Impact of Changes in US VIX on Equity Returns of Emerging ...model where risk innovations in VIX transmit to the emerging and frontier markets returns through mean return and volatility

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Impact of Changes in US VIX on Equity Returns of Emerging and

Frontier Markets: Global Evidence

Ghulam Sarwar* and Walayet Khan**

*Corresponding author, Department of Accounting and Finance, College of Business and Public

Administration, California State University, San Bernardino, CA, USA. Phone: 909-537-5711,

E-mail: [email protected].

**Schroeder School of Business, University of Evansville, Indiana, USA. E-mail:

[email protected].

mailto:[email protected]

mailto:[email protected]

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Abstract

We investigate the effects of US stock market uncertainty (VIX) on the equity returns in

emerging markets of Latin America, Asia, Europe, Middle East and Africa, and in the composite

emerging and frontier markets before, during, and the after the 2008 global financial crisis. We

find that increases in VIX lead to significant immediate and delayed declines in emerging and

frontier markets returns in all periods. However, changes in VIX have stronger effects on returns

in Latin American and European markets than in Asian markets, and these effects were more

pronounced during the financial crisis than in other periods. Changes in VIX Granger-cause

change in returns of emerging and frontier markets during the entire 2003-2014 and post-crisis

periods. The higher US stock market uncertainty exerts a much stronger depressing effect on

emerging and frontier markets returns than their own-lagged returns. Our risk transmission

model suggests that a heightened US stock market uncertainty lowers emerging and frontier

markets returns by both reducing the mean returns and raising the variance of returns. The VIX

fears raise the return volatility of emerging and frontier markets through GARCH-type volatility

transmission processes.

Keywords: VIX, emerging market returns, predictive ability.

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1. Introduction

The financial liberalization and capital markets development in emerging markets for

more than two decades produced substantial increase in information, labor, capital and trade

flows across countries enhancing global connectivity and market integration. The effect of this

interconnectedness is evident in capital markets as uncertainty and turmoil in one major market

immediately impacts global markets. However, the existence, magnitude, and duration of cross

border effects of heightened uncertainty in one market on other equity markets remain an

ongoing empirical question.

Despite the increase in return correlations across equity markets in recent years, emerging

markets are still not fully integrated with the developed markets and they should be treated as a

separate asset class (Bekaert, Harvey, Lunbald, and Seigel (BHLS) 2011; Phylaktis and Xia

2006; Bekart and Harvey 2014). Emerging markets continually offer attractive investment

opportunities for global investors due to their unmatched growth (including some of the fastest

growing economies in the world), high risk/return trade-off, enhanced investment opportunity

set, and a lower downside beta than the upside beta (Bekaert and Harvey 2014; BHLS 2011).

The Chicago Board Options Exchange’s Volatility Index (VIX), commonly known as the

investors’ fear index, has received greater attention after the global financial crisis as a key tool

to gauge the investors’ fears and market uncertainty. The VIX measures the market expectations

of short-run (30-day) U.S. stock market volatility implied by the S&P 500 index option prices

(CBOE 2014; Whaley 2009; Whaley 2000).

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The VIX (expected future volatility) is used as a measure to evaluate the cross country

volatility/returns relations due to its “forward looking” nature and its proven reliability of

investors’ fear gauge (Benelli and Ganguly 2007). Previous studies clearly demonstrate a

negative correlation between VIX and market turbulence since a high level of VIX is associated

with market turmoil and low level is associated with market recovery (Whaley 2009; Whaley

2000). Moreover, the IMF Global Financial Stability Report (IMF 2006) documents a significant

relation between VIX and future recessions. Also, VIX serves as an external (exogenous) shock

to emerging markets. Caballero and Panageas (2004) show that an increase in the level of VIX is

associated with significant curtailment in emerging markets capital inflows.

However, the predictive power of VIX has been mainly tested in the U.S. equity markets,

rather than in global markets particularly emerging markets (Fleming, Ostdiek,and Whaley 1995;

Copeland and Copeland 1999;Whaley 2000 &2009; Connolly, Stivers, and Licheng 2005; Giot

2005). That is, past studies have attempted to explain the emerging market returns based on

volatility of key global markets and historic returns, but very little empirical work has been

undertaken to explain these returns using forward looking volatility measures such as VIX.

If VIX has a predictive power with respect to emerging equity market returns, then investors can

potentially devise trading strategies to profit from such predictive relations. Such findings will be

in contradiction with the theory of market efficiency which dominated academic and business

scene for decades.

We study the impact of changes in US stock market uncertainty (VIX) on the equity

returns in emerging markets of Latin America (5 countries) , Asia (8 countries) , Europe (6

countries) , Middle East & Africa (6 countries) , composite emerging markets (23 countries), and

composite frontier markets (23 countries). Emerging markets offer an interesting opportunity to

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evaluate the question of predicting their returns based on an external market fear, VIX, in an era

of cross-border openness and interconnections across global economies and markets. Such an

examination will shed light on the virtue of global diversification, flight to safety choices, and

hedging portfolio risk through VIX options and futures.

Our analysis of cross-market influences of VIX on returns and volatilities of emerging

and frontier markets focuses on two important empirical dimensions. First, we investigate

whether changes in VIX have significant contemporaneous and Granger-causal (forward

looking) relations with the emerging and frontier market returns in pre-crisis, crisis, and post-

crisis periods? An evidence supportive of contemporaneous relations will suggest that the VIX-

triggered market fears of US investors spread quickly from the US stock market to emerging and

frontier stock markets. The existence of Granger-causal relations would imply that short-run

emerging market returns are predictable from changes in VIX. These relations will also show the

strength and duration of emerging market return reactions to risk shocks emanating from VIX.

Previous studies support the existence of information frictions and gradual information

diffusion from the US stock market to other international equity markets (Rapach, Strauss, and

Zhou, 2013; Rizova, 2013; Lo and MacKinlay 1990; Cohen and Frazzini 2008; and Menzly and

Ozbas 2010). A similar leading role of VIX in predicting the emerging and frontier market

returns may have important implications for hedging the cross-market risks of global portfolios

and realizing potential profit opportunities in emerging markets resulting from the delayed

effects of VIX on returns.

Bekaert and Harvey (2014) show that emerging stock markets exhibit wide disparity in

their return volatilities. To explore the distinctive differences between the region-specific

volatilities and the composite emerging market volatility, we examine the differential influences

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of VIX on the returns of each of the regional emerging markets indexes (5 in our case) and

compare it to the VIX’s effect on the returns of overall emerging markets (MSCI EM index).

Second, we examine the possible risk transmission channels through which VIX fears

enter the emerging and frontier markets. We propose and test a GARCH-type risk transmission

model where risk innovations in VIX transmit to the emerging and frontier markets returns

through mean return and volatility processes. Our model assumes that VIX and the volatilities of

emerging and frontier markets follow a GARCH-type process and is a variant of full-

transmission model used by Lin, Engle, Ito (1994) and Baur and Jung (2006).

The rest of the paper is organized as follows. Section 2 describes the data. The research

methods are explained in section 3. Section 4 explains the results, and the summary and

conclusions are drawn in the final section 5.

2. Data

Our study period spans from June 1, 2003 to December 31, 2014. The daily closing

values of VIX came from the CBOE web site. Our data period includes the global economic

recession period and the concomitant global stock market crisis since 2007. The daily closing

values of the overall MSCI emerging markets index (EM index) and the MSCI region-specific

indexes for the Latin American, Asian, European, Middle Eastern and African, and the

Emerging and the Frontier Markets are obtained from Morgan Stanley Capital International

(MSCI). The choice of sample period is dictated by our desire to examine the relations between

VIX and emerging market returns before, during, and after the global equity market crisis. We

follow Bekaert, Ehrmann, Fratzscher, and Mehl (2014) in specifying the time line for the global

financial crisis. They use the period from August 7, 2007 to March 15, 2009 as the global equity

market crisis period. The global equity markets initially fell on August 7, 2007 and major central

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banks started injecting liquidity into financial markets; equity markets hit bottom on March 15,

2009 and started recovering losses since then. Thus, our global equity crisis period runs from

August 7, 2007 to March 15, 2009; the pre-crisis period from June 1, 2003 to August 6, 2007;

and the post-crisis period from March 16, 2009 to September 30, 2014.

3. Research Methods

We use the following regression to study the contemporaneous and delayed relations

between emerging market stock returns and VIX changes (Fleming, Ostdek, and Whaley 1995;

Whaley 2000; Whaley 2009):1

Rt = α + + ∑ 𝑗𝑖=−𝑗 βs,i ∆Vt+i + β|s| |∆Vt| + εt , i = -j, . . . , j (1)

where Rt is the emerging market stock return at time t, ∆Vt+i is the change in VIX at time t+i,

|∆Vt| is the absolute change in VIX at time t, βs,i is the regression coefficient of the relation

between Rt and ∆Vt+i , β|s| is the regression coefficient for |∆Vt|, α is the regression intercept,

and εt is the error term. The coefficients βs,i and β|s| jointly measure the asymmetric relation

between Rt and ∆Vt given the existence of asymmetric relations between returns and stock

market volatility in previous studies (Schwert 1990). We use the Schwartz and Akaike

information criteria in selecting the number of lagged and lead terms to include in Equation (1).

A negative contemporaneous coefficient βs,0 would seem plausible on the basis of an

inverse relation between investors’ fears and returns and would be consistent with the return

predictions of the CAPM models of Merton (1973a) and Sharpe (1964). Indeed, a strong

negative association between US stock market returns and changes in VIX is reported by

Banerjee, Doran, and Peterson (2007), and Fleming, Ostdek, and Whaley (1995).

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A persistence in the market volatility as shown in previous studies (Wu, 2001; Bekaert

and Wu, 2000) would predict the regression coefficients βs,i (i<0) to have a negative sign.

Similarly, the presence of a mean-reversion in VIX (Giot 2005; Guo and Whitelaw 2006; Guo

and Wohar 2006) would suggest a positive sign for the regression coefficients βs,i (i>0). The joint

coefficients (βs,0 + β|s|) and (βs,0 - β|s|) represent the asymmetric effects of positive and negative

changes in VIX on emerging market stock returns, respectively. The asymmetric effects imply

that a decrease in emerging stock returns from an increase in VIX is expected to be larger than an

increase in stock returns from a similar drop in VIX.

Guo and Wohar (2006) document that the mean levels of VIX shift over time. Also, stock

market returns and VIX changes have shown significant autocorrelations as reported later in

Table 1. Hence, we estimate equation (1) using Hansen’s (1982) method of moments estimator to

obtain consistent regression coefficients and standard errors in the presence of possible

heteroscedasticity and autocorrelation problems.

While equation (1) captures the VIX-returns relations and the possible mean reversion

effects of VIX on these relations, it cannot examine the predictive power of VIX changes for the

emerging stock market returns. We analyze this predictive ability of VIX by examining the lead-

lag relationships between VIX changes and stock market returns using the Granger (1969)

causality tests. We follow Rapach, Strauss, and Zhou (2013) in estimating the following

augmented predictive regressions:2

Rt = β0 + ∑ 𝑛𝑖=1 βi ∆Vt-i +∑ 𝑛

𝑖=1 βri Rt-i + et , i = 1, . . . , n (2)

and

∆Vt = α0 + ∑ 𝑛𝑖=1 αi Rt-i + ∑ 𝑛

𝑖=1 αvi ∆Vt-i + εt , i = 1, . . . , n (3)

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where Rt and ∆Vt are the emerging stock market returns and changes in US stock market

volatility (VIX), respectively. The regression coefficients βi and αi capture the effects of lagged

VIX and lagged stock returns, respectively, βri and αvi reflect the respective effects of own-

lagged value of dependent variables, β0 and α0 are intercepts, and et and εt are error terms. The

Granger causality tests of equation (2) will allow us to examine if lagged changes in VIX have

predictive ability for the direction of emerging market returns. We use the Schwartz and Akaike

information criteria to specify the number of lagged terms in equations (2) and (3), and the

Phillips-Perrin unit root Z-test to check the stationarity of returns and changes in VIX to

circumvent the problem of spurious regressions.

To examine the transmission channels of VIX fears to emerging market returns, we

notice from previous spillover studies that changes in U.S. equity returns affect returns in other

markets both through a mean return equation and a volatility of return equation (Baur and Jung,

2006; King and Wadhwani, 1990; Lin, et a., 1994). Thus, the VIX fears may not only lower

emerging market returns but may also lead to higher return volatility (uncertainty) which in turn

will negatively affect the returns. Our dual-channel transmission model for the emerging market

returns is a variant of the aggregate-shock model of Lin, et al. (1994) and Baur and Jung (2006)

and can be written as follows:

Rt = µ1 + β1 Rt-1 + β2 ∆Vt + β3 Xt-1 + εt, with εt| ψt-1 ~N(0, ht) (4)

and ht = α1 + γ1 ε2

t-1 + γ2 ht-1 + γ3 ∆Vt (5)

∆Vt = ν1 + ξt, with ξt| ψt-1~N(0, gt) and gt = а1 + λ1 ξ2

t-1 + λ1 gt-1 (6)

In equations (4)-(6), µ1 and α1 are the constants of the mean return and variance,

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respectively, Rt is the emerging market return at time t, ∆Vt is the change in VIX at time t, the

regressor Xt-1 captures possible effects of regional market returns, N(, . ,) denotes a normal

distribution, and ht is the conditional variance of returns. The volatility (ht) of emerging

market returns and ∆Vt are assumed to follow GARCH processes. In equation (4), the

emerging market returns are influenced by the market’s own-lagged return, regional returns,

and US market risks. The US stock market uncertainty (∆Vt) enters the emerging stock

markets through the mean return equation (4) as well as through the volatility equation (5).

The full transmission model in equations (4) to (6) can be estimated using the quasi-maximum

likelihood procedures under the GARCH framework, as pointed out by Baur and Jung (2006).

4. Results

4.1. Returns-VIX Statistics and Regression Analysis

Table 1 presents the summary statistics of U.S. stock market uncertainty (VIX) and

equity returns of six regional emerging and frontier markets for the entire sample period 2003:6:-

2014:12, and for pre-crisis, crisis, and post-crisis sub periods. The daily VIX ranges from a

minimum of 9.89% to a maximum of 80.86%, its highest value on November 2, 2008. All the

emerging and frontier markets have positive mean daily returns for the entire period. The highest

mean returns are for the Latin American and Asian markets, while the lowest mean returns and

the highest volatility occur in European emerging markets. All emerging and frontier market

returns and VIX exhibit significant first-order autocorrelation.

The results of Table 1 are more revealing when separated by the three sub periods. All

the emerging and frontier markets have positive mean returns during both the pre- and post-crisis

periods. But all the emerging markets have negative mean returns in the crisis period. The

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volatility of daily returns was nearly twice as much during the crisis period (average 2.7%) than

in either the pre-crisis period (1.16%) or the post-crisis period (1.33%). The mean VIX value

rose 115% in the crisis period than in the pre-crisis period, and volatility of VIX (volatility of

volatility) jumped from 3.38% to 14.73%, a three-fold rise, from the pre-crisis period to the crisis

period. The mean and volatility of VIX dropped 33% and 50%, respectively, from the crisis

period to the post-crisis period. Table 1 shows that VIX and changes in VIX have significant

first-order autocorrelation in all three sub periods. Similarly, all returns show significant first-

order autocorrelation in the pre-crisis period and crisis period (except Latin America). Four of

the six emerging market returns also exhibit significant autocorrelation in the post-crisis period.

The significant change in the volatility of VIX and returns between sub periods and the

autocorrelations in returns and VIX, respectively, are accounted for later in our use of Hansen’s

method of moments procedure for estimating the VIX-returns relations.

Table 2 shows the contemporaneous correlations between the returns and changes in

VIX, and between the changes in VIX and returns. All the VIX-returns correlations are negative

and statistically significant at the 1% level, indicating that negative changes in returns occur

when VIX experiences positive changes (no causation implied).The highest VIX-returns

negative correlations exist for the Latin American and European emerging markets, the lowest

for Asian emerging markets. Thus, the coincidence of falling returns at times of rising VIX is

more pronounced in the Latin American and European emerging markets than in the Asian

markets.

Table 3 presents the regression results that examine the relations between US stock

market volatility (VIX) and emerging and frontier stock market returns. The Schwartz and

Akaike information criteria suggested the use of two lagged and lead terms in regression

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equation (1). The results show a strong negative contemporaneous relation (βs,0) between the

changes in VIX and returns in all emerging and frontier markets in all periods. The size of

contemporaneous coefficient ranges from a low of -0.24 for Asian markets to a high of -0.67 for

Latin American markets during the entire period 2003-2014. One percent rise in VIX is

accompanied by 0.37 and 0.27 percent drop in the broader emerging market and frontier market

returns, respectively. On average, the largest negative effects of changes in VIX are felt on the

same-day stock returns of Latin American and European markets during the entire period and in

all sub periods. Among the sub periods, the largest immediate negative effect of changes in VIX

on returns occurs in the crisis period than in pre- and post-crisis periods. The only exception is

the Latin American markets where the immediate negative effect of VIX on returns is strongest

in the pre-crisis period (-0.87) than in crisis period (-0.76). Further, the immediate negative effect

of changes in VIX on returns is larger in the broader emerging markets (23 countries) than in the

composite frontier markets (23 countries) in all sub periods. These results are consistent with

those of Fleming, Ostdiek, and Whaley (1995) and Whaley (2000) for the US stock market, and

suggest that VIX also serves as an investor fear gauge in all the regional emerging equity

markets and in the broader emerging and frontier markets.

Interestingly, the lag-one coefficient of changes in VIX was significant and negative for

all the emerging and frontier market returns during the entire period and three sub periods,

implying that the negative effects of changes in VIX on stock returns continue for an additional

day in the these markets. The lag-two coefficient of changes in VIX was statistically significant

for the European, Asian, and broader emerging markets during the crisis period, suggesting a

persistence in the negative effects of heightened U.S.-market risk fears on these three markets for

up to two days. In general, the regional emerging markets and the overall emerging and frontier

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markets show no relation to lead-one and lead-two changes in VIX, which fail to support the

mean reversion effects of VIX on returns. Further, the coefficient of the contemporaneous

absolute changes in VIX (β|s|) is generally not significant for the emerging and frontier markets in

any sub period, indicating that the mere size of changes in VIX, regardless of its direction, is not

a significant factor influencing the market returns. The non-significant value of β|s| does not

support the existence of asymmetric effects of changes in VIX on the emerging and frontier

market returns.

The results of Table 3 show that changes in VIX collectively explain a much bigger

percentage of daily returns during the crisis period than in pre- and post-crisis periods. The

adjusted coefficient of determination (R2) reveals that the VIX changes jointly explain nearly

twice as much of daily emerging and frontier market returns during the crisis period (average R2

0.43) than in the pre-crisis period (0.20) or post-crisis period (0.26). Thus, the daily emerging

and frontier market returns reacted more aggressively to the VIX fears of U.S. markets during

the financial crisis. Our results are consistent with those of Rapach, Strauss, and Zhou (2013)

who report that the explanatory power of US returns for returns of non-US industrialized

countries rises during business cycle recessions and also with that of Yunus (2013) who indicates

that integration among major world equity markets rises during a major financial crisis.

Overall, the contemporaneous and lag-one changes in VIX jointly capture the most

significant negative effects of VIX fears on the emerging and frontier stock market returns. The

immediate negative effects of VIX changes on returns are the strongest during the crisis period

with an average value of -0.49 for all markets (-0.38, pre-crisis period; -0.36, post-crisis period).

Hence, the depressing effects of daily changes in U.S. stock market uncertainty on the emerging

and frontier market returns are contained primarily to the same day and next trading day.

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The strong contemporaneous negative relations between VIX changes and emerging and

frontier market returns suggest that any global portfolio diversification efforts involving asset

allocations among U.S., emerging, and frontier equity markets may not achieve its risk

diversification objectives. Also, the spread of VIX fears to U.S., emerging, and frontier equity

markets simultaneously may not afford investors many flight-to-safety choices by shifting funds

between US and emerging and frontier equity markets. Furthermore, any promising strategies to

hedge the risks of equity portfolios invested in both US and emerging markets may involve

taking long positions in VIX and VXEEM (EM VIX) options or futures in order to offset the

losses on equity portfolios with the gains on volatility options and futures and vice versa.3

The transmission of VIX fears to emerging and frontier markets may be related to the

large US trading relations with Latin American countries (e.g., Mexico and Brazil), Asian

countries (e.g., China), and developed Euro-zone countries, and the resulting depressing effects

of these countries’ markets on other emerging and frontier markets through trading relations.

Hence, the VIX fears of US investors about US economy and businesses may signal instability

and weakness in such trading relations directly with US or indirectly through its major trading

partners. The persistence in the effect of current changes in VIX on emerging and frontier market

returns suggests that investors’ fears about the US market diffuse a bit slowly as investors

gradually analyze the effect of US macroeconomic and stock market information relevant to the

emerging and frontier markets. Our results are consistent with those of Rizova (2013) who

reports that a country’s stock market does not react immediately and fully to the stock market

movements of the country’s trading partners, and with the gradual information diffusion process

documented in other studies (Lo and MacKinlay 1990; Cohen and Frazzini 2008; Menzly and

Ozbas 2010; Hou 2007).

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4.2 Pairwise Granger Causality Tests

The results of Granger causality tests for equations (2) and (3) are presented in Table 4.

The Akaike and Schwartz information criteria suggest the use of three lagged terms for the

independent variables. The results of Phillips-Perrin unit root Z-tests, with values ranging from -

2013 to -2935, indicate that the null hypothesis of non-stationarity of return and change in VIX

(VIXC) series is rejected at the 1% level. Hence, the differencing of returns and VIXC is not

needed to attain stationarity for conducting Granger causality tests.

The Granger causality results in Table 4 show that none of the emerging and frontier

market returns have predictive power for the US stock market volatility (VIX) in any sub period.

This finding is consistent with the previous non-significant returns-VIX coefficients at leads 1

and 2 (βs,+1 and βs,+2) in Table 3.

The results of Table 4 for the three sub periods indicate that the US stock market

volatility does not Granger-cause the regional emerging market returns and the overall emerging

and frontier market returns during the pre-crisis and crisis periods. But changes in VIX have

significant predictive power for the daily stock market returns of all the regional emerging

markets and the broader emerging and frontier markets in the post-crisis period. This post-crisis

predictive ability of VIX seems to also produce a significant prediction power of VIX for returns

during the entire sample period. These results suggest that, after seeing the spread of 2008 U.S.

financial crisis to nearly all world equity markets, emerging and frontier market investors

became more responsive to changes in US stock market uncertainty in the post-crisis period. The

predictive ability of VIX for the emerging and frontier market returns in the post-crisis period

may be related to the evidence of information frictions and limited information-processing

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capabilities of investors in previous studies (Hong and Stein, 1999; Hong, Torous, and Valkonov

2007; Lo and MacKinlay 1990). Furthermore, since the US equity market is the world’s largest

and US is a major trading partner of larger emerging economies (e.g., China, Mexico, and

Brazil), any VIX fears of US markets will likely have significant implications for the equity

markets and trading patterns of U.S. trading partners, and in turn on their trading partners in

other emerging and frontier markets. The gradual transmission of US market risks to the

emerging and frontier markets due to the information-processing limitations of investors can

occur even when traders are aware of the information frictions and try to profit from them, as

demonstrated by Shleifer and Vishnay (1997) and Rizova (2010) in other market situations.

4.3. Returns-VIX Relations under the Full Transmission Model

Table 5 presents the results of the relations between the emerging and frontier market

returns and VIX changes under the full transmission model in which VIX innovations affect the

stock returns directly through the mean return process as well as indirectly via a GARCH-based

process of the volatility of return. The use of current changes in VIX (VIXCt) variable in both

the return and volatility processes of emerging market returns is consistent with, and supported

by, the significant contemporaneous VIX-returns coefficients in Table 3. The response of current

returns to lagged VIX changes is expected to be captured by the own-lagged return variable

included in the return equation.

The results of Table 5 indicate that, after controlling for the effects of a

country’s own-lagged returns, the latest changes in VIX exert a strong negative effect on the

stock returns of emerging and frontier markets. This result holds for the pre-crisis, crisis, and

post-crisis periods. The negative effect of current changes in VIX (VIXC) on the returns of

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regional and composite emerging market returns is much stronger (average value -0.34) than

the effect of the market’s own-lagged return (average effect 0.11) during the entire period and

all sub periods. The negative effect of VIX on returns was largest for the Latin American

markets with a -0.60 value, compared to a value of 0.13 for the market’s own-lagged return

effect. These results for the relation between emerging and frontier market returns and VIX

are consistent with those between foreign returns and US returns in Rapach, Strauss, and

Zhou (2013) who show that US stock returns are a more powerful force than even the

countries’ own economic variables in predicting the monthly stock returns of numerous non-

U.S. industrialized countries.

The Table 5 results also show that the latest changes in VIX raise the volatility of

emerging and frontier market returns as shown by the positive and significant coefficient of

VIXCt in the volatility equation. As expected, the positive effect of VIX on the volatility of

returns was larger in the crisis period than in the pre- and post-crisis periods. Hence, increases

in VIX (a fear gauge) lead to higher uncertainty of stock returns in all the regional emerging

markets and in the broader emerging and frontier markets. The positive coefficient of VIXC in

the volatility of return equation and its negative coefficient in the mean return equation jointly

suggest that higher US stock market uncertainty lowers emerging market returns by directly

lowering their mean returns and indirectly raising their volatility of returns which in turn

depresses returns further.

The GARCH coefficients in the volatility equations of the emerging and frontier

market returns are all positive and large (average value 0.93) and significant in all sub periods.

The large positive GARCH effects indicate persistence in the expected volatilities of

emerging and frontier market returns, which imply that higher current volatilities do not taper

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off quickly but linger on as higher future expected volatilities. These results are consistent

with the persistence in volatility of U.S. markets reported by Wu (2001) and Bekaert and Wu

(2000). The ARCH coefficients in the volatility equation are significant for all markets in all

sub periods, except for one case in the crisis period. Our results suggest that GARCH

framework may adequately capture the properties of volatilities of the emerging and frontier

market returns. The Chi-Square normality test for the residuals indicates that the null

hypothesis of normally distributed residuals under the GARCH framework for the emerging

and frontier market stock returns cannot be rejected. Overall, the results of our transmission

model underscore the importance of allowing VIX changes to influence emerging and frontier

market returns through both the mean and volatility equations of returns. Admittedly, there

may be other channels through which VIX can influence volatilities of the emerging and

frontier market returns.

5. Summary and Conclusions

We investigate the effects of US stock market uncertainty (VIX) on the equity returns in

emerging markets of Latin America, Asia, Europe, Middle East and Africa, and in the composite

emerging and frontier markets before, during, and the after the 2008 global financial crisis. Our

analyses focus on the immediate effects of VIX on returns, the Granger-cause effects of VIX on

returns, and the transmission mechanism of VIX fears onto returns via mean return and GARCH-

type volatility processes.

We find that increases in VIX lead to significant immediate and delayed declines in

emerging and frontier market returns in all periods. However, changes in VIX have stronger

effects on returns in Latin American and European markets than in Asian markets, and these

effects were more pronounced during the financial crisis than in other periods. Changes in VIX

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Granger-cause change in returns of emerging and frontier markets during the entire 2003-2014

and post-crisis periods. The higher US stock market uncertainty exerts a much stronger

depressing effect on emerging and frontier markets returns than their own-lagged returns. Our

risk transmission model suggests that a heightened US stock market uncertainty lowers emerging

and frontier markets returns by both reducing the mean returns and raising the variance of

returns. The VIX fears raise the return volatility of emerging and frontier markets through

GARCH-type volatility transmission processes.

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END NOTES

1. Whaley (2000, 2009) provides the rationale why US stock returns, rather than VIX, can

be treated as explanatory variables in this regression for examining the relations between

US returns and VIX.

2. Unlike these six studies, Koch (1993) and Easley, O’Hara and Srinivas (1998) argues for

the inclusion of a contemporaneous interaction term in the augmented predictive

regressions as the variables in question may be simultaneously determined. We will also

estimate equations (2) and (3) as a VAR model with contemporaneous terms in a

simultaneous-equation system.

3. These hedging strategies using volatility options and futures became possible since

March 2011 when CBOE started trading options and futures on the volatility indexes of

iShares MSCI emerging market ETF and iShares MSCI Brazil index fund ETF. Hence,

similar to VIX, the volatilities of the Brazilian and overall emerging markets are now

being traded.

21

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24

Table 1. Summary Statistics of Daily Volatility and Emerging Market Stock Returns

Variable Mean (%) SD (%) Min (%) Max (%) ρ1

2003:6-2014:12 (Entire Sample Period)

VIX 19.65 9.45 9.89 80.86 0.98*

VIX Change -0.001 1.76 -17.36 16.54 -0.15*

LA Return 0.042 1.82 -15.06 15.36 0.09*

EUR Return 0.02 2.04 -19.92 18.60 0.08*

EMEA Return 0.03 1.64 -15.54 12.82 0.08*

ASIA Return 0.04 1.39 -11.99 12.65 0.08*

FM Return 0.04 1.09 -9.80 8.02 0.19*

EM Return 0.04 1.32 -9.99 10.07 0.20*

2003:6-2007:7 (Pre-Crisis Period)

VIX 14.79 3.38 9.89 25.16 0.95*

VIX Change -0.002 0.94 -5.56 7.16 -0.12*

LA Return 0.145 1.37 -6.92 5.04 0.12*

EUR Return 0.124 1.51 -8.89 6.64 0.07*

EMEA Return 0.111 1.20 -6.82 4.82 0.09*

ASIA Return 0.104 1.06 -5.71 5.39 0.12*

FM Return 0.113 0.86 -4.05 3.94 0.22*

EM Return 0.115 0.95 -4.85 4.05 0.22*

Note: The daily data cover the period from June 1, 2003 to December 30, 2014 (i.e., 2003:6-

2014:12). ρ1 is the first-order autocorrelation coefficient and an asterisk ‘*’ on ρ1 indicates its

significance at the 5% level. The variables are defined as follows: LA, Latin America EM returns;

EUR, European EM returns; EMEA, European, Middle East, and Africa EM returns; ASIA, Asian

EM returns; FM, Frontier Market (23 countries) returns; EM, all emerging markets (23 countries)

index; and VIXCt, changes in VIX.

25

Table 1 (cont.). Summary Statistics of Daily Volatility and Stock Index Returns

Variable Mean (%) SD (%) Min (%) Max (%) ρ1

2007:8-2009:3 (Crisis Period)

VIX 31.78 14.73 16.129 80.86 0.97*

VIX Change 0.055 3.23 -17.36 16.54 -0.16*

LA Return -0.14 3.32 -15.06 15.36 0.05

EUR Return -0.27 3.47 -19.92 18.60 0.11*

EMEA Return -0.19 2.69 -15.54 12.82 0.11*

ASIA Return -0.17 2.46 -11.99 12.65 0.07*

FM Return -0.17 2.02 -9.80 8.02 0.18*

EM Return -0.17 2.35 -9.99 10.07 0.19*

2009:4-2014:12 (Post-Crisis Period)

VIX 20.05 7.10 10.32 48.06 0.97*

VIX Change -0.02 1.65 -12.94 16.04 -0.15*

LA Return 0.017 1.49 -8.65 7.73 0.10*

EUR Return 0.02 1.83 -9.43 7.95 0.04

EMEA Return 0.03 1.54 -8.24 6.05 0.05

ASIA Return 0.05 1.18 -5.87 5.32 0.06*

FM Return 0.05 0.82 -4.77 4.97 0.18*

EM Returns 0.043 1.13 -6.52 5.59 0.19*

Note: The subperiod 2007:8-2009:3 runs from August 7, 2007 to March 15, 2009, while

subperiod 2009:4-2014:9 covers the period from March 16, 2009 to September 30, 2014. ρ1 is

the first-order autocorrelation coefficient and an asterisk ‘*’ on ρ1 indicates its significance at the

5% level. The variables are defined as follows: LA, Latin America EM returns; EUR, European

EM returns; EMEA, European, Middle East, and Africa EM returns; ASIA, Asian EM returns; FM,

Frontier Market (23 countries) returns; EM, all emerging market (23 countries) index; and VIXCt,

changes in VIX.

26

Table 2. Correlation between VIX and Emerging Market Stock Returns, Full Sample Period

Return Variable VIX Change

Latin American EM Return (LA) -0.62*

Europe EM Return (EUR) -0.41*

Europe, Middle East, Africa (EMEA) EM Return -0.41*

Asian EM Return (ASIA) -0.22*

Frontier Markets Return (FM) -0.37*

All Emerging Markets Return (EM) -0.43*

LA Change -0.52*

EUR Change -0.34*

EMEA Change -0.35*

ASIA Change -0.20*

FM Change -0.37*

All EM Change -0.40*

Notes: An asterisk ‘*’ on the correlation value indicates its significance at the 5% level.

27

Table 3. Relations between VIX Changes and Emerging Market Stock Returns

Period Intercept βs,-2 βs,-1 βs,0 βs,+1 βs,+2 β|s| Adj. R2

__________________________________________________________________________________________________________________

__ Panel A. Latin American EM Returns

2003:6-2014:12 0.097* 0.01 -0.21* -0.67* -0.03 -0.03 -0.05 0.43

2003:6-2007:7 0.18* -0.09* -0.35* -0.87* -0.01 -0.01 -0.05 0.33

2007:8-2009:3 0.04 0.03 -0.25* -0.76* -0.03 -0.06 -0.07 0.55

2009:4-2014:12 0.01 -0.02 -0.16* -0.55* -0.05 -0.02 -0.01 0.36

Panel B. Europe EM Returns

2003:6-2014:12 0.124* -0.08* -0.33* -0.53* -0.05 -0.05 -0.11* 0.25

2003:6-2007:7 0.13* -0.14* -0.52* -0.36* 0.01 -0.03 -0.01 0.12

2007:8-2009:3 0.01 -0.09 -0.41* -0.59* -0.07 -0.10 -0.01 0.36

2009:4-2014:12 0.08 -0.08 -0.22* -0.52* -0.06* -0.01 -0.07 0.25

Panel C. EMEA EM Returns

2003:6-2014:12 0.117 -0.04 -0.31* -0.43* -0.05* -0.04 -0.09* 0.28

2003:6-2007:7 0.14* -0.14* -0.49* -0.33* 0.03 -0.02 -0.04 0.17

2007:8-2009:3 0.03 -0.03 -0.35* -0.45* -0.06 -0.05 -0.09 0.39

2009:4-2014:12 0.07 -0.05 -0.21* -0.45* -0.06* -0.01 -0.05 0.26

Note: The subperiods 2003:6-2007:7, 2007:8-2009:3, and 2009:4-2014:9 are the pre-crisis, crisis,

and post-crisis periods, respectively. Parameters βs,i indicate the effect of contemporaneous

(i=0) and non-contemporaneous (i≠0) daily changes in VIX on stock index returns. The

parameter β|s| captures the contemporaneous effect of absolute VIX changes on stock returns. An

asterisk ‘*’ on the coefficient indicates its significance at the 5% level. Adj R2 is the adjusted

coefficient of determination.

28

Table 3 (cont.): Relations between VIX Changes and Emerging Market Stock Returns

Period Intercept βs,-2 βs,-1 βs,0 βs,+1 βs,+2 β|s| Adj. R2

__________________________________________________________________________________________________________________

_ Panel D. Asian EM Returns

2003:6-2014:12 0.045 -0.09* -0.36* -0.24* -0.02 -0.01 -0.01 0.24

2003:6-2007:7 0.16* -0.17* -0.47* -0.15* 0.02 -0.02 -0.08 0.17

2007:8-2009:3 -0.21 -0.12 -0.38* -0.33* -0.03 -0.04 0.03 0.32

2009:4-2014:12 0.05 -0.07* -0.31* -0.17* -0.02 0.01 -0.01 0.22

Panel E. Frontier Markets Returns

2003:6-2014:12 0.09* -0.04 -0.24* -0.27* -0.01 -0.02 -0.04 0.29

2003:6-2007:7 0.13* -0.14* -0.38* -0.28* 0.01 -0.01 -0.04 0.21

2007:8-2009:3 -0.09* -0.06 -0.30* -0.37* -0.01 -0.05 -0.02 0.46

2009:4-2014:12 0.09* -0.03 -0.15* -0.18* -0.01 -0.01 0.04 0.20

Panel F. All Emerging Markets Returns

2003:6-2014:12 0.076 -0.065* -0.32* -0.37* -0.03 -0.028 -0.037 0.36

2003:6-2007:7 0.15 -0.15* -0.46* -0.34* 0.021 -0.001 -0.06 0.26

2007:8-2009:3 -0.09 -0.06* -0.34* -0.45* -0.04 -0.04 -0.02 0.48

2009:4-2014:12 0.05 -0.05* -0.26* -0.31* -0.04 -0.01 -0.024 0.31


and post-crisis periods, respectively. Parameters βs,i indicate the effect of contemporaneous

(i=0) and non-contemporaneous (i≠0) daily changes in VIX on stock index returns. The

parameter β|s| captures the contemporaneous effect of absolute VIX changes on stock returns. An

asterisk ‘*’ on the coefficient indicates its significance at the 5% level. Adj R2 is the adjusted

coefficient of determination.

29

Table 4. Granger Causality Tests

Period Ho: VIXC does not Ho: Returns do not

lead returns lead VIXC

___________________________________________________________________________________________________________________

Panel A. Latin American EM Returns

2003:6-2014:12 16.54* -12.87

2003:6-2007:7 0.33 0.04

2007:8-2009:3 3.39 0.70

2009:4-2014:12 18.08* 1.38

Panel B. Europe EM Returns

2003:6-2014:12 16.23* 6.79

2003:6-2007:7 2.21 0.60

2007:8-2009:3 1.43 3.65

2009:4-2014:12 16.17* -41.5

Panel C. EMEA EM Returns

2003:6-2014:12 18.89* 2.56

2003:6-2007:7 3.17 0.03

2007:8-2009:3 0.97 1.63

2009:4-2014:12 20.99* 0.21


and post-crisis periods, respectively. The VIXC is the change in VIX. An asterisk ‘*” on the Chi-

square test value indicates the rejection of the null hypothesis that VIX changes (returns) do not

Granger-cause stock returns (VIXC). The EMEA is the return for Europe, Middle East, and

Africa emerging markets (10 markets).

30

Table 4 (cont.). Granger Causality Tests

Period Ho: VIXC does not Ho: Returns do not

lead returns lead VIXC

___________________________________________________________________________________________________________________

Panel D. Asian EM Returns

2003:6-2014:12 12.95* 1.14

2003:6-2007:7 4.29 1.06

2007:8-2009:3 1.87 0.69

2009:4-2014:12 10.27* 0.07

Panel E. Frontier Markets Returns

2003:6-2014:12 10.60* 1.71

2003:6-2007:7 4.23 0.05

2007:8-2009:3 0.64 1.01

2009:4-2014:12 16.89* 0.05

Panel F. All Emerging Market Returns

2003:6-2014:12 11.01* -597

2003:6-2007:7 4.04 0.16

2007:8-2009:3 -304 0.329

2009:4-2014:12 9.15* 0.37


and post-crisis periods, respectively. The VIXC is the change in VIX. An asterisk ‘*” on the Chi-

square test value indicates the rejection of the null hypothesis that VIX changes (returns) do not

Granger-cause stock returns (VIXC).

31

Table 5. Linkages between VIX and Emerging Market Stock Returns under the Full

Transmission Model, 2003:6-2014:12

Regressor LA EUR EMEA ASIA FM EM

Mean Const. 0.072* 0.084* 0.08* 0.09* 0.043* 0.074*

LAt-1 0.13* ------ ------- ------ ------ ------

EURt-1 ------ 0.076* ------ ------ ------ ------

EMEAt-1 ------ ------ 0.08* ------ ----- ------

ASIAt-1 ------ ------ ------ 0.087* ----- ------

FMt-1 ------ ------ ------ ------ 0.065* ------

EMt-1 ------ ------ ------ ------ ----- 0.22*

VIXCt -0.60* -0.38* -0.34* -0.12* -0.14* -0.28*

Variance Con. 0.021 0.047* 0.02* 0.013* 0.009* 0.013*

ARCH 0.044* 0.062* 0.04* 0.038* 0.12* 0.048*

GARCH 0.94* 0.92* 0.94* 0.95* 0.88* 0.94*

VIXCt 0.113* 0.21* 0.17* 0.14* 0.04* 0.096*

Normality Test 84.10* 562.85* 162.86* 158.55* 283.19* 111.22*

(Chi Square)

Notes: The symbol * indicates significance at the 5% level. The full transmission model allows

for the VIX fears through the mean return equation as well as through the volatility of return

equation. The variables are defined as follows: LAt-1, Latin America EM returns day t-1; EURt-1,

European EM returns; EMEAt-1, European, Middle East, and Africa EM returns; ASIAt-1, Asian

EM returns; FMt-1, Frontier Market (23 countries) returns; EMt-1, MSCI emerging market (23

countries) index; and VIXCt, changes in VIX.

32

Table 5 (Cont.). Linkages between VIX and Emerging Market Stock Returns under the Full

Transmission Model, 2003:6-2007:7 (Pre-Crisis Period)


Mean Const. 0.16* 0.20* 0.17* 0.17* 0.13* 0.074*

LAt-1 0.155* ------ ------- ------ ------ ------

EURt-1 ------ 0.097* ------ ------ ------ ------

EMEAt-1 ------ ------ 0.12* ------ ----- ------

ASIAt-1 ------ ------ ------ 0.11* ----- ------

FMt-1 ------ ------ ------ ------ 0.22* ------

EMt-1 ------ ------ ------ ------ ----- 0.22*

VIXCt -0.78* -0.15* -0.17* -0.11* -0.16* -0.28*

Variance Con. 0.058* 0.118* 0.06* 0.03* 0.03* 0.013*

ARCH 0.036* 0.08* 0.06* 0.05* 0.06* 0.048*

GARCH 0.92* 0.86* 0.89* 0.91* 0.87* 0.94*

VIXCt 0.22* 0.34* 0.27* 0.20* 0.12* 0.096*

Normality Test 24.26* 181.20* 103.11* 29.99* 20.54* 111.22*

(Chi Square)







33


Transmission Model, 2007:8-2009:3 (Crisis Period)


Mean Const. 0.05 0.05 0.07* 0.04 0.04 0.074*

LAt-1 0.187* ------ ------- ------ ------ ------

EURt-1 ------ 0.078* ------ ------ ------ ------

EMEAt-1 ------ ------ 0.10* ------ ----- ------

ASIAt-1 ------ ------ ------ 0.07 ----- ------

FMt-1 ------ ------ ------ ------ 0.24* ------

EMt-1 ------ ------ ------ ------ ----- 0.22*

VIXCt -0.77* -0.35* -0.31* -0.16* -0.28* -0.28*

Variance Con. 0.015* 0.078* 0.07* 0.03* 0.015* 0.013*

ARCH 0.001 0.069* 0.037* 0.02* 0.02* 0.048*

GARCH 0.99* 0.92* 0.94* 0.98* 0.99* 0.94*

VIXCt 0.32* 0.36* 0.36* 0.41* 0.21* 0.096*

Normality Test 13.6* 78.50* 21.86* 9.65* 8.10* 111.22*

(Chi Square)







34


Transmission Model, 2009:4-2014:12 (Post-Crisis Period)

Regressor LA-EM EUR-EM EMEA ASIA-EM FM EM

Mean Const. 0.013* 0.015 0.019 0.058* 0.04* 0.074*

LAt-1 0.11* ------ ------- ------ ------ ------

EURt-1 ------ 0.042* ------ ------ ------ ------

EMEAt-1 ------ ------ 0.04 ------ ----- ------

ASIAt-1 ------ ------ ------ 0.058* ----- ------

FMt-1 ------ ------ ------ ------ 0.176* ------

EMt-1 ------ ------ ------ ------ ----- 0.22*

VIXCt -0.51* -0.45* -0.40* -0.13* -0.11* -0.28*

Variance Con. 0.018* 0.028* 0.10* 0.010* 0.019* 0.013*

ARCH 0.047* 0.034* 0.03* 0.032* 0.07* 0.048*

GARCH 0.94* 0.95* 0.97* 0.96* 0.91* 0.94*

VIXCt 0.075* 0.19* 0.15* 0.105* 0.024* 0.096*

Normality Test 52.7* 316.15* 59.57* 19.81* 154.23* 111.22*

(Chi Square)







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