Country and industry factors in tests of Capital Asset Pricing Models for partially integrated emerging markets

Journal Pre-proof Country and industry factors in tests of Capital Asset Pricing Models for partially integrated emerging markets Ye Bai, Christopher J. Green PII:

S0264-9993(19)30485-7

DOI:

https://doi.org/10.1016/j.econmod.2019.12.019

Reference:

ECMODE 5112

To appear in:

Economic Modelling

Received Date: 2 April 2019 Revised Date:

18 December 2019

Accepted Date: 26 December 2019

Please cite this article as: Bai, Y., Green, C.J., Country and industry factors in tests of Capital Asset Pricing Models for partially integrated emerging markets, Economic Modelling (2020), doi: https:// doi.org/10.1016/j.econmod.2019.12.019. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Country and Industry Factors in Tests of Capital Asset Pricing Models for Partially Integrated Emerging Markets* by Ye Bai1 and Christopher J. Green

Bai:

International Business School Suzhou, Xi’an Jiaotong-Liverpool University

Green:

School of Business and Economics, Loughborough University

1.

Correspondence to: Ye Bai: International Business School Suzhou, Xi’an Jiaotong-Liverpool University, No.111 Ren'ai Road, Dushu Lake Higher Education Town, Suzhou, China Tel.: +86 (0) 512 8816 1728; E-mail: [email protected] Christopher J. Green: School of Business and Economics, Loughborough University, Loughborough, Leicestershire, LE11 3TU, United Kingdom Tel: +44 (0)1509 222711; Fax: +44 (0)1509 223910; E-mail: [email protected]

*

We would like to thank the Editor, Sushanta Mallick, and two anonymous referees for their detailed and thoughtful comments. We also thank Isaac Otchere, Yuan Zhao for some very helpful comments on an earlier draft. We also thank participants in the Cross Country Perspectives in Finance (CCPF) Conference held on June 21-23, 2018 at Sun Yat-Sen Business School, Zhongshan University, China for their further comments.

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COUNTRY AND INDUSTRY FACTORS IN TESTS OF CAPITAL ASSET PRICING MODELS FOR PARTIALLY INTEGRATED EMERGING MARKETS

ABSTRACT Existing literature has produced broadly inconclusive evidence about the asset pricing model which best fits partially integrated markets. This paper examines whether industry and country factors are independent factors helping to determine returns in emerging stock markets, or are derived from the stocks’ risk-return characteristics. We link the countryindustry decomposition framework to the local and the Global CAPM in a new and more direct way. The results show that country factors are additional independent sources of crosssectional variation in stock returns before 1996 particularly under the Global CAPM. After 1996, the results suggest partial integration: industry and country factors are both additional independent determinants of cross-sectional variations in stock returns. .

JEL classification: G15 Keywords: Emerging equity markets; CAPM; Cross-sectional variation; Country factors; Industry factors

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1. Introduction Why should the study of financial integration be of interest to researchers and practitioners? A fundamental reason is that the expected gains from international portfolio diversification differ as among: local factor pricing models (if total segmentation), global factor pricing models (if total integration), and mixed factor pricing models (if partial integration). The cost of capital and other criteria for capital budgeting decisions also depend on which, if any, of these models are valid representations of asset prices and returns. One strand of the financial integration literature (e.g. Baele and Inghelbrecht, 2009; Bai et. al., 2012; Campa and Fernandes, 2006; Ferreira and Ferreira, 2006; Griffin and Karolyi, 1998; Philaktis and Xia, 2006; Serra, 2000) asks if investors should diversify across countries or across industries (or both) to achieve improvements in their portfolio risk-return tradeoffs. The answer to this question depends on how far international stock returns are driven by country factors and how far by industry factors. By decomposing returns into international industry and country factors as well as a world factor, using least squares dummy variables (Heston and Rouwenhorst, 1994), this research suggests overall that country effects are the more important of the two, but that international industry effects have been increasing over time (e.g. Marcelo, et. al., 2013). One explanation is that financial integration has led to a decrease in the relative importance of country factors and an increase in the relative importance of the industry factors over time. A second strand of the financial integration literature examines whether an international or domestic asset pricing model is more appropriate for estimating the cost of capital. Despite the continuing integration of intemational financial markets, practitioners and researchers often still estimate the cost of equity with a local version of the Capital Asset Pricing Model (CAPM1), using the market index for a specific country in which the firm is quoted (Graham and Harvey, 2001). The alternative in internationally-integrated markets is to base the calculation on an international formulation of the CAPM. The simplest version of this is the Global CAPM (GCAPM), which uses the world market 1

Rossi (2016) provides a comprehensive review of CAPM.

2

index to estimate a firm’s beta and hence its cost of capital (Grauer, et al., 1976). More general factor models can be used to estimate the cost of capital 2 (refer to Harvey et al. (2016) for a comprehensive survey) but the CAPM remains much the most widely-used for this purpose in practise (Graham and Harvey, 2001). The local CAPM and GCAPM can give the same estimate of the cost of capital if the local stock market portfolio contains all the information that is relevant to price domestic assets internationally. However, if risk that is diversifiable domestically contains risk that is systematic in the world market, the local CAPM incorrectly ignores such risk. Empirical studies such as Koedijk, et al. (2002) find that the local CAPM and GCAPM give different estimates of the cost of capital but there is a much closer correspondence between the local CAPM and the multi-factor international asset pricing model (ICAPM). They attribute the latter finding to the presence of a strong country factor in individual stock returns such that the exposure to international factors of companies in a single country is largely captured by movements in the local market index. They argue that this suggests a lack of complete capital market integration, due for example to cyclical, structural, and institutional country-specific factors. Bruner et al. (2008) obtain broadly similar results using a larger dataset including developed and emerging markets. It follows that diversification across industries within one country is insufficient to cope with a country’s systematic risk. These results are consistent with the findings of Heston and Rouwenhorst (1994), Griffin and Karolyi (1998) and others in the first strand of the financial integration literature, and also reinforce the home bias puzzle (Lewis, 1999). This study extends the financial integration literature by proposing a comprehensive test of financial integration using a new and more direct method that links the country-industry decomposition of stock returns to the local global CAPM models. We examine formally if in emerging stock markets (ESMs), country and industry factors are additional independent factors helping to determine stock returns as implied by Koedijk, et al. (2002). The decomposition of returns into country and industry effects provides a descriptive cross-sectional relationship among stock returns. Properly measured, a similar decomposition can be estimated for different measures of stock risks, whether total risk (Bai and

2

For example, the three- and five-factor models of Fama and French (1993, 2017), or the two-beta model of Malamud and Vilkov (2018).

3

Green, 2010), or systematic risk (ie. beta) as cross-sectional relationships. Meanwhile, all the different versions of the CAPM predict that there should be a direct cross-sectional relationship between stock returns and their systematic risks. In principle therefore we might expect to see congruence between the country-industry cross-sectional relationships and those implied by the CAPM: if CAPM is sufficient to estimate asset pricing under market segmentation or GCAPM is a more appropriate model under full integration. However as a voluminous literature suggests, there could be a third scenario: a time-varying partial integration in ESMs. In our research, we estimate the country-industry decomposition for a large sample of ESM returns and beta coefficients; we then derive and test the parameter restrictions implied by the (G)CAPM relationship between returns and betas within the country-industry decomposition framework. Depending on the different levels of integration we could expect to find some of the eight scenarios shown in Table 1 during different time periods. _____________________________________________________________________________ Table 1 about here _____________________________________________________________________________ The eight scenarios are ranked according to increasing levels of integration from full segmentation (1st) to full integration (8th). For instance, the first scenario means that on top of the CAPM, country factors are independent sources of cross-sectional variation in stock returns. The sixth scenario means that on top of GCAPM, both country and ESM industry factors are independent sources of crosssectional variation in stock returns, which suggests partial integration with both country specific risks and ESM systematic risk priced to different extents. To understand the level of integration and important factors for pricing equities in the ESM is crucial to portfolio managers in making international portfolio diversification decisions and for ESM firms to evaluate their cost of capital more accurately. The rest of the paper is organised as follows. In section two, we briefly review the relevant literature. In section three, we discuss the methodology of this paper. Section four discusses the sample data used; and this is followed by the empirical results in section five. Finally, we conclude.

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2. Literature Review 2.1 Stock return and risk decomposition The basic methodological framework for evaluating the sources of international share price movements is the orthogonal decomposition of share returns into country, industry and global factors introduced by Heston and Rouwenhorst (1994) (H-R). This procedure can be thought of as analogous to the choices of international portfolio managers: if country factors are the main determinants of share returns, country diversification should be an effective tool for risk reduction; if industry factors are more important, industrial diversification may be preferred. One might expect that the increased globalization of the world economy and financial markets of recent years has altered the relative importance of country and industry effects.

Some recent studies have shown that international

industry factors have increased in importance and may even have overtaken country effects in some OECD countries (Baele and Inghelbrecht, 2009; Campa and Fernandes, 2006; Philaktis and Xia, 2006; Ferreira and Ferreira, 2006). Others have argued that the persistence of country effects in liberalized OECD countries may be attributable in part to a small firm effect (Bekaert et al., 2009). Fewer studies have used ESM data (Campa and Fernandes, 2006; Phylaktis and Xia, 2006; Serra, 2000) and the evidence from these is inconclusive. Bai, et al. (2012) re-examined a large sample of ESMs and found that the composition of returns moved in parallel with industrial countries: initially, country effects dominate industry effects, but there is evidence of increased industry effects after the 1997 Asian financial crisis. Most studies focus on decompositions of stock returns. However, Bai and Green (2010) argued that, since one of the main benefits expected from portfolio diversification is risk reduction, movements in stock returns should reflect in part the outcome of portfolio shifts in response to perceived changes in stock risks. They therefore investigated the relative importance of country and industry effects from the risk perspective within a sample of ESMs. They find the decomposition of the conditional total risks followed a broadly similar pattern over time to those of ESM returns. By extending the Campbell et al. (2001) volatility decomposition method to an international setting, Ferreira and Gama (2005) decompose local industry risk measured as the variance of the local industry 5

return in excess of its country of origin return. Their results are also consistent with the common findings based on H-R method. Correspondence between returns and total risk is less expected than it would be as between returns and systematic risk. It suggests that based on the return decomposition results investors diversifying across countries (for example) may simply include equities with higher total risk in their portfolios which in turn could dilute the expected covariance risk reduction gain (Bai and Green, 2010).

In this paper we contribute to the decomposition literature by studying the

relationship between returns and systematic risks (betas) within the H-R framework. 2.2 Local CAPM vs. International CAPM Since CAPM was first introduced four decades ago, numerous papers have tried to explain the crosssection of expected returns. Empirical research suggests that there may be significant differences among estimates of the cost of capital in a country, dependent on the version of the CAPM used for this purpose. In the case of entirely segmented markets, only country-specific risks matter to investors hence are priced in the asset-pricing model. While if the market is fully integrated with the rest of the world, investors face both common and country-specific risks but only the common risk factors are priced since country-specific risks are fully diversified internationally. The use of the GCAPM in any particular country presumes that investors are fully diversified internationally. Therefore, expected returns should solely depend on global risk factors and such asset pricing relationships apply in all countries (Karolyi and Stulz, 2002). The well-known home bias puzzle suggests that investors do not in fact fully diversify their assets across international markets but appear to have a relatively strong preference for home-based assets (French and Poterba, 1991). Moerman (2005) finds that even in the highly-integrated euro area a domestic three-factor Fama-French model outperforms a euro area threefactor Fama-French model. Using more recent data, Dolde, et. al. (2011) draw similar conclusions. Mishra and O'Brien (2005) use data from 1990 to 2000 from 16 emerging stock markets (ESMs), and find that global beta offers some additional explanatory power for stocks that permit significant international investment, while local beta offers additional explanatory power for stocks that are more restricted in terms of international investment.

Meanwhile, Ejara et al. (2018) report that the 6

difference between cost of capital estimates provided by the local CAPM and the ICAPM are often quite large. Bruner et al. (2008) show that the choice of market portfolio makes much larger difference for ESM than for developed market stocks. The average absolute difference between local CAPM and GCAPM expected returns is 5.6% for ESM versus 3.6% for developed markets. Therefore the third and more common scenario is that the market is only partially integrated, so that investors face not only country-specific risks, but also common risks to different degrees. Shares that are more correlated with the global market than the home market have a higher expected return than that predicted by the local CAPM, and vice-versa. Despite significant contributions by several studies in empirically modelling the partial segmentation (e.g. Bekaert and Harvey, 1995; Carrieri et al., 2007; Pukthuanthong and Roll, 2009), these models are still built from a pure econometric combination of local and global risk factors and apply ad hoc tests of capital market integration (Arouri et al., 2012). For instance, the seminal study of Bekaert and Harvey (1995) combines the local CAPM and ICAPM when examining the explanatory power of local and common factors over expected returns, and empirically suggests that segmentation exists if the weights of the local factors are high. Pukthuanthong and Roll (2009) regress the index return on 10 global factors in principal component analysis. The conventional R2 (0 ≤ R2 ≤ 1) from their regression is used to measure the extent of market integration or segmentation. Studies on developed markets tend to find support for a global asset pricing model (Arouri et al., 2012; Harvey, 1991) while those on ESM find much weaker support for global determinants of risk. Instead they show support to a number of other important factors in ESM returns such as time variation, local factors, firm finanical variables and currency risk (Chaieb and Errunza, 2007; Carrieri et al., 2007; Cheng et al., 2010; Gupta and Modise, 2012; Harvey, 1995;). The inconclusive empirical findings on the relationship between stock returns and conditional variance/covariance has led numerous studies to look for missing variables or alternative model specifications under the partial integration situation in different markets during different time periods. Solnick (1983) derives a further multi-factor ICAPM in which the world market index factor of the GCAPM is augmented by factors corresponding to 7

exchange rate risk: as many factors as there are exchange rates faced by investors in international capital markets. Other than curency risk, based on a sample of five Asian ESMs, De Groot and Verschoor (2002) find a strong size and a significant market-to-book effect while Zhang et al. (2018) show that the price-earnings ratio performs better than book-to-market ratio for pricing in China. Zaremba and Maydybura (2019) find the Fama and French (2018) six-factor model outperforms other models based on a frontier country sample. Other than domestic factors, it may worth considering other global information and global risk factors when assessing the cost of capital at the domestic level (Iqbal et al., 2010). Chiang and Chen (2016) find that US stock returns and stress in the US market dominate in explaining ESM stock return variations. Sharma et al. (2019) find that in the last ten years, the Indonesian equity risk premium is under significant influence of regional and global credit risk factors. Harvey et al. (2016) provides a comprehensive survey on various factor models in the literature. Overall, the question of which CAPM is best for computing the cost of capital remains unresolved, especially if home investors are not fully diversified internationally. 3. Methodology 3.1 Country-Industry Decompositions: The Dummy Variable Model Following standard practice, the dummy variable model of Heston and Rouwenhorst (1994) is used to identify country and industry effects 3 . As the method is well-known, we explain it briefly. The decomposition of returns in any time period is given by: J

K

j =1

k =1

ri = α + ∑ λ jDi , j + ∑ γ k Ci ,k + ei

…(1)

In (1), ri is the return on equity i in industry j and country k; α is a base level of return; λ j is the industry j factor; γ k is the country k factor; ei is the idiosyncratic disturbance for firm i: E (ei ) = 0 ,

3

Some recent studies (Phylaktis and Xia, 2006) criticise the Heston-Rouwenhorst model (HR) because it assumes unit factor sensitivities. However, De Moor and Sercu (2011) argue that HR is not modelling a return generating process, but providing an algorithm to combine pre-defined country and industry portfolio returns so that different industry effects in country indices (and vice versa) can be modelled. De Moor and Sercu find that a Fama-MacBeth variant of HR, which allows non-unit sensitivities produces almost the same factors as H-R.

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Var (ei ) = σ 2 ,

and Cov(ei , eg ) = E (ei , eg ) = 0 , ∀i ≠ g .

Ci ,k and Di , j

are country and industry

dummies such that:

1 if security i belongs to country k 1 if security i belongs to industry j Ci ,k =  , Di , j =   0 otherwise  0 otherwise

The perfect collinearity problem in (1) is solved by measuring country and industry effects in relation to the average firm (eg. Bai and Green, 2012). Given vk and wj as the market share of country k and industry j (correspondingly) in the total market value of the sample, we impose the constraints: K

∑v γ k =1

k k

= 0 and

J

∑w λ j =1

j

j

= 0 , where

∑w = ∑v j

j

k

=1

…(2)

k

Applying (2) to (1) implies: J −1

K −1

j =1

k =1

ri = α + ∑ λ jdi , j + ∑ γ k ci ,k + ei

…(3)

where d i , j = ( Di , j − (w j wJ ) Di , J ) and ci ,k = (Ci ,k − (vk vK )Ci ,K ) . OLS estimation of (3) is equivalent to Weighted Least Squares on (1) with weights (pi,k, pi,j) equal to the share of firm i in the total market value of country k (industry j), and:

∑p i∈k

i,k

=1,

∑p i∈ j

i, j

= 1.

Hence Bai and Green (2012) decompose the value-weighted industry index return in any time period by multiplying (3) by pi,j and summing over all firms in industry j: K −1

rj = αˆ + ∑ p j ,k γˆk ci ,k + λˆ j

…(4)

k =1

Now pj,k is the total market value of industry j included in country k, and

∑

J −1 j =1

pi , j e j = 0 . Each value-

weighted country index return rk is correspondingly decomposed: J −1

rj = αˆ + ∑ pk , jγˆ j di , j + λˆk

…(5)

j =1

and pk,j is the total market value of country k included in industry j.

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Equations (4) and (5) are estimated on each cross-section of returns to yield industry and country ( αˆ + λˆ j ) is the time series of “pure industry” effects of a

effects for every time period.

geographically-diversified portfolio of firms in the jth industry, which has the same geographical make-up as the sample average firm. The standard deviations of the pure industry effects time series measures the absolute importance of these effects in determining the variation of industry portfolio returns (Heston and Rouwenhorst, 1994). The same applies to country effects. Betas can be decomposed in the same way, beginning with (3). Given a time-series of betas for each firm4, any cross-section of betas at a point in time can be written: J −1

K −1

j =1

k =1

βi = δ + ∑φ j di , j + ∑ψ k ci ,k + ε i

…(6)

As Bai and Green (2010) show for total risks, the decomposition of the betas can be analysed in the same way as the returns, to compute the “pure” industry and “pure” country components of the betas and their standard deviations.

In principle, the analysis might be extended to consider the

decomposition of factors other than market risk and returns, such as the three- and five-factor models of Fama and French (1993, 2017), or the two-beta model of Malamud and Vilkov (2018). However, since our main purpose here is to develop the methodology relating country-industry decompositions to asset-pricing theory, we concentrate on the basic two-factor CAPM. 3.2 A Test of the Systematic Risk-Return Relationship within the Country-Industry Decompositions Framework Returns and betas in each cross-section are decomposed in (3) and (6) respectively. Defining RM as the world market return and rF as the world risk-free rate, subtracting rF from both sides of (3) and taking expectations yields: J −1

K −1

j =1

k =1

E(ri − rF ) = ( RM − rF ) + ∑ λ j di , j + ∑ γ k ci ,k

…(7)

Likewise, taking expectations in (6) yields:

4

We explain how the time series of returns and betas are constructed for each firm in section 4 below.

10

J −1

K −1

j =1

k =1

Eβi = βM + ∑ϕ j di , j + ∑ψ k ci ,k

…(8)

Evidently these decompositions satisfy the CAPM only if E (ri − rF ) = ( RM − rF ) E β i

…(9)

Or, substituting (7) and (8) into (9), if: J −1 K −1 J −1 K −1   ( RM − rF ) + ∑ λ j di , j + ∑ γ k ci ,k = ( RM − rF )  β M + ∑ ϕ j di , j + ∑ψ k ci ,k  j =1 k =1 j =1 k =1  

or if:

J −1

K −1



J −1

K −1



j =1

k =1



j =1

k =1



∑ λ j di, j + ∑ γ k ci ,k = (RM − rF )  β M − 1 + ∑ϕ j di , j + ∑ψ k ci,k 

…(10)

…(11)

Since βM is the world market beta, βM ≈ 1. Therefore, comparing coefficents:

λ j = ( RM − rF )ϕ j ; So: λg / ϕg = λ j / ϕ j ; and:

∀j

and

γ k = ( RM − rF )ψ k ;

∀k

∀g, j industries and γ h /ψ h = γ k / ψ k ;

λg / ϕg = λ j / ϕ j = γ h / ψ h = γ k / ψ k ;

∀g, j, h, k

…(12) ∀h, k countries; …(13)

…(14)

Equation (13) provides (J – 1) industry + (K – 1) country testable restrictions in every cross-section, or a total of N(J + K – 2) in the whole time series. Equation (14) shows that, if (13) is accepted, one further independent restriction can be tested in each cross-section as between the country and industry effects, ie. N further restrictions in all. The idea therefore is to estimate equations (3) and (6) simultaneously by seemingly unrelated regression (SUR), and then perform Wald tests to check (13) and (14). We do this separately for the industry and country factors following (13); then test industry and country factors jointly following (14).

4. Emerging Market Data and Beta Calculation 4.1 Emerging market data The reviewed literature in section 2 shows that the different progress of financial integration in ESMs has contributed to the inconclusive empirical evidence in finding the appropriate asset pricing model in these markets. It is well documented in finance literature (e.g. Bekaert and Harvey, 1997; Jung et

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al., 2009) that ESM equities differ in their characteristics from those in industrial capital markets, exhibiting higher average returns, lower liquidity, higher commonality in liquidity, higher volatility, greater predictability, higher investor heterogeneity and lower correlation with developed market returns. To some extent, such correlation has been increasing with the progress of financial liberalization in ESMs over the last few decades (de Roon and de Jong, 2005). However, ESMs are still not as effectively integrated with other capital markets (Arouri et al., 2012). This may be due to specific structural and institutional country factors, such as investment barriers; asymmetric information; controlled exchange rates; absence of a high-quality regulatory system and underdeveloped capital markets. Furthermore, since ESM firms are mostly smaller on average than those in the developed markets, it may be argued that the dominant country effects in stock returns could actually be due to the predominance of small firms rather than any intrinsic differences between ESMs and the industrial countries. However, De Moor and Sercu (2010) suggest that the role of ESMs cannot be reduced to just a small-firm phenomenon. Therefore, relative to small open economy or well-integrated developed capital market, ESMs are particularly suitable for studying if country and/or industry factors are additional independent factors helping to determine stock returns. For the analysis in this paper, we choose the same dataset employed in Bai and Green (2010) and Bai et al. (2012) for the following reasons. We intend to make two contributions in this analysis: first we extend Bai and Green (2010) by examining the relationship between returns and systematic risks (betas) within the H-R framework; second, we use these results to help examine whether ESM returns are consistent with the CAPM, especially if country and industry factors are additional independent factors helping to determine stock returns by linking the country-industry decomposition with the local CAPM, and the GCAPM in a new and more direct way. For the first contribution, we want to be able to make a direct comparison to the total risk study in Bai and Green (2010), and this suggests using the same dataset as before. For the second contribution, literature has mixed findings in terms of the appropriate asset pricing model as well as the progress of the time-varying integration in ESMs. Schotman and Zalewska (2006) find that the comovement changes over time and rises significantly during the Asian and Russian Crisis. In contrast, Boubakri et al. (2016) suggest that ESMs exposed to 12

national and/or regional financial crises experience short-term reversals in their financial integration. However, they also show that the local market risk premium component has not diminished as much as expected when the financial integration progress. They argue that the 2017/18 crisis has induced a re-evaluation of the world market risk premium for all ESM. By employing the monthly returns of 1535 individual firms of 13 emerging stock markets and 11 industries from April 1988 to July 2004,5 we are able to augment these mixed findings more directly by focusing exclusively on ESMs which were at the relatively early stage of financial liberalization, integrated slowly and experienced a crisis which affected primarily ESMs. Furthermore, as this is first and foremost a methodological paper, the dataset we adopt enables illustration of the method and facilitates comparison with earlier work. In using the same dataset as Bai and Green (2012), we are able to keep this paper more focused and concise, especially by not reporting the return decomposition results, and emphasising the unity of the different approaches. In the following results section, we report only the beta decomposition results before we test the systematic risk-return relationship in the H-R framework. For this type of study, it is also important to use data at the monthly frequency. Kothari and Shanken (1998) argue that the use of annual returns to estimate betas helps to circumvent measurement problems caused by non-synchronous trading, seasonality in returns, and trading frictions. Time mismatch issue arises when stock returns employed in regressions are recorded in different exchanges at different times (Schotman and Zalewska, 2006). The alignment of non-synchronous data is a common problem in studies of market integration and contagion. Schotman and Zalewska (2006) show that at both daily and weekly frequencies, the time matching issue is significant. A further major problem with daily data is that thin trading is particularly endemic in ESMs, and individual daily stock returns include far too many non-trading days to be usable in H-R cross-sectional regressions. Using a weekly frequency loses information as compared with daily data and does not entirely overcome the

5

The available data for each country and industry are given in appendix table A1; appendix table A2 summarises the industry and country composition of the sample at December 2003. A common feature of emerging markets is thin (or infrequent) trading. Thin trading is likely to produce statistical biases in the time series of stock prices because prices documented at the end of one period could be (wrongly) used as the outcome of a transaction in an earlier period. Out-ofdate information may also induce serial correlation (Al-Khazali et al., 2007). We found some evidence of thin trading in the early part of the original Bai et. al. (2012) dataset. Therefore, to avoid the problem of thin trading, we deleted the early part of the data and the sample period in this study is from April 1988.

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time-mismatch or thin trading issues. Gençay et al. (2003) find that the relationship between the portfolio return and its beta is stronger at the medium-term than at short or long time horizons. Fernandez (2006) draws similar conclusions when he estimates the CAPM at different time-scales by applying wavelet analysis to a sample of individual Chilean stocks. He finds that the fraction of systematic risk contained in an individual stock at middle and lower frequencies is related more closely to lower frequencies of the market portfolio. Given the relatively recent provenance of ESMs, their annual returns have an unduly short time-span. To balance these issues and in light of previous empirical findings, we continue to use monthly data for our analysis. The data are summarised in appendix tables A1 and A2. 4.2 Beta calculation We calculate a time series of monthly betas for each security using a standard state-space (Kalman Filter) model. This approach has been used by, among others, Adrian and Franzoni (2009) and Choudhry and Wu (2009). Here, we largely follow the approach of Choudhry and Wu (2009). We use the market model as the measurement equation for each security (i):

ri ,t = αi ,t + βi ,t RM ,t + ε i ,t

…(15)

Thus, ri,t is the return on security i at time t, RM,t is the market return and βi,t is the time-varying beta of the security. For the parameter updating equation we use the random walk:

βi ,t = βi ,t −1 + ηi ,t ;

αi ,t = αi ,t −1 + ξi ,t

…(16)

This has the advantage of allowing for structural shifts in beta over the time series and, according to Faff, et al. (2000), it provides the best characterization of a time-varying beta. Estimating (15) and (16) simultaneously yields a time series of betas and abnormal returns (alphas). In estimating the subsequent country-industry decompositions of the betas, the first two observations are excluded from the sample as they are used to establish the initial conditions. Using the Kalman filter, we estimate the betas twice under different assumptions about the form of the CAPM. First, we use the local CAPM, assuming a degree of country myopia and employing the local 14

market return for each country’s stocks6, so the Brazilian market index is used for Brazilian stocks, the Chilean index for Chilean stocks and so on. Second, we use the GCAPM, employing the MSCI world index as the single world market index for all stocks in all countries. We then compare the results from these two approaches. Stulz (1995) shows that the cross-sectional relationship between the local CAPM and GCAPM can be conveniently summarized in:

E(ri − rF ) = ( RM ,W − rF )βi , H βH ,W

…(17)

Here, rF is the world risk-free rate; RM,H is the home market index return; RM,W is the world market return; βi,H is the beta coefficient calculated using RM,H; βH,W is the beta coefficient of the home market as a whole. In the market model, βH,W is the outcome of the regression of the excess return in the home market on the excess return in the world market. Evidently, the local CAPM and GCAPM give the same beta if the local market return always equals the world market return (βH,W =1). However, they also give the same beta under somewhat milder conditions, i.e. when the risk that is uncorrelated with the home market portfolio is also uncorrelated with the world market portfolio (Stulz, 1995). These two versions of the CAPM ignore exchange rate risk. In our analysis, we convert all returns into US dollar terms, calculate the betas from these returns and perform the decompositions on these data. Explicitly including exchange-rate factors to estimate the ICAPM would increase the dimensionality of the asset pricing tests to an unmanageable size, especially as Koedijk, et al. (2002) and Bruner et al. (2008) found that the CAPM and ICAPM gave comparable results for the cost of capital. Since our main objective in this paper is to explore the relationship between the CAPM and industry-country decompositions, it seems reasonable to focus on a comparison between the more tractable local CAPM and GCAPM.

6

The market index used for each country is listed in Appendix Table A1.

15

5. Empirical Results In this section, to avoid repetition, we do not report in detail the results of the decompositions for the returns. See Bai et al (2012) for these. We do report the decompositions and F test results for the betas. We then move on to examine the consistency of these decompositions with the CAPM. 5.1 F-test results of the dummy variable model We perform F-tests separately on the industry and country dummies in eq. (6) for the monthly beta series. The F-statistics provide a precise picture of the significance of the country and industry effects and their time varying properties, on top of their relative importance identified by the decomposition procedure. Following Bai et al. (2012), we split the sample into two sub-periods at June 1996 based on an inspection of the F-test results using the local market indices. This is consistent with the return decompositions results. Hence the results based on the MSCI world market index are also split at June 1996. June 1996 is very close to one of the splitting dates in the conditional variance decompositions of Bai and Green (2010) (April 1996), and both dates are close to but pre-date the East Asian financial crisis. Although this sample includes non-Asian markets, markets affected directly by the crisis account for 64.15% of all sampled firms and 57.02% of market capitalization by December 2003 (Table A2). Hence it would not be unreasonable to expect the crisis to be associated with changes in the underlying determinants of stock risks in most of the sample ESMs. _____________________________________________________________________________ Table 2 and Figure 1 about here _____________________________________________________________________________ According to Table 2 and Figure 1, country effects are highly important in determining systematic risks throughout the whole of the sample period, apart from some months at the beginning. This is true irrespective of whether the calculations are based on local market indices (BL) or on betas based on the MSCI world market index (BW). Country dummies are significant in more than 75% of the months for BL and in 95% of the months for BW. The stronger country effects for BW in the early months of the sample could be attributable to the greater degree of international market segmentation 16

in this period. It lends support to the argument made in Koedijk, et al. (2002) that GCAPM is not as adequate as CAPM at the early stages of ESM development. On the contrary, there are relatively more months with significant ESM industry effects based on BL than those on BW. There is some degree of integration among ESM which is better accounted for by GCAPM. This becomes even more obvious in the second sub-sample from 1996, when the importance of industry effects increases dramatically especially for BL. Throughout the second sub-period, country and ESM industry dummies are both highly significant. In addition, the pattern of the beta results especially that of BW, is generally in line with those of the return decompositions. 5.2 Decomposition results of the dummy variable model for beta The decomposition results of BL (Table 3) and BW (Table 4) in the whole sample show that most of the cross-sectional variation in the country and industry betas is explained by country-specific effects. Each pure country effect explains most of the variation in excess systematic risk for any country, in comparison with the sum of the ESM industry effects. The variation in excess systematic risk explained by the pure ESM industry effects is less than that explained by the sum of the country effects. Country effects also tend to dominate ESM industry effects in explaining the variation in the betas during the first sub-period (Tables 5 and 6). However, in line with the pattern of F-test results, ESM industry effects increase in importance in the second sub-period in the run-up to, during and after the East Asian crisis. The pure ESM industry effects explain the cross-sectional variation in the industry betas more than do the sum of the country effects. _____________________________________________________________________________ Tables 3, 4, 5 and 6 about here _____________________________________________________________________________ In Table 7, we compare the cross-country and industry average of the return, BL and BW decompositions. Their differences are consistent with those observed in the F-test. The predominant country effects in the first sub-period are even stronger in BW relative to BL. At the same time, the increasing importance of ESM industry effects in the second sub-period is also most obvious in BW. 17

The proportion of variation in BW explained by the pure ESM industry effect increased dramatically from 19% to 97%. The results lend support to the existing literature (e.g. Bruner et al., 2008; Koedijk et al., 2002) that the local CAPM and GCAPM differ in their estimates of the cost of capital. Under partial financial integration due to the significant impact of country-specific and/or ESM-specific factors in individual stock returns, there is a much closer correspondence between the local CAPM and the multi-factor international asset pricing model (ICAPM). _____________________________________________________________________________ Table 7 about here _____________________________________________________________________________ 5.3 Implications of the CAPM Table 8 summarizes the Wald test results of the CAPM restrictions (13) and (14). In the first subperiod, from April 1988 to May 1996, all Wald tests on the industry effect coefficients in SUR are insignificant, which is consistent for both BL and BW. This suggests that decompositions of return and beta across industry are consistent with the (G)CAPM. On the other hand, in around 64% (BL) or 75% (BW) of the months, Wald tests on the country effect coefficients in SUR are significant (at or below the 10% level), which means that decompositions of return and beta across country do not generally satisfy the (G)CAPM. This is consistent with the first and fifth expected partial integration scenarios in Table 1. It suggests that country factors are additional independent sources of crosssectional variation in stock returns in particular under the GCAPM model. The last column of the table presents the results for restrictions (14) and they show that the joint Wald tests on decompositions across both industry and country are insignificant in around 49% (BL) or 51% (BW) of the sample months especially before August 1990. The pattern changes in the second sub-period. From June 1996 to July 2004, the industry decompositions satisfy the (G)CAPM in around 83% of the months for both BL and BW.

However, overwhelmingly throughout these 98 months, neither the country

decompositions nor the overall decompositions satisfy the (G)CAPM. The results are consistent with the third and the sixth expected partial integration scenarios in Table 1, which means that partial integration with both country specific risks and ESM systematic risk priced to different extents in 18

stock returns during the second sub-period. Some ESM industry factors are captured in GCAPM but not at all by local CAPM. This is in line with the literature, F-test and decomposition results that whether it is due to short-term financial crisis or long-term financial liberalization, ESM industry factors have become priced risks along side country specific factors. A single factor model whether local CAPM or GCAPM is not sufficient to model stock returns in ESM during this time period. The month-by-month details of these Wald tests are shown in Figure 2, where the contrasts between the first and second sub-period and between the industry and country effects can be clearly seen. _____________________________________________________________________________ Table 8 and Figure 2 about here _____________________________________________________________________________ To examine these results further, we consider the relationship between the P-values of the CAPM Wald test and those of the decomposition F-test. We would expect that the power of our proposed test of the relationship between the CAPM and the country-industry effects will depend in part on the efficiency of the estimates of these effects. If estimates of the country-industry decompositions produce mostly insignificant coefficients, these are more likely to accept any postulated restrictions including those implied by the CAPM. Conversely, if the country-industry dummies are all significant in any particular month and the CAPM restrictions are also accepted in that month, this would constitute particularly strong evidence for the CAPM. Casual inspection of tables 2 and 8 might suggest that the CAPM is accepted primarily in months in which the test has lowest power, i.e. when the industry or country effects are not significant. _____________________________________________________________________________ Figure 3 about here _____________________________________________________________________________ To check this argument in more detail, we present scatter plots between the P-values of the CAPM Wald test and P-values of the decomposition F-test for BL and BW, broken down by sub-sample and as between industry and country effects (Figure 3). In general, if the CAPM test is weak, we would expect there to be a strong positive relationship between the P-values of the Wald test and those of the 19

F-test. As expected, Figure 3 shows a clear positive relationship in particular for the BL country dummies in the first sub-sample and full sample (panel A).

On the other hand, there is less clear

evidence of strong positive relationship between the P-values for industry dummies.7 Based on these results, we conclude that we do need to take into account the efficiency of the estimates of the underlying country-industry effects. However, the instances in which the CAPM is accepted cannot be attributed just to weak industry effects (both sub-periods) or to weak country effects (early part of the first sub-period). Likewise, rejections of the CAPM are not due just to strong country effects (the second sub-period) or to joint country-industry effects (the second sub-period). 6. Conclusions Several papers in the financial integration literature including Koedijk, et al. (2002) find that the local CAPM and Global CAPM (GCAPM) provides different estimates of the cost of capital while they find more similar estimates given by the local CAPM and the multi-factor international asset pricing model (ICAPM). Literature suggests that this is due to a lack of complete capital market integration, due for example to cyclical, structural, and institutional country-specific factors in individual stock returns, which is in line with the return decomposition literature (Bai and Green, 2012; Griffin and Karolyi, 1998; Heston and Rouwenhorst, 1994). The inconclusive empirical findings on the relationship between stock returns and conditional variance/covariance has led numerous studies to look for missing variables or alternative model specifications under the partial integration situation in different markets during different time periods. In this paper we have made two contributions to the financial integration literature: first, we extend Bai and Green (2010) by examining the relationship between returns and systematic risks (betas) within the Heston and Rouwenhorst (1994) decomposition framework; and second, using a new and more direct method, we examine the relationship between the estimated country-industry decompositions and the Capital Asset Pricing Model (CAPM) in order to test whether industry and country factors are derived from the risk-return characteristics of stocks or

7

We do not report any regressions because of the large number of observations of zero or unity.

20

are additional independent factors helping to determine stock returns and any time varying pattern in the country-industry factors. By applying the monthly individual firm returns of 13 emerging stock markets (ESM) from April 1988 to July 2004 in Bai et al. (2012) to our method, the empirical results show the following. First, consistent with the pattern of the total risk decomposition results in Bai and Green (2010), country effects also have dominant impacts on beta; and the relative importance of industry effects increases a year before the Asian financial crisis. The stronger country effects based on the MSCI world index in the early months of the sample could be attributable to the greater degree of international market segmentation in this period. Then in the second sub-sample from 1996, there is some degree of integration among ESMs which is better accounted for by GCAPM. The results lend support to the existing literature (e.g. Bruner et al., 2008; Koedijk et al., 2002). Furthermore, it is noticeable that the increase of relative importance of industry effects is much stronger for beta than for returns. If industry effects mainly contribute to beta, then diversifying across industries would not bring meaningful gains in the reduction of systematic risk. Second, the formal test of the relationship between G(CAPM) and risk-return decomposition suggests that country factors are additional independent sources of cross-sectional variation in stock returns before 1996 in particular under GCAPM model. While post 1996, the results suggest partial integration whether due to long-term market liberalization or the (short-term) Asian financial crisis: ESM industry factors are independent determinants of stock return variations jointly with country specific factors. Some ESM industry factors are captured in GCAPM but not at all by local CAPM. Therefore a single factor model whether local CAPM or GCAPM is not sufficient to model stock returns in ESMs during this time period. Moreover, these results do not appear to be an artefact of the test having low power because of weak country or industry effects. Overall, we believe that our results validate the testing procedure which directly relates the decomposition method with the corresponding CAPM test. The results not only make an important contribution to the so-far inconclusive empirical evidence on the appropriate model for estimating the cost of capital and implications for investment decision in 21

ESMs, but also have important implications for our understanding of the evolving financial integration progress in ESMs pre and post the Asian financial crisis. It means that for international portfolio investors, it is important to diversify across industries as well as across countries. For ESM firms, it is important to take into account ESM systematic risks as well as country-specific risks when estimating cost of capital. The current results are particularly applicable to markets at a relatively early stage of financial liberalization and with limited financial integration. Future research could be extended to examine the industrial countries and frontier markets. Also, if there is a sufficiently long tranquil period in ESMs, it could also be interesting to see how this affects the results. Last but not least, future research could also consider methods of estimating time-varying betas other than the random walk model, such as the random parameter model.

22

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28

Table 1 Eight scenarios of the roles played by country and industry factors in G(CAPM) Scenario CAPM 1 2 3 4

GCAPM

Country factors

× × × ×

ESM Industry factors

× ×

5 6 7 8

× × × ×

× ×

× ×

× ×

Table 2 A comparison of F-test results between return and beta dummy variable models Industry dummies Sample period

Sig. at 5% (%)

Sig. at 10% (%)

Insig. (%)

Country dummies Total (%)

Sig. at 5% (%)

Sig. at 10% (%)

Insig. (%)

Total (%)

Beta

04.88-05.96

24.49

21.43

54.08

100

75.52

2.04

22.44

100

(local index)

06.96-07.04

96.94

3.06

0

100

100

0

0

100

Beta

04.88-05.96

20.41

5.1

74.49

100

94.9

2.04

3.06

100

(MSCI)

06.96-07.04

85.71

0

14.29

100

97.96

0

2.04

100

Returns

03.86-03.97

28.6

6.8

64.7

100

99.3

0

0.8

100

04.97-07.04 73.9 12.5 13.6 100 100 0 0 100 Notes: 1. Table 2 shows the percentage of months with significant and insignificant country and industry dummies during each sub-sample period. 2. The F-tests have been done separately for industry and country dummies in each month. 3.. The pattern of the beta results especially those based on MSCI world market index, is generally in line with those of return decompositions.

1

Table 3 Decomposition of excess portfolio beta (based on local index) into country and industry effects The table gives the standard deviation (SD) of the components of the value-weighted excess country and industry index betas. Each country index excess beta is decomposed into a pure country effect and a sum of 11 industry effects. Each industry index beta is decomposed into the sum of 13 country effects and a pure industry effect. The ratio relative to the market gives the ratio of the SD of that component to the SD of the sum of that pure effect and the sum of the value weighted industry (country) effects.

Panel A Country Brazil Chile China India Israel Malaysia Mexico Pakistan South Africa South Korea Taiwan Thailand Turkey Cross-country average

VW country indices Pure country Sum of 11 effect industry effects St. Ratio St. Ratio Dev. Dev. 0.349 1.202 0.079 0.273 0.266 1.159 0.064 0.277 0.238 0.679 0.046 0.132 0.196 1.065 0.024 0.131 0.377 0.991 0.055 0.144 0.281 0.945 0.042 0.141 0.288 0.916 0.054 0.172 0.179 1.099 0.052 0.317 0.280 0.989 0.036 0.126 0.214 1.000 0.052 0.246 0.207 0.929 0.060 0.268 0.193 0.908 0.084 0.395 0.459 1.014 0.047 0.104 0.271 0.992 0.053 0.210

Panel B Industry IGS CG CS BM UT HC TEC TEL FI PHG BI Cross-industry average

VW industry indices Sum of 13 country Pure industry effects effects St. Dev. Ratio St. Ratio Dev. 0.121 0.876 0.068 0.492 0.162 0.897 0.066 0.364 0.194 0.830 0.083 0.357 0.166 0.881 0.093 0.495 0.204 0.874 0.187 0.802 0.201 0.798 0.102 0.403 0.175 0.669 0.163 0.622 0.211 0.978 0.121 0.560 0.209 0.985 0.175 0.826 0.184 0.787 0.111 0.475 0.159 0.600 0.216 0.815 0.181 0.834 0.126 0.565

Note: 1. i. The pure country effect measures the average beat of firms in a country relative to firms that are in the same industry but located in a different country. ii. The sum of the eleven industry effects represents the component of a country's beta that can be attributed to the difference between the industrial composition of its market and the industrial composition of the emerging markets. iii. The pure industry effect measures the average beta of firms in an industry relative to firms which are located in the same country but belong to a different industry. iv. The sum of the thirteen country effects represents the component of an industry's beta that can be attributed to the difference between the geographical composition of its index and the geographical composition of the emerging markets as a whole. 2. The decomposition results of BL in the whole sample show that most of the cross-sectional variation in the country and industry betas is explained by country-specific effects. Each pure country effect explains most of the variation in excess systematic risk for any country, in comparison with the sum of the industry effects. The variation in excess systematic risk explained by the pure industry effects is less than that explained by the sum of the country effects.

2

Table 4 Decomposition of excess portfolio beta (based on MSCI) into country and industry effects The table gives the standard deviation (SD) of the components of the value-weighted excess country and industry index betas. Each country index excess beta is decomposed into a pure country effect and a sum of 11 industry effects. Each industry index beta is decomposed into the sum of 13 country effects and a pure industry effect. The ratio relative to the market gives the ratio of the SD of that component to the SD of the sum of that pure effect and the sum of the value weighted industry (country) effects.

Panel A Country Brazil Chile China India Israel Malaysia Mexico Pakistan South Africa South Korea Taiwan Thailand Turkey Cross-country average

VW country indices Pure country Sum of 11 effect industry effects St. Ratio St. Ratio Dev. Dev. 1.664 1.085 0.150 0.098 1.912 1.057 0.168 0.093 1.326 1.094 0.080 0.066 1.979 1.060 0.073 0.039 1.403 1.151 0.127 0.104 1.883 0.986 0.145 0.076 2.064 0.982 0.147 0.070 0.550 1.219 0.128 0.284 2.070 0.986 0.120 0.057 1.902 0.991 0.114 0.059 1.923 0.999 0.187 0.097 1.936 0.987 0.152 0.078 3.922 0.995 0.117 0.030 1.887 1.046 0.132 0.089

Panel B Industry IGS CG CS BM UT HC TEC TEL FI PHG BI Cross-industry average

VW industry indices Sum of 13 country Pure industry effects effects St. Dev. Ratio St. Ratio Dev. 1.089 0.986 0.150 0.136 1.808 0.976 0.266 0.144 1.731 0.953 0.294 0.162 1.830 0.977 0.232 0.124 1.888 0.993 0.439 0.231 1.959 0.998 0.301 0.153 1.846 0.906 0.925 0.454 1.829 0.931 0.575 0.293 1.850 0.941 0.690 0.351 1.882 0.959 0.532 0.271 1.795 0.961 0.383 0.205 1.773 0.962 0.435 0.229

Note: 1. i. The pure country effect measures the average beat of firms in a country relative to firms that are in the same industry but located in a different country. ii. The sum of the eleven industry effects represents the component of a country's beta that can be attributed to the difference between the industrial composition of its market and the industrial composition of the emerging markets. iii. The pure industry effect measures the average beta of firms in an industry relative to firms which are located in the same country but belong to a different industry. iv. The sum of the thirteen country effects represents the component of an industry's beta that can be attributed to the difference between the geographical composition of its index and the geographical composition of the emerging markets as a whole. 2. The decomposition results of BW in the whole sample are in line with the decomposition results of BL.

3

Table 5 Decomposition of excess value-weighted beta (based on local index) into country and industry effects in two sub-sample periods Panel A: 04.88---05.96 Pure country Sum of 11 effect industry effects Country Brazil Chile China India Israel Malaysia Mexico Pakistan South Africa South Korea Taiwan Thailand Turkey Crosscountry average Industry

IGS CG CS BM UT HC TEC TEL FI PHG BI Crosscountry average

Panel B: 06.96---07.04 Sum of 11 Pure country effect industry effects

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

0.501 0.390 0.300 0.252 0.311 0.342 0.315 0.182

1.306 1.242 1.196 1.141 0.960 0.940 0.951 1.291

0.089 0.080 0.017 0.020 0.028 0.046 0.051 0.056

0.232 0.254 0.068 0.092 0.086 0.128 0.154 0.395

0.163 0.069 0.137 0.116 0.396 0.190 0.219 0.126

1.392 1.226 1.081 1.044 0.937 0.923 0.969 0.924

0.067 0.029 0.039 0.023 0.042 0.032 0.020 0.047

0.573 0.513 0.306 0.206 0.099 0.156 0.090 0.342

0.288

0.937

0.039

0.125

0.095

0.939

0.026

0.261

0.259

1.041

0.069

0.276

0.083

0.935

0.024

0.277

0.272 0.257 0.626

0.991 0.946 1.001

0.043 0.098 0.039

0.158 0.362 0.062

0.110 0.089 0.134

0.727 1.048 1.106

0.062 0.015 0.032

0.411 0.179 0.261

0.330

1.073

0.052

0.184

0.148

1.020

0.035

0.283

Sum of 13 country effects St. Dev.

Ratio

0.166 0.227 0.249 0.232 0.286 0.271 0.233 0.295 0.236 0.233 0.222

0.877 0.944 0.868 0.920 0.905 0.864 0.862 1.006 0.818 0.845 0.680

Pure industry effect St. Ratio Dev. 0.090 0.474 0.062 0.257 0.091 0.318 0.095 0.378 0.243 0.768 0.109 0.346 0.141 0.520 0.154 0.525 0.158 0.545 0.099 0.358 0.243 0.743

0.241

0.872

0.135

0.476

Sum of 13 country effects St. Dev.

Ratio

0.022 0.038 0.107 0.038 0.042 0.088 0.061 0.044 0.083 0.106 0.040

0.551 0.473 0.834 0.557 0.452 0.574 0.387 0.550 1.018 0.575 0.710

Pure industry effect St. Ratio Dev. 0.034 0.826 0.049 0.620 0.044 0.338 0.060 0.885 0.106 1.143 0.075 0.489 0.115 0.735 0.056 0.693 0.088 1.082 0.112 0.609 0.042 0.731

0.061

0.607

0.071

0.741

Note: 1. The setup of the table follows the same specifications as in Table 3. 2. Country effects tend to dominate industry effects in explaining the variation in the betas during the first subperiod. However, in line with the pattern of F-test results, industry effects increase in importance in the second sub-period in the run-up to, during and after the East Asian crisis. The pure industry effects explain the crosssectional variation in the industry betas more than do the sum of the country effects.

4

Table 6 Decomposition of excess value-weighted beta (based on MSCI) into country and industry effects in two sub-sample periods Panel A: 04.88---05.96 Pure country Sum of 11 effect industry effects Country Brazil Chile China India Israel Malaysia Mexico Pakistan South Africa South Korea Taiwan Thailand Turkey Crosscountry average Industry

IGS CG CS BM UT HC TEC TEL FI PHG BI Crosscountry average

Panel B: 06.96---07.04 Sum of 11 Pure country effect industry effects

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

2.571 2.859 2.295 3.007 0.486 2.622 2.899 0.671

1.199 1.116 1.375 1.145 0.821 0.989 0.988 1.648

0.150 0.222 0.055 0.087 0.130 0.160 0.147 0.110

0.070 0.087 0.033 0.033 0.219 0.060 0.050 0.270

0.370 0.113 0.416 0.317 1.623 0.475 0.298 0.455

1.058 1.039 1.037 0.993 1.001 0.963 0.954 1.174

0.148 0.042 0.075 0.055 0.074 0.112 0.125 0.143

0.424 0.388 0.186 0.173 0.046 0.226 0.400 0.369

2.902

0.989

0.145

0.050

0.254

0.879

0.078

0.269

2.639

0.994

0.147

0.055

0.451

0.910

0.062

0.125

2.682 2.723 5.302

0.992 0.988 0.996

0.107 0.205 0.104

0.040 0.074 0.019

0.301 0.286 1.590

1.300 1.045 0.978

0.220 0.040 0.096

0.950 0.147 0.059

2.589

1.095

0.136

0.082

0.535

1.026

0.098

0.289

Sum of 13 country effects St. Dev.

Ratio

1.543 2.561 2.439 2.586 2.674 2.744 2.611 2.589 2.610 2.646 2.540

0.988 0.985 0.957 0.982 0.995 0.995 0.965 0.935 0.943 0.966 0.966

Pure industry effect St. Ratio Dev. 0.197 0.126 0.239 0.092 0.378 0.148 0.261 0.099 0.585 0.218 0.273 0.099 0.715 0.264 0.776 0.281 0.853 0.308 0.707 0.258 0.514 0.195

2.504

0.971

0.500

0.190

Sum of 13 country effects St. Dev.

Ratio

0.054 0.062 0.121 0.109 0.109 0.425 0.116 0.139 0.252 0.215 0.067

0.539 0.210 0.600 0.413 0.785 1.431 0.120 0.450 0.945 1.023 0.512

Pure industry effect St. Ratio Dev. 0.074 0.737 0.256 0.867 0.163 0.805 0.190 0.724 0.193 1.390 0.269 0.908 1.011 1.042 0.247 0.797 0.435 1.628 0.200 0.954 0.101 0.773

0.152

0.639

0.285

0.966

Note: 1. The setup of the table follows the same specifications as in Table 4. 2. The sub-sample results based on MSCI are consistent with the results based on local index.

5

Table 7 A comparison of cross country/industry average between return and beta decomposition results Cross country/industry averages Panel A: full sample Pure country effect

Panel B: the first sub-sample

Sum of 11 industry effects

Pure country effect

Sum of 11 industry effects

Panel C: the second sub-sample Pure country effect

Sum of 11 industry effects

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

Beta (Local)

0.271

0.992

0.053

0.210

0.330

1.073

0.052

0.184

0.148

1.020

0.035

0.283

Beta (MSCI)

1.887

1.046

0.132

0.089

2.589

1.095

0.136

0.082

0.535

1.026

0.098

0.289

Return

0.201

0.904

0.034

0.141

0.245

0.933

0.041

0.115

0.088

0.997

0.013

0.184

Sum of 13 country effects

Pure industry effect

Sum of 13 country effects

Pure industry effect

Sum of 13 country effects

Pure industry effect

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

St. Dev.

Ratio

Beta (Local)

0.181

0.834

0.126

0.565

0.241

0.872

0.135

0.476

0.061

0.607

0.071

0.741

Beta (MSCI)

1.773

0.962

0.435

0.229

2.504

0.971

0.500

0.190

0.152

0.639

0.285

0.966

0.281 0.823 0.092 0.334 0.376 0.825 0.118 0.331 0.033 0.777 0.028 0.688 Return Notes: 1. Table 7 presents the cross country/industry average decomposition results over the full-sample period and two sub-periods. All return and beta dummy results are compared. The predominant country effects in the first sub-period are even stronger in BW relative to BL and the returns. At the same time, the increasing importance of industry effects in the second sub-period is also most obvious in BW.

6

Table 8 Wald test results of SUR coefficients

Period

No. of months

H0: ρ g / φ g = ρ j / φ j ; ∀g, j Panel: A Wald test on industry effect coefficients sig at sig at Insig. total 5% 10%

H0: γ h /ψ h = γ k /ψ k ; ∀h, k Panel B: Wald test on country effect coefficients sig at sig at Insig. total 5% 10%

H0: ρ g / φ g = ρ j / φ j = λh /ψ h = λk /ψ k ; ∀g, j, h, k Panel C: Wald test on industry and country effect coefficients sig at 5%

sig at 10%

Insig.

total

Panel A: beta based on local market index 04.8805.96 06.9607.04 04.8805.96 06.9607.04

98

0

0

100

100

58.16

6.12

35.71

100

42.86

8.16

48.98

100

98

9.18

8.16

82.65

100

100

0

0

100

100

0

0

100

42.86

6.12

51.02

100

92.86

1.02

6.12

100

98

0

0

100

100

98

6.12

10.2

83.68

100

Panel B: beta based on MSCI 67.35 8.16 24.49 100 94.9

0

5.1

100

Notes: 1. Table 8 presents the percentages of the months with significant and insignificant Wald test on the industry and/or country effect coefficients in SUR over two sub-periods. 2. Wald test degrees of freedom varies from month to month because of availability of industries and countries in the sample period. Table A1 gives the detailed starting date of each industry and country in the sample. The maximum degrees of freedom of χ2 for Wald tests in panels A, B and C correspondingly are: 9, 11 and 20.

7

Appendix Table A1. Country and Industry sample starting dates Panel A. Country Data

1 2 3 4 5 6 7 8 9 10 11 12 13

Emerging Market Brazil Chile China India Israel Malaysia Mexico Pakistan South Africa South Korea Taiwan Thailand Turkey

Local market index

First date of available data

MSCI MSCI MSCI Datastream market index MSCI MSCI MSCI MSCI Datastream market index MSCI MSCI Datastream market index MSCI

08/1990 08/1989 03/ 1991 02/ 1990 03/ 1986 02/ 1973 03/ 1988 03/ 1991 02/1973 12/1980 11/ 1987 03/ 1987 03/ 1988

Starting date 12/1987 12/1987 12/1992 01/1990 12/1992 12/1987 12/1987 12/1992 01/1973 12/1987 12/1987 02/1987 12/1987

Panel B. Industry Data Industry groups (ICB level 3)

Key

First Date of available data

1

Industrial goods & services

IGS

02/1984

2

Consumer goods

CG

02/1973

3

Consumer services

CS

02/1973

4 5 6 7 8 9 10 11

Basic material Utility Health care Technology Telecom Financials Personal & household goods Basic Industrials

BM UT HC TEC TEL FI PHG BI

02/1973 02/1983 08/1984 08/1984 03/1986 07/1973 08/1984 02/1973

Note:

Sub-industry groups (DS industry level 4) Aerospace & Defence, Electronic & Electric support services, Transport, Engineering & Machinery Automobiles and Parts, Beverages, Food producer & Products, Household goods & Textiles Food & drug retailers, Leisure & Hotels, Media & Entertainment, Retail general Chemical, Forestry paper Electricity, Other utility Health care, Pharmacy + Biotech I/T hardware, Software + Services Telecom Non-life insurance Personal care & Household goods Construction & Materials

1.

Table 1 panel A shows countries and panel B shows industries used in the analysis. “First date of available data” is the date of the first published firm-specific data for each country or industry. “August 1984” shows the number of firms available in August 1984. Few firms were available before that date, but the total available increased from 25 in July 1984 to 138 in August. 2. Industries were classified based on Datastream (DS) industry level 4 groups. 25 DS industry groups were classified into 11 industry sectors, based on the Dow Jones/FTSE Industry Classification Benchmark (ICB) level 3. This gives 11 industry groups in 13 countries. 3. For the purpose of this study, only common shares are included. The data were extracted from Datastream. .

8

Table A2 Industry and Country Composition of the Sample: December 2003 Industry IGS Country Brazil 12 [0.25] Chile 7 [5.45] China 36 [1.28] India 33 [1.87] Israel 7 [0.17] Malaysia 34 [0.64] Mexico 1 [0.68] Pakistan 2 [0.01] South Africa 16 [0.74] South Korea 24 [1.73] Taiwan 37 [3.2] Thailand 16 [0.47] Turkey 5 [0.12] Sum 230 [15.93]

CG 14 [4.43] 16 [0.37] 23 [0.77] 38 [1] 4 [0.08] 82 [2.05] 16 [0.68] 14 [0.07] 13 [0.6] 75 [3.24] 51 [2.14] 67 [0.49] 22 [0.8] 435 [16.7]

CS 5 [0.34] 8 [0.7] 32 [0.95] 9 [0.26] 3 [0.1] 26 [1.39] 12 [2.23] [-] 19 [1.09] 18 [0.51] 6 [0.21] 24 [0.44] 3 [0.09] 165 [8.33]

BM 8 [0.29] 6 [0.6] 5 [0.48] 42 [2.4] 5 [0.28] 16 [0.13] 2 [0.01] 9 [0.16] 3 [0.54] 33 [0.57] 29 [3.42] 21 [0.89] 5 [0.02] 184 [9.78]

UT 9 [1.17] 12 [1.69] 7 [1.05] 9 [0.78] [-] 8 [1.99] [-] 5 [0.29] [-] 5 [1.6] 1 [0.01] 2 [0.13] 1 [0.06] 59 [8.76]

HC TEC TEL FI PHG 1 9 2 2 [-] [-] [0.99] [0.03] [0.01] 1 4 [0.04] [-] [0.54] [-] [-] 20 6 3 3 [0.66] [0.16] [0.11] [-] [0.05] 18 9 3 5 [1.42] [2.18] [0.3] [-] [1.12] 2 2 1 3 [2.01] [0.06] [0.3] [0.3] [-] 4 4 5 5 [0.03] [0.13] [0.96] [0.06] [-] 2 1 [-] [-] [0.63] [-] [0.18] 4 1 2 [0.04] [-] [0.28] [0.01] [-] 2 6 1 3 [0.14] [0.11] [0.81] [0.17] [-] 14 12 2 7 2 [0.11] [7.23] [1.56] [0.43] [0.14] 29 1 [-] [10.11] [-] [0.02] [-] 8 6 6 14 4 [0.07] [0.41] [0.74] [0.09] [0.08] 2 3 3 1 [0.02] [0.05] [-] [0.06] [-] 76 77 38 39 18 [4.52] [20.44] [7.22] [1.17] [1.57]

BI Total 4 66 [0.02] [7.51] 10 64 [0.23] [9.62] 9 144 [0.28] [5.77] 13 179 [0.58] [11.9] 2 29 [0.08] [3.39] 53 237 [0.77] [8.15] 4 38 [0.07] [3.81] 7 44 [0.03] [0.89] 6 69 [0.23] [4.42] 51 243 [0.95] [18.08] 24 178 [0.69] [19.78] 16 184 [1.44] [5.24] 17 62 [0.22] [1.43] 216 1537 [5.59] [100]

Note: Table A2 gives the number of stocks included in the sample for each country by industry. The corresponding average market capitalization expressed as percentage of the total sampled market value are included in the apprences.

9

Figure 1 Panel A: Probability of each month F-test for beta based on local market index

Panel B: Probability of each month F-test for beta based on MSCI

Panel C: Probability of each month F-test for return

Note: country effects are highly important in determining systematic stock risks throughout the whole of the sample period, apart from some months at the beginning. This is true irrespective of whether the calculations are based on local market indices (BL) or on betas based on the MSCI world market index (BW)

1

Figure 2 Probabilities of Wald-tests on SUR country and/or industry coefficients Panel A: beta based on local market index Wald-test P values of industry effect coeff. in SUR

Wald-test P values of country and industry effects coeff. in SUR

Wald-test P values of country effect coeff. in SUR

1/1/04

1/4/03

1/7/02

1/1/01

1/10/01

1/4/00

1/7/99

1/1/98

1/10/98

1/4/97

1/7/96

1/1/95

1/10/95

1/4/94

1/7/93

1/1/92

1/10/92

1/4/91

1/7/90

1/1/89

1/10/89

1/1/04

1/4/03

1/7/02

1/1/01

1/10/01

1/4/00

1/7/99

1/1/98

1/10/98

1/4/97

1/7/96

1/1/95

1/10/95

1/4/94

1/7/93

1/1/92

1/10/92

1/4/91

1/7/90

1/1/89

1/10/89

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1/4/88

1/1/04

1/4/03

1/7/02

1/1/01

1/10/01

1/4/00

1/7/99

1/1/98

1/10/98

1/4/97

1/7/96

1/1/95

1/10/95

1/4/94

1/7/93

1/1/92

1/10/92

1/4/91

1/7/90

1/1/89

1/10/89

1/4/88

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

1/4/88

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Panel B: beta based on MSCI

Note: Figure 2 presents the probabilities of Wald tests on the industry and/or country effect coefficients in SUR. The top three figures are beta based on local index and the bottom three figures are beta based on MSCI.

2

Figure 3 Scatter plots of CAPM Wald test P values and decomposition F-test P values Panel A: beta based on local market index

3

Panel B: beta based on MSCI

Note: These plots show that there is a clear positive relationship in the cases of the BL country dummies in the first sub-sample and full sample (panel A). However, there is less obvious positive relationship in the case of industry dummies.

4

Country and Industry Factors in Tests of Capital Asset Pricing Models for Partially Integrated Emerging Markets Highlights • • • • •

Country factors are independent sources of stock return variations before 1996. Post 1996 industry-country factors are jointly sources of stock return variations. Global CAPM cannot capture the strong country effects due to market segmentation. Post Asian crisis, integration among emerging markets is better captured by GCAPM. Beta decompositions are in line with return and conditional risk decompositions.