Download Boom and Bust of Equity Portfolio Flows to Emerging Markets A

Boom and Bust of Equity Portfolio Flows to Emerging Markets
A South African Perspective
Hayley Reynolds
Francis Kemegue
Extended abstract
Abstract
This paper attempts to provide an alternative explanation to capital flows to South Africa. Boom and
Busts have characterized capital flows to emerging markets for centuries. Early documented booms of
capital flows are portfolio investments in the new world (Peru) in the late 1800’s.
Between 1994 and 2005 there was a surge of portfolio flows to South Africa, and these flows
represented a higher proportion of GDP than portfolio flows to other emerging markets.
Current explanations of such the relative importance of portfolio equity flows to South Africa have
hinged on the characteristics of the South African economy, identifying policy variables affecting the
risk profile of the country.
This paper attempts the pinpoint the effect of the global context on the flows by empirically assuming
that country characteristics can be accurately perceived by the returns on a stock market index. It thus
compares the resulting behavior of returns across countries and establishes that there have been wide
co-movements in risk and returns profiles to portfolio equity to emerging markets.
Furthermore, the paper establishes that South Africa catches a wave of capital flows to emerging
markets, and the characteristics of portfolio equity flows makes them more likely to be important in a
country joining the wave. This would not necessarily have something to do with other types of flows.
We conclude that it is difficult to attribute the surge in portfolio equity inflows to absolute domestic
policy therefore; discouraging portfolio equity would not necessary lead to an increase in other types
of flows.
Francis Kemegue: University of Pretoria and Framingham State University,
[email protected]
Hayley Reynolds; South African Treasury
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Introduction
The political and consequent economic reform in South Africa, following the transition to a democracy,
resulted in a transformation of the balance of payments from 1994 onwards. This transformation has
partially been realised by an influx of non-residential capital flows, enabling the South African
government to run a continued current account deficit, particularly over the 2004 to 2007 period
(Aron, Leape & Thomas, 2010:5). Interestingly, capital flows into South Africa have consisted primarily
of portfolio investment (mainly in the form of bonds and equities), relative to foreign direct
investment (FDI). This is in contrast with many other developing and emerging economies that have
seen FDI play a more dominant role (Ahmed, Arezki, & Funke, 2005:3). Ahmed et al. (2005:3) show
that portfolio flows constituted 70 per cent of capital inflows between 1994 and 2002, while FDI
inflows only amounted to 30 per cent. According to Aron et al. (2010:6-7), average annual net FDI
inflows have also been low relative to other middle-income economies. For the period 2003 to 2007,
FDI inflows amounted to 2.9 per cent of GDP for middle-income countries and only 1.1 per cent of GDP
in South Africa. South Africa might have different drivers affecting its capital inflows and composition.
Because of globalisation, there are many factors that play a role in the decisions made by investors,
including the locations they choose to invest in and whether they prefer investments with a shorter or
longer time horizon. Markets and financial instruments have become more and more sophisticated
and it is not always easy to understand why investors make the choices they do. Add to this:
speculation, information asymmetries, the heterogeneity inherent in individuals, as well as the
differing and in some cases volatile circumstances across countries; one is bound to come up with a
variety of factors that influence individuals and firms in their investing activities, and thus capital
flows.
Different flows can have very different implications for any economy. Foreign Direct Investment (FDI)
is by and large considered to be the most resilient and least volatile form of private capital flows.
These flows are associated with the transfer of new technologies and provide additional sources of
financing that may otherwise not have been available in the recipient economy. Improvements in
labour skills, improved access to markets and increasing welfare are additional benefits associated
with longer-term foreign capital investments. The benefits of FDI are especially amplified in
economies with well-functioning, transparent institutions, trade liberalisation, good governance and
predictable legal environments (Ahmed et al., 2005; Albuquerque, 2003; Alfaro, Kalemli-Ozcan &
Sayek, 2009; Blalock & Gertler, 2004; Lesher & Miroudot, 2008).
In contrast to FDI flows, portfolio investment flows can be very volatile: they can be good for the
recipient economy in that they aid countries in financing current account deficits – particularly in
South Africa’s case where the domestic level of savings is extremely low. However, they can also lead
to detrimental economic effects through shifts in market sentiment that can result in sudden and large
reversals of such flows (Ahmed et al., 2005; Cardoso & Goldfajn, 1998; Frankel, Smit & Sturzenegger,
2008; IMF, 2011).
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It is widely documented in the literature that factors that play a role in attracting FDI inflows are quite
different to those that encourage portfolio flows. For this reason, as well as the fact that South Africa’s
capital inflows are predominantly in the form of portfolio inflows, this paper focuses on portfolio
inflows, and more specifically portfolio equity inflows. While bond inflows are also important, this
paper is concerned with equity markets and equity flows. The drivers of equity and bond flows are
different as investors have different objectives with respect to the two. Bonds are primarily “carry
trade”/interest differential driven and are influenced by inflation expectations, while equity flows are
return driven and investors look at economic growth expectations (Bezuidenhout, 2010).
This paper differs from the majority of empirical studies in this area in that it seeks to understand the
co-movements of booms and busts of portfolio equity flows using the perspective of a well-diversified
global investor. In doing so, it seeks to relate South Africa to other emerging market economies with
respect to surges in equity portfolio inflows, and risk and return profiles, and ultimately establishes
the degree of co-movement across countries.
Most studies have focused on financial liberalization and on other countries, while not many have tried
to look at South Africa specifically and its relationship with other countries in respect of risk and
portfolio flows. The paper seeks to explain whether such flows were driven by South Africa’s unique
characteristics or whether South Africa’s transition to democracy in 1994 and concomitant financial
liberalization merely coincided with a global surge in portfolio flows.
We find that portfolio equity to South Africa is influenced by global factors affecting the diversification
strategy of a global investor. It results that marginal policy changes affecting domestic risk profile are
less likely to be significant in changing the composition of flows.
The remainder of this extended abstract is the empirical methodology, the preliminary results and
conclusion.
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EMPIRICAL ANALYSIS
The analysis considers stock prices and returns from the point of view of a well-diversified investor.
It is always useful to have an initial glimpse of the data being analysed. A graphical
representation of the log-transformed stock market indexes is shown in Figure 4. All show a
clear upward trend and a glance with the naked eye could easily lead one to think they are a
replica of one another from 2004 onwards. The shock from the financial crisis led to a sudden
downward movement in all stock indexes towards the end of 2008, after which there was a
moderate improvement from 2009 onwards. However, it is evident that there has not been as
much enthusiasm in equities since the crisis when compared to the lead-up to the crisis
during 2004 to 2007.
Figure 1: The log-transformed stock market indexes
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ln_southafrica
ln_msciem
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ln_sp500
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ln_brazil
ln_india
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ln_indonesia
7
ln_malaysia
ln_mexico
6
ln_pakistan
5
ln_philippines
ln_southkor
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1.1
RETURN PROFILES
The actual returns of the stock market indexes were generated through the log-transformation of the
indexes, after which first differences were applied. Classical methods of estimation are based on the
assumption that the variables are stationary. A process is stationary if 𝐸[𝑌𝑡 ] is same for all t; 𝑉𝑎𝑟[𝑌𝑡 ] is
the same for all t; and 𝐶𝑜𝑣(𝑌𝑡 , 𝑌𝑡−𝑘 ) is the same for all t, for every 𝑘 > 0. However, many
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macroeconomic time-series variables are characterized by common trends or unit roots. Economic
theory regarding stock markets suggests that stock market data is volatile and one can safely assume a
stochastic, not deterministic trend. A joint test for stationarity by Levin, Lin and Chu, as well as a
Dickey Fuller test for the individual series shows that the log-transformed series in differences are
indeed stationary processes (I(1)), i.e. they are mean reverting.
The Hodrick-Prescott (HP) Filter was applied to the data in order to extract a smoothed series for each
country’s returns. The HP Filter is a smoothing technique that is widely used among macroeconomists
to obtain a smooth estimate of the long-term trend component of a series. The HP Filter arose from a
paper by Hodrick and Prescott (1997) in which they devised a method of separating a time series into
two components: a smoothly varying trend component and a cyclical component. Their conceptual
framework is that a given time series (𝑦𝑡 ) is the sum of a growth component (𝑔𝑡 ) and a cyclical
component (𝑐𝑡 ):
𝑦𝑡 = 𝑔𝑡 + 𝑐𝑡
for t = 1, …, T.
They measured the smoothness of 𝑔𝑡 as the sum of the squares of its second difference and saw 𝑐𝑡
(which average near zero over long time horizons) as deviations from 𝑔𝑡 . The growth component can
thus be determined by the following equation:
𝑇
𝑇
min {∑ 𝑐𝑡2 + 𝜆 ∑[(𝑔𝑡 − 𝑔𝑡−1 ) − (𝑔𝑡−1 − 𝑔𝑡−2 )]2 }
{𝑔𝑡 }𝑇
𝑡=−1
𝑡=1
𝑡=1
𝜆 is a positive number that penalises variability in the growth component and the larger it is, the
smoother the series. In the instance of monthly data, the help function on Stata recommends using a
value of 129 600 for the smoothing parameter (λ).
With the series being smoothed, it is possible to explore the co-movement of actual returns. This was
done by regressing the returns on one another and extracting the R-squared values. R-squared refers
to the fraction of variance explained by a model, and in this instance refers to the fraction of variance
in stock Y explained by the variance in stock X. Importantly, there are many transformations that can
be applied to a variable – deflation, seasonal adjustment, logging, and differencing. All of them alter the
variance and can even change the units in which variance is measured. Thus, if the dependent variable
in the regression model has already been transformed in some way, it is possible that much of the
variance has already been "explained" merely by the choice of an appropriate transformation.
Whether the R-squared be measured on the variance of the original series, the deflated series, the
seasonally adjusted series, and/or the differenced series depends very much on the situation.
A common question is what a good value is for R-squared. This depends on how it is measured. If it is
measured as a percentage of the variance of the original real series, then a simple time series model
will easily achieve an R-squared of more than 90 per cent. Alternatively, if you measured it as a
percentage of a stationary series, a value of 25 per cent is even good. Decision 411 Forecasting (2005)
states that an R-squared of 10 per cent or even as little as 5 per cent may be statistically significant
with respect to predicting stock returns.
It has thus been established that an R-squared can be used to determine how well the variance in one
stock describes the variance in another stock. Some stocks naturally move in similar directions at
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similar times and, by implication, the highest R-Squared value will give an indication of which stocks
are the best. With this in mind, a well-diversified investor would most likely pick a combination of
equities that have low R-squared values in relation to one another so as to spread his risk and diversify
his investment portfolio.
The results are quite interesting in that higher values are obtained for countries that are
geographically close to one another. A regression encompassing Brazilian and Mexican returns, from
South and Central America respectively, resulted in an R-squared of 0.8, while the only strong
variance-explanatory relationship within the BRICS grouping was between India and South Africa,
where the R-squared was 0.79. These values are very high considering the above explanation of Rsquared – even though the series has been transformed into a stationary process and further
smoothed using the HP filter, the values are still very high – showing a strong link between the
variance of returns in these countries being explained substantially by the variance in returns of their
emerging market peers. There seems to be a strong relationship between the stock market returns of
the countries in Asia – India, Indonesia, Malaysia, the Philippines and South Korea. India displayed an
R-squared of 0.64 and 0.52 with Indonesia and South Korea respectively; Indonesia showed an of 0.77,
0.85 and 0.58 with Malaysia, the Philippines and South Korea respectively; Malaysia resulted in Rsquared values of 0.78 and 0.84 against Philippines and South Korea respectively; while Pakistan and
South Korea had an R-squared of 0.68.
These results are valuable in that they imply that the stock markets of countries in close geographic
proximity to one another are definitely influenced by one another’s variance. This is most likely due to
regional shocks that spill over from one economy to another and are perhaps more pronounced or
take quicker effect on countries near to the source of change in market sentiment. For this reason, a
well-diversified global investor may take precaution against investing all his/her funds into one
region.
This is particularly interesting in light of the results obtained by the IMF (2011) paper, where they
found that (in addition to domestic factors) regional factors have increasingly dominated global
factors in attracting capital inflows. It is perhaps this strong variance-explaining relationship found
between the stock market returns of countries in close proximity to one another that leads investors to
treat countries in the same region similarly with respect to investing decisions.
In order to test whether there is synchronicity1 between the actual returns, a Vector Autoregressive
(VAR) model was specified. A multivariate k-dimensional stochastic process 𝑦⃗𝑡 follows a VAR(p) model
if
𝑦⃗𝑡 = 𝑐 + 𝐴1 𝑦⃗𝑡−1 + ⋯ + 𝐴𝑝 𝑦⃗𝑡−𝑝 + 𝜀⃗𝑡
With c a k-vector and 𝐴1 , …, 𝐴𝑝 matrices of size k x k. it is a direct extension of an AR(p) process used to
model the relationship among several time series (Croux, 2011).
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Synchronicity refers to the simultaneous occurrence of events that appear significantly related but have no
discernible causal connection. This is important because it is not the aim of this paper to determine causal links.
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Within a VAR framework, the development of a variable is explained by the development of potential
explanatory variables. The value is explained by its own history and simultaneously by considering
various variables and their history (Füss, 2008).
One drawback is that VAR models themselves do not allow one to draw conclusions about causal
relationships, which require an underlying economic model. Accordingly, causality is not the objective
of this paper and so VAR models are seen as the most appropriate technique to capture co-movements.
VAR models do permit interpretations about the dynamic relationship between the indicated variables
(Füss, 2008).
One very important drawback of a VAR model is that one can not include too many variables as doing
so severely reduces the degrees of freedom. For this reason, it is not prudent to include all nine
countries into one VAR model as doing so would result in biased estimates. In light of this, it was
decided to split the countries up into three groups – the BRICS countries, the Central and South
American countries, and the Asian countries.
Before conducting this analysis, it was necessary to determine the most appropriate lag length for the
models. Doing so is essentially a trade-off between the curse of dimensionality and condensed models,
which do not appropriately show the dynamic adjustment. If the lag length is too short,
autocorrelation of the error terms could lead to apparently significant and inefficient estimators.
Hence, results would be biased. However, even with a relatively small lag length, one would still need
a large number of parameters if required. On the other hand, with increasing number of parameters,
the degrees of freedom decrease, potentially leading to inefficient estimators (Füss, 2008).
The lag lengths were selected using the Akaike Information Criterion (AIC), which Torres-Reyna
suggests to be the best information criterion procedure for monthly data. The information criteria are
combined out of the squared sum of residuals and a penalty term for the number of lags (Füss, 2008).
The most appropriate lag length for the BRICS is one, while it is two for the Central/South American
group and Asian countries.
The resulting system of equations (showing only the statistically significant2 relationships) for the VAR
models is as follows:
VAR(1):
𝑠𝑜𝑢𝑡ℎ𝑎𝑓𝑟𝑖𝑐𝑎𝑟 = 0.007 + 0.23𝑠𝑜𝑢𝑡ℎ𝑎𝑓𝑟𝑖𝑐𝑎𝑟𝑡−1
𝑏𝑟𝑎𝑧𝑖𝑙𝑟 = 0.009 + 0.19𝑏𝑟𝑎𝑧𝑖𝑙𝑟𝑡−1
𝑖𝑛𝑑𝑖𝑎𝑟 = 0.19𝑏𝑟𝑎𝑧𝑖𝑙𝑡−1
VAR(2):
𝑖𝑛𝑑𝑖𝑎𝑟 = 0.21𝑖𝑛𝑑𝑜𝑛𝑒𝑠𝑖𝑎𝑡−2 + 0.16𝑚𝑎𝑙𝑎𝑦𝑠𝑖𝑎𝑟𝑡−1 + 0.2𝑝ℎ𝑖𝑙𝑖𝑝𝑝𝑖𝑛𝑒𝑠𝑡−1
𝑖𝑛𝑑𝑜𝑛𝑒𝑠𝑖𝑎𝑟 = 0.01 + 0.19𝑖𝑛𝑑𝑜𝑛𝑒𝑠𝑖𝑎𝑟𝑡−1 − 0.26𝑖𝑛𝑑𝑜𝑛𝑒𝑠𝑖𝑎𝑟𝑡−2 + 0.3𝑚𝑎𝑙𝑎𝑦𝑠𝑖𝑎𝑟𝑡−1 +
0.24𝑝ℎ𝑖𝑙𝑖𝑝𝑝𝑖𝑛𝑒𝑠𝑟𝑡−1
𝑚𝑎𝑙𝑎𝑦𝑠𝑖𝑎𝑟 = 0.16𝑝ℎ𝑖𝑙𝑖𝑝𝑝𝑖𝑛𝑒𝑠𝑟𝑡−1 + 0.21𝑝ℎ𝑖𝑙𝑖𝑝𝑝𝑖𝑛𝑒𝑠𝑟𝑡−2
2
At the 95 per cent confidence interval
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𝑝ℎ𝑖𝑙𝑖𝑝𝑝𝑖𝑛𝑒𝑠𝑟 = −0.22𝑖𝑛𝑑𝑜𝑛𝑒𝑠𝑖𝑎𝑟𝑡−1 + 0.25𝑚𝑎𝑙𝑎𝑦𝑠𝑖𝑎𝑟𝑡−1
VAR(2):
𝑏𝑟𝑎𝑧𝑖𝑙𝑟 = −0.12𝑏𝑟𝑎𝑧𝑖𝑙𝑟𝑡−2 + 0.5𝑚𝑒𝑥𝑖𝑐𝑜𝑟𝑡−1
𝑚𝑒𝑥𝑖𝑐𝑜𝑟 = 0.014 + 0.11𝑏𝑟𝑎𝑧𝑖𝑙𝑟𝑡−2 − 0.15𝑚𝑒𝑥𝑖𝑐𝑜𝑟𝑡−2
For the BRICS group, the only significant relationship between countries is that of India’s returns and
Brazil’s returns lagged by one month. Looking at the Asian group, strong regional influences seem to
be at play. India’s returns have positive relationships with those of Indonesia, Malaysia and the
Philippines. Indonesia’s returns are positively related to those of Malaysia and the Philippines, and
interestingly a negative relationship exists with its own returns lagged by two months. This is most
likely due to the volatile nature of stock markets that are characterized by constant up and down
movements in value. Malaysian returns have a positive relationship with both monthly lags of the
Philippine returns, while the Philippine returns have a negative relationship with Indonesian returns
and a positive one with those of Malaysia. Regional forces seem to be evident for Brazil and Mexico too.
Brazilian returns are strongly positively correlated with those of Mexico (lagged by one month). In
addition, Mexican returns show a positive relationship with those of Brazil (lagged by two months).
Again, the results are fascinating as regional relationships seem to be quite strong in that there are
positive correlations between the returns of stock indexes where the countries are located close to one
another. Because stock market prices are driven by investor sentiment, i.e. the purchasing of equities
(by both residents and non-residents), and because the countries studied experience high trading
volumes by foreigners, one could assume that a large deal of foreign purchases do in fact influence
share prices. This implies then that portfolio equity inflows would follow, or rather lead, similar trends
with respect to regional influence if stock market returns are linked in this way.
1.2
RISK PROFILES
Analysing the risk profiles of countries with equity investors in mind can be done through two
measures. The first involves establishing market risk profiles for each country. Beta coefficients are
used for this purpose in that they measure the contribution of the one country’s stock market index to
the variance of the global stock market index as a fraction of the total variance of the global stock index
(Bodie, Kane & Marcus, 2008:300). The second is to consider stand-alone risk profiles for each
country. This involves calculating expected return-beta relationships as predicted by the Capital Asset
Pricing Model (CAPM). An asset with a high expected return is generally assumed to encompass more
risk (Bodie, Kane & Marcus, 2008:300).
1.2.1
BETAS
Beta values were generated for all countries (using the returns already obtained through logdifferences). In finance, the Beta of a stock or portfolio is a number describing the relation of its
returns with those of the financial markets as a whole. Essentially, Beta measures the extent to which
returns on the stock/index and the market move together (Bodie, Kane & Marcus, 2008:295). Beta is
often referred to as financial elasticity or correlated relative volatility, and can be called a measure of
the sensitivity of the asset’s returns to market returns, its non-diversifiable risk, or its systematic risk.
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For the purposes of this paper, the calculated betas will measure the extent to which returns from each
of the emerging economy’s stock indexes and the Morgan Stanley Capital International Emerging
Market (MSCI EM) index move together. This allows each country’s returns to be compared to the
emerging market benchmark.
An asset has a beta of zero if its changes in returns are not linked to those of the market’s returns. A
positive beta implies that the asset’s returns generally follow the market’s returns, while a negative
beta means that the asset’s returns will normally move opposite to those of the market. The formula
for the Beta of a country’s stock index in relation to the emerging market index is:
𝛽𝑐 =
𝐶𝑜𝑣(𝑟𝑐 , 𝑟𝑒𝑚 )
𝑉𝑎𝑟(𝑟𝑒𝑚 )
Where 𝑟𝑐 measures the rate of return of the country’s index, 𝑟𝑒𝑚 is a measure of the rate of return of
the MSCI EM index, and 𝑐𝑜𝑣(𝑟𝑐 , 𝑟𝑒𝑚 ) is the covariance between the rates of return.
Figure 5 shows the generated beta coefficients for the three BRICS countries included in the paper. All
three are positive (except for India until August 1998), showing that their returns generally follow that
of the MSCIEM Index. They also seem to follow similar trends, although further analysis is needed to
prove this. Brazil’s beta exceeded one during parts of 2001 and 2003, which implies that during these
periods, the Brazilian Bovespa index could be considered as more risky than that of the market.
Figure 2: Beta coefficients for South Africa, India and Brazil
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sa_beta
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brazil_beta
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india_beta
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Testing for Stationarity, the Augmented Dickey-Fuller test indicates stationarity for all the countries,
except Malaysia (p-value = 0.25). This is not a major problem as the next step is the extraction of a
smoothed trend using the HP Filter – one advantage of this technique being that it does not require the
original series to be mean reverting. Hence, Malaysia is kept in the analysis. The Hodrick-Prescott
Filter was applied to the obtained smoothed beta series that are illustrated in Figure 6.
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Figure 1: Smoothed beta series
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sabeta_hp
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brazilbeta_hp
indiabeta_hp
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indonesiabeta_hp
0.6
malaysiabeta_hp
mexicobeta_hp
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pakistanbeta_hp
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philippinesbeta_hp
southkoreabeta_hp
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0
The betas tell an interesting story. One will notice that none of the countries’ betas fall below zero,
implying that none of the studied stock market returns move in an opposite direction to those of the
benchmark MSCIEM Index. The second interesting finding is that South Korea’s beta falls above 1
spanning 2000 to 2005. This means that Korea’s market was more risky than the than the global
market during this time. A third interesting finding is that, at first glance, there appears to be a positive
relationship between the betas of South Africa and Brazil, and to an extent India, but it crosses the
other two towards the end of 2006 (South Africa) and 2009 (Brazil).
Comparing these betas to the results obtained by Bekaert and Harvey (2000) is interesting for the
countries that fully liberalised their stock markets near to the start of the data series. These authors
found that subsequent to liberalisation (along with financial market development and macroeconomic
development), the beta value for a country increased by 0.12. Considering Figure 6, India, which fully
liberalised towards the end of 1992, saw an increase in its beta value. This is also the case for South
Korea, which fully liberalised in May 1998 (although the beta was increasing prior to this date), and
South Africa. the Malaysian case is difficult to interpret as it went through a phase of full liberalisation
between January 1984 and November 1997, after which it had phases of repression, partial
liberalisation and the full liberalisation again in May 2001.
Indonesia and Pakistan both experience downward trends in their betas. This confirms the results
obtained by Kaminsky and Schmukler (2008) who asserted that volatility (and perhaps risk) increases
in the short-run subsequent to liberalisation, but decrease again in the long-run (after four years).
Interestingly enough, both these countries liberalised their stock markets in the early 1990s and so,
the decrease in risk compared to that of the market appears to be due to the long-run results of
financial liberalisation. One must be careful not to over interpret this however, as volatility does not
necessarily imply risk. A stock can be very volatile, but achieve overall high returns over time, while a
stock can be predictable, but achieve low or negative growth over time. Even so, investors tend to
associate emerging markets with volatility and thus risk.
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To test whether there is synchronicity between the country betas, a Vector Autoregressive (VAR)
model was specified. Again, the countries were split into three groupings to avoid having too many
parameters in the model. Here, the most appropriate lag length for the BRICS and Central/South
American groups is three, while it is four for the Asian countries.
The resulting system of equations for the VAR(3) model for the BRICS group is as follows:
𝑠𝑎𝑏𝑒𝑡𝑎 = 𝟎. 𝟎𝟎𝟎𝟎𝟓 + 𝟐. 𝟗𝟓𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟏 − 2.91𝑠𝑎𝑏𝑒𝑡𝑎𝑡−2 + 𝟎. 𝟗𝟔𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟑 + 𝟎. 𝟎𝟑𝒃𝒓𝒂𝒛𝒊𝒍𝒃𝒆𝒕𝒂𝒕−𝟏
− 0.05𝑏𝑟𝑎𝑧𝑖𝑙𝑏𝑒𝑡𝑎𝑡−2 + 𝟎. 𝟎𝟐𝒃𝒓𝒂𝒛𝒊𝒍𝒃𝒆𝒕𝒂𝒕−𝟑 − 𝟎. 𝟎𝟔𝒊𝒏𝒅𝒊𝒂𝒃𝒆𝒕𝒂𝒕−𝟏 + 0.12𝑖𝑛𝑑𝑖𝑎𝑏𝑒𝑡𝑎𝑡−2
− 𝟎. 𝟎𝟔𝒊𝒏𝒅𝒊𝒂𝒃𝒆𝒕𝒂𝒕−𝟑
𝑏𝑟𝑎𝑧𝑖𝑙𝑏𝑒𝑡𝑎 = 0.000003 − 𝟎. 𝟎𝟓𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟏 + 𝟎. 𝟎𝟖𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟐 − 𝟎. 𝟎𝟑𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟑
+ 𝟑. 𝟎𝟒𝒃𝒓𝒂𝒛𝒊𝒍𝒃𝒆𝒕𝒂𝒕−𝟏 − 3.08𝑏𝑟𝑎𝑧𝑖𝑙𝑏𝑒𝑡𝑎𝑡−2 + 𝟏. 𝟎𝟒𝒃𝒓𝒂𝒛𝒊𝒍𝒃𝒆𝒕𝒂𝒕−𝟑
− 𝟎. 𝟏𝟐𝒊𝒏𝒅𝒊𝒂𝒃𝒆𝒕𝒂𝒕−𝟏 + 0.25𝑖𝑛𝑑𝑖𝑎𝑏𝑒𝑡𝑎𝑡−2 − 𝟎. 𝟏𝟑𝒊𝒏𝒅𝒊𝒂𝒃𝒆𝒕𝒂𝒕−𝟑
𝑖𝑛𝑑𝑖𝑎𝑏𝑒𝑡𝑎 = 𝟎. 𝟎𝟎𝟎𝟐 − 𝟎. 𝟎𝟏𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟏 + 𝟎. 𝟎𝟐𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟐 − 𝟎. 𝟎𝟏𝒔𝒂𝒃𝒆𝒕𝒂𝒕−𝟑 + 𝟎. 𝟎𝟐𝒃𝒓𝒂𝒛𝒊𝒍𝒃𝒆𝒕𝒂𝒕−𝟏
− 0.04𝑏𝑟𝑎𝑧𝑖𝑙𝑏𝑒𝑡𝑎𝑡−2 + 𝟎. 𝟎𝟏𝒃𝒓𝒂𝒛𝒊𝒍𝒃𝒆𝒕𝒂𝒕−𝟑 + 𝟐. 𝟖𝟔𝒊𝒏𝒅𝒊𝒂𝒃𝒆𝒕𝒂𝒕−𝟏 − 2.75𝑖𝑛𝑑𝑖𝑎𝑏𝑒𝑡𝑎𝑡−2
+ 𝟎. 𝟖𝟗𝒊𝒏𝒅𝒊𝒂𝒃𝒆𝒕𝒂𝒕−𝟑
Where all terms have been smoothed by the HP filter and statistically significant regressors at the one
per cent confidence level are highlighted in bold. Because it was expected that these countries would
follow similar trends concerning risk in relation to the market, the a priori expectation was that the
betas and their lags would be positively correlated with one another for the most part. This is mostly
true for the beta relationship between South Africa and Brazil. South Africa’s beta coefficient is
positively related to Brazil’s beta coefficient one and three months back. In addition, it seems inversely
related to India’s beta coefficient at the same time intervals, which is not necessarily the result sought.
This may have to do with the trend developing in the results that regional factors have a big influence
on factors relating to equity markets, and accordingly these three countries are on different corners of
the map. The only significant positive relationships driving Brazil’s beta coefficient are with that of
itself (at lags one and three) and South Africa (at lag 2). In the case of India, its beta coefficients are
positively driven by those of South Africa (at lag 2), Brazil (at lags one and three), and itself (at lags
one and three).
1.2.2
EXPECTED RETURN-BETA RELATIONSHIPS
This step in the analysis required calculating expected return-beta relationships as predicted by the
Capital Asset Pricing Model. As stated in the literature review, the expected returns, implied risk, or
discount rate can be calculated for each country with the following formula:
𝐸(𝑅𝑖 ) = 𝑅𝑓 + 𝛽𝑖 (𝐸(𝑅𝑒𝑚 ) − 𝑅𝑓 )
- 11 -
Where 𝑅𝑖 is the expected return on country i; 𝑅𝑓 is the risk-free rate, i.e. the market yield on three
month U.S. Treasury Bills; 𝛽𝑖 is beta of country i; and 𝑅𝑒𝑚 is the expected return of the Morgan Stanley
Capital International Emerging Market Index.
Figure3 shows the expected returns for South Africa, India and Brazil.
Figure3: Expected return-beta relationships generated by the CAPM
0.15
0.1
0.05
-0.05
Jan-98
Aug-98
Mar-99
Oct-99
May-00
Dec-00
Jul-01
Feb-02
Sep-02
Apr-03
Nov-03
Jun-04
Jan-05
Aug-05
Mar-06
Oct-06
May-07
Dec-07
Jul-08
Feb-09
Sep-09
Apr-10
Nov-10
Jun-11
Jan-12
0
-0.1
ri_bra
ri_ind
ri_saf
-0.15
-0.2
-0.25
-0.3
From 2004 onwards, they are almost replicas of one another. There is a definite drop in expected
returns following the financial crisis where the lowest expected returns occurred in November 2008.
Another mild drop was experienced during September 2011 – most likely linked to the sovereign-debt
crisis in Europe. The other EME’s not show on the graph showed very similar trends.
Again, R-squared is used to measure the co-movements in expected returns. The a priori expectation is
high values due to the strong evidence of co-movement from Figure 8. This turned out to be the case
with almost all of the R-squared values being quite a bit higher than 0.5. Thus, making use of the
expected return-beta relationships from the CAPM, it can be said that the variance in expected returns
(and risks by implication) of this group of EMEs strongly explain the variance in one another’s
expected returns. The highest values were obtained for the following pairs: Philippines and Malaysia
(0.93), Brazil and Mexico (0.92), Brazil and Philippines (0.89), Mexico and Malaysia (0.88), Philippines
and Mexico (0.88), Malaysia and Brazil (0.86), South Africa and India (0.85) and South Africa and
Brazil (0.79).
1.3
VOLATILITY IN PORTFOLIO EQUITY INFLOWS
The third research objective was to analyse actual portfolio equity inflows to the group of emerging
markets. The quarterly data are measured in millions of U.S. Dollars and were deflated into real terms
using the U.S. consumer price index (2005 = 100). The volatilities for portfolio equity flows were
obtained through extracting the variances from the data series.
- 12 -
Figure 8 shows the net portfolio equity inflows to South Africa, Brazil and India. Flows to Brazil and
India appear to be more volatile in some periods.
Figure 4: Net portfolio equity inflows
15000
10000
5000
01-Nov-2011
01-Mar-2011
01-Jul-2010
01-Nov-2009
01-Mar-2009
01-Jul-2008
01-Nov-2007
01-Jul-2006
01-Mar-2007
01-Nov-2005
01-Mar-2005
01-Jul-2004
01-Nov-2003
01-Mar-2003
01-Jul-2002
01-Nov-2001
01-Mar-2001
01-Jul-2000
01-Nov-1999
01-Mar-1999
01-Jul-1998
01-Nov-1997
01-Mar-1997
-10000
01-Jul-1996
-5000
01-Nov-1995
0
01-Mar-1995
Millions of U.S. Dollars (2005 = 100)
20000
-15000
Brazil_pf_e
India_pf_e
South Africa_pf_e
Again, R-squared is used to determine how much of the variance in one series is explained by the
variance in the other series. In this instance, it involves the variance of the volatility – thus, by how
much is the variation in the volatility of country X’s portfolio inflows explained by the variance in the
volatility of country Y’s.
Prior to generating volatilities, the series were not log-transformed or differenced as the true volatility
of the inflows is sought. It was expected a priori that this may lead to quite high R-squared values as
the variation in the data will not be “explained away” by transformation. This was the case, with many
of the relationships being above 0.5. Notable relationships in respect of the volatility of equity inflows
include: Brazil with India (0.77); India with Malaysia (0.82), Mexico (0.85), Pakistan (0.71), Philippines
(0.86) and South Korea (0.92); Indonesia with South Africa (0.71); Malaysia with Mexico (0.89),
Pakistan (0.92), Philippines (0.87) and South Korea (0.96); Mexico with Pakistan (0.76), Philippines
(0.79) and South Korea (0.9); Pakistan with Philippines (0.85) and South Korea (0.85); and Philippines
with South Korea (0.89).
Of course there will naturally be more factors that explain the variance in the volatility of the flows
other than the variance from peer country flows (this is likely to be the reason for the R-squared
values being so high – no control variables were included). However, this paper is merely concerned
with the co-movement of volatilities and does not seek to investigate control variables or explain
causality. These results confirm just how volatile portfolio equity flows to emerging markets are and
that in many cases volatility in one emerging market is often mirrored by that of other emerging
markets. The regional factors do not seem as pronounced here as in previous results.
- 13 -
CONCLUSION
There has been much talk about the volatility of portfolio flows to emerging markets since the end of
the 1980s when many developing economies began liberalizing their stock markets by reducing
regulations and allowing foreign access to domestic assets. This resulted in a wealth of literature on
the booms and busts of portfolio flows, much of which focuses on the effect of liberalization, most
remarkably by Kaminsky and Schmukler (2008) who created a very valuable chronology of the
process and degree of liberalization for 28 countries. Many studies have also tried to determine the
factors that play a role in attracting such flows and whether these are mainly globally or domestically
driven (Ahmed et al., 2005; Aron et al., 2010; IMF, 2011).
This paper approaches inflows of portfolio equity to emerging markets as the co-movement of the
booms and busts of flows. Using the perspective of a well-diversified global investor, the paper
empirically establishes that surges in inflows of portfolio equity to EMEs are concomitant, evidenced
by the high R-squared values obtained when analysing the flows. This implies that exogenous factors
do play an important role in driving investor decisions. This contrasts slightly with the IMF (2011)
finding in which domestic factors were explained by the residual in the model. Their results showed
that while global and regional factors have been continually growing over time, domestic factors still
play an important role. However, this paper did not attempt to analyse causation or specific
determining factors; it aimed to establish the degree of co-movement between flows and the resultant
high R-squared values point to much of the variance in one country’s flows being explained by the
variance in those of another country. High R-squared values were obtained for most countries and one
can interpret this as investors seeing investing opportunities in these countries as comparable.
It was discovered there is co-movement of smoothed actual and predicted return profiles, stand-alone
and market risk in groupings of studied emerging markets. These results were particularly fascinating
as they confirm the IMF’s (2011) results that within exogenous factors, regional characteristics are
becoming far more dominant. There was strong explanatory power (as a result of high R-squared
values) between countries in close proximity to one another. This was evidenced by Brazil and Mexico
showing high values, as well as the Asian group.
Thus, in light of the above, portfolio equity flows are exogenously influenced by global and regional
factors. This links well with the herd-type behaviour alluded to in the section on portfolio choice
theory. Many investors act on the behavior of the few informed investors who make their choices
based on market fundamentals. However, this has lead to consequences for emerging markets – when
global sentiment is good, investors scramble to benefit from the high returns in emerging market
economies, while in times of crisis, they panic and exhibit herd behaviour by looking to safe havens,
such as gold and developed country3 stocks and bonds. This results in tremendous volatility of
portfolio inflows for EMEs.
3
Typically Japan, Germany and the United States, although various events have lead to the tainting of these countries’ appeal
of late. Even so, they are still preferred to developing economies.
- 14 -
The results obtained have some implications for South African policymakers. The volatility in portfolio
flows does not seem likely to decrease and so no policy measures will stop this. South Africa has deep
and liquid financial markets, more so than many of its peers, and investors will continue to look for
profit-making opportunities when the global economy is doing well and risk aversion is low. However,
there is always the danger of reversal when times are bad. One measure to consider is imposing a tax
on capital inflows such as the IOF in Brazil; however the country would not want to dissuade investors.
South Africa’s biggest problem with respect to the volatility is that it relies so heavily on these flows to
finance its current account deficit due to low levels of domestic saving. This is an area that the
government is striving to improve on. An additional implication from global factors influencing
portfolio flows is that policies on these flows should in no way be linked to those of foreign direct
investment.
Because this paper only sought to focus on co-movements and the degree thereof, an area for further
research would be digging deeper and finding underlying causation, i.e. what actual factors played a
role in determining portfolio equity flows to South Africa. Ahmed et al. (2005) and Aron et al. (2010)
would provide good bases from which to add.
- 15 -
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