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Transcript of Stocks for the Long Run? Evidence from Emerging Markets · Stocks for the Long Run? Evidence from...
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
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.
2
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.
3
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
4
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.
5
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).
6
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.
7
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
8
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
9
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.
10
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.
11
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
12
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.
13
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
14
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.
15
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.
16
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
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
18
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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22
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
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
Tabl
e3:
Dom
estic
inve
stor
s’to
tal,
myo
pic
and
inte
rtem
pora
lhed
ging
dem
and
for
asse
ts
Arg
entin
aB
razi
lC
hile
Stoc
ksB
ills
Stoc
ksB
ills
Stoc
ksB
ills
CR
RA
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
225
.910
.315
.674
.189
.7-1
5.6
56.7
45.7
11.0
43.3
54.3
-11.
042
2.9
375.
447
.5-3
22.9
-275
.4-4
7.5
L-5
2.1
-48.
5-7
.0-3
.931
.0-3
8.3
-86.
8-7
4.8
-15.
1-1
00.3
-66.
2-3
7.2
159.
519
8.2
-102
.5-5
86.3
-452
.5-1
97.6
U10
3.9
69.0
38.3
152.
114
8.5
7.0
200.
316
6.2
37.2
186.
817
4.8
15.1
686.
355
2.5
197.
6-5
9.5
-98.
210
2.5
530
.05.
025
.070
.095
.0-2
5.0
27.4
18.4
9.0
72.6
81.6
-9.0
185.
515
0.1
35.3
-85.
5-5
0.1
-35.
3L
-11.
4-1
8.6
2.6
28.6
71.4
-47.
4-3
5.3
-29.
8-8
.09.
833
.4-2
6.1
20.6
79.3
-96.
2-2
50.3
-121
.0-1
66.8
U71
.428
.647
.411
1.4
118.
6-2
.690
.266
.626
.113
5.3
129.
88.
035
0.3
221.
016
6.8
79.4
20.7
96.2
730
.83.
926
.869
.296
.1-2
6.8
21.2
13.2
8.0
78.8
86.8
-8.0
136.
110
7.2
28.9
-36.
1-7
.2-2
8.9
L-2
.5-1
3.0
5.6
36.0
79.1
-48.
0-2
4.5
-21.
2-5
.433
.152
.4-2
1.4
4.6
56.6
-80.
4-1
67.6
-57.
8-1
38.2
U64
.020
.948
.010
2.5
113.
0-5
.666
.947
.621
.412
4.5
121.
25.
426
7.6
157.
813
8.2
95.4
43.4
80.4
1031
.33.
228
.268
.796
.8-2
8.2
16.4
9.3
7.1
83.6
90.7
-7.1
98.2
75.0
23.1
1.8
25.0
-23.
1L
3.9
-8.7
7.8
41.2
84.9
-48.
5-1
6.1
-14.
8-3
.451
.166
.6-1
7.5
-2.8
39.7
-63.
3-9
9.2
-10.
4-1
09.5
U58
.815
.148
.596
.110
8.7
-7.8
48.9
33.4
17.5
116.
111
4.8
3.4
199.
211
0.4
109.
510
2.8
60.3
63.3
Chi
naC
olom
bia
Egy
ptSt
ocks
Bill
sSt
ocks
Bill
sSt
ocks
Bill
sC
RR
ATo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ng2
46.3
22.9
23.3
53.7
77.1
-23.
315
5.3
170.
7-1
5.4
-55.
3-7
0.7
15.4
54.8
53.1
1.7
45.2
46.9
-1.7
L-5
4.0
-31.
8-2
3.5
-46.
622
.4-7
0.2
-23.
413
.7-7
3.5
-234
.0-2
27.7
-42.
6-5
0.2
-42.
0-2
1.5
-59.
8-4
8.1
-25.
0U
146.
677
.670
.215
4.0
131.
823
.533
4.0
327.
742
.612
3.4
86.3
73.5
159.
814
8.1
25.0
150.
214
2.0
21.5
543
.410
.333
.156
.689
.7-3
3.1
62.3
68.2
-5.9
37.7
31.8
5.9
26.2
21.5
4.7
73.8
78.5
-4.7
L-3
1.8
-11.
5-2
1.3
-18.
767
.8-8
7.5
-20.
95.
4-4
7.2
-45.
4-3
1.0
-35.
4-2
0.9
-16.
6-1
3.3
26.7
40.4
-22.
6U
118.
732
.287
.513
1.8
111.
521
.314
5.4
131.
035
.412
0.9
94.6
47.2
73.3
59.6
22.6
120.
911
6.6
13.3
741
.27.
933
.258
.892
.1-3
3.2
45.8
48.7
-2.9
54.2
51.3
2.9
20.9
15.5
5.4
79.1
84.5
-5.4
L-2
3.2
-7.7
-16.
5-5
.576
.5-8
3.0
-16.
33.
8-3
6.2
-7.8
6.5
-30.
4-1
4.2
-11.
8-1
0.0
44.0
57.2
-20.
8U
105.
523
.583
.012
3.2
107.
716
.510
7.8
93.5
30.4
116.
396
.236
.256
.042
.820
.811
4.2
111.
810
.010
38.4
6.1
32.3
61.6
93.9
-32.
333
.634
.0-0
.466
.466
.00.
417
.011
.06.
083
.089
.0-6
.0L
-14.
8-4
.8-1
0.9
8.3
83.0
-75.
5-1
1.9
2.6
-26.
620
.834
.6-2
5.9
-9.0
-8.1
-7.4
56.9
69.9
-19.
5U
91.7
17.0
75.5
114.
810
4.8
10.9
79.2
65.4
25.9
111.
997
.426
.643
.130
.119
.510
9.0
108.
17.
4In
dia
Indo
nesi
aM
alay
sia
Stoc
ksB
ills
Stoc
ksB
ills
Stoc
ksB
ills
CR
RA
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
299
.166
.532
.60.
933
.5-3
2.6
55.8
38.9
17.0
44.2
61.1
-17.
028
6.8
144.
314
2.5
-186
.8-4
4.3
-142
.5L
23.9
19.3
0.3
-74.
4-1
3.7
-65.
0-1
04.8
-80.
1-2
6.8
-116
.5-5
7.8
-60.
810
3.8
50.9
47.3
-369
.7-1
37.7
-237
.6U
174.
411
3.7
65.0
76.1
80.7
-0.3
216.
515
7.8
60.8
204.
818
0.1
26.8
469.
723
7.7
237.
6-3
.849
.1-4
7.3
558
.826
.532
.341
.273
.5-3
2.3
28.5
15.6
12.9
71.5
84.4
-12.
923
4.4
57.5
176.
9-1
34.4
42.5
-176
.9L
8.1
7.6
-3.8
-9.5
54.6
-68.
3-4
5.8
-32.
0-1
5.4
-2.9
36.8
-41.
285
.320
.160
.3-2
83.4
5.1
-293
.4U
109.
545
.468
.391
.992
.43.
810
2.9
63.2
41.2
145.
813
2.0
15.4
383.
494
.929
3.4
14.7
79.9
-60.
37
47.2
18.9
28.3
52.8
81.1
-28.
322
.211
.211
.077
.888
.8-1
1.0
205.
340
.916
4.3
-105
.359
.1-1
64.3
L4.
55.
4-4
.710
.167
.6-6
1.2
-32.
4-2
2.8
-11.
023
.254
.8-3
3.0
75.2
14.2
56.7
-235
.432
.4-2
71.9
U89
.932
.461
.295
.594
.64.
776
.845
.233
.013
2.4
122.
811
.033
5.4
67.6
271.
924
.885
.8-5
6.7
1037
.113
.223
.962
.986
.8-2
3.9
17.2
7.9
9.3
82.8
92.1
-9.3
172.
828
.514
4.2
-72.
871
.5-1
44.2
L1.
63.
8-5
.427
.477
.3-5
3.2
-22.
0-1
6.0
-7.3
43.6
68.3
-26.
063
.99.
850
.6-1
81.7
52.8
-237
.8U
72.6
22.7
53.2
98.4
96.2
5.4
56.4
31.7
26.0
122.
011
6.0
7.3
281.
747
.223
7.8
36.1
90.2
-50.
6
Not
es:
Ford
omes
ticin
vest
ors
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.
25
Tabl
e4:
Dom
estic
inve
stor
s’to
tal,
myo
pic
and
inte
rtem
pora
lhed
ging
dem
and
for
asse
ts(c
ontin
ued)
Mex
ico
Paki
stan
Peru
Stoc
ksB
ills
Stoc
ksB
ills
Stoc
ksB
ills
CR
RA
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
216
1.6
126.
834
.7-6
1.6
-26.
8-3
4.7
108.
969
.040
.0-8
.931
.0-4
0.0
143.
313
8.3
5.0
-43.
3-3
8.3
-5.0
L23
.921
.7-1
8.0
-199
.3-1
32.0
-87.
527
.516
.46.
4-9
0.4
-21.
5-7
3.6
-5.9
13.1
-46.
0-1
92.5
-163
.5-5
6.0
U29
9.3
232.
087
.576
.178
.318
.019
0.4
121.
573
.672
.583
.6-6
.429
2.5
263.
556
.010
5.9
86.9
46.0
587
.151
.236
.012
.948
.8-3
6.0
69.6
27.9
41.7
30.4
72.1
-41.
766
.755
.611
.133
.344
.4-1
1.1
L11
.79.
1-1
0.6
-62.
56.
8-8
2.6
19.0
6.8
8.1
-20.
351
.0-7
5.4
-5.9
5.5
-27.
1-3
9.2
-5.7
-49.
3U
162.
593
.282
.688
.390
.910
.612
0.3
49.0
75.4
81.0
93.2
-8.1
139.
210
5.7
49.3
105.
994
.527
.17
70.6
36.7
33.8
29.4
63.3
-33.
857
.720
.137
.642
.379
.9-3
7.6
52.3
39.8
12.5
47.7
60.2
-12.
5L
11.8
6.7
-5.2
-29.
333
.2-7
2.9
16.8
5.0
8.3
1.4
64.8
-66.
9-2
.14.
1-1
8.0
-6.8
24.4
-43.
1U
129.
366
.872
.988
.293
.35.
298
.635
.266
.983
.295
.0-8
.310
6.8
75.6
43.1
102.
195
.918
.010
57.5
25.9
31.6
42.5
74.1
-31.
647
.214
.233
.052
.885
.8-3
3.0
41.7
28.0
13.6
58.3
72.0
-13.
6L
12.8
4.9
0.1
-2.3
53.0
-63.
115
.03.
68.
420
.675
.1-5
7.5
1.8
3.0
-9.9
18.5
46.9
-37.
2U
102.
347
.063
.187
.295
.1-0
.179
.424
.957
.585
.096
.4-8
.481
.553
.137
.298
.297
.09.
9Ph
ilipp
ines
Pola
ndR
ussi
aSt
ocks
Bill
sSt
ocks
Bill
sSt
ocks
Bill
sC
RR
ATo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ng2
83.8
58.6
25.1
16.2
41.4
-25.
115
.910
.05.
884
.190
.0-5
.810
8.0
69.7
38.4
-8.0
30.3
-38.
4L
-53.
5-3
8.0
-25.
5-1
21.0
-55.
3-7
5.8
-88.
1-5
4.4
-34.
9-1
9.8
25.6
-46.
633
.720
.87.
3-8
2.4
-18.
5-6
9.5
U22
1.0
155.
375
.815
3.5
138.
025
.511
9.8
74.4
46.6
188.
115
4.4
34.9
182.
411
8.5
69.5
66.3
79.2
-7.3
555
.923
.532
.444
.176
.5-3
2.4
11.9
4.0
7.8
88.1
96.0
-7.8
69.1
27.6
41.6
30.9
72.4
-41.
6L
-28.
5-1
5.1
-21.
3-4
0.2
37.9
-86.
0-5
3.8
-21.
7-3
3.5
22.5
70.2
-49.
122
.98.
010
.1-1
5.4
52.9
-73.
0U
140.
262
.186
.012
8.5
115.
121
.377
.529
.849
.115
3.8
121.
733
.511
5.4
47.1
73.0
77.1
92.0
-10.
17
49.5
16.8
32.7
50.5
83.2
-32.
710
.82.
97.
989
.297
.1-7
.958
.019
.538
.542
.080
.5-3
8.5
L-1
9.5
-10.
8-1
5.4
-18.
455
.6-8
0.7
-42.
5-1
5.5
-28.
435
.978
.7-4
4.2
20.6
5.6
11.0
4.5
66.5
-66.
0U
118.
444
.480
.711
9.5
110.
815
.464
.121
.344
.214
2.5
115.
528
.495
.533
.566
.079
.494
.4-1
1.0
1044
.311
.832
.655
.788
.2-3
2.6
9.9
2.0
7.8
90.1
98.0
-7.8
48.5
13.5
35.0
51.5
86.5
-35.
0L
-10.
8-7
.5-8
.80.
568
.9-7
4.0
-32.
3-1
0.9
-22.
947
.985
.1-3
8.6
18.9
3.8
11.8
21.9
76.7
-58.
1U
99.5
31.1
74.0
110.
810
7.5
8.8
52.1
14.9
38.6
132.
311
0.9
22.9
78.1
23.3
58.1
81.1
96.2
-11.
8So
uth
Afr
ica
Sout
hK
orea
Taiw
anSt
ocks
Bill
sSt
ocks
Bill
sSt
ocks
Bill
sC
RR
ATo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ng2
185.
713
8.2
47.5
-85.
7-3
8.2
-47.
510
3.4
63.6
39.8
-3.4
36.4
-39.
834
.720
.014
.765
.380
.0-1
4.7
L67
.452
.21.
5-2
04.0
-124
.2-9
3.5
14.1
8.6
3.2
-92.
6-1
8.6
-76.
4-5
4.4
-31.
1-2
4.1
-23.
728
.9-5
3.4
U30
4.0
224.
293
.532
.647
.8-1
.519
2.6
118.
676
.485
.991
.4-3
.212
3.7
71.1
53.4
154.
413
1.1
24.1
510
2.6
55.2
47.4
-2.6
44.8
-47.
465
.025
.539
.535
.074
.5-3
9.5
22.1
7.6
14.6
77.9
92.4
-14.
6L
34.1
20.8
2.9
-71.
110
.4-9
1.9
7.8
3.5
2.1
-22.
352
.5-7
7.0
-41.
6-1
2.8
-29.
714
.172
.1-5
8.8
U17
1.1
89.6
91.9
65.9
79.2
-2.9
122.
347
.577
.092
.296
.5-2
.185
.927
.958
.814
1.6
112.
829
.77
82.4
39.4
43.0
17.6
60.6
-43.
052
.218
.234
.047
.881
.8-3
4.0
17.1
5.2
11.9
82.9
94.8
-11.
9L
28.1
14.8
4.8
-36.
836
.0-8
1.3
5.9
2.5
1.5
1.5
66.1
-66.
5-3
6.7
-9.3
-28.
229
.180
.3-5
2.0
U13
6.8
64.0
81.3
71.9
85.2
-4.8
98.5
33.9
66.5
94.1
97.5
-1.5
70.9
19.7
52.0
136.
710
9.3
28.2
1066
.127
.538
.533
.972
.5-3
8.5
40.5
12.8
27.7
59.5
87.2
-27.
712
.13.
48.
787
.996
.6-8
.7L
24.1
10.3
7.2
-8.0
55.2
-69.
94.
41.
81.
123
.476
.2-5
4.4
-31.
7-6
.8-2
5.8
44.0
86.4
-43.
2U
108.
044
.869
.975
.989
.7-7
.276
.623
.854
.495
.698
.2-1
.156
.013
.643
.213
1.7
106.
825
.8
26
Tabl
e5:
Dom
estic
inve
stor
s’to
tal,
myo
pic
and
inte
rtem
pora
lhed
ging
dem
and
for
asse
ts(c
ontin
ued)
Tha
iland
Turk
eyU
SSt
ocks
Bill
sSt
ocks
Bill
sSt
ocks
Bill
sC
RR
ATo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ng2
99.5
71.4
28.1
0.5
28.6
-28.
1-3
.21.
3-4
.610
3.2
98.7
4.6
46.0
28.6
17.4
54.0
71.4
-17.
4L
17.6
13.7
-0.9
-81.
4-2
9.1
-57.
0-7
5.9
-47.
2-2
9.7
30.5
50.1
-20.
6-1
25.2
-69.
6-5
7.9
-117
.2-2
6.9
-92.
7U
181.
412
9.1
57.0
82.4
86.3
0.9
69.5
49.9
20.6
175.
914
7.2
29.7
217.
212
6.9
92.7
225.
216
9.6
57.9
554
.628
.925
.745
.471
.1-2
5.7
-1.9
3.1
-5.0
101.
996
.95.
029
.511
.418
.170
.588
.6-1
8.1
L9.
15.
8-0
.40.
048
.0-5
1.7
-42.
4-1
6.3
-27.
461
.477
.6-1
7.4
-94.
1-2
7.9
-68.
6-5
3.2
49.2
-104
.8U
100.
052
.051
.790
.994
.20.
438
.622
.417
.414
2.4
116.
327
.415
3.2
50.8
104.
819
4.1
127.
968
.67
42.9
20.8
22.1
57.1
79.2
-22.
1-1
.23.
4-4
.610
1.2
96.6
4.6
23.6
8.2
15.5
76.4
91.8
-15.
5L
7.5
4.3
0.1
21.8
62.7
-44.
0-3
2.7
-10.
4-2
3.7
69.7
82.8
-14.
4-8
0.9
-20.
0-6
3.1
-28.
263
.7-9
4.1
U78
.237
.344
.092
.595
.7-0
.130
.317
.214
.413
2.7
110.
423
.712
8.2
36.3
94.1
180.
912
0.0
63.1
1033
.114
.718
.466
.985
.3-1
8.4
-0.5
3.7
-4.2
100.
596
.34.
217
.85.
712
.182
.294
.3-1
2.1
L6.
33.
10.
740
.073
.7-3
6.1
-24.
7-6
.1-2
0.1
76.4
86.6
-11.
7-6
7.4
-14.
0-5
5.4
-3.1
74.6
-79.
6U
60.0
26.3
36.1
93.7
96.9
-0.7
23.6
13.4
11.7
124.
710
6.1
20.1
103.
125
.479
.616
7.4
114.
055
.4A
rgen
tina
with
outb
ook-
pric
era
tioC
hile
with
outb
ook-
pric
era
tioM
alay
sia
with
outb
ook-
pric
era
tioSt
ocks
Bill
sSt
ocks
Bill
sSt
ocks
Bill
sC
RR
ATo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ngTo
tal
Myo
pic
Hed
ging
Tota
lM
yopi
cH
edgi
ng2
13.3
10.2
3.1
86.7
89.8
-3.1
364.
037
5.0
-11.
1-2
64.0
-275
.011
.118
2.9
140.
642
.3-8
2.9
-40.
6-4
2.3
L-1
00.2
-107
.5-7
.5-2
6.8
-27.
9-1
3.7
135.
114
9.2
-55.
5-4
92.8
-500
.9-3
3.4
26.9
25.4
-13.
4-2
38.9
-155
.7-9
8.1
U12
6.8
127.
913
.720
0.2
207.
57.
559
2.8
600.
933
.4-3
5.1
-49.
255
.533
8.9
255.
798
.173
.174
.613
.45
10.6
4.9
5.7
89.4
95.1
-5.7
143.
515
0.0
-6.5
-43.
5-5
0.0
6.5
89.7
55.9
33.8
10.3
44.1
-33.
8L
-34.
6-4
2.2
-1.9
44.3
48.1
-13.
349
.359
.6-3
4.0
-137
.6-1
40.4
-20.
96.
69.
8-1
3.8
-72.
9-2
.0-8
1.5
U55
.751
.913
.313
4.6
142.
21.
923
7.6
240.
420
.950
.740
.434
.017
2.9
102.
081
.593
.490
.213
.87
10.1
3.9
6.2
89.9
96.1
-6.2
102.
410
7.1
-4.7
-2.4
-7.1
4.7
67.3
39.7
27.6
32.7
60.3
-27.
6L
-22.
4-2
9.7
-0.5
57.5
62.6
-13.
034
.542
.5-2
5.8
-70.
3-7
1.7
-16.
43.
46.
8-1
1.7
-31.
227
.3-6
6.9
U42
.537
.413
.012
2.4
129.
70.
517
0.3
171.
716
.465
.557
.525
.813
1.2
72.7
66.9
96.6
93.2
11.7
109.
73.
16.
690
.396
.9-6
.671
.875
.0-3
.128
.225
.03.
149
.327
.621
.650
.772
.4-2
1.6
L-1
3.4
-20.
40.
467
.273
.4-1
2.8
23.7
29.7
-18.
9-1
9.9
-20.
2-1
2.6
1.5
4.5
-9.4
3.0
49.2
-52.
7U
32.8
26.6
12.8
113.
412
0.4
-0.4
119.
912
0.2
12.6
76.3
70.3
18.9
97.0
50.8
52.7
98.5
95.5
9.4
27
Tabl
e6:
Inte
rnat
iona
linv
esto
rs’t
otal
,myo
pic
and
inte
rtem
pora
lhed
ging
dem
and
for
asse
ts
Arg
entin
aB
razi
lSt
ocks
USA
Stoc
ksA
rgen
tina
Bill
sUSA
Stoc
ksU
SASt
ocks
Bra
zil
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
252
.735
.217
.4-1
0.1
-8.5
-1.6
57.5
73.3
-15.
8-2
56.7
-202
.5-5
4.2
165.
514
7.1
18.4
191.
215
5.4
35.8
L-1
45.6
-76.
6-8
3.2
-121
.2-1
03.1
-24.
9-1
49.7
-43.
2-1
12.4
-580
.0-4
14.7
-197
.343
.538
.6-4
.4-5
6.9
25.7
-100
.6U
250.
914
7.1
118.
010
1.0
86.0
21.7
264.
718
9.7
80.7
66.6
9.7
88.9
287.
525
5.6
41.2
439.
328
5.1
172.
25
30.0
13.9
16.2
-4.4
-3.3
-1.1
74.4
89.4
-15.
1-1
25.9
-81.
5-4
4.4
72.1
59.1
13.0
153.
812
2.4
31.4
L-1
15.2
-30.
8-9
1.9
-56.
0-4
1.1
-21.
2-6
9.9
42.8
-116
.6-3
28.0
-166
.4-1
81.1
18.8
15.7
-3.4
-22.
370
.5-1
01.5
U17
5.3
58.6
124.
247
.234
.519
.021
8.6
136.
086
.576
.13.
492
.312
5.3
102.
529
.433
0.0
174.
316
4.3
722
.09.
812
.2-2
.9-2
.3-0
.680
.992
.5-1
1.6
-96.
4-5
8.5
-37.
952
.842
.310
.414
3.6
116.
127
.4L
-100
.7-2
2.1
-84.
3-4
1.5
-29.
3-1
7.6
-39.
459
.2-1
01.8
-260
.2-1
19.1
-156
.213
.911
.3-2
.6-2
.779
.1-8
8.1
U14
4.8
41.8
108.
735
.624
.716
.420
1.2
125.
878
.667
.52.
180
.491
.673
.423
.528
9.8
153.
214
3.0
1014
.26.
87.
4-1
.6-1
.6-0
.187
.594
.8-7
.3-7
3.2
-41.
2-3
2.0
38.0
29.8
8.2
135.
311
1.4
23.9
L-8
5.7
-15.
6-7
4.4
-29.
9-2
0.5
-13.
9-9
.371
.5-8
3.4
-201
.9-8
3.7
-129
.510
.28.
1-1
.918
.585
.5-7
1.6
U11
4.1
29.1
89.2
26.6
17.3
13.8
184.
311
8.1
68.8
55.4
1.2
65.4
65.7
51.5
18.2
252.
113
7.4
119.
3C
hile
Chi
naSt
ocks
USA
Stoc
ksC
hile
Bill
sUSA
Stoc
ksU
SASt
ocks
Chi
naB
illsU
SAC
RA
ATo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
ge2
-73.
2-1
38.9
65.7
252.
622
0.3
32.3
-79.
418
.7-9
8.0
34.1
9.8
24.3
72.3
41.5
30.8
-6.3
48.7
-55.
0L
-297
.5-2
92.1
-59.
778
.273
.3-1
2.4
-303
.3-9
1.7
-229
.4-1
97.9
-122
.1-7
9.8
-20.
6-1
0.1
-15.
5-2
73.4
-90.
4-1
85.5
U15
1.0
14.3
191.
142
6.9
367.
277
.014
4.6
129.
033
.326
6.0
141.
712
8.3
165.
193
.077
.126
0.7
187.
975
.45
11.5
-55.
867
.311
2.7
88.3
24.4
-24.
267
.5-9
1.7
33.1
3.8
29.3
52.9
16.6
36.3
14.0
79.6
-65.
6L
-140
.1-1
17.0
-52.
330
.529
.5-1
1.0
-191
.023
.4-2
21.9
-131
.2-4
9.0
-85.
8-2
0.2
-4.0
-19.
9-1
87.6
23.9
-213
.6U
163.
05.
418
6.9
194.
914
7.1
59.7
142.
711
1.6
38.6
197.
456
.614
4.3
126.
037
.292
.621
5.6
135.
382
.47
17.1
-40.
057
.082
.863
.219
.70.
176
.8-7
6.7
28.4
2.6
25.8
45.1
11.9
33.2
26.5
85.5
-59.
0L
-109
.0-8
3.7
-47.
221
.421
.2-9
.3-1
40.3
45.3
-191
.1-1
09.1
-35.
1-7
7.1
-18.
5-2
.9-1
8.6
-144
.745
.7-1
92.2
U14
3.2
3.7
161.
314
4.3
105.
248
.714
0.5
108.
337
.716
5.9
40.4
128.
610
8.7
26.6
85.1
197.
612
5.3
74.2
1016
.4-2
8.1
44.5
59.5
44.3
15.2
24.1
83.8
-59.
722
.31.
820
.537
.18.
328
.840
.789
.9-4
9.2
L-8
4.9
-58.
7-4
2.1
14.6
14.9
-7.7
-89.
561
.7-1
55.1
-88.
5-2
4.6
-66.
4-1
6.0
-2.0
-16.
3-9
8.7
62.0
-162
.3U
117.
62.
513
1.0
104.
573
.738
.113
7.7
105.
835
.813
3.0
28.2
107.
390
.218
.773
.918
0.0
117.
863
.8C
olom
bia
Egy
ptSt
ocks
USA
Stoc
ksC
olom
bia
Bill
sUSA
Stoc
ksU
SASt
ocks
Egy
ptB
illsU
SAC
RA
ATo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
ge2
30.5
-95.
212
5.7
215.
719
3.5
22.2
-146
.21.
7-1
48.0
3.8
-20.
424
.162
.274
.2-1
2.0
34.0
46.2
-12.
2L
-175
.5-2
08.6
-22.
446
.855
.9-3
0.7
-409
.9-1
28.7
-298
.2-1
94.0
-137
.8-8
3.7
-81.
3-6
2.8
-42.
8-1
83.4
-89.
0-1
17.2
U23
6.5
18.1
273.
838
4.7
331.
275
.111
7.4
132.
22.
320
1.6
97.1
132.
020
5.8
211.
318
.925
1.3
181.
392
.95
64.4
-38.
210
2.6
96.3
77.4
18.9
-60.
760
.8-1
21.5
5.6
-8.5
14.1
22.3
29.9
-7.6
72.1
78.6
-6.5
L-8
7.1
-83.
5-2
9.7
16.2
22.3
-18.
7-2
41.5
8.6
-258
.5-1
37.9
-55.
5-9
4.9
-39.
8-2
4.9
-29.
2-7
4.2
24.5
-110
.9U
215.
97.
223
4.8
176.
413
2.4
56.5
120.
111
3.0
15.5
149.
138
.512
3.0
84.4
84.7
14.1
218.
313
2.6
97.9
755
.6-2
7.3
82.9
71.0
55.3
15.8
-26.
672
.0-9
8.7
2.9
-6.2
9.1
15.6
21.5
-5.9
81.5
84.8
-3.2
L-7
0.4
-59.
7-2
9.6
11.5
15.9
-14.
1-1
74.4
34.7
-215
.5-1
18.0
-39.
8-8
7.3
-30.
0-1
7.7
-23.
5-3
9.9
46.2
-95.
4U
181.
65.
119
5.4
130.
694
.645
.612
1.2
109.
318
.112
3.9
27.4
105.
661
.160
.611
.720
3.0
123.
488
.910
43.7
-19.
262
.851
.338
.712
.65.
080
.5-7
5.5
-0.3
-4.5
4.2
10.7
15.1
-4.4
89.7
89.4
0.3
L-5
6.8
-41.
8-2
8.4
8.1
11.2
-10.
2-1
11.1
54.4
-170
.1-9
8.6
-28.
1-7
7.1
-22.
2-1
2.3
-18.
4-7
.962
.4-7
7.1
U14
4.2
3.5
154.
094
.566
.235
.412
1.1
106.
619
.197
.919
.085
.443
.542
.69.
518
7.2
116.
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
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)
Indi
aIn
done
sia
Stoc
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
-74.
9-2
2.2
134.
190
.843
.362
.984
.1-2
1.2
2.1
-50.
352
.488
.273
.314
.99.
877
.1-6
7.3
L-3
41.6
-219
.3-1
36.3
40.7
27.5
7.5
-142
.3-2
6.4
-126
.7-1
97.6
-201
.2-7
5.1
-73.
8-6
4.3
-14.
5-2
17.5
-27.
6-2
09.2
U14
7.6
69.5
92.0
227.
515
4.1
79.2
268.
219
4.6
84.4
201.
810
0.5
180.
025
0.1
210.
844
.323
7.0
181.
774
.65
-42.
8-3
0.0
-12.
876
.736
.240
.566
.193
.7-2
7.7
18.2
-20.
238
.540
.329
.411
.041
.490
.8-4
9.4
L-2
12.1
-87.
8-1
34.0
21.3
10.9
5.3
-88.
149
.4-1
44.3
-116
.6-8
0.5
-77.
6-3
2.7
-25.
6-1
0.5
-120
.749
.0-1
77.6
U12
6.6
27.9
108.
313
2.2
61.6
75.7
220.
313
8.0
89.0
153.
140
.115
4.5
113.
384
.432
.420
3.6
132.
778
.87
-30.
6-2
1.4
-9.2
60.2
25.8
34.4
70.4
95.6
-25.
115
.1-1
4.5
29.6
29.8
21.0
8.8
55.1
93.5
-38.
4L
-171
.4-6
2.7
-116
.316
.17.
74.
1-6
0.4
63.9
-129
.5-9
7.1
-57.
5-7
0.2
-23.
8-1
8.3
-8.4
-79.
063
.6-1
48.2
U11
0.1
19.9
97.8
104.
343
.964
.620
1.3
127.
279
.312
7.3
28.6
129.
483
.560
.326
.018
9.1
123.
471
.410
-22.
1-1
5.0
-7.1
45.8
18.0
27.8
76.3
96.9
-20.
610
.6-1
0.2
20.8
21.6
14.7
6.8
67.8
95.4
-27.
7L
-134
.8-4
3.9
-96.
711
.95.
43.
1-3
0.4
74.7
-109
.1-7
9.4
-40.
3-6
1.1
-17.
0-1
2.8
-6.4
-38.
874
.5-1
17.3
U90
.514
.082
.479
.730
.752
.418
3.0
119.
167
.810
0.7
20.0
102.
760
.142
.220
.117
4.4
116.
462
.0M
alay
sia
Mex
ico
Stoc
ksU
SASt
ocks
Mal
aysi
aB
illsU
SASt
ocks
USA
Stoc
ksM
exic
oB
illsU
SAC
RA
ATo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
ge2
-87.
7-8
5.1
-2.5
299.
119
8.4
100.
8-1
11.5
-13.
2-9
8.2
-437
.3-3
48.8
-88.
537
6.7
318.
358
.316
0.7
130.
530
.2L
-299
.5-2
17.1
-104
.413
4.6
87.4
30.4
-315
.6-1
15.6
-212
.9-8
09.5
-612
.6-2
37.4
159.
814
0.1
-5.6
-66.
111
.6-1
00.7
U12
4.2
46.9
99.3
463.
630
9.3
171.
192
.789
.116
.4-6
5.2
-85.
160
.459
3.5
496.
612
2.3
387.
524
9.4
161.
05
-18.
5-3
4.1
15.6
184.
079
.410
4.6
-65.
554
.6-1
20.2
-206
.7-1
40.6
-66.
117
3.0
128.
144
.913
3.7
112.
521
.2L
-166
.4-8
6.9
-95.
378
.835
.029
.4-2
36.4
13.7
-258
.0-4
23.0
-246
.1-2
02.8
68.7
56.7
-6.7
-33.
364
.9-1
09.7
U12
9.4
18.8
126.
528
9.3
123.
917
9.7
105.
495
.617
.79.
7-3
5.0
70.6
277.
219
9.4
96.5
300.
616
0.1
152.
07
-6.7
-24.
417
.614
9.2
56.8
92.4
-42.
567
.6-1
10.0
-154
.0-1
00.9
-53.
112
8.3
91.8
36.5
125.
710
9.1
16.7
L-1
31.4
-62.
1-8
2.0
62.1
25.0
25.2
-192
.738
.4-2
37.3
-326
.0-1
76.3
-169
.850
.040
.9-6
.0-1
4.2
75.0
-97.
7U
118.
013
.411
7.2
236.
388
.515
9.6
107.
896
.817
.218
.0-2
5.4
63.6
206.
614
2.8
78.9
265.
714
3.1
131.
110
-0.6
-17.
116
.511
7.3
39.8
77.5
-16.
777
.3-9
4.0
-113
.0-7
1.1
-41.
893
.164
.628
.411
9.9
106.
513
.4L
-102
.3-4
3.5
-68.
547
.317
.620
.3-1
43.5
56.8
-205
.1-2
45.7
-124
.0-1
36.9
35.6
28.9
-5.1
7.1
82.7
-81.
6U
101.
19.
410
1.5
187.
462
.113
4.7
110.
097
.717
.019
.8-1
8.3
53.2
150.
510
0.3
61.9
232.
613
0.3
108.
4Pa
kist
anPe
ruSt
ocks
USA
Stoc
ksPa
kist
anB
illsU
SASt
ocks
USA
Stoc
ksPe
ruB
illsU
SAC
RA
ATo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
geTo
tal
Myo
pic
Hed
ge2
30.8
-1.2
32.0
111.
274
.336
.8-4
1.9
26.9
-68.
837
.9-6
9.9
107.
926
9.0
205.
863
.2-2
06.9
-35.
9-1
71.0
L-1
87.1
-115
.5-8
2.1
20.5
15.0
1.0
-284
.5-1
00.9
-192
.0-1
67.2
-174
.4-2
7.8
88.3
78.9
-8.9
-475
.1-1
71.6
-318
.0U
248.
711
3.1
146.
020
1.8
133.
672
.720
0.7
154.
754
.424
3.1
34.6
243.
544
9.7
332.
713
5.2
61.3
99.9
-24.
15
31.5
-0.6
32.1
61.4
29.8
31.6
7.1
70.8
-63.
875
.8-2
8.4
104.
213
5.9
83.0
52.9
-111
.745
.4-1
57.1
L-1
33.3
-46.
3-9
3.0
8.5
6.1
-1.4
-170
.719
.7-1
95.7
-82.
6-7
0.2
-29.
236
.432
.3-8
.3-3
02.4
-8.8
-301
.4U
196.
345
.115
7.3
114.
353
.564
.618
4.9
122.
068
.223
4.1
13.4
237.
723
5.4
133.
811
4.1
79.0
99.7
-12.
97
25.4
-0.5
25.9
47.1
21.3
25.7
27.5
79.2
-51.
767
.2-2
0.5
87.7
103.
759
.644
.1-7
0.9
60.9
-131
.8L
-114
.0-3
3.2
-85.
45.
64.
4-2
.0-1
21.5
42.7
-168
.3-6
6.6
-50.
4-2
8.6
26.9
23.4
-6.3
-228
.822
.2-2
56.9
U16
4.8
32.1
137.
388
.538
.353
.517
6.6
115.
864
.920
1.0
9.3
204.
018
0.5
95.9
94.5
87.0
99.7
-6.7
1017
.8-0
.518
.334
.714
.919
.847
.585
.5-3
8.1
54.4
-14.
669
.077
.442
.135
.3-3
1.8
72.5
-104
.3L
-95.
1-2
3.3
-75.
23.
33.
1-2
.3-7
2.5
59.9
-135
.6-5
4.0
-35.
5-2
7.2
20.0
16.7
-4.2
-157
.445
.4-2
07.3
U13
0.8
22.4
111.
866
.226
.841
.916
7.4
111.
159
.416
2.8
6.3
165.
213
4.8
67.4
74.8
93.8
99.7
-1.4
29
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
-90.
0-8
7.5
-65.
1-2
2.4
81.3
66.4
14.9
106.
298
.77.
6L
-134
.9-1
67.2
-38.
4-1
52.4
-100
.1-6
3.3
-313
.7-8
1.6
-238
.7-4
02.8
-264
.8-1
48.1
-27.
9-2
0.3
-16.
1-1
31.3
-33.
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
.934
3.7
230.
311
9.7
553
.0-1
1.0
64.0
50.1
35.3
14.7
-3.0
75.6
-78.
7-4
1.0
-26.
7-1
4.2
38.2
26.9
11.3
102.
899
.82.
9L
-72.
8-6
6.8
-47.
5-9
1.3
-40.
2-6
0.3
-198
.027
.4-2
28.5
-239
.0-1
06.5
-139
.9-1
6.4
-7.8
-15.
7-6
3.3
47.2
-114
.6U
178.
744
.817
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
-47.
7-4
5.1
-72.
9-2
8.7
-51.
8-1
45.5
48.2
-196
.1-1
90.6
-76.
4-1
20.4
-13.
3-5
.4-1
3.9
-35.
462
.5-1
01.3
U15
1.2
32.0
150.
414
7.8
79.1
76.3
181.
111
7.1
66.3
129.
537
.698
.269
.844
.131
.724
0.0
137.
610
5.7
1034
.6-5
.540
.127
.217
.69.
738
.287
.9-4
9.7
-23.
1-1
3.9
-9.1
20.3
13.7
6.6
102.
810
0.2
2.6
L-5
2.0
-33.
4-4
1.3
-56.
5-2
0.2
-42.
3-9
3.1
63.7
-158
.6-1
48.1
-53.
8-9
9.3
-10.
7-3
.6-1
1.9
-7.1
73.9
-83.
7U
121.
322
.412
1.5
110.
955
.361
.616
9.4
112.
159
.110
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
Stoc
ksR
ussi
aB
illsU
SASt
ocks
USA
Stoc
ksSo
uth
Afr
ica
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
2-2
74.4
-165
.3-1
09.1
172.
113
1.4
40.6
202.
313
3.8
68.5
-145
.2-1
51.4
6.2
182.
816
1.1
21.6
62.4
90.3
-27.
9L
-612
.9-3
59.3
-272
.251
.943
.2-1
.6-4
8.4
8.4
-71.
0-4
03.7
-312
.8-1
27.7
59.3
53.1
-5.2
-151
.7-1
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
.6-6
6.8
-88.
884
.253
.031
.317
1.3
113.
857
.5-5
3.2
-61.
17.
982
.164
.917
.271
.196
.3-2
5.1
L-3
72.1
-144
.5-2
38.2
21.3
17.7
-3.7
-5.5
63.6
-76.
3-2
33.2
-125
.7-1
27.7
26.2
21.7
-3.2
-92.
954
.1-1
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
.7-1
03.6
-200
.415
.012
.8-3
.611
.974
.1-6
7.6
-188
.8-9
0.0
-113
.819
.815
.7-2
.0-6
1.7
67.3
-134
.5U
53.5
7.4
54.3
111.
263
.353
.730
4.1
145.
916
3.7
112.
22.
212
5.0
102.
377
.431
.121
6.3
127.
594
.210
-92.
5-3
4.0
-58.
546
.026
.819
.214
6.4
107.
139
.3-2
8.7
-31.
02.
344
.932
.812
.183
.898
.2-1
4.4
L-2
28.5
-72.
9-1
61.8
10.4
9.2
-3.2
30.3
82.0
-55.
7-1
50.0
-63.
3-9
7.7
15.2
11.2
-0.8
-29.
377
.2-1
10.3
U43
.64.
944
.881
.644
.541
.626
2.6
132.
313
4.3
92.6
1.3
102.
374
.654
.425
.019
6.9
119.
381
.5
30
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
.8-2
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
-449
.9-3
21.9
-151
.832
.724
.31.
6-1
22.0
0.9
-136
.9-2
00.3
-118
.3-8
6.1
-58.
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
.931
7.9
245.
986
.022
7.2
128.
710
2.6
129.
783
.148
.322
3.9
169.
257
.75
-66.
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
.6-1
29.4
-138
.215
.210
.1-0
.2-7
9.1
60.6
-147
.9-1
42.7
-47.
5-9
8.7
-37.
3-1
4.7
-24.
5-5
6.3
49.9
-108
.7U
118.
815
.611
9.3
160.
284
.680
.923
6.6
158.
686
.216
0.1
51.3
112.
383
.433
.352
.019
2.9
127.
867
.67
-47.
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
.9-9
2.8
-118
.711
.17.
4-0
.6-5
1.4
71.9
-129
.6-1
21.6
-34.
0-9
0.5
-30.
2-1
0.5
-21.
4-2
9.3
64.3
-95.
7U
103.
310
.910
5.1
123.
360
.666
.921
2.6
141.
976
.913
2.1
36.5
98.5
68.7
23.8
46.6
180.
211
9.9
62.5
10-3
5.0
-29.
0-6
.050
.024
.026
.084
.910
5.0
-20.
01.
30.
80.
516
.04.
711
.382
.894
.5-1
1.8
L-1
54.1
-65.
3-9
8.5
7.9
5.4
-0.8
-21.
280
.4-1
06.3
-101
.1-2
3.9
-79.
7-2
3.3
-7.3
-17.
5-2
.175
.0-7
8.9
U84
.27.
386
.592
.142
.752
.819
1.1
129.
566
.210
3.7
25.5
80.7
55.3
16.7
40.1
167.
611
4.0
55.4
Tha
iland
Turk
eySt
ocks
USA
Stoc
ksT
haila
ndB
illsU
SASt
ocks
USA
Stoc
ksTu
rkey
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
211
.4-6
0.7
72.1
103.
888
.415
.4-1
5.1
72.4
-87.
5-8
6.0
-70.
3-1
5.7
71.7
51.9
19.8
114.
311
8.4
-4.1
L-1
99.6
-203
.1-6
2.3
-42.
7-3
4.7
-13.
8-2
62.4
-43.
7-2
34.9
-335
.6-2
17.7
-130
.36.
14.
5-0
.8-1
06.7
-2.6
-113
.5U
222.
381
.620
6.5
250.
221
1.4
44.6
232.
118
8.4
59.8
163.
777
.198
.913
7.3
99.3
40.4
335.
323
9.4
105.
25
39.0
-24.
463
.350
.235
.614
.610
.888
.8-7
8.0
-36.
4-2
8.0
-8.4
36.3
20.6
15.7
100.
010
7.3
-7.3
L-1
13.2
-81.
4-6
6.7
-19.
2-1
3.7
-9.9
-171
.842
.3-2
20.9
-207
.3-8
6.9
-128
.81.
51.
7-2
.4-6
0.2
58.9
-124
.6U
191.
132
.619
3.3
119.
684
.839
.219
3.4
135.
364
.913
4.6
31.0
112.
171
.239
.633
.726
0.2
155.
711
0.0
734
.2-1
7.4
51.7
38.3
25.5
12.8
27.4
91.9
-64.
5-2
6.2
-19.
9-6
.327
.014
.712
.399
.210
5.2
-6.0
L-9
3.6
-58.
2-6
1.2
-13.
6-9
.7-7
.6-1
24.7
58.6
-188
.2-1
67.8
-62.
0-1
12.5
0.3
1.1
-2.7
-35.
170
.6-1
10.0
U16
2.1
23.3
164.
590
.360
.733
.217
9.6
125.
259
.311
5.5
22.2
100.
053
.728
.327
.323
3.5
139.
898
.010
26.7
-12.
238
.928
.918
.010
.944
.494
.3-4
9.8
-19.
1-1
3.9
-5.2
19.2
10.2
8.9
99.9
103.
6-3
.7L
-76.
5-4
0.8
-54.
1-9
.1-6
.7-5
.3-7
7.2
70.9
-151
.6-1
32.1
-43.
4-9
4.0
-0.7
0.7
-2.9
-8.3
79.4
-91.
0U
129.
916
.313
2.0
66.8
42.6
27.0
166.
111
7.6
51.9
94.0
15.6
83.6
39.1
19.7
20.8
208.
112
7.8
83.5
31
Supplementary Material (for online publication only)
1
Tabl
eI:
Est
imat
ion
resu
ltsfo
rdo
mes
ticin
vest
or’s
VAR
mod
el
real
.bill
stoc
kbp
R2
real
.bill
stoc
kbp
R2
real
.bill
stoc
kbp
R2
real
.bill
stoc
kbp
R2
Arg
entin
aB
razi
lC
hile
Chi
nare
al.b
ill0.
50-0
.03
0.01
0.39
0.67
-0.0
10.
000.
500.
490.
000.
000.
250.
120.
000.
000.
04(0
.13)
(0.0
1)(0
.47)
(0.0
0)(0
.10)
(0.4
4)(0
.00)
(0.5
9)(0
.38)
(0.1
9)(0
.89)
(0.0
9)st
ock
0.80
0.18
0.02
0.05
0.24
0.17
0.06
0.08
1.39
0.10
0.00
0.04
-1.4
60.
040.
050.
04(0
.21)
(0.1
3)(0
.52)
(0.8
2)(0
.05)
(0.0
2)(0
.08)
(0.2
4)(0
.71)
(0.2
3)(0
.68)
(0.0
4)bp
-0.7
6-0
.08
0.97
0.93
2.58
-0.1
40.
890.
82-0
.59
-0.1
00.
980.
961.
42-0
.02
0.96
0.93
(0.4
4)(0
.23)
(0.0
0)(0
.10)
(0.3
0)(0
.00)
(0.5
0)(0
.34)
(0.0
0)(0
.27)
(0.8
1)(0
.00)
Col
ombi
aE
gypt
Indi
aIn
done
sia
real
.bill
0.69
-0.0
10.
000.
510.
45-0
.01
0.00
0.23
0.97
0.00
0.00
0.94
0.30
-0.0
10.
000.
11(0
.00)
(0.2
0)(0
.56)
(0.0
0)(0
.15)
(0.2
6)(0
.00)
(0.4
0)(0
.96)
(0.0
1)(0
.05)
(0.2
8)st
ock
0.00
0.16
0.00
0.03
0.13
0.25
0.01
0.06
-2.2
90.
140.
050.
04-0
.48
0.20
0.06
0.12
(1.0
0)(0
.06)
(0.9
2)(0
.88)
(0.0
1)(0
.70)
(0.2
3)(0
.10)
(0.0
6)(0
.35)
(0.0
4)(0
.00)
bp0.
38-0
.21
0.99
0.98
0.61
-0.1
90.
970.
952.
27-0
.18
0.93
0.90
0.49
-0.2
50.
940.
88(0
.81)
(0.0
6)(0
.00)
(0.5
0)(0
.04)
(0.0
0)(0
.29)
(0.0
5)(0
.00)
(0.5
7)(0
.09)
(0.0
0)
Mal
aysi
aM
exic
oPa
kist
anPe
rure
al.b
ill0.
32-0
.01
0.00
0.13
0.57
0.00
0.00
0.39
0.23
0.00
0.01
0.12
0.33
0.00
0.00
0.30
(0.0
0)(0
.32)
(0.0
6)(0
.00)
(0.5
7)(0
.05)
(0.0
1)(0
.71)
(0.0
1)(0
.00)
(0.6
1)(0
.00)
stoc
k-1
.29
0.16
0.08
0.07
-1.6
70.
120.
020.
030.
720.
090.
050.
041.
060.
060.
000.
01(0
.23)
(0.0
6)(0
.00)
(0.0
9)(0
.20)
(0.1
4)(0
.57)
(0.2
4)(0
.02)
(0.3
2)(0
.56)
(0.6
9)bp
1.36
-0.1
30.
940.
891.
10-0
.08
0.97
0.95
-0.1
7-0
.12
0.93
0.90
-2.9
3-0
.06
0.99
0.98
(0.2
4)(0
.15)
(0.0
0)(0
.29)
(0.3
7)(0
.00)
(0.9
0)(0
.11)
(0.0
0)(0
.09)
(0.6
4)(0
.00)
Phili
ppin
esPo
land
Rus
sia
Sout
hA
fric
are
al.b
ill0.
530.
000.
000.
340.
620.
000.
000.
390.
55-0
.01
0.00
0.37
0.27
0.00
0.01
0.22
(0.0
0)(0
.64)
(0.0
1)(0
.00)
(0.5
8)(0
.70)
(0.0
0)(0
.14)
(0.0
2)(0
.00)
(0.9
9)(0
.00)
stoc
k-1
.70
0.16
0.02
0.04
-2.0
30.
100.
050.
06-0
.07
0.17
0.04
0.04
-0.2
40.
080.
040.
02(0
.12)
(0.0
3)(0
.23)
(0.0
3)(0
.17)
(0.0
7)(0
.96)
(0.0
9)(0
.07)
(0.7
7)(0
.41)
(0.1
3)bp
2.44
-0.1
30.
970.
961.
68-0
.04
0.95
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
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
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
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
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
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
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
-0.1
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
Figure I: Domestic investors’ total and intertemporal hedging demand for stocks
9
Figure II: Domestic investors’ total and intertemporal hedging demand for stocks
10
Figure III: Domestic investors’ total and intertemporal hedging demand for stocks
11
Figure IV: Domestic investors’ total and intertemporal hedging demand for stocks
12
Figure V: Domestic investors’ total and intertemporal hedging demand for stocks
13
Figure VI: Domestic investors’ total and intertemporal hedging demand for stocks
14
Figure VII: Domestic investors’ total and intertemporal hedging demand for stocks
15
Figure VIII: International investors’ total and intertemporal hedging demand for stocks
16
Figure IX: International investors’ total and intertemporal hedging demand for stocks
17
Figure X: International investors’ total and intertemporal hedging demand for stocks
18
Figure XI: International investors’ total and intertemporal hedging demand for stocks
19
Figure XII: International investors’ total and intertemporal hedging demand for stocks
20
Figure XIII: International investors’ total and intertemporal hedging demand for stocks
21
Figure XIV: International investors’ total and intertemporal hedging demand for stocks
22