. (2001) and Leledakis et al. (2001) have documented that stock returns are closely
correlated with company-specific variables such as MV, BV/MV and D/E ratio. Fama and French (1993)
consider these characteristics as proxies for non-diversifiable factor risk. Empirically, Fama and French
(1992) find that BV/MV ratio and MV are able to explain the cross-sectional variations in stock returns in
the US market in the 1963-1990 period. Similarly, Chui et al. (2003) examine the cross-sectional
determinants of expected returns for REITs. Perez-Quiros and Timmermann (2000) indicate that
especially in recessions, small firms’ returns and risk are more strongly affected than large firms. Since
stock risks are multidimensional and can be represented by these firm characteristics, in this study we
consider their possible influence on the relations between stock excess return and volatility. Specifically,
in addition to the variables, namely the MV and D/E factors considered by Duffee (1995), we also
include the MV/BV ratios of individual firms as an additional risk variable in our investigation.2
To the best of our knowledge, this paper is the first comprehensive study to investigate the
return-volatility dynamics at the individual firm level in international real estate literature though few
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papers have reported leverage effect on volatility asymmetry by applying EGARCH model on
securitized real estate indexes (see for examples, Stevenson, 2002; Liow et al., 2004). With the growth
of international investing opportunities in securitized real estate vehicles such as Asian REITs and listed
real estate stocks, it is of great significance to assess the fuller investment perspective of return-volatility
dynamics of securitized real estate markets at both the aggregate index and individual firm levels. The
current paper focuses on real estate firms of four major securitized real estate markets.
3.
SAMPLE AND DATA CHARACTERISTICS
Four securitized real estate markets are included in this study, namely, Hong Kong (HK),
Singapore (SG), the UK and the USA. The two Asian markets (HK and SG) have enjoyed remarkable
economic growth in the past decade and have reasonable track record of listed real estate companies
that play a relatively important role in the general stock indexes. The USA is the world’s largest
economy and about 48% of the global real estate securities market is based on US companies (Worzala
and Sirmans, 2003). The UK is a world major economy and its real estate stock market, which is about
10% of the global real estate securities market, is one of the major and established European
securitized real estate markets. The inclusion of the USA and UK securitized real estate markets can
provide comparative evidence and thereby generate significant interest in international real estate.
We include all real estate firms listed on the New York Stock Exchange (USA), London Stock
Exchange (UK), Hong Kong Stock Exchange (HK) and Singapore Stock Exchange (SG) that are
continuously listed for the period November 30, 1987 – 30, November 2002, the longest interval for
which all required data are available. Hence our sample procedure may introduce survivorship bias and
care must therefore be taken in the interpretation of results (Duffee, 1995). The number of real estate
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firms included are 80 (HK), 14 (SG), 42 (UK) and 76 (USA). A further point to note in the US securitized
real estate market is the dominance of REITs. This is not surprising given a history of more than 40
years of REIT operation in the USA. Out of the USA 76 firms, at least 50 are REITs. For the other three
markets, no REITs are considered either because of non-existence (in the case of the UK) or immaturity
(in the cases of SG and HK). For the purpose of this study, no distinction is made between the USA
REITs and other real estate operating companies. We compile daily stock price data and annual
financial data (i.e. MV, D/E and MV/BV) of these 212 firms from Datastream.
For each firm, we construct monthly stock excess returns and estimates of the standard
deviation of monthly excess returns from November 1987 through November 2002 in US dollar.
Monthly excess returns are defined as the sum of log daily returns in the month less the USA
three-month Treasury bill return (a usual proxy for risk free rate). Standard deviations are estimated by
the square root of the sum of squared log daily returns in the month. Specifically, if there are N t days in
month t, the estimated standard deviation is:
⎡ Nt ⎤
σ t = ⎢∑ rt 2,i ⎥
⎣ i =1 ⎦
1/ 2
…………. (1)
Panel 1 of Table 1 reports the average monthly excess returns and excess return volatility for all
securitized real estate markets as well as for two subgroups: mature markets (USA and UK as a group)
and developing markets (HK and SG as another group). As the figures indicate, all excess returns are
negative, ranging between -6.41% (HK) and -4.48% (the USA). With the exception of the UK, the
average monthly return volatilities for other three markets are above 10%. Additionally, results from
using Augmented Dicky Fuller (ADF) test shows that all 212 excess return series are stationary and
most of the return volatility series are stationary too at the 10% significant level.3 Finally, Panel B of
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Table 1 displays the average values of MV, D/E and MV/BV of all real estate firms for the individual
markets.
(Table 1 here)
4.
RESEARCH METHODOLOGY
The empirical foundation for investigating the relation between excess return and volatility is
equation (2) below adopted by Christie (1982):
ln(
σ i , t +1
) = α i , 0 + λ i , 0 ri ,t + ε i 0 ,t +1
σ i ,t
( λi , 0 < 0 )
…………….(2)
where r i t and σ i t are firm i ’s stock excess return and return volatility (standard deviation) respectively.
Following Duffee (1995), equations (2) can be decomposed into (3) and (4):
ln(σ i ,t +1 ) = α i , 2 + λi , 2 ri ,t + ε i 2,t
ln(σ i ,t ) = α i ,1 + λi ,1ri ,t + ε i1,t
………………(3)
……………(4)
where ( λ 2 − λ1 ) equals the coefficient λ0 of equation (2). Duffee (1995) finds that a positive
r t corresponds to an increase in σ t (i.e. positive λ1 ). On the other hand, the sign of λ 2 is positive at
the daily frequency and negative at the monthly frequency. Furthermore, λ1 is greater than λ 2 in both
cases, so λ0 is negative in equation (2).
Our empirical procedures involve three steps. We first estimate λ0 , λ1 and λ 2 of
equations (2)-(4) for every individual firm using monthly data. Then, we perform portfolio and correlation
tests to examine the bivariate relations between λ0 , λ1 , λ2 and MV, Debt/MV, MV/BV using cross
sectional data. Specifically all 212 firms are sorted in descending order into four portfolios of
approximately equal numbers according to their MV, D/E and MV/BV respectively. The parametric
7
analysis of variance (ANOVA) and non-parametric Kruskal-Wallis (KW) tests are employed to determine
whether there are significant variations in λ0 , λ1 and λ 2 across the four portfolios with firms’ MV, D/E
and MV/BV. Furthermore, the Pearson (parametric) and Spearman (non-parametric) correlation tests
are conducted for the full sample. Finally, we run multiple-factor stepwise regressions with dependent
variables λ0 , λ1 and λ2 respectively on independent variables: log(MV), log(D/E), log(MV/BV) as
indicated in equation (5). The null hypothesis to be tested is that there are significant relations between
the return-volatility coefficients ( λ0 , λ1 and λ2 ) and firm financial characteristics.
Equation 5
λ = C + β1Log(MV) + β2 Log(Debt/ MV) + β3 Log(MV / BV) + γ 1Dusa+ γ 2 Duk + γ 3 Dhk + ζ
where λ0 , λ1 and λ2 are the dependent variables;, γ 1 , γ 2 and γ 3 are market dummy
variables, with γ 1 , a one for the USA firms (Dusa) and zero otherwise. The remaining market dummies
are defined accordingly.
5.
(I)
EMPIRICAL RESULTS
Relations between excess returns and volatility: λ0 , λ1 , λ2
Table 2 reports the estimated results of equations (2) to (4). The mean regression coefficients
are reported for (a) four sets of firms in the individual markets (b) two sets of firms grouped by
developing markets (HK and SG) and mature markets (USA and UK) and (c) full sample. For the full
sample of 212 firms, firstly, stock excess returns and future changes in return volatility are significantly
negatively related. This finding confirms the sign of the relation examined by Christie (1982), Cheung
and Ng (1992) and Duffee (1995) in their stock market research. Specifically, the mean negative λ0 from
the monthly regressions implies that an increase in month t’ s excess stock returns of 1% corresponds
8
to a decline of approximately 0.64% (UK), 0.28% (USA), 0.13% (SG) and 0.04% (HK) respectively in
stock return volatility from month t to month t+1. Based on the estimates, it appears that the strength of
the relation depends on the individual securitized real estate markets. In particular, the relation between
firm stock excess return and future changes in return volatility in the two mature markets as a whole
(mean λ0 is -0.34%) is much stronger (although with a higher standard error) than that in the two
developing markets as a whole (mean λ0 is -0.05%). Secondly, with the exception of HK, stock excess
returns and volatility are contemporaneously positively correlated. The mean estimated coefficients ( λ1 )
from the monthly regressions imply that an increase of one percentage point in month t ’s excess return
corresponds to a 0.26% (UK), 0.22% (USA) and 0.14% (SG) increase, respectively in month t ‘s return
volatility. The estimated mean λ1 of -0.01% for HK firms is however statistically insignificant. Again, there
is a stronger positive relation between current stock excess returns and volatility in the mature markets
(mean λ1 is 0.15%) than in the developing markets (mean λ1 is 0.005%). Thirdly, with the exceptions of
Singapore and HK, stock excess returns are negatively correlated with one-month ahead return volatility.
Based on our estimates, month t+1return volatility for the entire sample falls 0.12% ( λ 2 ). For the
individual markets, the λ 2 estimates are, respectively, -0.41% (UK), -0.07% (USA), -0.03% (HK) and
0.01% (SG).
Overall our results on securitized real estate markets are consistent with Duffee (1995)
regarding the signs of the relations between stock excess returns and current volatility, future volatility
and future volatility change. In addition, there are two other interesting observations from the results.
Firstly, our three mean coefficient estimates (in absolute value) for the USA securitized real estate
market (-0.28%, 0.22% and -0.07% for λ0 λ1 and λ 2 , respectively) are much weaker than the
corresponding monthly results of Duffee (1995) for the USA stock market (-0.74%, 0.46% and -0.28%
9
for λ0 λ1 and λ 2 , respectively). Since the USA securitized real estate market is more volatile than the
stock market over the study period (Liow and Sim, 2004), we can therefore expect some other factors in
addition to price decline to trigger return volatility and changes in the USA securitized real estate market.
Of course, our comparison here must be viewed with great caution due to different time period and
sample firms included in both studies. Secondly, the relations between stock excess returns and future
change in volatility, current volatility and future volatility, respectively, are significantly weaker for the
developing markets than the mature markets ( λ0 λ1 and λ 2 are -0.05%, 0.005% and -0.02% for the
developing markets and -0.34%, 0.15% and -0.20% for the mature markets). Our results imply that if
excess return alone is not able to account for higher volatility experienced in Asian securitized real
estate markets (Liow and Sim, 2004) 4, then it is likely that other factors in addition to price decline may
lead to volatility and changes in volatility . As Bekaert and Harvey (1995) propose, some possible
factors include: stage of stock market development, microstructure effects, macroeconomic influences,
political risks and property market factors.
(Table 2 here)
(ii)
Influence of MV, D/E and MV/BV on λ0 , λ1 and λ 2
To reveal any relations between λ0 , λ1 , λ 2 and firms’ MV, D/E and MV/BV, Panels A – C of
Table 3 reports the mean of λ0 , λ1 , λ2 for four sorted-portfolios and the respective significance test
results.
(Table 3 here)
A positive correlation between MV and λ0 (Panel A) implies that larger firms exhibit stronger
negative relations between period t excess returns and the change in volatility between t and t+1 than
10
do smaller firms. However, the portfolio results reveal a different picture. Based on the correlation
results, property firms in the smallest MV category (P4) would derive the weakest negative λ0 ; instead
they report a mean λ0 of -0.2803 which is the highest of the four portfolios (P1: -0.1843; P2: -0.1959; P3:
-0.1750 and P4 -0.2803). Furthermore, the ANOVA and KW tests confirm no significant variations
of λ0 with MV across the four portfolios. The correlation results also indicate that λ1 and λ 2 are
negatively correlated with MV. However, the respective portfolio results are unable to detect consistent
patterns of variations to support the correlation results.
Based on the estimated portfolio results of Panel B, with some exceptions, it appears that
higher–geared real estate firms display weaker negative relations between excess returns and the
change in volatility between t and t+1 than do lower-geared real estate firms ( λ0 ). For example, the
highest D/E portfolio (P1) reports a mean λ0 of -0.2366, followed by P2 ( λ0 = -0.2038) and P3
( λ0 =0.1848). However the lowest D/E portfolio, P4 reports a mean λ0 of -0.2418. In addition, the
correlation results are inconclusive. With respect to λ1 , it is observed that firms with lower D/E ratios (in
P4, P3 and P2 groups, with P4 firms having the lowest mean D/E ratio) display stronger positive
relations between excess returns and volatility. Additionally, the portfolio results for λ 2 indicates that
higher D/E firms are associated with a stronger negative λ 2 . Significant variation of λ 2 with D/E is
further confirmed by a statistically significant Spearman rank correlation coefficient at the 5% level.
Finally, the correlations of λ0 and λ 2 with MV/BV are negative (Panel C). They are consistent
with the portfolio results that indicate, with the exceptions of the smallest MV/BV portfolio (P4), that (a)
higher MV/BV real estate firms display weaker negative relations between excess returns and the
change in volatility between t and t+1 than do lower MV/BV firms, and (b) higher MV/BV real estate
firms also display weaker negative relations between excess returns and future volatility than do lower
11
MV/BV firms. On the contrary, λ1 is positively correlated with MV/BV although the portfolio results do not
reveal a consistent pattern that higher MV/BV firms derive stronger positive relations (i.e. a higher
positive λ1 ) between excess returns and volatility.
(iii)
Multivariate Relations between λ0 , λ1 , λ2 and MV,D/E and MV/BV
One major limitation of the above univariate analyses is that firms in the four portfolios are not
sorted by individual markets due to smaller sample size. As a consequence, the overall results may not
be able to reveal the individual market differences. To confirm the various relations detected above, we
now report the results from equation (5) that links λ0 , λ1 , λ2 (individual dependent variables) to D/E, MV,
MV/BV and market dummies jointly. Table 4 contains the results of the stepwise regressions for the
overall sample of 212 firms (Panel A); two sub-samples: mature markets (118 firms) and developing
markets (94 firms) (Panel B) and 4 individual markets (Panel C). We focus our attention on the
significance (or otherwise) of the three firm-specific variables. The major findings are discussed below.
(Table 4 here)
Firstly, the F-statistics for the three overall stepwise models are all significant at the 1% level
(between 10.90 and 63.35) (Panel A). Thus, as a whole, the selected explanatory variables are
significantly related to λ0 , λ1 and λ2 . However, the range of adjusted R2 is between 12.9% and 32.9%
indicating that there are factors other than the three selected firm-specific characteristics in
influencing λ0 , λ1 and λ2 . In terms of significance, for the full set of 212 firms, the D/E ratio emerges as
the most important and only firm variable for λ0 and λ1 . The MV and MV/BV variables fail to enter into
the two models. Specifically, there is a significantly positive relation at the 5% level between λ0 and the
D/E ratio (t statistic = 2.54). On the other hand, the D/E ratio is strongly negative with λ1 at the 1% level
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(t statistic = -3.35). Another finding is that none of the firm-specific characteristics is significantly related
to λ2 . In this case, the UK market dummy explains the majority of the variations in λ2 .
Secondly, the relations of λ0 , λ1 and λ2 with the three firm-specific characteristics vary across
real estate firms of the mature markets and developing markets (Panel B). Notably, firm size emerges
as the only significant variables to explain the variations in λ0 , λ1 and λ2 for the two developing markets
as a whole. Specifically, the MV variable is very important with t-statistics of 2.45 ( λ0 ), -4.18 ( λ1 ) and
-4.57 ( λ2 ), respectively. For the two mature markets as a whole, only the D/E ratio is significantly
positive in the λ1 model (t-statistic = -2.81) and none of the three firm-specific variables is important
for λ0 and λ2 .
Thirdly, the significance of the three firm-specific variables in the λ0 , λ1 and λ2 models are not
uniform and depend upon the individual listed property markets (Panel C). Of the 12 individual models,
five of them are not statistically significant (USA: 2, UK: 1 and SG: 2). For Hong Kong, the adjusted R2
of the three stepwise regression models are between 9.8% and 29%. In terms of importance, MV is the
most significant variables for λ0 , λ1 and λ2 . Whilst the firm size effect is significantly positive for λ0 (t =
3.01), it is strongly negative for λ1 (t statistic = -4.75) and λ2 (t statistic = -4.20). Additionally, the D/E
ratio is important for λ1 (t-statistic =2.38) and λ2 (t statistic = -2.05). On the contrary, the MVBV ratio is
not important in all three models. As a consequence, two profiles of HK property firms emerge from the
findings are: (a) smaller and lower-geared real estate firms display greater positive contemporaneous
relations between excess returns and volatility than do larger and higher-geared firms, and (b) larger
and higher-geared real estate firms exhibit weaker negative relations between excess returns and future
volatility than do smaller and lower-geared real estate firms.
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In explaining the cross-sectional variations of λ0 and λ1 for the UK firms, the D/E and MV/BV
ratios are important. In particular, the D/E ratio is very significant with t-statistics of 3.09 and -3.02 for
λ0 and λ1 , respectively. The significance for the MV/BV factor are slightly weaker with t-statistics of
2.35 (for λ0 ) and -2.28 (for λ1 ) respectively. In these instances, two profiles of the UK real estate firms
emerge from the analyses are: (a) Real estate firms with higher D/E and MV/BV ratios exhibit stronger
negative relations between month t excess returns and the change in volatility between t and t+1 than
do real estate firms with lower D/E and MV/BV ratios and (b) Real estate firms with lower D/E and
MV/BV ratios derive stronger positive contemporaneous relations between excess returns and volatility
than do real estate firms with higher D/E and MV/BV ratios.
The USA and Singapore results are much weaker. Specifically, only the D/E ratio is significantly
negative for the USA λ0 model (t = -2.14). The significance of the MV/BV factor in the λ2 model for
Singapore (t statistic = -2.63) has to be interpreted with caution as there are only 14 firms in this
category to draw any strong conclusions.
Thus an overall picture emerge from the study is that whilst the variations
of λ0 , λ1 and λ2 with firms’ debt/equity ratios, size and market-to-book valuation ratios are generally
significant, the importance of the three firm-specific characteristics varies across the mature and
developing markets as well as across the individual securitized real estate markets. Based on the
estimated results, the significance of the D/E ratio in influencing real estate firms’ λ0 , λ1 and λ2 across
the markets is broadly confirmed. For the two Fama and French factors, firm size is very important only
for real estate firms of the developing markets, in particular, for HK real estate firms. The significance of
the MV/BV factor is largely for the UK property firms
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6.
CONCLUSION
In this paper, we examine the risk-return characteristics of 212 firms in four major securitized
real estate markets with respect to their return and volatility relations and the effects of three popular
firm characteristics: firm size, financial leverage and market-to-book valuation ratio on the relations.
Such extension of previous stock market literature to international securitized real estate markets is
timely and meaningful given the increased significance of real estate stocks as property investment
vehicles for international investors to gain property exposure in Asia and internationally.
Collectively, the evidence from this study may be summarized as follows. Overall, there is a
negative relation between firm stock excess returns and future volatility change. We also detect a
similar negative relation between firm stock excess returns and future volatility. On the contrary, a
significantly positive contemporaneous relation exists between firm stock excess returns and volatility.
These results are broadly consistent with those of Duffee (1995) for the USA stock market. Results also
indicate that the relations between firm stock excess returns and future change in volatility, current
volatility and future volatility, respectively, are significantly weaker for the developing market group than
those of mature market group. One major implication is that if return alone is unable to account for
higher volatility experienced in Asian securitized real estate markets (most of them are developing), then
it is likely that other factors in addition to price decline can lead to stock volatility and changes in stock
volatility. Some possible factors include, stage of stock market development, microstructure effects,
macroeconomic influences, political risks and property market factors. Explaining these patterns and
factors awaits future research. Furthermore, although the variations of λ0 , λ1 and λ2 with firms’
debt/equity ratios, size and market-to-book valuation ratios are generally significant, the importance of
the three firm-specific characteristics varies across the mature and developing markets as well as
15
across the individual securitized real estate markets. In particular, the significance of the D/E ratio in
influencing real estate firms’ λ0 , λ1 and λ2 across the markets is broadly confirmed. For the two Fama
and French factors, firm size is very important only for real estate firms of the developing markets, in
particular, for HK real estate firms. The significance of the MV/BV factor is largely for the UK real estate
firms.
Finally, our results are based on the securitized real estate returns and three firm
characteristics of four major markets and any generalization of the findings to other securitized real
estate markets that have different institutional characteristics and market structure must be treated with
caution. As more and more Asian economies are interested in developing REIT type of securitized real
estate products, our study that focuses on major securitized real estate markets and their constituents
around the globe will enhance investors’ understanding of the role of Asian real estate in local and
international investment portfolios.
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18
Table 1
Summary Statistics of Sample Firms
Panel A Excess Returns and Volatility
Market
No of firms
Mean of
excess returns
% of excess
returns that is
I(0)
Mean of
volatility
% of volatility
that is I(0)
All
212
-0.0537
100
0.0945
88.7
Mature
118
-0.0471
100
0.0856
83.9
Developing
94
-0.0620
100
0.1056
94.7
USA
76
-0.0448
100
0.1064
84.2
UK
42
-0.0514
100
0.0479
83.3
HK
80
-0.0641
100
0.1080
96.3
SG
14
-0.0501
100
0.0920
85.6
Panel B Firm Variables
Market
All
Mature
Developing
USA
UK
HK
Singapore
Notes:
Mean of MV(mill)
636.432
471.443
889.162
417.217
558.594
909.329
716.692
Mean of D/E
1.26
1.75
0.65
1.91
1.44
0.65
0.68
Mean of MV/BV
1.22
1.53
0.83
1.87
0.89
0.75
1.29
The number of real estate firms included are 80 (HK), 14 (SG), 42 (UK) and 76 (USA). Mature markets refer to USA and UK as a
group. Developing markets refer to HK and Singapore as a group. For each firm, daily stock return data from Nov 30, 1990 to Nov30, 2002 are
extracted from the Datastream. Monthly excess returns are defined as the sum of log daily returns in the month less the equivalent USA
three-month Treasury-bill returns. The three firm specific variables for the period (market capitalization: MV; Debt/equity ratios: D/E and
market-to-book ratios: MV/BV) are extracted from Datastream. ADF is the Augmented Dickey-Fuller unit root value to determine whether the
excess returns and volatility are I (0) variables at the 10% level, i.e. they are stationary. This test is necessary to provide a pre-requisite for any
regression tests.
19
Table 2
Summary of Ordinary Least-Square
Regressions of Firm Stock Return Volatility on Firm Stock Returns
ln(
σ i , t +1
) = α i , 0 + λ i , 0 ri ,t + ε i 0 ,t +1
σ i ,t
ln(σ i ,t +1 ) = α i , 2 + λ i , 2 ri ,t + ε i 2,t
…………(3)
ln(σ i ,t ) = α i ,1 + λi ,1ri ,t + ε i1,t
…………(4)
Sample
All (212 firms)
Mature markets (118 firms)
Developing markets (94
firms)
USA (76 firms)
UK (42 firms)
HK (80 firms)
SINGAPORE (14 firms)
Notes:
……………………………..(2)
λ0
λ1
λ2
-0.2088***
0.0850***
(0.0271)
-0.3375***
(0.0916)
-0.0474
(0.0302)
-0.2783***
(0.0335)
-0.6406***
(0.0895)
-0.0367**
(0.0175)
-0.1278**
(0.0340)
(0.0225)
0.1488*
(0.0800)
0.0050
(0.0374)
0.2166***
(0.0393)
0.2583***
(0.0719)
-0.0117
(0.0196)
0.1366*
(0.0479)
-0.1204***
(0.0200)
-0.1973**
(0.0703)
-0.0240
(0.0258)
-0.0720**
(0.0286)
-0.4050***
(0.0645)
-0.0268
(0.0155)
0.0089
(0.0303)
OLS regressions are estimated for different samples. R i t and σ i t are firm i ’ s stock return and return volatility (standard deviation)
respectively. For each firm, we construct monthly excess stock returns and estimates of the standard deviation of monthly excess stock returns
from November 1987 through November 2002. Excess monthly returns are defined as the sum of log daily returns in the month less the USA
three-month Treasury bill return (a usual proxy for risk free rate). Standard deviations are estimated by the square root of the sum of squared
log daily returns in the month. Standard errors for mean coefficients are in parenthesis. ***, **, * - indicates two tailed significance at the 1%, 5%
and 10% levels.
20
Table 3
Variations of λ0 , λ1 and λ2 with Firms’ Sizes (MV),
Debt/equity ratios (D/E) and Market-to-Book Ratios (MV/BV): Portfolio Results
Panel A: Firm Sizes (MV)
MV Portfolios
No of firms
MV (USD$)
P1
53
2256591.2
P2
53
240252.2
P3
53
83110.4
P4
53
22866.9
F-stat a
212
18.60***
Chi-sq b
212
197.82***
Pearson correlation with MV
Spearman correlation with MV
λ0
Mean
-0.1843
-0.1959
-0.1750
-0.2803
0.80
0.94
0.062
0.014
λ1
λ2
0.0658
0.0452
0.0815
0.1475
0.97
0.52
-0.106
-0.065
-0.1253
-0.1469
-0.0632
-0.1464
0.99
5.82
-0.041
-0.102
Panel B: Debt/equity ratios (D/E)
D/E Portfolios
No of firms
D/E
P1
51
3.704
P2
51
0.901
P3
50
0.501
P4
50
0.141
F-stat a
202
31.53***
Chi-sq b
202
188.44***
Pearson correlation with D/E
Spearman correlation with D/E
λ0
Mean
-0.2366
-0.2038
-0.1848
-0.2418
0.23
1.94
0.022
-0.098
λ1
λ2
0.0492
0.0189
0.1410
0.1623
2.28*
5.36
-0.101
-0.128*
-0.2038
-0.1667
-0.0648
-0.0624
3.13**
19.52***
-0.089
-0.204**
Panel C: Market-to-Book Ratios (MV/BV)
MV/BV
No of firms
Portfolios
MV/BV
P1
53
2.6764
P2
53
1.0551
P3
53
0.7165
P4
53
0.4050
F-stat a
212
44.61***
Chi-sq b
212
197.82***
Pearson correlation with MV/BV
Spearman correlation with MV/BV
λ0
-0.1887
-0.2017
-0.3203
-0.1248
2.31*
5.58
-0.037
-0.102
Mean
λ1
λ2
0.0915
0.0078
0.1568
0.0840
1.86
2.51
0.087
0.027
-0.0945
-0.1497
-0.1934
-0.0442
2.74**
10.34**
-0.032
-0.096
Notes
To investigate the variations of λ0 , λ1 and λ2 with the three firm-specific characteristics, we divide the full sample of 212 firms into four
portfolios of approximately equal size in descending order of firm size (Panel A), debt/equity ratios (Panel B) and market-to-book ratio (Panel C).
Results of univariate comparison for the four portfolios as well as the ANOVA test (F-statistics) and non-parametric Kruska Wallis significance
test are reported. For the full sample, the Pearson and Spearman correlation coefficients between λ0 , λ1 and λ2 and the three firm
variables are also reported. a Obtained from SPSS ANOVA test. b Obtained from SPSS Kruskal-Wallis test.
***
, **, * -indicates two tailed
significance at the 1%, 5% and 10% levels respectively.
21
Table 4
λ = C + β 1 Log ( MV ) + β 2 Log (
Panel A Full Sample
Regression
coefficient
Intercept
β1 (Log MV)
β 2 (Log D/E)
β 3 (Log MV/BV)
γ1
γ2
γ3
Stepwise Regression Results
Debt
MV
) + β 3 Log (
) + γ 1 Dusa
MV
BV
Dependent variable =
-0.157 (-4.40***)
0.046 (2.54**)
-0.484 (-7.82***)
0.177 (3.21***)
32.9%
33.83***
212
λ0
Dependent variable
= λ1
0.094 (2.79***)
-0.057 (-3.35***)
0.165 (2.83***)
-0.179 (-3.45***)
12.9%
10.90***
212
+ γ 2 Duk + γ 3 Dhk + ζ
Dependent variable = λ2
-0.052 (-2.54**)
-0.354 (-7.96***)
Adjusted R2
23.7%
F value
63.35***
N
212
Panel B Sub-samples
Dependent variable = λ1
Dependent variable = λ2
Regression
Dependent variable = λ0
coefficient
USA/UK
HK/SG
USA/UK
HK/SG
USA/UK
HK/SG
Intercept
-0.351(-2.81**)
0.155(4.30***)
0.572(4.16***)
0.094(4.21***)
β 1 (Log MV)
0.025 (2.45**)
-0.047(-4.18***)
-0.035(-4.57***)
***
β 2 (Log D/E)
-0.092(-2.81 )
β 3 (Log MV/BV)
Adjusted R2
5.4%
5.9%
15.9%
18.6%
F value
5.98**
7.92***
17.50***
20.9***
N
118
94
118
94
118
94
Panel C Individual Results
USA (N=76)
UK (N=42)
HK (N=80)
SG (N=14)
Dependent variable = λ0
Intercept
-0.537***
-0.425(-3.26***)
β 1 (Log MV)
0.032(3.01***)
β 2 (Log D/E)
0.298(3.09***)
β 3 (Log MV/BV)
0.552(2.35**)
2
Adjusted R
20.0%
9.8%
F value
6.05**
9.05***
Dependent variable = λ1
Intercept
0.098(2.48**)
0.177(2.38**)
0.572(4.26***)
β 1 (Log MV)
-0.052(-4.75***)
β 2 (Log D/E)
-0.069(-2.14**)
-0.235(-3.02**)
-0.027(-2.38**)
β 3 (Log MV/BV)
-0.433(-2.28**)
Adjusted R2
4.8%
19.0%
29.0%
F value
4.60**
5.82***
16.09***
Dependent variable = λ2
Intercept
0.361(3.55***)
-0.027(-1.09)
β 1 (Log MV)
-0.035(-4.20***)
β 2 (Log D/E)
-0.018(-2.05**)
β 3 (Log MV/BV)
-0.119(-2.63**)
Adjusted R2
23.6%
33.1%
F value
12.38
6.95**
Notes: We run stepwise regressions to determine the firm variables (firm size: log MV; financial leverage: log D/E and market-to-book value:
log MV/BV) most significantly related to λ0 , λ1 and λ2 . For each dependent variable, we run the overall model of 212 firms, two
sub-groups (mature markets: USA/UK firms and developing market sample: HK /SG firms) and 4 individual markets. For the overall models,
three market dummies ( γ 1 , γ 2 , γ 3 ) are included. The t-statistics for regression coefficients are in parenthesis. ***, ** indicate two tailed
significance at the 1% and 5% levels respectively.
22
NOTES
These studies include Asia (Garvey et al, 2001), Singapore (Liow, 2001a, 2001b), Hong Kong (Chau
et al, 2001, 2003; Newell and Chau, 1996; Schwann and Chau, 2003) and Australia (Newell and
Acheampong).
1
Broadly speaking, the MV/BV ratio in the real estate context is used to measure the relative market
valuation of a firm’s real estate holdings. It can also be regarded as a proxy for the presence of growth
opportunities of the real estate firms.
2
The ADF tests the excess return and volatility series are I(0) variables. These test are necessary to
provide a pre-requisite for regression analyzes.
3
Liow and Sim (2004) find that Asian real estate stock markets (many of them are emerging markets)
have not produced high level of compounded returns relative to the US REIT and UK real estate stock
market over the 1990-2003 time period. Furthermore, Asian real estate stock markets have also
experienced a higher level of volatility compared to their USA and UK counterparts.
4
23