Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from...

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Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijk a,, Zaghum Umar a a University of Groningen, Faculty of Economics and Business, Department of Economics, Econometrics and Finance, P.O. Box 800, 9700 AV Groningen, The Netherlands. Abstract We estimate the myopic (single-period) and intertemporal hedging (long-run) demand for stocks in 20 growth-leading emerging market economies during the 1999 – 2012 period. We consider two types of investors: a domestic investor who invests in emerging-market assets only (with returns in the local currency) and an international investor who invests in both US and emerging-market assets (with returns in US dollars). We establish significant short-run and long-run domestic demand for stocks in several emerging market economies. For international investors only the short-run demand for emerging-market stocks tends to be significant. Hence, only for domestic investors emerging-market stocks are assets for the long run. Keywords: emerging-market stocks, predictability, myopic demand, intertemporal hedging demand JEL Classification: G11, G14, E44 Corresponding author Email addresses: [email protected] (Laura Spierdijk), [email protected] (Zaghum Umar) Preprint submitted to Elsevier June 1, 2013

Transcript of Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from...

Page 1: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Stocks for the Long Run? Evidence from Emerging Markets

Laura Spierdijka,∗, Zaghum Umara

aUniversity of Groningen, Faculty of Economics and Business, Department of Economics, Econometrics and Finance, P.O.Box 800, 9700 AV Groningen, The Netherlands.

Abstract

We estimate the myopic (single-period) and intertemporal hedging (long-run) demand for stocks in 20

growth-leading emerging market economies during the 1999 – 2012 period. We consider two types of

investors: a domestic investor who invests in emerging-market assets only (with returns in the local

currency) and an international investor who invests in both US and emerging-market assets (with returns

in US dollars). We establish significant short-run and long-run domestic demand for stocks in several

emerging market economies. For international investors only the short-run demand for emerging-market

stocks tends to be significant. Hence, only for domestic investors emerging-market stocks are assets for

the long run.

Keywords: emerging-market stocks, predictability, myopic demand, intertemporal hedging demand

JEL Classification: G11, G14, E44

∗Corresponding authorEmail addresses: [email protected] (Laura Spierdijk), [email protected] (Zaghum Umar)

Preprint submitted to Elsevier June 1, 2013

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1. Introduction

For both domestic and international investors, emerging markets may offer attractive investment op-

portunities. According to the IMF (2011), published by the IMF, emerging markets’ growth prospects

are among the main considerations for asset allocation by long-term real-money investors. Also from

a diversification perspective, emerging markets provide potentially interesting investment opportunities

(Harvey, 1994; Bekaert and Harvey, 2003).

The aforementioned considerations suggest that emerging market equities can be an interesting port-

folio component for both domestic and international investors with a multi-period investment horizon.

The present study therefore analyses the intertemporal effects of return predictability on optimal con-

sumption and portfolio choice for long-term emerging-market investors. We decompose the demand for

emerging-market stocks in a myopic and an intertemporal hedging part. The myopic demand owes to the

current risk premium in a single-period setting, whereas the intertemporal hedging demand stems from

the investor’s strategic objective to hedge against adverse future events in a multi-period setting. To our

best knowledge, the impact of myopic and intertemporal hedging motives on the total demand for stocks

as proposed by Campbell et al. (2003) has not yet been applied to emerging markets.1

In Campbell et al. (2003), the intertemporal hedging demand for US stocks stems from the pre-

dictability of stock returns from the dividend yield, due to which negative stock returns are associated

with positive expected returns in the future. In this way, stocks can be used to hedge the variation in their

own future returns. The empirical evidence for return predictability is mixed though, even for the US

and other developed markets. For example, Goyal and Welch (2008) and Boudoukh et al. (2008) present

evidence against return predictability in the US, whereas Campbell and Thompson (2008) establish ev-

idence in favour of it. Schrimpf (2010) investigates return predictability in an international context and

finds large differences across markets. In his study of four industrialised stock markets, he finds weak

evidence in favour of stock return predictability in Germany, Japan and the United Kingdom.

There is even more ambiguity in the literature about return predictability in emerging markets. Har-

vey (1994) finds that emerging market equity returns are more predictable than stock returns in developed

economies, while Karemera et al. (1999) cannot reject the random walk model for equity returns in their

study of fifteen emerging markets. Bekaert and Harvey (2007) establish more evidence in favour of pre-

dictability of emerging market stock returns, whereas Hjalmarsson (2010) finds only little evidence for

predictability. There is also uncertainty about mean-reversion effects in emerging market stock returns;

see e.g. Chaudhuri and Wu (2003) and Malliaropulos and Priestley (1999). Mean reversion is defined as

negative autocorrelation in asset returns and arises in the presence of strong return predictability. Because

1A notable exception is De Vries et al. (2011), who apply the approach of Campbell et al. (2003) to South Africa.

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of the mixed results regarding predictability and mean reversion in emerging markets stock returns, it is

not a priori clear whether emerging-market stocks will turn out to be assets ‘for the long run’.

This study focuses on the group of the emerging and growth-leading economies (known as EAGLEs

and EAGLEs Nest countries), created in 2010 by Banco Bilbao Vizcaya Argentaria.2 The EAGLEs group

includes economies whose expected contribution to the world’s economic growth in the next ten years

is larger than the average of the G-6 economies (i.e., G-7 excluding the US). The performance of group

members is reviewed annually for possible inclusion or removal. A related classification is the EAGLEs

Nest, which includes countries whose expected incremental GDP in the next decade is lower than the

average of the G-6 economies but higher than that of the smallest G-6 group’s contributor. Currently,

there are 9 countries in the EAGLEs group (Brazil, China, India, Indonesia, Mexico, Russia, South Korea,

Taiwan, and Turkey), while there are 15 countries in the EAGLEs Nest group (Argentina, Bangladesh,

Chile, Colombia, Egypt, Malaysia, Nigeria, Pakistan, Peru, Philippines, Poland, South Africa, Thailand,

Ukraine, Vietnam). We study the demand for stocks in 20 of these countries, from June 1999 until July

2012.

We analyse the intertemporal portfolio choice problem for two types of emerging-market investors:

a domestic investor with returns in the local currency and an international investor whose returns are

denominated in US dollars. Both investors have an asset menu consisting of a benchmark asset and

a stock index. The domestic investor invests in the local market only, whereas the international investor

can invest in both the local market and the US. The benchmark asset for each country’s domestic investor

is a short-term money market instrument, while the international investor’s benchmark asset is a 3-month

US T-bill. For various levels of risk aversion, we provide point estimates of the mean total, myopic and

intertemporal hedging demand for assets. In addition to point estimates, we provide statistical confidence

intervals to quantify the amount of parameter uncertainty involved with the estimates of the demand

for assets (Rapach and Wohar, 2009). Accounting for estimation uncertainty is particularly relevant in

emerging markets, where data availability is often limited. Finally, we investigate the monthly changes

in the demand for stocks over time.

Our study builds on the work of Rapach and Wohar (2009) who apply the approach of Campbell

et al. (2003) to the US and six other developed countries (Australia, Canada, France, Germany, Italy, and

the UK). They find that the intertemporal hedging motive explains a significant portion of the domestic

demand for US and UK stocks, whereas the hedging demand for domestic stocks turns out insignificant

in the other countries under consideration. For international investors, the hedging and myopic demand

for Australian, Canadian, French and Italian stocks is significant.

We establish the following main results. In several emerging market economies the hedging motive

2See http://www.bbvaresearch.com/KETD/ketd/ing/nav/eagles.jsp.

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explains a significant part of the total demand for stocks by domestic investors, emphasizing the impor-

tance of accounting for time variation in investment opportunities. Stock returns in these countries are

predictable to some extent, which explains the long-run demand for these assets. The main predictor

of stock returns turns out to be the book-price ratio, which may proxy for a firm’s risk of distress. For

international investors, whose asset menu contains both US and emerging markets equity (with returns

in US dollars), it is mainly the myopic demand for stocks that turns out significant. Emerging market

stocks do generally not provide a hedge against adverse future changes in investment opportunities for

these investors, which is likely to be due to emerging market countries’ volatile exchange rates. Hence,

only for domestic investors emerging-market stocks are for the long run.

The remainder of the paper is organised as follows. Section 3 provides some background to the multi-

period asset allocation problem, followed by a model description in Section 3. The details of emerging

markets data are given in Section 4. The estimation results are discussed in Sections 5 (domestic investor)

and 6 (international investor). Finally, Section 7 concludes.

2. Literature review

Ever since the seminal work of Markowitz (1952) on mean-variance analysis, financial economists

have investigated various facets of portfolio choice. The traditional mean variance analysis is myopic in

nature and is appropriate only for a single-period investment horizon with constant investment oppor-

tunities. In practice, however, most investors face multi-period investment horizons with time-varying

investment opportunities (Campbell and Viceira, 1999).

In a multi-period setting, an investor looks beyond the single-period investment horizon and seeks

protection against adverse events affecting future returns. Mossin (1968) was among the first to examine

the multi-period portfolio choice problem. His study showed that myopic portfolio choice is optimal for

an investor with log utility in terminal wealth and unhedgeable investment opportunities that are either

constant or time-varying. Samuelson (1969) and Merton (1969) incorporated the role of consumption

in the multi-period portfolio choice problem and highlighted the difference between myopic and multi-

period portfolio choice. They showed that myopic portfolio choice is optimal for an investor with either

log utility or unpredictable (idd) asset returns.

During the last two decades a large volume of literature has documented predictability of equity

returns, particularly for longer investment horizons (e.g., Keim and Stambaugh, 1986; Campbell, 1987,

1991; Fama and French, 1988, 1989; Harvey, 1994; Barberis, 2000; Lynch, 2001; Bekaert and Ang,

2002; Chapados, 2011; Ang and Bekaert, 2007). Merton (1973) introduced the concept of intertemporal

hedging demand and showed that the variation in expected returns over time can lead to horizon effects

for long-term investors. Intertemporal hedging demand arises when an investor wants to hedge against

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future shocks in investment opportunities (Campbell and Viceira, 2002).

Despite the theoretical soundness of Merton’s model, analytical solutions of the model (in terms

of portfolio weights as a function of state variables) were not available for a long time. The progress

in advanced computing towards the end of the twentieth century led to the development of a number

of numerical solutions of the Merton model that were calibrated to US data (see Balduzzi and Lynch,

1999; Barberis, 2000; Brennan et al., 1997; Lynch, 2001). Campbell and Viceira (1999) go one step

further by incorporating consumption in the portfolio choice problem. They develop an approximate

analytical solution to the Merton model for an infinitely-lived investor with one risky asset and one state

variable. They show that the accounting for the hedging motive can almost double the total demand for

US stocks. Campbell et al. (2003) extend Campbell and Viceira (1999) model to include more assets and

state variables by applying a simple numerical procedure in conjunction with an approximate analytical

solution. They calculate the myopic and the intertemporal hedging component of the total optimal asset

demand for an infinitely lived investor with Epstein-Zin utility. The myopic demand corresponds to

the single-period demand for an asset when there are no changes in the investment opportunity set.

The myopic demand is similar to the demand in the traditional single-period portfolio choice problem.

The hedging demand reflects the additional demand for an asset when the changes in the investment

opportunity set are incorporated into the portfolio choice problem, as in the multi-period portfolio choice

problem of Merton (1973). The empirical implementation of the Campbell et al. (2003) approach uses a

vector autoregressive (VAR) model to capture the time-varying nature of expected asset returns. There are

no borrowing or short-selling constraints on asset allocations to isolate the intertemporal effects of return

predictability. The authors show that the intertemporal hedging demand motive explains a substantial

part of the total demand for US stocks.

Campbell et al. (2003) explain the intertemporal hedging demand from the positive coefficient of

the lagged dividend yield in the VAR model’s stock excess return equation and the strongly negative

correlation between the innovations of the stock return and dividend yield equations. Investors are gen-

erally long in stocks that have high Sharpe ratios. A negative shock to stock excess returns implies a

deterioration of investment opportunities for such investors. A strongly negative correlation between the

innovations of the stock excess return and dividend yield equations means that a negative shock to stock

excess returns is generally accompanied by a positive shock to the dividend yield. The positive coefficient

of the lagged dividend yield in the stock excess return equation implies that the positive dividend yield

will have a positive impact on expected excess returns in the next period. Therefore, low stock returns in

the present tend to be followed by higher expected excess returns in the future, thus providing a hedge in

the long run. Hence, predictability of asset returns is crucial in explaining the hedging demand. We will

formalise this in the next section.

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3. Methodology

This section outlines the approach of Campbell et al. (2003).

3.1. Theoretical framework

Let Rp,t+1 be the real return of a portfolio of n assets, for an infinitely long-lived investor with Epstein-

Zin preferences defined over a fixed stream of consumption. Let R1,t+1 be the simple real return on the

benchmark asset (a short-term money instrument) and Ri,t+1 (i = 2, 3, . . . , n) the simple real return on the

remaining n − 1 assets. The portfolio return is given by

Rp,t+1 =

n∑i=2

αi,t(Ri,t+1 − R1,t+1) + R1,t+1, (1)

where αi,t is the portfolio weight for asset i. The log real return is denoted as ri,t+1 = log(Ri,t+1 + 1), for

all i. Let xt+1 be the vector of excess returns such that

xt+1 = [r2,t+1 − r1,t+1, . . . , rn,t+1 − r1,t+1]′. (2)

Let st+1 be the vector of the other state variables (also referred to as instruments), such as the dividend

yield and the short rate. We stack r1,t+1, xt+1 and st+1 in a m × 1 vector, zt+1, yielding the state vector

zt+1 = [r1,t+1, xt+1, st+1]′. (3)

The system of state variables is assumed to follow a first-order vector autoregression for zt+1, such that

zt+1 = ϕ0 + ϕ1zt + νt+1, (4)

where ϕ0 is the m × 1 vector of intercepts, and ϕ1 is the m ×m matrix of slope coefficients, and νt+1 is an

m-dimensional vector of shocks that is iid normally distributed with mean 0 and covariance matrix Σν,

such that

Σν = Var t(νt+1) =

σ2

1 σ′1x σ′1s

σ1x Σxx Σ′xs

σ1s Σxs Σss

. (5)

Innovations are assumed to be homoscedastic and independently distributed over time, but cross-sectional

correlation is allowed. σ21 denotes the variance of the innovations to the return on the benchmark asset;

σ1x is the vector of covariances between the innovations to the benchmark asset returns and the other

asset returns (with (n − 1) elements); σ1s is the vector of covariances between the innovations to the

benchmark asset returns and the instruments (with (m − n) elements); Σxx is the covariance matrix of

the innovations to the excess returns (with (n − 1) × (n − 1) elements); Σxs is the covariance matrix of

the innovations to the excess returns and the instruments (with (m − n) × (n − 1) elements); Σss is the

covariance matrix of the innovations to the instruments (with (m − n) × (m − n) elements).

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The homoskedasticity assumption is restrictive in the sense that it rules out the possibility that the

state variables predict changes in risk. However, the literature has shown that the impact of the state

variables on risk seems modest relative to their influence on expected returns; see Campbell et al. (2003).

Following Epstein and Zin (1989, 1991), Campbell et al. (2003) define the recursive preferences of

an investor over an infinite investment horizon as

U(Ct, IEt(Ut+1)

)=((1 − δ)C(1−γ)/θ

t + δ(IEt(U

1−γt+1 ))1/θ)θ/(1−γ)

, (6)

where Ct is the consumption at time t, IEt(·) is the conditional expectation given all information at time t,

0 < δ < 1 is the time-discount factor, γ > 0 is the coefficient of relative risk aversion, θ ≡ (1−γ)/(1−ψ−1)

and ψ > 0 is the elasticity of intertemporal substitution. A higher value of δ reflects a more patient

investor and a higher value of γ corresponds with a more risk averse investor. One notable advantage of

using this utility function is the separation of the notion of risk aversion (γ) from that of the elasticity of

intertemporal substitution (ψ).3

The budget constraint for an investor with wealth Wt is

Wt+1 = (Wt −Ct)Rp,t+1. (7)

Given the budget constraint defined in Equation (7), Epstein and Zin (1989, 1991) derive the Euler

equation for consumption of asset i in portfolio p as

IEt((δ(Ct+1

Ct

)−1/ψ)θR−(1−θ)

p,t+1 Ri,t+1)= 1 (8)

For the power utility case (with γ = ψ−1 and θ = 1), the first-order condition in Equation (8) reduces to the

standard one. Campbell et al. (2003) point out that the optimal portfolio choice with constant investment

opportunities is the same as the single-period or myopic portfolio. However, with time varying investment

opportunities, exact analytical solutions are generally not available except for specific values of γ and

ψ. Campbell et al. (2003) combine the approximate analytical solution of Campbell and Viceira (1999,

2001) with a simple numerical procedure to calculate the optimal portfolio and consumption choices

for any values of γ and ψ. Campbell et al. (2003) approximate the log real return on the portfolio in a

way that is exact in continuous time and highly accurate for short time intervals. Furthermore, log-linear

approximations of the budget constraint (first order) and the Euler equation (second order) are used.4 The

optimal portfolio (αt) and consumption (ct − wt) rules are given by

αt = A0 + A1zt; ct − wt = b0 + B′1zt + z′t B2zt, (9)

3Equation (6) reduces to a special case of time-separable power utility when γ = ψ−1 and to log utility under the additionalconstraint γ = ψ−1 = 1.

4These approximations are exact for ψ = 1.

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where ct and wt are the log levels of Ct and Wt, respectively. A0 (dimension (n− 1)× 1), A1 ((n− 1)×m),

b0 (1× 1), B1 (m× 1) and B2 (m×m) are constant coefficient matrices that are functions of γ, ψ, δ, ρ, ϕ0,

ϕ1 and Σν, with ρ = 1 − exp[IE(ct − wt)].

3.2. Myopic and intertemporal hedging demand

Because we focus on an investor’s portfolio choice, we are interested in the parameters of Equa-

tion (9). Following Merton (1969, 1971), Campbell et al. (2003) derive expressions for A0 and A1 in (9)

that divide the total demand into myopic and intertemporal hedging components:

A0 = (1/γ)Σ−1xx(Hxϕ0 + 0.5σ2

x + (1 − γ)σ1x)︸ ︷︷ ︸

myopic demand

+(1 − (1/γ)

)Σ−1

xx(−Λ0/(1 − ψ)

)︸ ︷︷ ︸intertemporal hedging demand

(10)

and

A1 = (1/γ)Σ−1xx Hxϕ1︸ ︷︷ ︸

myopic demand

+(1 − (1/γ)

)Σ−1

xx(−Λ1/(1 − ψ)

)︸ ︷︷ ︸intertemporal hedging demand

, (11)

where Hx denotes the selection matrix that selects the vector of excess returns (xt) from zt. σ2x denotes

the vector of diagonal elements of Σxx; Λ0 and Λ1 denote matrices whose values depend on B0, B1, B2

γ, ψ, δ, ρ, ϕ0, ϕ1 and Σν. For full details we refer to Campbell et al. (2003). The two terms related to the

myopic demand in Equations (10) and (11) sum to the total myopic demand for an asset. The myopic

demand refers to the demand for an asset in a single-period context, similar to the demand derived in

a static mean-variance framework, with a constant investment opportunity set. The two terms related to

the intertemporal hedging demand in Equations (10) and (11) sum to the total intertemporal hedging

demand. The latter demand arises in a multi-period portfolio choice problem, when an investor accounts

for changes in the investment opportunity set and tries to hedge against adverse future shocks.

When the benchmark asset is riskless (σ1x = 0) the myopic demand equals the vector of expected

excess returns on the risky assets, scaled by the inverse of the covariance matrix of the risky asset returns

and the reciprocal of the coefficient of relative risk aversion. When the benchmark asset is risky, investors

use the term (1−γ)σ1x to adjust this allocation. An investor with logarithmic utility (i.e., γ = 1 and θ = 0)

will have a zero hedging demand. The hedging demand will also be zero when investment opportunities

are constant over time. More specifically, Rapach and Wohar (2009) point out two necessary conditions

for the existence of intertemporal hedging demand in a multi-period portfolio choice framework with

non-logarithmic utility: (1) the returns should be predictable and (2) the variance-covariance matrix for

the VAR innovations (Σν) should not be diagonal.

The more risk-averse the investor, the more eager she is to hold assets that deliver wealth when

investment opportunities are poor. Consequently, the hedging demand is usually positive for sufficiently

risk-averse investors. By contrast, a less risk-averse investor prefers to hold assets when investment

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opportunities are favourable. For such an investor the hedging demand tends to be negative. Hence, the

risk-averse investor is usually long in stocks. However, if the expected stock excess return are sufficiently

negative, it becomes attractive for the investor to take a short position in equity. With a short position in

stocks, a drop in expected future stock returns provides an improvement in the investment opportunity

set. In this scenario the hedging demand becomes negative.

As noticed by Rapach and Wohar (2009), the estimated asset demands can be given a normative or

positive interpretation. According to the normative interpretation, the optimal asset demand for a given

asset return process corresponds to an investor whose investment preferences coincide with the assump-

tions made by Campbell et al. (2003). The positive interpretation, following Lynch (2001), states that the

optimal asset demand reflects the behavior of a unique individual or of a small group of investors, who

exploit the return predictability created by a large number of other investors with different preferences,

such as the habit-formation preferences of Campbell and Cochrane (1999).

3.3. Limitations of the approach

Campbell et al. (2003) discuss several limitations of their approach that we briefly summarise here.

First, their approach boils down to a partial equilibrium model, focusing on the demand-side of the asset

allocation problem. For a given exogenous asset return process and a set of investor preferences, a long-

term investor’s optimal demand for assets is calculated. This partial approach assumes that asset returns

are exogenous, which implies (among others) that the asset demand by individual investors does not have

any market impact. The implied model of investor behavior is not used to explain observed asset returns

such as in e.g. Lynch (2003). Second, the approach of Campbell et al. (2003) provides an approximate

solution, which is only exact under certain assumptions. Although it has been shown that this solution is

generally accurate, the approximation error increases when the state variable is more than two standard

errors away from its mean.

4. Data

We consider two types of investors: A domestic investor with returns denominated in the local cur-

rency (domestic investors) and an international investor whose returns are denominated in US dollars.

Both investors can invest in a short-term money market asset (the benchmark asset) and a stock market

index. The domestic investor’s benchmark asset is a domestic money market instrument, while it is a

3-month US T-bill for the international investor.

Motivated by data availability constraints in emerging markets, we opt for the sample period June

1999 – July 2012, resulting in 158 monthly observations for the emerging economies of Argentina,

Brazil, Chile, China, Colombia, Egypt, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines,

Poland, Russia, South Africa, South Korea, Taiwan, Thailand, and Turkey. The sample covers a very

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turbulent period, including the aftermath of the financial crises in East Asia, Russia and Latin America,

the dot-com crisis in the US and the subsequent recovery, and the global financial crisis that started with

the US subprime crisis in 2007 – 2008.

The relevant data series on equity indices, money market instruments, inflation rates, and instruments

have been obtained from Thompson Reuters Datastream and the IMFs International Financial Statistics

(IFS). We use Datastream’s global equity total return indices to obtain the stock returns in each country

under consideration. Throughout, we work with continuously compounded returns and inflation rates.

Real returns are calculated by subtracting the monthly inflation rate from the monthly nominal return.

We obtain real stock excess returns by subtracting the nominal return on the short-term money market

instrument from the nominal stock return. For the domestic investor, we use the inflation rate of the

relevant domestic market to calculate real T-bill returns. For the international investor we use the US

inflation rate. The book-price ratio corresponding to a stock index is calculated as the ratio of the index’

total book value divided by its total market value.5

4.1. Data choices

The first panel of Table 1 lists the domestic investor’s benchmark asset in each country, which is

a short-term money market instrument. The choice of the money market instrument in each country is

mainly driven by data availability. The inflation series are listed in the second panel of Table 1 and the

equity indices are given in the first panel of Table 2. The main return predictor variable we consider is the

book-price ratio on the corresponding stock market index; see the third panel of Table 2. Campbell et al.

(2003) and Rapach and Wohar (2009), like several other studies, use the nominal yield on the short-term

money market instrument, the dividend yield and the term spread as predictor variables in their VAR

model. Campbell et al. (2003) find that the term spread has a very little effect on the demand for stocks

due to its low predictive ability. We find that both the nominal yield and the term spread hardly have any

predictive power for stock excess returns. We therefore omit these two predictor variables for the sake

of parsimony. As to be explained in Section 5.3, we find that the book-price ratio is a better predictor of

stock returns than the dividend yield during our sample period. We therefore replace the dividend yield

by the book-price ratio in our VAR model.6 We will come back to this issue in Section 5.3.

The second panel of Table 2 also lists the equity indices used by the international investor, whose

returns are denominated in US dollars. This investor’s benchmark asset is a 3-month US T-bill. The

equity indices for Colombia, Egypt, India, Pakistan, Peru and Russia are available in local currency only.

To calculate the international investor’s excess returns, we therefore use the relevant exchange rate to

convert the local currency return to the US dollar equivalent (see again the third panel of Table 2). Also

5The book-price ratio is alternatively referred in the literature as the book-to-market ratio.6We do use the dividend yield for Brazil, because of the unavailability of book-price ratio data in Datastream.

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for the international investor the stock return predictor is the book-price ratio.

4.2. Sample statistics

Tables 1 and 2 reports monthly sample means and standard errors for the various data series at hand.

The money market instrument in Turkey has the highest average monthly return of 0.92%, whereas the

benchmark assets for Russia and Chile have the lowest average return of -0.10% and -0.22%, respectively

(all in local currency). The returns on the benchmark assets for India and Malaysia have the lowest

volatility of 0.34% and 0.41%, respectively, whereas the returns on the benchmark assets for Argentina

and Turkey have the highest volatility of 1.2% and 2.3%, respectively.

Domestic investors in Chile and Colombia (with returns in the local currency) have the highest mean

excess return on stocks, which equal 1.0% and 0.95%, respectively. Argentina, China, Poland, Taiwan,

and Turkey all have negative excess returns on average. Excess returns in Russia and Turkey show the

highest volatility: 12.5% and 9.9%, respectively. With 4.8% and 3.9% excess returns in the US and Chile

have the lowest volatility.

International investors in Colombia and Russia (with returns in US dollars) have the highest mean

excess returns on stocks (1.4% and 1.3%, respectively). The average excess return is lowest in Argentina,

where it equals 0.38% during the sample period. In Turkey and Russia excess returns are the most

volatile, with monthly sample volatilities of 15.0% and 11.1%, respectively. Chile and Malaysia have

the lowest volatility during the sample period (0.07% and 0.18%, respectively).

In addition to sample means and standard deviations, we also report the ratio of the mean excess

return and the standard deviation for stocks. We refer to this as the (sample) Sharpe ratio (SR) in Table 2.

A higher value of the Sharpe ratio implies a higher myopic demand for stocks, ceteris paribus. In local

currency, Chile has the highest Sharpe ratio (0.26), followed by Colombia (0.16). Argentina, China,

Poland, Taiwan, and Turkey have negative Sharpe ratios. In US dollars the equity indices of Chile and

Russia exhibit the highest Sharpe ratio (both 0.18), whereas Argentina’s Sharpe ratio of -0.04 is lowest

across all countries under consideration.

All in all, the risk-return profiles of the money market instruments and the equity indices show

substantial variation across countries.

5. Empirical results: domestic investor

This section considers a domestic investor and interprets its optimal asset allocations in the various

emerging markets countries and the US.

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5.1. Model parameters

We use a VAR model to capture time-varying investment opportunities. In line with Rapach and

Wohar (2009), we keep this model tractable by imposing that the investor can only invest in one emerging

market country at the time. This means that we estimate a 3-dimensional first-order VAR model for each

country in our sample, involving the real return on the money market instrument (the benchmark asset),

the real excess return on the aggregate stock index, and the natural logarithm of the book-price ratio of

the aggregate stock index. Complete OLS estimation results for the domestic investor’s VAR models are

provided in the appendix with supplementary material; see Table I of the latter document.

In addition to the VAR coefficients, we need realistic values of ψ (the elasticity of intertemporal

substitution), γ (the coefficient of relative risk aversion), and δ (annual time-discount factor) to estimate

(9). Campbell et al. (2003) calculate the mean optimal demand for US stocks for ψ = 0.5, 1 while Rapach

and Wohar (2009) use values of ψ = 0.3, 1, 1.5 for developed markets. Buffie et al. (2009) show that the

elasticity values tend to be relatively low for developing countries, where they range between 0.1 and

0.5. We therefore start our analysis with ψ = 1 and later also obtain results for ψ = 0.1, 0.2, 0.3, 0.4, 0.5,

0.6, 0.7, and 0.8. Regarding the coefficient of relative risk aversion (γ), Campbell et al. (2003) use values

of 1, 2, 5 and 20, while Rapach and Wohar (2009) consider values 4, 7 and 10. We calculate the demand

for assets using the values 2, 5, 7, and 10. Campbell et al. (2003) and Rapach and Wohar (2009) use an

annual time-discount factor δ = 0.92 for developed markets. We opt for discount factors of 0.92, 0.95

and 0.99 to cater for different time preferences across investors.7

5.2. Optimal asset allocations

We start with a domestic investor in each of the emerging market countries and (for the sake of

comparison) the US. The domestic investors can invest in an aggregate stock index and a short-term

money market instrument available in the domestic market and denominated in the local currency.

Tables 3 – 5 report the mean optimal equity demand for ψ = 1 (intertemporal elasticity of substitu-

tion), δ = 0.92 (annual time-discount factor) and γ = 2, 5, 7, 10 (coefficient of relative risk aversion). The

mean optimal asset demand is derived from (9) by replacing A0 and A1 by their estimated counterparts

and zt by its time average. The demand for each asset is decomposed into the total, myopic and hedg-

ing demand. Both the total and the myopic demand over all assets sum to 100%, whereas the hedging

demand over all assets in the investment set sums to 0%. The total, myopic, and hedging demand are ex-

pressed as a percentage of the real initial wealth that is invested in a particular asset. A negative demand

for an asset indicates a short position.8 The point estimates of the mean optimal asset demand are subject

7Fuentes (2011) report discount factors between 0.97 – 0.99 for Chile, while Rebelo and Vegh (1995) use a value of 0.99for Argentina. A comprehensive review of time-discount factors is provided by Frederick et al. (2002).

8We notice that many markets do not allow short selling. However, we do not impose borrowing or short-selling constraints

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to parameter uncertainty, because they boil down to functions of the estimated VAR-model parameters.

To quantify this uncertainty, we follow Rapach and Wohar (2009) and estimate confidence intervals in

addition to the point estimates. We use the Delta method for this purpose.

We start with the US domestic investor, whose optimal asset allocations are displayed in Table 5. For

this investor only the myopic demand for 3-month US T-bills (the benchmark asset) turns out significant

(but only for more conservative investors). Neither the myopic nor the hedging demand for US stocks is

significant. The book-price ratio has a significantly positive coefficient in the VAR model’s stock excess

return equation (see Table I in the appendix) and the correlation between the innovations of the stock

return and book-price ratio equations is strongly negative (see Table II of the appendix). Consequently,

the hedging demand turns out positive in Table 5. However, parameter uncertainty is too large, due to

which the hedging demand turns out insignificant.

Several other patterns emerge from Tables 3 – 5. Various countries (such as Chili, Colombia, and

Malaysia) have a total stock demand in excess of 100% for at least one of the lower values of γ (cor-

responding with less risk-averse investors). Although the confidence intervals show that this demand is

not significantly larger than 100%, we provide some explanation anyhow. Demand in excess of 100%

means that the wealth invested in stocks exceeds the total available amount of wealth. This is only pos-

sible when the investor borrows money to invest in stocks. Stock demand exceeding 100% will result in

negative demand for T-bills. This means that the investor is short in T-bills, as the total demand for stocks

and T-bills sums to 100%. By allocating more than their total wealth to stocks, less risk-averse investors

can benefit from the high Sharpe ratios of domestic equity investments.

The demand for stocks is statistically significant in Argentina (where only the hedging demand is sig-

nificant, for more conservative investors), Chile (myopic), Colombia (myopic), India (myopic; hedging

only for the least risk-averse investors), Malaysia (myopic and hedging), Mexico (myopic; hedging only

for very conservative investors), Pakistan (myopic and hedging), Peru (myopic), Russia (myopic and

hedging), South Africa (myopic and hedging), South Korea (myopic and hedging), and Thailand (my-

opic; hedging only for the more conservative investors). Hence, in several emerging market countries

both the myopic and the hedging demand for domestic stocks turn out significant. By contrast, neither

the myopic nor the hedging demand for stocks is significant for the US. In all countries, the myopic

demand for the money market instrument is significant for at least the more risk-averse investors (with

exception of Chile, where only the demand for stocks is significant). The myopic demand for the stock

indices in the aforementioned countries turns out positive and significant due to favourable Sharpe ra-

tios. The hedging demand for stocks, that turns out significantly positive in 9 countries, stems from the

because we want to isolate the intertemporal effects of return predictability. In the presence of such constraints, it seems difficultto decompose the total demand for assets in myopic and hedging components.

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predictability of stock returns from the book-price ratio. We will extensively discuss the predictability

effects in Section 5.3.

As expected, the total and hedging demand for stocks tends to be lower (in absolute terms) for higher

levels of the risk aversion parameter γ. However, the percentage of hedging demand in the total demand

for stocks increases with the level of risk aversion, implying that the hedging motive gains importance

for more risk averse investors.

Throughout, the confidence intervals’ substantial width indicates that it is important to quantify the

amount of parameter uncertainty associated with the point estimates of the demand for emerging-market

assets. The use of a relatively short data sample is likely to account for the large estimation uncertainty.

5.3. Return predictability

In our VAR model, predictability stems from the positive coefficient of the lagged book-price ratio in

the VAR model’s stock return equation. In combination with a strongly negative correlation between the

innovations of the stock return and book-price ratio equations (see Table II in the appendix) this results

in intertemporal hedging demand for stocks. Investors are generally long in stocks that have high Sharpe

ratios. A negative shock to stock excess returns implies a deterioration of investment opportunities for

such investors. A strongly negative correlation between the innovations of the stock excess return and the

book-price ratio equations means that a negative shock to stock excess returns is generally accompanied

by a positive shock to the book-price ratio. The positive coefficient of the lagged book-price ratio in

the stock excess return equation implies that the positive book-price ratio will have a positive impact on

expected excess returns in the next period. Therefore, low stock returns in the present tend to be followed

by higher expected excess returns in the future, thus providing a hedge in the long run.

Campbell et al. (2003) find that the dividend yield is the most influential predictor of stock returns.

Their finding is consistent with many other studies establishing a positive effect of the dividend yield on

stock excess returns. The causes of the positive dividend yield effect remain unresolved, but a tax effect

is among the possible explanations (Brennan, 1970). An alternative explanation is that the dividend yield

acts as a proxy for omitted risk factors (Lemmon and Nguyen, 2008).

We establish a much less prominent role for the dividend yield in the US and the emerging markets

under consideration during the 1999 – 2012 period. Specifically, we find that the dividend yield as predic-

tor of stock excess returns tends to underestimate the hedging demand for stocks. To accurately estimate

the hedging demand, the book-price ratio is required as a predictor variable. To illustrate the important

role of the book-price ratio in the VAR models, Table 5 shows the demand for stocks in Argentina,

based on the VAR model that only contains the dividend yield as the predictor of stock excess returns

(see the caption ‘Argentina (without book-price ratio)’). The hedging demand for stocks reported here is

substantially lower than the demand for stocks based on the VAR model including the book-price ratio

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and reported in Table 3. Moreover, the hedging demand based turns out insignificant in the VAR model

without the book-price ratio. The relatively low (hedging) demand for stocks, arising in the absence of

the book-price ratio, can be explained from the weakly negative correlation between the innovations of

the stock return and dividend yield equations. The inclusion of the book-price ratio in the VAR model

results in more sizable and significant total and hedging demand for stocks. This does not only hold for

Argentina, but also for other countries. To save space, we only report the results without book-price ratio

for Argentina, Chile and Malaysia.9

The VAR model including both the dividend yield and the book-price ratio results in very similar

outcomes as the model including the book-price ratio only. We prefer the most parsimonious specification

and therefore only consider the book-price ratio as predictor of stock returns.

Many studies have established a positive book-price equity premium; see e.g. Fama and French

(1992, 1998); Chan et al. (1991); Capaul et al. (1993); Griffin (2002); Lewellen (2004). Chan and Chen

(1991) argue that the book-price ratio acts as a proxy for a firm’s relative level of distress. According to

Fama and French (1992), the book-price equity premium stems from the higher risk premiums assigned

to high book-price firms due to their increased risk of distress. Our results suggest that it is crucial to

account for distress risk during the sample period 1999 – 2012. This seems intuitive, given that the

sample spans a very turbulent period in both developed and emerging economies.

5.4. Changes over time

Until so far, we have only discussed the estimates of the mean optimal asset demand. We now analyse

the changes over time in the demand for assets. Instead of using the time average of zt in (9), we use zt

itself and depict the time-varying demand for assets as a function of time. We are particularly interested

in the time-varying demand for stocks; see Figures I – VII in the appendix. The chosen parameter values

are ψ = 1, γ = 7 and δ = 0.92.

The total and hedging demand follow similar patterns in China, India, Malaysia, Poland, South Ko-

rea, Taiwan, Turkey, and the US. In these countries, the hedging demand is relatively close to the total

demand, which means that the hedging motive explains a large part of the total demand for stocks. In

Argentina, Brazil, Chile, Colombia, Egypt, Indonesia, Mexico, Pakistan, Peru, the Philippines, Russia,

and Thailand the relatively volatile myopic demand accounts for the largest part of the total demand.

In most countries there are periods of negative total and positive hedging demand, which implies

negative myopic demand. Negative myopic demand means that the current implied risk premium for a

single period is low, so that it is optimal to go short in the asset. In such a scenario stocks can still be used

as a hedge against adverse changes in long-run investment opportunities. Negative total demand tends to

9The results for the other countries are available on request.

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occur during financial crises, such as the aftermath of the Latin American crisis at the start of the sample

period (see e.g. Colombia and Mexico). Also the end of the crisis in East-Asia is visible at the start of the

sample period in Malaysia, South-Korea, and Taiwan. For the US the dot-com crisis is clearly visible in

the early years of the sample period. The US subprime crisis of 2007 – 2008 is visible in many countries,

either from a drop in the demand for stocks or a subsequent rise in that demand.

5.5. Robustness checks

We have seen that the coefficient of relative risk aversion has a considerable influence on the mean

optimal asset demand. As a robustness check, we also analyse the effect of change in the values of the

other parameters. We calculate the mean demand for stocks for ψ = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8

(intertemporal elasticity of substitution) and δ = 0.95, 0.99 (time-discount factor).10 The general trend is

that a ceteris paribus increase in δ results in an increase in the magnitude of the total and hedging demand

for stocks. This result is intuitive because an investor with a focus on the present uses a lower discount

factor than a more future-oriented consumer. Thus, a consumer who is more future-oriented will forego

more consumption today in exchange for a better future return. Another effect that we observe is that a

ceteris paribus increase in ψ results in an increase in the magnitude of the total and hedging demand for

stocks. The intuition for this result is that investors are willing to hold more stocks for higher values of

the intertemporal elasticity of substitution.

6. Empirical results: international investor

This section discusses the mean (total, myopic and hedging) demand for an international investor

whose returns are denominated in US dollars.

6.1. Demand for stocks

We consider an international investor who can invest in US stocks and T-bills, as well as foreign

stocks in one of the emerging market countries. Again we use a VAR model to capture this investor’s

time-varying investment opportunities. We follow the existing literature and include the following vari-

ables in the international investor’s VAR model: the real return on the 3-month US T-bill, the US stock

excess return, the emerging market’s stock excess return, the log book-price ratio on the US stock index,

and log the book-price ratio on the emerging market stock index.11 Estimation results for the interna-

tional investor’s 5-dimensional VAR model can be found in the appendix; see Tables III – VI (estimated

coefficients and p-values) and Table VII (residual correlations).

10To save space, we do not report detailed results here, but complete results are available upon request.11Similarly to Rapach and Wohar (2009), we assume that the exchange rates used to convert local currency asset prices into

US dollars are exogenous. Furthermore, they play no role in the international investor’s VAR models.

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The total, myopic and hedging demand for the international investor are calculated in a similar way as

for the domestic investor. The estimation results for the various countries are reported in Tables 6 – 9, for

ψ = 1 (intertemporal elasticity of substitution), δ = 0.92 (annual time-discount factor) and γ = 2, 5, 7, 10

(coefficient of relative risk aversion). The international investor’s myopic demand for emerging-market

stocks is significant for Brazil, Chile, Columbia, India, Malaysia, Mexico, Pakistan, Peru, Russia, South-

Africa, South Korea, and Turkey. The hedging demand for emerging market equity is significant in India

and Malaysia only. Hence, for international investors emerging market tend to offer attractive short-run

investment opportunities only. Emerging market stocks do generally not provide a hedge against adverse

future changes in investment opportunities for these investors, which is likely to be due to the volatile

exchange rates of the emerging market countries. We also observe that the myopic demand for US T-bills

is significant in all countries, whereas the demand for US stocks is never significant.

Tables III – VI and Table VII in the appendix display positive coefficients for the book-price ratio in

the stock return equations, as well as negative residual correlations. However, because various variables

are involved it is rather complex to disentangle the various predictability effects. The overall outcome is

that the hedging demand for stocks and T-bills does generally not turn out significant, with exception of

India and Malaysia.

6.2. Changes over time

We depict the international investor’s time-varying total and hedging demand for stocks over the

sample period in Figures VIII – XIV in the appendix. The graph for each emerging market country and

the US displays the corresponding total and myopic demand for ψ = 1, γ = 7 and δ = 0.92.

We observe the following patterns. In countries such as Chile, Colombia, Indonesia, Peru, and the

Philippines the contribution of the hedging demand to the total demand for US stocks is sizable through-

out the sample period, implying that hedging demand is the main ingredient of the total demand for US

stocks. By contrast, the total and hedging demand for emerging-market stocks are generally far apart

(with the hedging demand relatively low), implying that the myopic demand tends to be the main com-

ponent of the total demand for these stocks.

During the dot-com crisis in the early 2000s the total and hedging demand for US stocks tend to be

negative. For many countries the figures in the appendix show a sharp increase in the demand for both

US and emerging markets stocks towards the end of 2008, reflecting the recovery just after the turbulent

stock market period at the end of 2008.

6.3. Benefits of international diversification

Instead of merely looking at the statistical significance of the international investor’s demand for

emerging-market stocks, we follow Rapach and Wohar (2009) and quantify the international investor’s

17

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utility gains from international diversification. We calculate the ‘value function’ per unit of wealth (for

ψ = 1, γ = 7 and δ = 0.92). For example, a domestic US investor (with US stocks and T-bills in

her investment set) would need 4.8 times as much initial wealth to realise the same expected utility as

the international investor with US stocks and T-bills as well as Turkish stocks on her asset menu. For the

other countries, a domestic US investor would require the following factor increases in wealth to enjoy the

same utility as an international investor with equity diversification opportunities in each of the following

emerging market economies: Poland, 2.1; Egypt, 2.3; Taiwan, 2.4; Argentina, 2.9; China, 2.6; Brazil,

2.9; Chile, 3.3; South Africa, 3.3; Russia, 4.2; Indonesia, 4.9; Turkey, 4.8; Philippines, 4.8; Pakistan,

5.4; India, 5.3; Thailand, 6.1; South Korea, 7.0; Peru, 6.9; Mexico, 7.7; Malaysia, 7.9; Colombia, 8.3.

Although it is difficult to interpret the factor increases in wealth in an absolute sense, we can say that for

the aforementioned parameter values the diversification benefits of investing in Mexico, Colombia, and

Malaysia are largest. For each of these countries we found a significantly positive myopic demand for

stocks and for Malaysia also the hedging demand turned out significantly positive.

6.4. Robustness checks

Changes in the intertemporal elasticity of substitution and the time-discount factor lead to the same

changes in the hedging demand for stocks as we established previously for the domestic investor. How-

ever, the effects of a change in ψ (intertemporal elasticity of substitution) and δ (the annual time-discount

factor) on the total and myopic demand for stocks are weaker. We have seen that the myopic demand is

the main motivation for an international investor to hold emerging-market stocks. Because the myopic

demand is independent of the latter two parameters (Campbell et al., 2003), the effect on the total and

myopic demand for emerging-market stocks is minor.

7. Conclusions

This paper investigates the myopic and hedging demand for stocks in 20 emerging markets from

the perspective of a domestic investor (whose returns are denominated in the local currency) and an

international investor (with returns in US dollars) during the period 1999 – 2012. We establish sig-

nificant long-run domestic demand for stocks in several emerging market countries. For international

investors, who can invest in both US and emerging-market stocks, we hardly find significant hedging de-

mand. Hence, emerging-market stocks do generally not provide a hedge against adverse future changes

in investment opportunities for these investors, which is likely to be due to emerging market countries’

volatile exchange rates. Hence, only for domestic investors emerging-market stocks tend to be for the

long run.

Our model of emerging market equity returns reveals that local-currency emerging market stock

returns are to some extent predictable. These predictability effects accounted for the domestic investor’s

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significant hedging demand for emerging-market stocks. We find the book-price ratio to be the main

determinant of the hedging demand for stocks, rather than the dividend yield that has often been used

as a predictor of stock excess returns in previous studies. The literature relates the book-price ratio to a

firm’s relative level of distress. The turbulent sample analysed in this paper may explain why the book-

price ratio acts as the main predictor of stock excess returns.

Throughout, we provide statistical confidence intervals in addition to the point estimates of the de-

mand for stocks. Because data availability for emerging-market assets is still limited, the amount of

parameter uncertainty turned out to be considerable. As a consequence, the estimation results have to be

interpreted with some caution.

There are several directions for future research. First, more parsimonious models could help to re-

duce the amount of estimation uncertainty. Second, to ensure tractability of our approach, we consider

investors that invest in one emerging market at the time. In practice, international diversification is likely

to be attractive for investors. Third, the approach followed in this study does not impose any borrowing or

short-selling constraints on asset allocations as to isolate the intertemporal effects of return predictabil-

ity. In practice, however, such constraints exist in many countries. It would be interesting to investigate

whether the approach can be extended as to allow for restrictions on borrowing and shortselling.

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Page 22: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

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22

Page 23: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Table 1: Sample statistics

benchmark asset (real return) nominal yield inflation rate

country type of money market asset mnemonic mean std.dev. mean std.dev. mnemonic mean std.dev.Argentina 90-day deposit AG90DPP 0.32 1.17 1.008 0.894 AGCONPRCF 0.69 1.03

Brazil Selic target rate BRSELIC 0.74 0.46 1.273 0.362 BRCONPRCF 0.53 0.41

Chile CD 90-day CLCD90D -0.22 0.44 0.031 0.014 CLCONPRCF 0.25 0.44

China 3-month Relending rate CHDIS3M 0.26 0.66 0.287 0.031 CHCONPRCF 0.03 0.65

Colombia 90-day CD rate CB90CDR 0.24 0.45 0.684 0.286 CBCONPRCF 0.44 0.43

Egypt T-bills IFS database 0.15 0.82 0.770 0.168 EYCONPRCF 0.62 0.78

India 91- day T-bills INTB91D 0.02 0.34 0.545 0.151 INCPANNL 0.52 0.26

Indonesia 1-month SBI discount rate IFS database 0.25 0.90 0.842 0.314 IFS database 0.60 0.86

Malaysia 3-month T-bills IFS database 0.06 0.41 0.232 0.035 MYCONPRCF 0.18 0.41

Mexico 91-day cetes MXCTM91 0.36 0.42 0.758 0.375 MXCONPRCF 0.40 0.35

Pakistan Money Market rate IFS database 0.01 0.80 0.706 0.302 IFS database 0.70 0.79

Peru Interbank rate PEINBIR 0.21 0.46 0.422 0.328 PECONPRCF 0.22 0.31

Philipines 91-day T-bills PHTBL3M 0.06 0.46 0.462 0.218 PHCONPRCF 0.40 0.42

Poland Interbank rate POIBKON 0.32 0.54 0.607 0.435 POCONPRCF 0.29 0.42

Russia Interbank rate RSIBK90 -0.10 0.67 0.865 0.593 RSCONPRCF 0.97 0.66

South Africa 91-day T-bills SATBL3M 0.25 0.46 0.715 0.178 SACONPRCF 0.46 0.45

South Korea 91-day COD KOCD91D 0.12 0.41 0.373 0.111 KOCONPRCF 0.25 0.40

Taiwan Money market rate TAMM90D 0.09 0.80 0.182 0.118 TWCONPRCF 0.09 0.79

Thailand Interbank rate THBTIBN -0.02 0.57 0.198 0.095 IFS database 0.22 0.55

Turkey Money Market rate IFS database 0.92 2.32 2.379 2.899 IFS database 1.46 1.67

US 3-month T-bills FRTBS3M -0.01 0.42 0.191 0.166 IFS database 0.20 0.41

Notes: This table shows monthly sample statistics. The sample mean and standard deviations are in percentage and on amonthly basis.

23

Page 24: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Table 2: Sample statistics (continued)

stocks (real excess returns local currency) stocks (real excess returns US dollars) book-price ratio

mnemonic mean std.dev. SR mnemonic exchange rate mean std.dev. SR mean std.dev.TOTMKAR -0.23 8.94 -0.03 TOTMAR$ -0.38 9.17 -0.04 0.73 0.27

TOTMKBR 0.16 6.59 0.02 TOTMBR$ 1.13 10.36 0.14 3.91 0.95

TOTMKCL 1.02 3.94 0.26 TOTMCL$ 0.87 6.10 0.18 0.61 0.16

TOTMKCH -0.04 8.40 0.00 TOTMCA$ 0.22 8.34 0.08 0.39 0.12

TOTMKCB 0.95 5.91 0.16 TOTMKCB COLUPE$ 1.39 7.80 0.13 1.30 0.92

TOTMKEY 0.36 8.31 0.04 TOTMKEY EGYPTN$ 0.58 8.48 0.09 0.55 0.22

TOTMKIN 0.59 8.60 0.07 TOTMKIN INDRUP$ 0.78 9.74 0.06 0.48 0.15

TOTMKID 0.15 7.94 0.02 TOTMID$ 0.70 10.96 0.12 0.37 0.12

TOTMKMY 0.52 4.90 0.11 TOTMMY$ 0.75 5.58 0.07 0.58 0.09

TOTMKMX 0.56 5.37 0.10 TOTMMX$ 0.93 6.98 0.08 0.53 0.15

TOTMKPK 0.70 9.24 0.08 TOTMKPK PAKRUP$ 0.83 9.59 0.09 0.51 0.15

TOTMKPE 0.76 5.82 0.13 TOTMKPE PERUSO$ 1.14 6.29 0.18 0.67 0.40

TOTMKPH 0.23 5.93 0.04 TOTMPH$ 0.44 6.97 0.06 0.66 0.21

TOTMKPO -0.14 7.09 -0.02 TOTMPO$ 0.38 9.86 0.04 0.61 0.14

TOTMKRS 0.83 9.87 0.08 TOTMKRS CISRUB$ 1.34 11.07 0.12 1.00 0.36

TOTMKSA 0.61 5.26 0.12 TOTMSA$ 0.95 8.13 0.12 0.46 0.07

TOTMKKO 0.43 7.92 0.05 TOTMKO$ 0.64 9.72 0.07 0.86 0.18

TOTMKTA -0.05 7.47 -0.01 TOTMTA$ 0.00 8.23 0.00 0.52 0.10

TOTMKTH 0.60 8.27 0.07 TOTMTH$ 0.71 9.49 0.08 0.46 0.14

TOTMKTK -0.72 12.51 -0.06 TOTMTK$ 0.53 14.97 0.04 0.63 0.20

TOTMKUS 0.02 4.82 0.00 TOTMKUS 0.02 4.82 0.00 0.38 0.10

Notes: This table shows monthly sample statistics. The sample mean and standard deviations are in percentage and on amonthly basis. SR stands for the (sample) Sharpe ratio. Because of unavailability of the book-price ratio, we report thedividend yield for Brazil.

24

Page 25: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

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25

Page 26: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

e4:

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26

Page 27: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

e5:

Dom

estic

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tal,

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pic

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and

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27

Page 28: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

e6:

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rnat

iona

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rs’t

otal

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pic

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lhed

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9.8

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6-1

0.1

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5-2

73.4

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85.5

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1.0

14.3

191.

142

6.9

367.

277

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4.6

129.

033

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141.

712

8.3

165.

193

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0.7

187.

975

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867

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2.7

88.3

24.4

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267

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52.9

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330

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87.6

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163.

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418

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037

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382

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17.1

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057

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176

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11.9

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92.2

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3.7

161.

314

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105.

248

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0.5

108.

337

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5.9

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128.

610

8.7

26.6

85.1

197.

612

5.3

74.2

1016

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44.5

59.5

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722

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820

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328

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561

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117.

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835

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390

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863

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bia

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bia

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pic

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pic

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pic

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pic

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ge2

30.5

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212

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318

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760

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672

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318

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477

.7

Not

es:F

orin

tern

atio

nali

nves

tors

with

diff

eren

tcoeffi

cien

tsof

rela

tive

risk

aver

sion

(CR

RA

)thi

sta

ble

show

sth

em

ean

tota

l,m

yopi

can

din

tert

empo

ralh

edgi

ngde

man

d.T

hede

man

dfo

rass

ets

isba

sed

onψ=

1(e

last

icity

ofin

tert

empo

rals

ubst

itutio

n)an

dδ=

0.92

(ann

ualt

ime-

disc

ount

fact

or).

Poin

test

imat

esof

the

dem

and

fora

sset

sar

ein

bold

face

,whi

leth

elo

wer

and

uppe

rbou

nds

ofth

eco

rres

pond

ing

90%

confi

denc

ein

terv

alar

eca

ptio

ned

‘L’a

nd‘U

’,re

spec

tivel

y.

28

Page 29: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

e7:

Inte

rnat

iona

linv

esto

rs’t

otal

,myo

pic

and

inte

rtem

pora

lhed

ging

dem

and

for

asse

ts(c

ontin

ued)

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aIn

done

sia

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ksU

SASt

ocks

Indi

aB

illsU

SASt

ocks

USA

Stoc

ksIn

done

sia

Bill

sUSA

CR

AA

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

2-9

7.0

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9-2

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134.

190

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352

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877

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97.6

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alay

sia

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ico

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192

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612

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387.

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05

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118.

013

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388

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896

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206.

614

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78.9

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547

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kist

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pic

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pic

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tal

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pic

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geTo

tal

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pic

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geTo

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pic

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geTo

tal

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pic

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ge2

30.8

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32.0

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837

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926

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863

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248.

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34.6

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544

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332.

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61.3

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15

31.5

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875

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8.4

104.

213

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83.0

52.9

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3-9

3.0

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0.2

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236

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196.

345

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114.

353

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4.9

122.

068

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13.4

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723

5.4

133.

811

4.1

79.0

99.7

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97

25.4

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25.9

47.1

21.3

25.7

27.5

79.2

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767

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0.5

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103.

759

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67.4

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93.8

99.7

-1.4

29

Page 30: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

e8:

Inte

rnat

iona

linv

esto

rs’t

otal

,myo

pic

and

inte

rtem

pora

lhed

ging

dem

and

for

asse

ts(c

ontin

ued)

Phili

ppin

esPo

land

Stoc

ksU

SASt

ocks

Phili

ppin

esB

illsU

SASt

ocks

USA

Stoc

ksPo

land

Bill

sUSA

CR

AA

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

245

.5-2

7.6

73.0

105.

788

.717

.0-5

1.2

38.9

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0-8

7.5

-65.

1-2

2.4

81.3

66.4

14.9

106.

298

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6L

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67.2

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4-1

52.4

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3.3

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1.6

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02.8

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48.1

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9-2

0.3

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1-1

31.3

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0-1

04.6

U22

5.8

112.

118

4.4

363.

927

7.5

97.3

211.

415

9.3

58.6

227.

813

4.6

103.

219

0.5

153.

245

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3.7

230.

311

9.7

553

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1.0

64.0

50.1

35.3

14.7

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75.6

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7-4

1.0

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7-1

4.2

38.2

26.9

11.3

102.

899

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8-6

6.8

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1.3

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28.5

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-7.8

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7-6

3.3

47.2

-114

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178.

744

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5.4

191.

511

0.9

89.7

191.

912

3.9

71.2

157.

153

.111

1.5

92.8

61.6

38.3

268.

815

2.4

120.

57

44.8

-7.8

52.6

37.5

25.2

12.3

17.8

82.6

-64.

9-3

0.5

-19.

4-1

1.1

28.3

19.3

8.9

102.

310

0.1

2.2

L-6

1.6

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7-4

5.1

-72.

9-2

8.7

-51.

8-1

45.5

48.2

-196

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90.6

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3-5

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3.9

-35.

462

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01.3

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1.2

32.0

150.

414

7.8

79.1

76.3

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111

7.1

66.3

129.

537

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610

5.7

1034

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738

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9.7

-23.

1-1

3.9

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20.3

13.7

6.6

102.

810

0.2

2.6

L-5

2.0

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4-4

1.3

-56.

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0.2

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3-9

3.1

63.7

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48.1

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1.9

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73.9

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7U

121.

322

.412

1.5

110.

955

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.616

9.4

112.

159

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2.0

25.9

81.1

51.2

31.0

25.1

212.

712

6.5

88.8

Rus

sia

Sout

hA

fric

aSt

ocks

USA

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ksR

ussi

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illsU

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USA

Stoc

ksSo

uth

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sUSA

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AA

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lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

2-2

74.4

-165

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09.1

172.

113

1.4

40.6

202.

313

3.8

68.5

-145

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51.4

6.2

182.

816

1.1

21.6

62.4

90.3

-27.

9L

-612

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59.3

-272

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8.4

8.4

-71.

0-4

03.7

-312

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27.7

59.3

53.1

-5.2

-151

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5.0

-154

.1U

64.2

28.8

54.0

292.

321

9.7

82.8

452.

925

9.2

207.

911

3.4

9.9

140.

130

6.2

269.

148

.427

6.5

195.

698

.35

-155

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6.8

-88.

884

.253

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.317

1.3

113.

857

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3.2

-61.

17.

982

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.917

.271

.196

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5.1

L-3

72.1

-144

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38.2

21.3

17.7

-3.7

-5.5

63.6

-76.

3-2

33.2

-125

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27.7

26.2

21.7

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-92.

954

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54.6

U60

.910

.860

.714

7.2

88.3

66.2

348.

216

4.0

191.

412

6.8

3.5

143.

513

7.9

108.

137

.623

5.1

138.

410

4.4

7-1

21.1

-48.

1-7

3.1

63.1

38.1

25.1

158.

011

0.0

48.0

-38.

3-4

3.9

5.6

61.0

46.5

14.5

77.3

97.4

-20.

1L

-295

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03.6

-200

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7.6

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0.0

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67.3

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53.5

7.4

54.3

111.

263

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145.

916

3.7

112.

22.

212

5.0

102.

377

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6.3

127.

594

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4.0

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546

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6.4

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139

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02.

344

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4.4

L-2

28.5

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61.8

10.4

9.2

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30.3

82.0

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50.0

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377

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313

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102.

374

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6.9

119.

381

.5

30

Page 31: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

e9:

Inte

rnat

iona

linv

esto

rs’t

otal

,myo

pic

and

inte

rtem

pora

lhed

ging

dem

and

for

asse

ts(c

ontin

ued)

Sout

hK

orea

Taiw

anSt

ocks

USA

Stoc

ksSo

uth

Kor

eaB

illsU

SASt

ocks

USA

Stoc

ksTa

iwan

Bill

sUSA

CR

AA

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

Tota

lM

yopi

cH

edge

2-1

63.1

-140

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2.3

165.

111

7.4

47.8

98.0

123.

4-2

5.4

13.4

5.2

8.3

35.8

23.1

12.7

50.8

71.8

-21.

0L

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21.9

-151

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6-1

22.0

0.9

-136

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00.3

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6.1

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1-3

7.0

-22.

9-1

22.4

-25.

7-9

9.7

U12

3.8

40.4

107.

229

7.5

210.

593

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7.9

245.

986

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7.2

128.

710

2.6

129.

783

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3.9

169.

257

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4-5

6.9

-9.5

87.7

47.4

40.3

78.7

109.

6-3

0.8

8.7

1.9

6.8

23.0

9.3

13.7

68.3

88.8

-20.

5L

-251

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29.4

-138

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.210

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9.1

60.6

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42.7

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5-9

8.7

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3-1

4.7

-24.

5-5

6.3

49.9

-108

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118.

815

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9.3

160.

284

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6.6

158.

686

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0.1

51.3

112.

383

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2.9

127.

867

.67

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8-4

1.0

-6.8

67.2

34.0

33.2

80.6

106.

9-2

6.3

5.3

1.3

4.0

19.3

6.7

12.6

75.5

92.1

-16.

6L

-198

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2.8

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0-9

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64.3

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103.

310

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5.1

123.

360

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2.6

141.

976

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2.1

36.5

98.5

68.7

23.8

46.6

180.

211

9.9

62.5

10-3

5.0

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0-6

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30.

80.

516

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711

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3-9

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280

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566

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3.7

25.5

80.7

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40.1

167.

611

4.0

55.4

Tha

iland

Turk

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ocks

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edge

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edge

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lM

yopi

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edge

Tota

lM

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edge

211

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0.7

72.1

103.

888

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5.1

72.4

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5-8

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5.7

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51.9

19.8

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311

8.4

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99.6

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7-3

4.7

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8-2

62.4

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7-2

34.9

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17.7

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14.

5-0

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06.7

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222.

381

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250.

221

1.4

44.6

232.

118

8.4

59.8

163.

777

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40.4

335.

323

9.4

105.

25

39.0

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463

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4-6

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20.9

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51.

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0.2

58.9

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191.

132

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3.3

119.

684

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3.4

135.

364

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4.6

31.0

112.

171

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0.2

155.

711

0.0

734

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7.4

51.7

38.3

25.5

12.8

27.4

91.9

-64.

5-2

6.2

-19.

9-6

.327

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.399

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5.2

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L-9

3.6

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2-6

1.2

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6-9

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.6-1

24.7

58.6

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67.8

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0-1

12.5

0.3

1.1

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170

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10.0

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2.1

23.3

164.

590

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9.6

125.

259

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5.5

22.2

100.

053

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3.5

139.

898

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26.7

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238

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8.9

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111

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39.1

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20.8

208.

112

7.8

83.5

31

Page 32: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Supplementary Material (for online publication only)

1

Page 33: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eI:

Est

imat

ion

resu

ltsfo

rdo

mes

ticin

vest

or’s

VAR

mod

el

real

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stoc

kbp

R2

real

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stoc

kbp

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real

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stoc

kbp

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real

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stoc

kbp

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entin

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nare

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500.

490.

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000.

250.

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0)(0

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4)(0

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9)(0

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(0.1

9)(0

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(0.0

9)st

ock

0.80

0.18

0.02

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60.

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050.

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(0.1

3)(0

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(0.8

2)(0

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(0.0

2)(0

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(0.2

4)(0

.71)

(0.2

3)(0

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0.89

-0.0

1-0

.10

0.95

0.91

0.42

-0.0

60.

920.

86(0

.04)

(0.0

7)(0

.00)

(0.1

1)(0

.60)

(0.0

0)(0

.99)

(0.4

5)(0

.00)

(0.6

4)(0

.55)

(0.0

0)

Sout

hK

orea

Tha

iland

Turk

eyU

Sre

al.b

ill0.

26-0

.01

0.00

0.08

0.34

-0.0

10.

000.

150.

07-0

.03

0.00

0.03

0.49

-0.0

20.

000.

32(0

.00)

(0.1

9)(0

.66)

(0.0

0)(0

.07)

(0.7

1)(0

.71)

(0.1

8)(0

.96)

(0.0

0)(0

.02)

(0.1

5)st

ock

1.89

0.11

0.09

0.07

-0.4

6-0

.01

0.06

0.06

0.32

-0.0

80.

070.

05-0

.60

0.13

0.03

0.06

(0.1

7)(0

.14)

(0.0

1)(0

.74)

(0.9

1)(0

.01)

(0.6

7)(0

.40)

(0.0

4)(0

.48)

(0.1

7)(0

.04)

bp-3

.01

-0.1

20.

890.

803.

120.

010.

880.

79-0

.15

0.20

0.93

0.84

1.34

-0.1

10.

970.

96(0

.08)

(0.1

7)(0

.00)

(0.1

4)(0

.95)

(0.0

0)(0

.86)

(0.0

6)(0

.00)

(0.1

6)(0

.32)

(0.0

0)

Not

es:T

his

tabl

esh

ows

the

dom

estic

inve

stor

’sVA

Res

timat

ion

resu

lts,w

ithth

eco

effici

ents

’p-

valu

esin

pare

nthe

ses.

The

row

vari

able

sar

eth

ede

pend

entv

aria

bles

inea

chVA

Req

uatio

nan

dth

eco

lum

nva

riab

les

are

the

expl

anat

ory

vari

able

s(a

llof

lag

1).T

hein

terc

epts

are

notr

epor

ted

tosa

vesp

ace.

Not

atio

n:re

al.b

ill=

real

retu

rnon

dom

estic

shor

t-te

rmm

oney

mar

keti

nstr

umen

t(b

ench

mar

kas

set)

;sto

ck=

real

exce

ssre

turn

ondo

mes

ticst

ock

inde

x;no

m.b

ill=

nom

inal

retu

rnon

dom

estic

shor

t-te

rmm

oney

mar

keti

nstr

umen

t;bp=

natu

rall

ogar

ithm

ofbo

ok-p

rice

ratio

.Fo

rBra

zil,

the

log

pric

e-ea

rnin

gra

tioon

the

dom

estic

stoc

kin

dex

inst

ead

ofth

elo

gbo

ok-p

rice

ratio

isus

edas

pred

icto

rvar

iabl

e.

2

Page 34: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eII

:Res

idua

lcor

rela

tions

indo

mes

ticin

vest

or’s

VAR

mod

el

stoc

kbp

stoc

kbp

stoc

kbp

stoc

kbp

stoc

kbp

Arg

entin

aB

razi

lC

hile

Chi

naC

olom

bia

real

.bill

-0.2

20.

10-0

.11

0.18

0.07

0.05

-0.2

30.

220.

03-0

.03

stoc

k-0

.74

-0.5

3-0

.84

-0.9

2-0

.66

Egy

ptIn

dia

Indo

nesi

aM

alay

sia

Mex

ico

real

.bill

-0.0

10.

010.

04-0

.01

0.00

0.10

0.07

-0.0

3-0

.11

0.04

stoc

k-0

.80

-0.8

9-0

.54

-0.9

1-0

.84

Paki

stan

Peru

Phili

ppin

esPo

land

Rus

sia

real

.bill

-0.0

10.

03-0

.03

-0.0

20.

02-0

.08

0.02

-0.1

10.

21-0

.41

stoc

k-0

.76

-0.7

8-0

.91

-0.8

9-0

.88

Sout

hA

fric

aSo

uth

Kor

eaTa

iwan

Tha

iland

Turk

eyre

al.b

ill0.

06-0

.07

0.00

-0.0

40.

11-0

.07

-0.1

3-0

.02

-0.3

90.

18st

ock

-0.8

5-0

.87

-0.9

2-0

.54

-0.8

3

US

real

.bill

-0.0

40.

02st

ock

-0.9

0

Not

es:

Thi

sta

ble

repo

rts

the

resi

dual

corr

elat

ions

corr

espo

ndin

gw

ithth

edo

mes

ticin

vest

or’s

VAR

mod

el.N

otat

ion:

real

.bill=

real

retu

rnon

dom

estic

T-bi

ll(b

ench

mar

kas

set)

;sto

ck=

real

exce

ssre

turn

ondo

mes

ticst

ock

inde

x;no

m.b

ill=

natu

rall

ogai

rthm

ofth

eno

min

alyi

eld

ondo

mes

ticT-

bill;

bp=

natu

rall

ogar

ithm

ofbo

ok-p

rice

ratio

.For

Bra

zil,

the

log

pric

e-ea

rnin

gra

tioon

the

dom

estic

stoc

kin

dex

inst

ead

ofth

elo

gbo

ok-p

rice

ratio

isus

edas

pred

icto

rvar

iabl

e.

3

Page 35: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eII

I:E

stim

atio

nre

sults

for

inte

rnat

iona

linv

esto

r’sV

AR

mod

el

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

Arg

entin

aB

razi

lre

al.b

ill0.

50-0

.01

-0.0

10.

000.

000.

340.

48-0

.01

-0.0

10.

000.

000.

33(0

.00)

(0.1

0)(0

.04)

(0.2

1)(0

.95)

(0.0

0)(0

.41)

(0.1

1)(0

.12)

(0.8

1)st

.us

-0.6

90.

090.

050.

030.

010.

07-0

.74

0.07

0.04

0.04

0.02

0.08

(0.4

2)(0

.37)

(0.2

6)(0

.05)

(0.6

5)(0

.38)

(0.5

5)(0

.50)

(0.0

3)(0

.21)

st.e

m4.

350.

360.

090.

060.

020.

110.

460.

180.

060.

040.

070.

05(0

.01)

(0.0

3)(0

.30)

(0.0

1)(0

.50)

(0.7

8)(0

.49)

(0.7

1)(0

.14)

(0.0

4)bp

.us

1.42

-0.0

9-0

.03

0.97

-0.0

10.

961.

45-0

.09

-0.0

20.

97-0

.01

0.96

(0.1

4)(0

.42)

(0.6

5)(0

.00)

(0.6

1)(0

.13)

(0.5

4)(0

.84)

(0.0

0)(0

.43)

bp.e

m-2

.58

-0.4

0-0

.03

-0.0

50.

950.

94-0

.06

-0.0

8-0

.04

-0.0

20.

900.

82(0

.12)

(0.0

1)(0

.61)

(0.0

6)(0

.00)

(0.9

6)(0

.76)

(0.7

4)(0

.71)

(0.0

0)

Chi

leC

hina

real

.bill

0.49

-0.0

1-0

.01

0.00

0.00

0.34

0.50

-0.0

1-0

.01

0.00

0.00

0.34

(0.0

0)(0

.25)

(0.0

3)(0

.21)

(0.5

9)(0

.00)

(0.0

3)(0

.06)

(0.1

6)(0

.63)

st.u

s-0

.60

0.14

0.00

0.04

0.01

0.06

-0.6

90.

100.

060.

030.

010.

07(0

.47)

(0.2

4)(0

.96)

(0.0

6)(0

.76)

(0.4

0)(0

.34)

(0.1

9)(0

.11)

(0.3

5)st

.em

0.98

0.21

0.03

0.07

0.04

0.08

1.37

-0.0

30.

050.

010.

030.

03(0

.31)

(0.1

1)(0

.79)

(0.0

0)(0

.08)

(0.3

8)(0

.84)

(0.5

6)(0

.62)

(0.2

5)bp

.us

1.34

-0.1

30.

030.

970.

000.

961.

41-0

.08

-0.0

50.

970.

000.

96(0

.16)

(0.3

1)(0

.78)

(0.0

0)(0

.94)

(0.1

3)(0

.47)

(0.3

4)(0

.00)

(0.8

5)bp

.em

1.31

-0.2

00.

07-0

.05

0.94

0.97

0.42

0.04

-0.0

50.

010.

960.

93(0

.10)

(0.0

4)(0

.39)

(0.0

0)(0

.00)

(0.8

0)(0

.79)

(0.5

8)(0

.75)

(0.0

0)

Col

ombi

aE

gypt

real

.bill

0.49

-0.0

10.

000.

000.

000.

330.

47-0

.01

-0.0

10.

000.

000.

35(0

.00)

(0.0

3)(0

.34)

(0.3

4)(0

.79)

(0.0

0)(0

.08)

(0.0

2)(0

.18)

(0.3

3)st

.us

-0.6

10.

130.

030.

060.

020.

09-0

.46

0.07

0.09

0.03

0.00

0.08

(0.4

6)(0

.14)

(0.6

0)(0

.01)

(0.0

7)(0

.58)

(0.4

2)(0

.06)

(0.0

4)(0

.88)

st.e

m0.

630.

410.

090.

120.

040.

161.

720.

090.

280.

030.

000.

11(0

.67)

(0.0

0)(0

.37)

(0.0

0)(0

.01)

(0.3

1)(0

.52)

(0.0

0)(0

.18)

(0.9

6)bp

.us

1.36

-0.1

1-0

.02

0.95

-0.0

10.

961.

25-0

.06

-0.0

70.

970.

000.

96(0

.15)

(0.2

9)(0

.68)

(0.0

0)(0

.15)

(0.1

9)(0

.52)

(0.2

4)(0

.00)

(0.9

0)bp

.em

-0.0

4-0

.21

-0.1

5-0

.08

0.96

0.98

0.66

-0.0

2-0

.23

-0.0

10.

960.

95(0

.98)

(0.1

5)(0

.09)

(0.0

6)(0

.00)

(0.7

2)(0

.89)

(0.0

1)(0

.63)

(0.0

0)

Not

es:

Thi

sta

ble

show

sth

ein

tern

atio

nali

nves

tor’

sVA

Res

timat

ion

resu

lts,w

ithth

eco

effici

ents

’p-

valu

esin

pare

nthe

ses.

The

row

vari

able

sar

eth

ede

pend

entv

aria

bles

inea

chVA

Req

uatio

nan

dth

eco

lum

nva

riab

les

are

the

expl

anat

ory

vari

able

s(a

llof

lag

1).T

hein

terc

epts

are

notr

epor

ted

tosa

vesp

ace.

Not

atio

n:re

al.b

ill=

real

retu

rnon

US

T-bi

ll(b

ench

mar

kas

set)

;st.u

s=

real

exce

ssre

turn

onU

Sst

ock

inde

x;st

.em=

real

exce

ssre

turn

onem

ergi

ngm

arke

tsto

ckin

dex

(in

US

dolla

rs);

nom

.bill=

natu

rall

ogar

ithm

ofno

min

alyi

eld

onU

ST-

bill;

bp.u

s=

natu

rall

ogar

ithm

ofbo

ok-p

rice

ratio

onU

Sst

ock

inde

x;bp

.em=

natu

rall

ogar

ithm

ofbo

ok-p

rice

ratio

onem

ergi

ngm

arke

tsto

ckin

dex

(in

US

dolla

rs).

For

Bra

zil,

the

log

pric

e-ea

rnin

gra

tioon

the

dom

estic

stoc

kin

dex

inst

ead

ofth

elo

gbo

ok-p

rice

ratio

isus

edas

pred

icto

rvar

iabl

e.

4

Page 36: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eIV

:Est

imat

ion

resu

ltsfo

rin

tern

atio

nali

nves

tor’

sVA

Rm

odel

(con

tinue

d)

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

Indi

aIn

done

sia

real

.bill

0.51

0.00

-0.0

10.

000.

000.

360.

49-0

.01

0.00

0.00

0.00

0.33

(0.0

0)(0

.46)

(0.0

1)(0

.15)

(0.5

8)(0

.00)

(0.0

6)(0

.21)

(0.2

0)(0

.87)

st.u

s-0

.66

0.11

0.02

0.03

0.00

0.06

-0.6

30.

19-0

.05

0.03

0.01

0.07

(0.4

4)(0

.26)

(0.6

9)(0

.04)

(0.8

0)(0

.45)

(0.0

5)(0

.24)

(0.0

3)(0

.56)

st.e

m3.

070.

290.

080.

050.

050.

080.

900.

61-0

.02

0.09

0.07

0.17

(0.0

8)(0

.18)

(0.4

1)(0

.09)

(0.0

5)(0

.63)

(0.0

0)(0

.86)

(0.0

0)(0

.00)

bp.u

s1.

33-0

.12

0.01

0.97

0.00

0.96

1.39

-0.2

00.

070.

970.

000.

96(0

.17)

(0.2

8)(0

.81)

(0.0

0)(0

.91)

(0.1

4)(0

.07)

(0.1

1)(0

.00)

(0.9

7)bp

.em

-5.0

4-0

.22

-0.0

8-0

.05

0.94

0.91

1.03

-0.6

10.

05-0

.02

0.94

0.88

(0.0

0)(0

.31)

(0.4

1)(0

.12)

(0.0

0)(0

.62)

(0.0

0)(0

.65)

(0.7

5)(0

.00)

Mal

aysi

aM

exic

ore

al.b

ill0.

49-0

.01

-0.0

10.

000.

000.

350.

480.

00-0

.01

0.00

0.00

0.33

(0.0

0)(0

.22)

(0.0

1)(0

.12)

(0.3

8)(0

.00)

(0.5

7)(0

.16)

(0.1

3)(0

.82)

st.u

s-0

.65

0.18

-0.0

70.

030.

010.

07-0

.51

-0.0

50.

170.

040.

020.

10(0

.45)

(0.0

8)(0

.40)

(0.0

4)(0

.57)

(0.5

3)(0

.68)

(0.0

8)(0

.01)

(0.1

2)st

.em

1.29

0.25

-0.0

10.

030.

050.

100.

640.

140.

100.

050.

040.

07(0

.15)

(0.0

3)(0

.92)

(0.0

8)(0

.07)

(0.6

2)(0

.46)

(0.4

7)(0

.06)

(0.0

6)bp

.us

1.30

-0.1

40.

060.

970.

000.

961.

270.

04-0

.14

0.97

-0.0

20.

96(0

.17)

(0.2

2)(0

.54)

(0.0

0)(0

.90)

(0.1

7)(0

.78)

(0.2

0)(0

.00)

(0.3

0)bp

.em

-0.4

6-0

.30

0.06

-0.0

20.

950.

900.

34-0

.19

-0.0

1-0

.04

0.96

0.95

(0.5

8)(0

.00)

(0.5

5)(0

.15)

(0.0

0)(0

.82)

(0.2

6)(0

.93)

(0.0

8)(0

.00)

Paki

stan

Peru

real

.bill

0.49

-0.0

20.

000.

000.

000.

320.

46-0

.01

-0.0

10.

000.

000.

35(0

.00)

(0.0

4)(0

.58)

(0.1

7)(0

.53)

(0.0

0)(0

.08)

(0.0

3)(0

.34)

(0.8

6)st

.us

-0.3

90.

090.

120.

030.

000.

11-0

.54

0.12

0.06

0.05

0.01

0.07

(0.5

7)(0

.35)

(0.0

0)(0

.03)

(0.9

3)(0

.50)

(0.2

1)(0

.33)

(0.0

3)(0

.32)

st.e

m-1

.34

0.20

0.07

0.00

0.07

0.06

0.29

0.28

-0.0

40.

090.

030.

10(0

.73)

(0.2

9)(0

.39)

(0.9

8)(0

.01)

(0.7

9)(0

.03)

(0.6

5)(0

.00)

(0.0

3)bp

.us

1.18

-0.0

7-0

.09

0.97

0.00

0.96

1.31

-0.1

0-0

.04

0.96

-0.0

10.

96(0

.16)

(0.5

1)(0

.06)

(0.0

0)(0

.91)

(0.1

5)(0

.36)

(0.5

2)(0

.00)

(0.4

9)bp

.em

2.66

-0.2

4-0

.08

0.02

0.93

0.90

1.59

-0.1

30.

00-0

.08

0.96

0.98

(0.4

7)(0

.21)

(0.3

5)(0

.55)

(0.0

0)(0

.27)

(0.3

8)(0

.97)

(0.0

1)(0

.00)

5

Page 37: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eV

:Est

imat

ion

resu

ltsfo

rin

tern

atio

nali

nves

tor’

sVA

Rm

odel

(con

tinue

d)

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

Phili

ppin

esPo

land

real

.bill

0.49

-0.0

10.

000.

000.

000.

330.

480.

00-0

.01

0.00

0.00

0.35

(0.0

0)(0

.03)

(0.3

7)(0

.31)

(0.9

0)(0

.00)

(0.6

1)(0

.02)

(0.3

7)(0

.39)

st.u

s-0

.57

0.17

-0.0

50.

040.

010.

07-0

.49

-0.0

30.

120.

030.

010.

09(0

.51)

(0.1

2)(0

.53)

(0.0

2)(0

.57)

(0.5

5)(0

.81)

(0.0

4)(0

.11)

(0.4

3)st

.em

1.64

0.40

-0.0

80.

100.

020.

170.

71-0

.12

0.19

0.01

0.05

0.04

(0.1

5)(0

.01)

(0.4

1)(0

.00)

(0.1

6)(0

.73)

(0.6

1)(0

.08)

(0.7

6)(0

.17)

bp.u

s1.

29-0

.16

0.06

0.97

0.00

0.96

1.27

0.01

-0.0

90.

98-0

.01

0.96

(0.1

9)(0

.20)

(0.4

2)(0

.00)

(0.9

2)(0

.18)

(0.9

1)(0

.14)

(0.0

0)(0

.69)

bp.e

m0.

87-0

.30

0.06

-0.0

60.

970.

960.

150.

10-0

.11

0.00

0.94

0.89

(0.4

8)(0

.04)

(0.5

0)(0

.00)

(0.0

0)(0

.92)

(0.5

7)(0

.18)

(0.9

8)(0

.00)

Rus

sia

Sout

hA

fric

are

al.b

ill0.

450.

00-0

.01

0.00

0.00

0.36

0.49

0.00

-0.0

20.

000.

000.

38(0

.00)

(0.7

3)(0

.00)

(0.0

4)(0

.41)

(0.0

0)(0

.73)

(0.0

0)(0

.10)

(0.7

2)st

.us

-0.5

90.

130.

010.

040.

010.

07-0

.67

0.12

0.02

0.04

0.03

0.07

(0.4

7)(0

.22)

(0.9

0)(0

.03)

(0.5

5)(0

.42)

(0.2

4)(0

.73)

(0.0

3)(0

.16)

st.e

m-0

.96

0.41

0.08

0.02

0.04

0.07

1.60

0.21

0.02

0.05

0.07

0.05

(0.6

1)(0

.07)

(0.4

9)(0

.55)

(0.0

4)(0

.35)

(0.3

0)(0

.88)

(0.0

4)(0

.07)

bp.u

s1.

32-0

.10

-0.0

10.

970.

000.

961.

42-0

.19

0.06

0.97

-0.0

30.

96(0

.15)

(0.4

2)(0

.82)

(0.0

0)(0

.77)

(0.1

4)(0

.11)

(0.4

0)(0

.00)

(0.2

6)bp

.em

3.16

-0.4

20.

03-0

.03

0.94

0.92

-2.3

6-0

.22

0.04

-0.0

30.

920.

86(0

.11)

(0.0

5)(0

.80)

(0.3

8)(0

.00)

(0.0

2)(0

.07)

(0.5

2)(0

.11)

(0.0

0)

Sout

hK

orea

Taiw

anre

al.b

ill0.

49-0

.01

-0.0

10.

000.

000.

330.

500.

00-0

.01

0.00

0.00

0.37

(0.0

0)(0

.34)

(0.2

3)(0

.14)

(0.9

4)(0

.00)

(0.7

3)(0

.00)

(0.1

4)(0

.38)

st.u

s-0

.67

0.24

-0.0

60.

030.

020.

08-0

.57

0.10

0.03

0.04

-0.0

10.

07(0

.42)

(0.1

1)(0

.45)

(0.0

5)(0

.34)

(0.5

1)(0

.34)

(0.7

0)(0

.10)

(0.7

4)st

.em

2.33

0.48

-0.0

70.

060.

100.

101.

220.

200.

050.

010.

080.

07(0

.22)

(0.0

9)(0

.55)

(0.1

0)(0

.02)

(0.4

0)(0

.28)

(0.6

8)(0

.72)

(0.1

7)bp

.us

1.42

-0.2

30.

070.

97-0

.02

0.96

1.30

-0.0

8-0

.02

0.97

0.01

0.96

(0.1

3)(0

.15)

(0.3

8)(0

.00)

(0.3

7)(0

.19)

(0.4

9)(0

.78)

(0.0

0)(0

.72)

bp.e

m-1

.01

-0.3

90.

09-0

.03

0.88

0.80

0.56

-0.2

60.

070.

010.

910.

89(0

.50)

(0.0

7)(0

.43)

(0.3

6)(0

.00)

(0.7

0)(0

.10)

(0.4

6)(0

.87)

(0.0

0)

6

Page 38: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eV

I:E

stim

atio

nre

sults

for

inte

rnat

iona

linv

esto

r’sV

AR

mod

el(c

ontin

ued)

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

real

.bill

st.u

sst

.em

bp.u

sbp

.em

R2

Tha

iland

Turk

eyre

al.b

ill0.

48-0

.01

-0.0

10.

000.

000.

370.

49-0

.02

0.00

0.00

0.00

0.32

(0.0

0)(0

.25)

(0.0

2)(0

.03)

(0.1

2)(0

.00)

(0.0

5)(0

.63)

(0.3

6)(0

.43)

st.u

s-0

.56

0.27

-0.1

30.

040.

000.

11-0

.62

0.11

0.01

0.03

0.00

0.06

(0.5

1)(0

.02)

(0.0

2)(0

.09)

(0.9

2)(0

.47)

(0.3

3)(0

.71)

(0.0

7)(0

.96)

st.e

m3.

410.

63-0

.20

0.08

0.02

0.14

3.09

0.79

-0.1

80.

000.

090.

08(0

.03)

(0.0

0)(0

.01)

(0.0

4)(0

.57)

(0.2

1)(0

.01)

(0.0

6)(0

.95)

(0.0

1)bp

.us

1.26

-0.2

90.

160.

970.

000.

971.

42-0

.09

-0.0

10.

970.

010.

96(0

.19)

(0.0

3)(0

.01)

(0.0

0)(0

.92)

(0.1

4)(0

.47)

(0.8

4)(0

.00)

(0.6

6)bp

.em

0.76

-0.8

80.

210.

100.

830.

811.

99-0

.64

0.26

0.04

0.91

0.85

(0.7

6)(0

.00)

(0.1

3)(0

.06)

(0.0

0)(0

.38)

(0.0

1)(0

.01)

(0.3

8)(0

.00)

7

Page 39: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Tabl

eV

II:R

esid

ualc

orre

latio

nsin

inte

rnat

iona

linv

esto

r’sV

AR

mod

el

st.u

sst

.em

bp.u

sbp

.em

st.u

sst

.em

bp.u

sbp

.em

st.u

sst

.em

bp.u

sbp

.em

st.u

sst

.em

bp.u

sbp

.em

Arg

entin

aB

razi

lC

hile

Chi

nare

al.b

ill0.

01-0

.04

-0.0

40.

140.

100.

13-0

.15

-0.0

40.

05-0

.10

-0.1

2-0

.04

0.03

0.00

-0.0

7-0

.11

st.u

s0.

41-0

.91

-0.4

30.

76-0

.92

-0.2

70.

60-0

.91

-0.3

30.

27-0

.92

-0.2

8st

.em

-0.2

7-0

.72

-0.6

6-0

.38

-0.5

2-0

.71

-0.1

8-0

.93

bp.u

s0.

360.

160.

240.

26

Col

ombi

aE

gypt

Indi

aIn

done

sia

real

.bill

0.02

0.02

-0.0

7-0

.09

0.04

-0.0

6-0

.08

-0.0

3-0

.12

-0.0

70.

110.

060.

02-0

.05

-0.0

3-0

.08

st.u

s0.

42-0

.92

-0.2

00.

38-0

.92

-0.2

90.

58-0

.93

-0.4

80.

49-0

.91

-0.2

6st

.em

-0.3

6-0

.57

-0.3

1-0

.77

-0.5

4-0

.85

-0.4

9-0

.41

bp.u

s0.

200.

320.

460.

24

Mal

aysi

aM

exic

oPa

kist

anPe

rure

al.b

ill-0

.08

-0.2

5-0

.01

0.16

0.04

-0.0

5-0

.08

-0.1

50.

03-0

.02

-0.0

70.

070.

03-0

.16

-0.0

7-0

.02

st.u

s0.

49-0

.89

-0.4

50.

81-0

.92

-0.6

50.

20-0

.92

-0.2

20.

37-0

.92

-0.2

3st

.em

-0.3

9-0

.86

-0.7

7-0

.78

-0.1

7-0

.80

-0.3

2-0

.71

bp.u

s0.

310.

700.

180.

25

Phili

ppin

esPo

land

Rus

sia

Sout

hA

fric

are

al.b

ill0.

010.

04-0

.06

-0.1

30.

05-0

.04

-0.0

9-0

.05

0.02

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1-0

.07

0.03

0.02

-0.0

8-0

.04

-0.0

2st

.us

0.46

-0.9

2-0

.46

0.67

-0.9

2-0

.61

0.65

-0.9

2-0

.58

0.66

-0.9

2-0

.53

st.e

m-0

.41

-0.8

7-0

.62

-0.8

7-0

.56

-0.8

5-0

.60

-0.6

5bp

.us

0.47

0.61

0.55

0.48

Sout

hK

orea

Taiw

anT

haila

ndTu

rkey

real

.bill

0.01

-0.0

8-0

.05

0.02

0.03

0.00

-0.0

7-0

.09

-0.0

4-0

.10

-0.0

1-0

.09

0.02

0.06

-0.0

6-0

.21

st.u

s0.

74-0

.92

-0.5

60.

61-0

.92

-0.6

10.

53-0

.91

-0.1

70.

62-0

.92

-0.5

1st

.em

-0.6

7-0

.80

-0.5

1-0

.92

-0.4

9-0

.54

-0.5

5-0

.80

bp.u

s0.

580.

600.

200.

51

Not

es:T

his

tabl

esh

ows

the

resi

dual

corr

elat

ions

corr

espo

ndin

gw

ithth

ein

tern

atio

nali

nves

tor’

sVA

Rm

odel

.Not

atio

n:re

al.b

ill=

real

retu

rnon

US

T-bi

ll(b

ench

mar

kas

set)

;st.u

s=

real

exce

ssre

turn

onU

Sst

ock

inde

x;st

.em=

real

exce

ssre

turn

onem

ergi

ngm

arke

tsto

ckin

dex

(in

US

dolla

rs);

nom

.bill=

natu

rall

ogar

ithm

ofno

min

alyi

eld

onU

ST-

bill;

bp.u

s=

natu

rall

ogar

ithm

ofbo

ok-p

rice

ratio

onU

Sst

ock

inde

x;bp

.em=

natu

rall

ogar

ithm

ofbo

ok-p

rice

ratio

onem

ergi

ngm

arke

tsto

ckin

dex

(in

US

dolla

rs).

ForB

razi

l,th

elo

gpr

ice-

earn

ing

ratio

onth

edo

mes

ticst

ock

inde

xin

stea

dof

the

log

book

-pri

cera

tiois

used

aspr

edic

torv

aria

ble.

8

Page 40: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure I: Domestic investors’ total and intertemporal hedging demand for stocks

9

Page 41: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure II: Domestic investors’ total and intertemporal hedging demand for stocks

10

Page 42: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure III: Domestic investors’ total and intertemporal hedging demand for stocks

11

Page 43: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure IV: Domestic investors’ total and intertemporal hedging demand for stocks

12

Page 44: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure V: Domestic investors’ total and intertemporal hedging demand for stocks

13

Page 45: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure VI: Domestic investors’ total and intertemporal hedging demand for stocks

14

Page 46: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure VII: Domestic investors’ total and intertemporal hedging demand for stocks

15

Page 47: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure VIII: International investors’ total and intertemporal hedging demand for stocks

16

Page 48: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure IX: International investors’ total and intertemporal hedging demand for stocks

17

Page 49: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure X: International investors’ total and intertemporal hedging demand for stocks

18

Page 50: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure XI: International investors’ total and intertemporal hedging demand for stocks

19

Page 51: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure XII: International investors’ total and intertemporal hedging demand for stocks

20

Page 52: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure XIII: International investors’ total and intertemporal hedging demand for stocks

21

Page 53: Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from Emerging Markets Laura Spierdijka,∗, Zaghum Umara aUniversity of Groningen, Faculty

Figure XIV: International investors’ total and intertemporal hedging demand for stocks

22