Emerging Markets Finance and Trade
ISSN: 1540-496X (Print) 1558-0938 (Online) Journal homepage: http://www.tandfonline.com/loi/mree20
The Risk-Return Trade-Off in a Liberalized
Emerging Stock Market: Evidence from Vietnam
Kuangnan Fang, Ji Wu & Cuong Nguyen
To cite this article: Kuangnan Fang, Ji Wu & Cuong Nguyen (2015): The Risk-Return Trade-Off in
a Liberalized Emerging Stock Market: Evidence from Vietnam, Emerging Markets Finance and
Trade, DOI: 10.1080/1540496X.2015.1103129
To link to this article: http://dx.doi.org/10.1080/1540496X.2015.1103129
Published online: 07 Dec 2015.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=mree20
Download by: [Ji Wu]
Date: 09 December 2015, At: 02:04
Emerging Markets Finance & Trade, 1–18, 2015
Copyright © Taylor & Francis Group, LLC
ISSN: 1540-496X print/1558-0938 online
DOI: 10.1080/1540496X.2015.1103129
The Risk-Return Trade-Off in a Liberalized Emerging Stock
Market: Evidence from Vietnam
Kuangnan Fang1, Ji Wu2, and Cuong Nguyen3
1
Downloaded by [Ji Wu] at 02:04 09 December 2015
School of Economics and Collaborative Innovation Center for Peaceful Development of Cross-Strait
Relations, Xiamen University, Xiamen, Fujian, P. R. China; 2Institute for Financial and Accounting
Studies (IFAS), Xiamen University, Xiamen, Fujian, P. R. China; 3Department of Accounting,
Economics, and Finance, Faculty of Agribusiness and Commerce, Lincoln University, Christchurch,
New Zealand
ABSTRACT: We empirically examine the risk-return trade-off in a liberalized emerging stock market:
Vietnam during the period 2007–2014. We find that (1) neither realized idiosyncratic volatility nor
conditional idiosyncratic volatility has been priced; (2) rational multifactor models could well explain the
stock portfolio returns; (3) there is a flat trend for equal-weighted idiosyncratic volatility (IVOL), but a
downward trend for market volatility. Our results indicate that the idiosyncratic risk plays an unimportant
role in pricing stocks and that the systematic risks still dominate asset returns in emerging stock markets.
Results imply that Vietnamese investors can get increased benefit from portfolio diversification.
KEY WORDS: conditional idiosyncratic volatility, cross-sectional, idiosyncratic volatility, liberalization,
Vietnam
Introduction
Motivated by recent research, Huang, Wald, and Martell (2013) suggest that the idiosyncratic risk
plays a less important role in pricing stocks in emerging stock markets after the markets’ liberalization.
When emerging countries liberalize their capital markets, domestic investors will have more choices to
diversify their portfolios, thus idiosyncratic risk would be diversified away from their portfolios.
Huang et al.’s (2013) findings support traditional finance theory, which indicate that idiosyncratic risk
could be costly for diversified by portfolio selections.
However, empirical evidence suggests that the idiosyncratic risk is priced in the developed stock
markets. For example, Ang et al. (2006, 2009) find a negative relationship between the lagged
idiosyncratic volatility and expected stock returns (refer to IVOL puzzle) in the United States and
twenty-three other developed stock markets. Levy (1978) and Merton (1987) theoretically support the
finding of Ang et al. (2006, 2009) that if investors are constrained from holding a market portfolio,
investors should ask for compensation for bearing the idiosyncratic risk contained in their portfolios.
Malkiel and Xu (2006) further state that if one group of investors is not able to hold a market portfolio
in a stock market, then other groups of investors are also not able to because the constrained investors
create excess demand for some stocks and excess supply of others. Goetzmann and Kumar (2008) find
that over half of investors hold no more than three stocks according to 62,000 household trading
accounts in the United States (Campbell et al. 2001); however, they suggest that to form a diversified
portfolio one needs at least fifty stocks in the U.S. stock markets. In this sense, idiosyncratic risk
represented by idiosyncratic volatility (IVOL) should be imputed in stock market prices.
Ang et al.’s (2006, 2009) results are questioned by other researchers. For example, Fu (2009) argues
that the lagged IVOL is not a good proxy for the expected IVOL. Fu (2009) employs the EGARCH
Address correspondence to Ji Wu, Institute for Financial & Accounting Studies (IFAS), Xiamen University,
422 Siming South Road, Xiamen, Fujian, 361005, P. R. China. E-mail: [email protected]
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/MREE.
Downloaded by [Ji Wu] at 02:04 09 December 2015
2
K. FANG ET AL.
model to estimate a conditional idiosyncratic risk proxy (E-IVOL) and finds a significantly positive
relationship between the E-IVOL and expected stock returns. In emerging stock markets, the IVOL
puzzle is also absent in the Philippines (Nartea, Ward, and Yao 2011) and Hong Kong (Nartea and Wu
2013), because, as Diamonte, Liew, and Stevens (1996) suggest, the systematic risks still dominate the
asset returns in developing stock markets. Therefore, we estimate both IVOL and E-IVOL in this study
to examine their explanatory power on stock returns in the Vietnamese stock market (VSM). If the
idiosyncratic risk is not priced in the VSM, we will further test whether the multifactor asset pricing
models do a good job; that is, the Fama-French three-factor model and the Carhart (1997) four-factor
model.
Whether there are time series trends of both IVOL (E-IVOL) and market volatility (MVOL) is of
interest to both academics and investors. Campbell et al. (2001) suggest that investors could increase
the benefits over time from portfolio diversification if there is an upward trend of the IVOL with a flat
MVOL. However, Bekaert and Harvey (2000) suggest that the liberalization in the emerging stock
markets reduces the market volatility. De Santis and İmrohoroglu (1997) also point out foreign
investors would not increase the market volatility for an emerging stock market. We thus are interested
in whether the market volatility has declined after the VSM’s liberalization.
The Vietnamese stock market is an interest setting in which to examine these research questions.
First, the VSM is one of the fastest growing emerging stock markets in the world. The VSM officially
came into operation on July 28, 2000, with only five companies listing during the year. By the end of
2014, the numbers of listed companies had increased to 684. Market capitalization grew from less than
USD $1 billion to nearly USD $20 billion, with investor accounts rising from 2,908 in 2000 to nearly
1.4 million in 2014. The market capitalization accounted for 33 percent of gross domestic product
(GDP) in 2014 as illustrated in Table 1. However, the market size is still small compared to other
regional markets, implying enormous potential for growth. Second, the VSM started its liberalization
in 2009. Of the 1.2 million accounts, 16, 000 accounts are those of foreign investors and institutions.
As the VSM continues to open itself to foreign investors, it is worth understanding the factors that are
driving stock price movements in the market. Third, the VSM is one of the few capital markets under
the management of a communist government. This study provides insight into the possible coexistence
of elements of the different ways of managing financial markets and politics.
We contribute to the literature in three ways. First, we find that neither IVOL nor E-IVOL has a
significant relationship with one-month-ahead stock returns in the VSM. Our findings support Huang,
Wald, and Martell's (2013) arguments that the idiosyncratic risk is not priced in an emerging stock
market after the market’s liberalization. Second, our findings indicate that multifactor asset pricing
models could well explain the stock portfolio returns. Our findings contribute empirical evidence to
Table 1. Vietnamese stock market information
Year
2007
2008
2009
2010
2011
2012
2013
2014*
Mcap
Accounts
%GDP
Listed co.
IVOL
E-IVOL
492,900
225,935
620,551
695,186
616,226
685,830
834,800
N/A
312,139
531,428
822,914
925,955
1,188,824
1,263,056
1,312,845
1,374,686
40.00
19.76
37.71
42.25
33.60
32.80
30.00
33.00
253
342
457
557
705
704
687
684
0.0226
0.0195
0.0248
0.0230
0.0235
0.0232
0.0245
0.0243
0.1007
0.1138
0.1110
0.1035
0.1044
0.1073
0.1043
0.1030
Notes: Mcap is market capitalization in billion Vietnamese dong (VND); Accounts is the number of investors’ accounts
investing in the Vietnamese stock market; %GDP is the percentage of Vietnamese stock market in GDP; Listed co. is the
number of listed companies each year; IVOL is the lagged idiosyncratic volatility estimated by Ang et al.’s (2006)
method; E-IVOL is the conditional idiosyncratic volatility estimated by Fu’s (2009) method.*Figures are as of June 2014.
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
3
Diamonte, Liew, and Stevens's (1996) and Perotti and Oijen’s (2001) arguments that the systematic
risks dominate asset returns in the emerging stock markets. Finally, we find that there are flat trends of
both IVOL and E-IVOL with a downward trend of MVOL in the VSM. Our findings empirically
support Bekaert and Harvey's (2000) conclusions that the market liberalization could reduce markets’
volatilities. Our results imply that Vietnamese investors could gain benefits from portfolio
diversification.
Literature Review
Downloaded by [Ji Wu] at 02:04 09 December 2015
Idiosyncratic Volatility, Conditional Idiosyncratic Volatility, and Stock Returns
Ang et al. (2006) report that stocks with high IVOL generate low average returns in the U.S. stock
markets, even after controlling for size, book-to-market ratio (BM), leverage, liquidity, volume,
turnover, bid-ask spreads, coskewness, dispersion in the forecasts, and momentum effect, and the
results are robust in twenty-three international developed stock markets, which is called the IVOL
puzzle. However, the empirical evidence on the IVOL puzzle is not consistent. For example, Bali and
Cakici (2008) suggest that the weighting scheme used to compute average portfolio returns affects the
relationship between the expected returns and IVOL. Ang et al. (2009) explain the IVOL puzzle being
due to IVOL being inversely related to future earning shocks of firms.
In emerging stock markets, most results suggest that there is an absence of the IVOL puzzle. For
example, Nartea, Ward, and Yao (2011) find that there is no relationship between IVOL and stock
portfolio returns in the Philippine stock market, and the IVOL effect can be due to the Fama-French
factors; that is, size and BM. Nartea and Wu (2013) indicate that the IVOL puzzle in the Hong Kong
stock market is weak, but it unveils a strong negative total volatility effect in the Hong Kong stock
market (Lee and Wei 2012).
Hou and Loh (2014) conclude that the explanations of the IVOL puzzle can be sorted into three
groups. The first group of researchers argues that the IVOL puzzle is caused by investors’ lottery
preferences; such as investors prefer stocks with features of skewness, coskewness, realized and
expected idiosyncratic skewness, and maximum daily return. Second, researchers suggest that the
IVOL puzzle is attributed to the market frictions; that is, one-month return reversal, illiquidity, shortsale constraints, and investors’ attention. Finally, other explanations about the IVOL puzzle can be
sorted into the last group, including uncertainty, leverage, financial distress, growth options, and
others. More important, Hou and Loh (2014) find that incorporation of investors’ lottery preferences,
short-term reversal, and earnings shocks together could only explain 60 to 80 percent of the IVOL
puzzle.
Furthermore, Fu (2009) suggests that investors would rely on the newly revealed information in the
last period to update their expectation of means and variances of returns each period, thus the
idiosyncratic volatilities are time varying. Therefore, Fu (2009) employs the EGARCH model to
estimate the conditional IVOL and relate it to the expected stock returns. Fu (2009) argues that the
E-IVOL not only captures the time-series properties of the volatility—for example fat tails, clustering,
and asymmetry—but is also less noisy than the IVOL. Fu (2009) finds that the expected stock returns
are actually positively related to the E-IVOL. Pukthuanthong-Le and Visaltanachoti (2009) report a
positive relationship between the E-IVOL and expected stock returns across thirty-six countries from
1973 to 2007.
Empirical evidence indicates that the idiosyncratic risk has a strong effect on stock returns in the
developed stock markets, but it is often missing in the emerging stock markets. This might be due to
two reasons. First, most emerging stock markets are suffering processes of market liberalization.
Huang, Wald, and Martell (2013) hypothesize that if the number of assets in a small economy is
limited, the idiosyncratic risk might be priced in the stock market because investors lack other assets to
diversify their portfolios. After the market’s liberalization, investors have more choices to diversify
their portfolios, thus this reduces the pricing power on the idiosyncratic risk. Furthermore, Bae, Bailey,
4
K. FANG ET AL.
and Mao (2006) suggest that the market liberalization increases the efficiency of the information
environment; this also could reduce the pricing power of the idiosyncratic risk in portfolios. Second,
Diamonte, Liew, and Stevens (1996) suggest that systematic risk, such as political risk and market risk,
plays a more important role in pricing stocks in the emerging stock markets than in the developed
markets. Perotti and Oijen (2001) point out that the progress of privatization also increases the
systematic risk and has a strong effect on the local market development in emerging stock markets.
In this study, we are going to estimate both IVOL and E-IVOL to test their explanatory power on
Vietnamese stock returns. To consider the characteristics of VSM, liberalizing its stock markets in
2009 and processing privatization since 2000, we expected that none of our idiosyncratic risk proxies
would have significant relations to Vietnamese stock returns. Thus, we hypothesize as following:
Hypothesis 1: There is no significant relationship between the IVOL (E-IVOL) and one-month-ahead stock
returns in the VSM during the study period.
Downloaded by [Ji Wu] at 02:04 09 December 2015
Systematic Risk and Stock Returns
Sharpe (1964) presents the capital asset pricing model (CAPM) and asserts that the asset return
should depend only on its systematic risk, measured by β, since the unsystematic risk of an asset
could be diversified by portfolio selection. Fama and French (1993) include two factors in the
CAPM: firm size and book-to-market equity ratio. The authors report that their three-factor model
is more powerful in explaining asset returns than is the traditional CAPM in the U.S. stock markets
and thirteen other international stock markets over the period 1975–1995 (Fama and French 1998).
Carhart (1997) presents a four factor model by adding a factor of momentum on Fama-French’s
(1993) three factors and announces that it has a stronger explanatory power than the Fama-French
three-factor model.
So far, we emphasize three features of the VSM. First, the VSM is an emerging stock market in
which the systematic risk, such as political risk and market risk, may dominate the asset returns
(Diamonte, Liew, and Stevens 1996). Second, the VSM started a process of liberalization in 2009;
thus, Vietnamese investors should have more choices to diversify their portfolios than in the past
(Huang, Wald, and Martell 2013). Finally, the VSM is in the process of privatization, which is
normally correlated with an improvement of political risk or market risk (Perotti and Oijen 2001).
Thus, we expect that stock returns in the VSM should be significantly related to their systematic risks.
Therefore, we propose the second hypothesis:
Hypothesis 2: The multifactor asset pricing models could well explain the portfolio returns in the VSM.
Trends of Volatility
Malkiel and Xu (1997) conclude that the market volatility in the United States is stable over time, but
the volatility at the firm level has increased in recent years. Campbell et al. (2001) conclude that the
aggregate volatility at the firm level in the United States is large and has increased significantly, more
than doubling from 1962 to 1997. This implies that correlations among individual stocks and the
explanatory power of the market model have declined (Campbell et al. 2001).
Campbell et al. (2001) indicate several reasons behind the increase in the IVOL at firm level as
examined by other researchers: tough competition in product markets (Irvine and Pontiff 2009),
increased numbers of younger and risky firms listed on stock exchanges (Fink et al. 2009), retail
investors’ irrational trading behavior (Brandt et al. 2010), growth options and earnings (Cao,
Simin, and Zhao 2008), and increased institutional ownership (Bennett, Sias, and Starks 2003).
Bekaert, Hodrick, and Zhang (2012), however, find no evidence of a trend in the IVOL in twentythree developed stock markets excluding the United States. Bekaert, Hodrick, and Zhang (2012)
further explain that the upward trends of the IVOL in previous studies could be because previous
Downloaded by [Ji Wu] at 02:04 09 December 2015
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
5
studies may have started in a low-IVOL regime and ended in a high-IVOL regime. Brandt et al. (2010)
argue that the time-series behavior of the IVOL is actually an episodic phenomenon rather than a time
trend in the U.S. stock markets. Nartea, Wu, and Liu (2013) report that there is no trend of IVOL in the
Chinese stock markets during period 1993–2012.
However, other empirical evidence suggests that there might be a decreased trend of volatility in
either developed or emerging stock markets. For example, Hamao, Mei, and Xu (2003) report an
increased market volatility but falling firm-level idiosyncratic volatility in the Japanese stock market
during the period 1975–1999 because stocks in the Japanese market lost their individual characteristics
and started to move together, making it more difficult for investors to distinguish the “good” from the
“bad” firms after the 1990 market crash. In the emerging markets, Chen and Huang (2009) find that
there is a downward trend of volatility with the market liberalization and increment of economic
freedom. Their findings have been confirmed by Nartea, Wu, and Liu's (2013) study in the Chinese
stock markets, in which a high degree of economic freedom was found to lead to a low volatility in
stock markets. Moreover, De Santis and İmrohoroǧlu (1997) also suggest that the market liberalization
in the emerging markets would not increase the market volatility.
In summary, the VSM has experienced major market liberalization since 2009, which has allowed
foreign investors to invest in the VSM. There was also a price-limit policy imposed on the VSM in
2008. We thus propose the last research hypothesis:
Hypothesis 3: Volatility in the VSM shows a downward trend over the study period.
Data and Methods
The research data were obtained from DataStream. Daily and monthly individual stock returns in the
VSM from 2007 to 2014 were collected from the database as well as market capitalization and bookto-market ratio (BM). The interbank offered rate was used to represent the risk-free rate. Our sample
excluded investment trusts, closed-end funds, exchange-traded funds, and preference shares, which is
consistent with previous studies. We also excluded stocks with either daily returns of less than −100
percent or monthly returns greater than 200 percent, as well as stocks with a negative book-to-market
(BM) ratio, in order to reduce the noise in computing variables for each stock. Stocks that did not
continually have past-twenty-two-days return records in a particular month were automatically
excluded from the sample. We have 158 stocks in 2007, and this increased to 684 stocks at the end
of 2014; this results in 42,828 monthly return observations and approximately 1 million daily return
observations.
Estimating Idiosyncratic Volatility
We employ the method of Ang et al. (2006, 2009) to estimate the realized IVOL for Vietnamese
stocks. We first compute the size factor (SMB) and book-to-market factor (HML) for the VSM by using
an adapted Fama-French three-factor model introduced by Ang et al. (2009). We do not employ the
original Fama-French three-factor model (Fama and French 1993) due to the limited numbers of stocks
in the sample.1 The IVOL of each firm was computed at the beginning of every month to obtain the
standard deviation of the residuals (σɛi) from the adopted Fama-French three-factor model using the
daily data from the previous twenty-two trading days:
Ri;t Rf ¼ α þ βMKT ;i;m MKTt þ βSMB;i;m SMBt þ βHML;i;m HMLt þ εi;t ;
(1)
where t represents the twenty-two trading days ending on the last trading day of the month, m-1; Rf is
the daily risk-free rate; the MKT (value-weighted market return), SMB, and HML are the three factors.
6
K. FANG ET AL.
Therefore, σɛi is a daily volatility measure computed monthly. The model was reestimated every
month; thus, the three betas for each firm were changed every month.
Estimating Conditional Idiosyncratic Volatility
To estimate the conditional idiosyncratic volatility, we employ Fu’s (2009) method. We employ the
EGARCH (3, 1) as Fu (2009) suggested the best-fitting model for the fewest numbers of firm-month
observations. Therefore, we estimate the E-IVOL by EGARCH (p, q) model, in which p = 3 and q = 1.
The model can be written as follows:
Ri;t Rf ¼ α þ βMKT ;i;m MKTt þ βSMB;i;m SMBt þ βHML;i;m HMLt þ εi;t , N ðσ 2it Þ;
Downloaded by [Ji Wu] at 02:04 09 December 2015
ln σ 2it¼αi
þ
p
X
bi;l ln σ 2i;t1
l¼1
εi;tk εi;tk
1=2
þγ ð2=π Þ
þ
ci;k θ
σ i;tk
εi;tk k¼1
q
X
(2)
(3)
The monthly return process is described by the adopted Fama-French three-factor model as in
Equation (2). The conditional (on the information set at time t-1) distribution of residual εi;t is assumed
to be normal with the mean of zero and the variance of σ 2it . We estimate the conditional variance σ 2it ,
which is the function of the past p period of residual variance and 1 period of return shocks as
specified by Equation (3). To follow Fu (2009), firms should have at least thirty monthly returns to be
included in the sample.
Table 2. Descriptive statistics of monthly volatility measures
Mean
Median
Standard deviation
CV
Panel A: Summary statistics
0.0232
0.0235
0.0034
0.1468
IVOLEW
0.0150
0.0149
0.0029
0.1949
IVOLVW
0.1064
0.1050
0.0065
0.0612
E-IVOLEW
0.0842
0.0836
0.0147
0.1748
E-IVOLVW
MVOL
0.0142
0.0117
0.0072
0.5104
Panel B: Summary statistics—Subsample (June 2009–December 2014)
0.0239
0.0237
0.0020
0.0854
IVOLEW
0.0148
0.0149
0.0024
0.1693
IVOLVW
0.1053
0.1045
0.0048
0.0452
E-IVOLEW
0.0799
0.0807
0.0133
0.1666
E-IVOLVW
MVOL
0.0124
0.0111
0.0060
0.4819
Panel C: Correlation table
IVOLVW
E-IVOLEW
E-IVOLVW
IVOLEW
1.0000
IVOLEW
0.5859
1.0000
IVOLVW
–0.0998
0.3057
1.0000
E-IVOLEW
–0.2274
0.3870
0.5860
1.0000
E-IVOLVW
MVOL
0.2331
0.6036
0.3018
0.5651
Maximum
Minimum
0.0288
0.0223
0.1331
0.1284
0.0355
0.0086
0.0061
0.0942
0.0555
0.0040
0.0288
0.0200
0.1187
0.1159
0.0325
0.0187
0.0099
0.0942
0.0555
0.0040
MVOL
1.0000
Notes: IVOLEW and IVOLVW are average equal- and value-weighted aggregate realized idiosyncratic volatilities;
E-IVOLEW and E-IVOLVW are aggregate conditional idiosyncratic volatilities; MVOL is the value-weighted market
volatility. CV represents the coefficient of variation. The IVOL of each firm is estimated at the beginning of every month
as the standard deviation of the residuals of the Fama-French three-factor model, using daily data for the previous
twenty-two trading days. E-IVOL is estimated by EGARCH (3, 1) model by using lagged thirty monthly returns. MVOL
is the standard deviation of daily value-weighted market returns for the past twenty-two trading days ending on the last
trading day of the month. The sample period is January 2007–December 2014.
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
7
Downloaded by [Ji Wu] at 02:04 09 December 2015
Descriptive Statistics of IVOL, E-IVOL, and MVOL
Table 2 presents the descriptive statistics of the monthly volatility series in the VSM from 2007 to
2014. IVOLEW and IVOLVW are average equal- and value-weighted aggregate realized idiosyncratic
volatilities; E-IVOLEW and E-IVOLVW are also aggregate conditional idiosyncratic volatilities across
all firms in the VSM. MVOL represents the value-weighted market volatility and is computed as the
standard deviation of daily value-weighted market returns for the past twenty-two trading days.
Therefore, the MVOL is also a daily volatility measure computed monthly. Panel A shows that both
mean and median of IVOLEW are higher than those of IVOLVW, which implies that smaller firms are
more volatile than big firms. The same pattern of E-IVOLEW and E-IVOLVW are observed in Panel A.
These results are consistent with results from studies in the U.S stock markets and other developing
markets (Campbell et al. 2001; Nartea, Ward, and Yao 2011; and Nartea et al. 2013; Nartea and Wu
2013).2 The mean and median of MVOL are similar to those of IVOLVW, which are 0.0142 and
0.0117, respectively. More important, both IVOLEW and IVOLVW have approximately the same
coefficient of variation, thus indicating that they are equally variable. However, the coefficient of
variation of E-IVOLVW (0.1748) is nearly double the coefficient of variation of E-IVOLEW (0.0612).
This implies that the E-IVOLVW is more variable than E-IVOLEW. The MVOL is on the highest level,
at 0.5104, among all volatility series, which indicates that the MVOL is more variable than the average
IVOL and E-IVOL in our sample. Panel B reports the subsample analysis from 2009 to 2014. Results
in Panel B are the same as those in Panel A.
Panel C shows that IVOLEW and IVOLVW are highly correlated as expected; E- IVOLEW and
E-IVOLVW are also highly correlated, with correlation coefficients of 0.5859 and 0.5860, respectively.
The correlation coefficient between IVOLVW and E- IVOLVW is 0.3870, which is approximately the
same as Fu’s (2009) result. The MVOL is moderately correlated with both IVOLEW and E-IVOLEW,
which are 0.2331 and 0.3018, respectively; however, MVOL is highly correlated with both IVOLVW
and E- IVOLVW, which are 0.6036 and 0.5651, respectively.
Idiosyncratic Volatility Versus Expected Portfolio Returns
We employ the portfolio method to study the relationship between the IVOL (E-IVOL) and onemonth-ahead raw and risk-adjusted stock returns. All sample stocks are sorted into three portfolios
according to their IVOL (E-IVOL), based on the breakpoints for the top 66.67 percent and the bottom
33.33 percent at the beginning of every month: high IVOL (E-IVOL), medium IVOL (E-IVOL), and
low IVOL (E-IVOL). Next, we compute each portfolio’s equal- and value-weighted raw returns for the
current month, and the process is repeated every month. To test the IVOL (E-IVOL) effect, we
compute the differences between the high- and low-IVOL (E-IVOL) portfolios of the average
portfolios’ raw returns. Finally, we compute the alpha (abnormal returns) for each IVOL (E-IVOL)
portfolio to further test the IVOL (E-IVOL) effect. Following Ang et al. (2006, 2009), alpha for each
IVOL (E-IVOL) portfolio is computed with respect to the Fama-French three-factor model:
Rra
i;m ¼ rfm þ βMTK;i;m MTKm þ βSMB;i;m SMBm þ βHML;i;m HMLm ;
(4)
where rfm is the risk-free rate for month m, and MTKm, SMBm, and HMLm are excess returns as defined
previously, but for month m. The alpha for firm i, αi,m, is
αi;m ¼ Ri;m Rra
i;m ;
(5)
where Rrai,m is the actual return of firm i in month m. Hence, the alpha is the return in excess of the
risk-adjusted return.
8
K. FANG ET AL.
Robustness Tests for the IVOL (E-IVOL) Effect
We conduct firm-level Fama-MacBeth cross-sectional regressions of stock returns as a robustness
test for the IVOL (E-IVOL) effect. The portfolio analysis has two major shortcomings. First, the
portfolio analysis could lose too much information by using aggregate portfolios rather than
individual firms. Second, the portfolio method cannot control for the simultaneous multiple effects
of the dependent bivariate sorts. To address the serial correlation problem among our control
variables, we report the Newey and West (1987) t-statistics instead of the standard t-statistics. We
perform the following model:
Ri;t ¼ β0;t1 þ β1;t1 IVOLi;t1 þ β2;t1 E-IVOLi;t1 þ β3;t1 SIZEi;t1 þ β4;t1 BMi;t1
þ β5;t1 MOMi;t1 þ β6;t1 REVi;t1 ;
(6)
Downloaded by [Ji Wu] at 02:04 09 December 2015
where Ri,t,, is the realized stock return in month t.3
Empirical Results
Idiosyncratic Volatility, Conditional Idiosyncratic Volatility, and Cross-Sectional Stock Returns
Table 3 shows the relationship between the IVOL (E-IVOL) and the one-month-ahead stock portfolio
returns in the VSM over the period 2007–2014. Panel A shows the results for IVOL-sorted portfolios,
and Panel B shows the corresponding results sorted by E-IVOL. The results show the average monthly
raw returns of stock portfolios sorted by IVOL (E-IVOL), the average abnormal returns with respect to
the Fama-French three-factor model, the average size, and the book-to-market ratio of the three IVOL(E-IVOL-) sorted portfolios. Results in both the equal-weighted (EW) portfolios and the valueweighted (VW) portfolios are reported. First, the results in Panel A show that the size of High –
Low is significantly negative (−2,652,632; t = −2.4578), but the BM of High – Low is not (0.2410;
t = 0.3222). This implies that the high-IVOL portfolio contains mostly small-sized stocks. Second,
Panel A also shows that the differences of raw returns between the high- and low-IVOL portfolios are
statistically insignificant, regardless of the weighting scheme. Further, all six IVOL-sorted portfolios in
Panel A present negative but statistically insignificant returns. From the raw returns analysis, we do
not observe the IVOL puzzle in the VSM. Third, alpha spreads in Panel A further confirm the absence
of the IVOL puzzle since none are statistically significant (−0.0018; t = −0.4057 for EW sorting, and
−0.0091; t = −1.4226 for VW sorting).
Results in Panel B also point to an absence of the E-IVOL effect in the VSM during the testing
period. However, when we use E-IVOL to sort Vietnamese stocks, we find that the differences of raw
returns (alpha) between high- and low-E-IVOL portfolios are positive but still insignificant: 0.0061
(0.0059) for EW portfolio and 0.0029 (0.0043) for VW portfolio, respectively.
We do not detect either the IVOL or the E-IVOL effect in the VSM during the study period by the
portfolio sorting method. Due to the obvious deficiencies in the portfolio analysis, we conduct an
additional robustness test by running firm-level Fama-MacBeth regressions for the data to further
check the reliability of the previous findings. Table 4 reports the Fama-MacBeth regression results.
Panel A reports results in the full sample period, and Panel B reports results after 2009. The univariate
regression in Panel A shows significant positive BM effect as well as a marginal significant negative
size effect in the VSM. There are no momentum or short-term reversal effects in the VSM during the
study period. More important, the coefficients of IVOL and E-IVOL are statistically insignificant at all
levels. Thus, the results indicate that neither IVOL nor E-IVOL is priced in the VSM during the full
sample period.
Results in Panel B are slightly different from the results in Panel A. First, the univariate results still
show insignificant coefficients of IVOL and E-IVOL in the testing period, but the multivariate results
indicate a significant negative IVOL effect in the VSM during the period 2009–2014. Second, the
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
9
Table 3. Returns of portfolios sorted by idiosyncratic volatility
Raw returns
Mean
Standard deviation
Downloaded by [Ji Wu] at 02:04 09 December 2015
Panel A: Portfolios sorted by conditional
Equal-weighted
High IVOL
−0.0055
t-statistic
−0.0446
Medium IVOL
−0.0046
t-statistic
−0.0406
Low IVOL
−0.0059
t-statistic
−0.0572
High - Low
0.0004
t-statistic
0.0027
Value-weighted
High IVOL
t-statistic
Medium IVOL
t-statistic
Low IVOL
t-statistic
High-Low
t-statistic
−0.0136
−0.1133
−0.0056
−0.0537
−0.0076
−0.0782
−0.0060
−0.0389
Panel B: Portfolios sorted by conditional
High E-IVOL
−0.0023
t-statistic
−0.0178
Medium E-IVOL
−0.0076
t-statistic
−0.0700
Low E-IVOL
−0.0084
t-statistic
−0.0850
High-Low
0.0061
0.0374
t-statistic
Value-weighted
High E-IVOL
t-statistic
Medium E-IVOL
t-statistic
Low E-IVOL
t-statistic
High-Low
t-statistic
−0.0059
−0.0502
−0.0081
−0.0750
−0.0088
−0.1001
0.0029
0.0199
Jensen’s alpha
Size
B/M
Mean
Standard error
−0.0017
−0.5422
−0.0009
−0.2663
0.0001
0.0283
−0.0018
−0.4057
0.0031
Idiosyncratic Volatility
0.1230
309,984.5
1.3111
0.1142
860,195.7
1.2573
0.1035
2,962,616.0
1.0701
0.1607
−2,652,631.5
0.2410
0.1203
309,984.5
1.3111
0.1042
860,195.7
1.2573
0.0973
2,962,616.0
1.0701
0.1547
−2,652,631.5
0.2410
Idiosyncratic Volatility
0.1292
727,482.7
1.2448
0.1090
1,243,635.0
1.2393
0.0984
2,171,823.0
1.1529
0.1624
−1,444,340.3
0.0919
0.1168
727,482.7
1.2448
0.1074
1,243,635.0
1.2393
0.0876
2,171,823.0
1.1529
0.1460
−1,444,340.3
0.0919
−0.0072
−1.1740
0.0014
0.3631
0.0019
1.0545
−0.0091
−1.4226
0.0019
0.4165
−0.0029
−1.0951
−0.0040
−2.2208
0.0059
1.2066
0.0029
0.4992
0.0006
0.1752
−0.0014
−0.7102
0.0043
0.7006
0.0035
0.0031
0.0044
0.0062
0.0039
0.0018
0.0064
0.0046
0.0026
0.0018
0.0049
0.0059
0.0033
0.0020
0.00618
Notes: At the beginning of every month we sorted stocks into tertiles according to the previous month’s IVOL (E-IVOL):
high IVOL (high E-IVOL), medium IVOL (medium E-IVOL), and low IVOL (low E-IVOL). The method in estimating
IVOL (E-IVOL) is introduced previously. We computed each portfolio’s equal- and value-weighted raw returns for the
current month. We also estimated each portfolio’s alpha (α coefficient) from the Fama-French three-factor model
(Equation (4)) using the full sample of monthly value- or equal-weighted returns for each portfolio. The last row of each
panel presents the difference in the monthly returns and the differences in alpha between the high- and low-IVOL (EIVOL) portfolios. T-statistics are reported in a separate row. We conducted the analysis for the full sample period, August
2007–December 2014. Size is market value in millions of Vietnamese dong; BM is book-to-market value; IVOL is
idiosyncratic volatility; E-IVOL is conditional idiosyncratic volatility. Panel A: Portfolios sorted by lagged idiosyncratic
volatility.
10
IVOL
E-IVOL
SIZE
BM
REV
MOM
Intercept
IVOL
E-IVOL
SIZE
BM
REV
Panel B: Subsample (June 2009—December 2014
MOM
Notes: Each month from August 2007 to December 2014 we ran a firm-level Fama-MacBeth cross-sectional regression of the return on that month, with one-month
lagged values of the control variables: Ri,t = β0,t-1 + β1,t-1 IVOLi, t-1 +β2,t-1 E-IVOLi, t-1 + β3,t-1 SIZEi, t-1 + β4,t-1 BMi, t-1 +β5,t-1 MOMi, t-1+ β6,t-1 REVi, t-1. Each row
reports the time-series averages of the slope coefficients. IVOL is the standard deviation of the residuals of the Fama-French three-factor model, using daily data for the
previous twenty-two trading days; E-IVOL is conditional idiosyncratic volatility. Size at the end of month t is defined as the log of the firm’s market capitalization at the
end of month t; BM is the firm’s book-to-market ratio six months prior. Following Jegadeesh and Titman (1993), momentum at time t (MOM) is the stock’s elevenmonth past return lagged one month. Reversal in month t (REV) is short-term reversal defined as the return on the stock in month t-1. Numbers in parentheses are NewyWest t-statistics.
−0.0077
0.1745
0.0049 −0.1152
(−0.6538) (0.6015)
(0.5991) (−0.7006)
−0.0115
0.0497
−0.0035
0.0424
(−1.0858)
(1.2099)
(−0.4874)
(1.0985)
0.0281
−0.0027
0.0212
−0.0016
(1.0809)
(−1.6642)
(0.9825)
(−1.1194)
−0.0130
0.0077
−0.0085
0.0096
(−1.1354)
(2.0915)
(−1.2244)
(2.7291)
−0.0073
−0.0106
0.0024
−0.0378
(−0.6955)
(−0.4388)
(0.2992)
(−2.0442)
−0.0091
−0.0061
0.0040
0.0004
(−0.8586)
(−0.9887) (0.4951)
(0.0577)
0.0015
−0.1817
−0.0013 0.0102 −0.0341 −0.0033
0.0134 −0.2871
−0.0012 0.0107 −0.0440 0.0036
(0.0688) (−0.9525)
(−0.9129) (4.0110) (−1.9216) (−0.5323) (0.5906) (−2.1056)
(−0.7264) (4.2313) (−2.5236) (0.6016)
−0.0239
0.0516 −0.0001 0.0103 −0.0470 −0.0044 −0.0151
0.0587
0.0002 0.0112 −0.0539 0.0021
(1.2545) (−0.0971) (3.9032) (−2.7421) (−0.7271) (−0.7527)
(1.5326) (0.1190) (4.1446) (−3.2138) (0.3532)
(−1.1230)
Intercept
Panel A: Full sample
Table 4. Fama-Macbeth regression results of single-variable effect
Downloaded by [Ji Wu] at 02:04 09 December 2015
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
11
Downloaded by [Ji Wu] at 02:04 09 December 2015
negative size effect has disappeared the negative size effect still remains negative during the period
2009–2014. Instead, we find a consistently significant negative short-term reversal effect in this
period. Finally, the BM effect remains significantly positive.
The findings from the current study add to the literature from emerging stock markets that neither
IVOL nor E-IVOL matters in pricing stocks. We explain our results as follows. First, the VSM starts
its liberalization in 2009, and then investors have more choices to diversify their portfolios (Huang,
Wald, and Martell 2013). This could be the reason we do not observe a significant IVOL or E-IVOL
effect on either portfolio level or individual stock level. Moreover, as Bae, Bailey, and Mao (2006)
suggest, the liberalization would reduce the pricing power of the idiosyncratic risk in portfolios.
Second, unlike the developed stock markets, the systematic risks still nominate asset returns in the
emerging stock markets (Diamonte, Liew, and Stevens 1996). The process of privatization since 2000
also increases the systematic risk in the VSM (Perotti and Oijen 2001). We thus find a consistently
significant BM effect and marginal size effect in the full sample period. Finally, our results imply that
there is no preference among Vietnamese investors for high- or low-IVOL (E-IVOL) stocks, and thus
there is no compensation for bearing idiosyncratic risk in the VSM.
Multifactor Asset Pricing Models and Expected Stock Returns
We are going to examine the explanatory power of both the Fama-French three-factor model and the
Carhart (1997) four-factor model in explaining the expected stock portfolio returns in the VSM. The
time series regression approach to estimate each pricing model for the same three IVOL- (E-IVOL-)
sorted portfolios is used in this section. Table 5 reports the results of IVOL-sorted portfolios, and
Table 6 reports the results of E-IVOL-sorted portfolios. Panels A and B report results of the FamaFrench three-factor model for the IVOL-sorted equal- and value-weighted portfolios, respectively;
Panels C and D report the corresponding results of the Carhart (1997) four-factor model. To correct for
either heteroskedasticity or serial correlation, we reestimate all models using M-L ARCH (Marquardt).
Panels A and B in Table 5 show that the Fama-French three-factor model could well explain the
expected stock portfolio returns for all six IVOL-sorted portfolios. First, the coefficients of MKT play
the most significant role in explaining portfolio returns for all six IVOL-sorted portfolios, regardless of
the weighting scheme. Second, none of the alphas are statistically significant in all six IVOL-sorted
portfolios. This suggests that the Fama-French three-factor model could well explain the IVOL-sorted
portfolio returns. Third, the average R2 for six IVOL-sorted portfolios is 0.9107, which indicates about
91 percent portfolio return variance explained by the Fama-French three-factor model. However, we
also find that the coefficients of SMB are all in expected signs, but none of them are significant for VW
portfolios. Moreover, results also show that the coefficients of HML for low-IVOL-sorted portfolios
are insignificant for EW portfolios and significantly negative for VW portfolios. Panel C shows that
coefficients of both MKT and SMB are statistically significant with expected signs. Two coefficients of
HML are significantly positive, but the coefficient for low-IVOL is insignificantly positive. However,
Panel C shows that none of the coefficients of WML are with expected signs, and only two of them are
statistically significant at the 10 percent level: −0.1068 for the high-IVOL portfolio and −0.1375 for
the medium-IVOL portfolio, respectively. Results in Panel D are less significant than results in Panel
C. The three coefficients of MKT are still significantly positive at all levels, but most coefficients of
SMB, HML, and WML are statistically insignificant, except the coefficient of HML for high-IVOL
portfolio is significant at the 5 percent level: 0.3429.
Results in Panels A and B of Table 6 are similar to the corresponding results in Table 5, but none of
the coefficients of SMB and HML for VW portfolios are significant. All six alphas are statistically
insignificant. The average R2 for six E-IVOL portfolios is 0.9142. Results in Panels C and D are the
same as the corresponding results in Table 5.4
Our results are qualitatively consistent with most previous studies in testing the explanatory power
of the multifactor asset pricing models in the emerging stock markets (Drew and Veeraraghavan 2002;
12
K. FANG ET AL.
Table 5. The predictabilities of the Fama-French four-factor model versus the Carhart fourfactor model in IVOL-sorted portfolios
Downloaded by [Ji Wu] at 02:04 09 December 2015
Intercept
b
s
h
Adjusted R2
ARCH
B-G LM
Panel A: Fama-French three-factor model with EW portfolio returns
High IVOL
−0.0040
1.0805
−0.9890
0.2453
t-statistics
−1.3833
37.5135 −13.0240 3.4316
Medium IVOL 0.0011
1.1351
−0.8123
0.2573
t-statistics
0.3192
31.2837 −8.4913
2.8579
Low IVOL
0.0025
1.0576
−0.6277
0.0114
t-statistics
0.8195
34.4361 −7.7528
0.1498
0.9501
NA
0.9270
NA
0.9360
NA
0.4859
NA
0.4250
NA
0.2064
NA
0.0973
NA
0.2539
NA
0.0542
NA
Panel B: Fama-French three-factor model with VW portfolio returns
High IVOL
−0.0105
1.0889
−0.2445
0.3234
t-statistics
−1.7272
17.7889 −1.5149
2.1290
Medium IVOL 0.0014
1.0495
−0.0467
0.1863
t-statistics
0.3289
24.8936 −0.4205
1.7802
Low IVOL
0.0023
1.0021
−0.0013
−0.0850
t-statistics
1.2687
53.7359 −0.0273
−1.8360
0.7981
NA
0.8857
NA
0.9733
NA
0.1212
NA
0.6481
NA
0.9645
NA
0.4738
NA
0.1646
NA
0.2787
NA
Intercept
b
s
h
w
Adjusted R2
ARCH
B-G LM
Panel C: Carhart four-factor
High IVOL
−0.0040
t-statistics
−1.4281
Medium IVOL 0.0010
t-statistics
0.2951
Low IVOL
0.0025
t-statistics
0.8214
model with EW portfolio returns
1.0625
−1.0023
0.1945
35.0614 −13.2947 2.5427
1.1119
−0.8294
0.1919
29.1515 −8.7404
1.9938
1.0621
−0.6244
0.0242
32.2939 −7.6303
0.2921
−0.1068
−1.7295
−0.1375
−1.7683
0.0270
0.4021
0.9513
NA
0.9289
NA
0.9354
NA
0.7230
NA
0.1092
NA
0.2226
NA
0.0890
NA
0.2037
NA
0.0594
NA
Panel D: Carhart four-factor
High IVOL
−0.0105
t-statistics
−1.7121
Medium IVOL 0.0013
t-statistics
0.3088
Low IVOL
0.0023
t-statistics
1.2621
model with VW portfolio returns
1.0958
−0.2394
0.3429
16.7088 −1.4672
2.0716
1.0285
−0.0622
0.1271
23.0262 −0.5598
1.1277
1.0027
−0.0009
−0.083
50.1603 −0.0174
−1.648
0.0409
0.3057
−0.1243
−1.3648
0.0038
0.0942
0.7958
NA
0.8869
NA
0.9730
NA
0.1294
NA
0.7764
NA
0.9628
NA
0.4304
NA
0.1831
NA
0.2746
NA
Notes: We test the explanatory power of the Fama-French three-factor model as RP(t)—RF(t) = a + b[RM(t)—RF(t)]+
sSMB(t) + hHML(t) and the Carhart (1997) four-factor model as RP(t)—RF(t) = a + b[RM(t)—RF(t)]+ sSMB(t) + hHML
(t) + wWML(t), using Vietnamese stock returns. At the beginning of every month we sort stocks into tertiles based on
IVOL: high IVOL, medium IVOL, and low IVOL. IVOL of each firm is computed at the beginning of every month as
the standard deviation of the residuals of the Fama-French three-factor model using daily data for the previous twentytwo trading days. T-statistics are reported in a separate row. ARCH represents the autoregressive conditional heteroskedasticity test with p-value. B-G LM is Breusch-Godfrey serial correlation Lagrange multiplier test with p-value.
Fama and French 1998). Since the systematic risk proxies well explain the stock portfolio returns, this
could be another reason why there is no IVOL (E-IVOL) effect in the VSM.
The Time Trend of Volatilities
We first plot IVOLEW, IVOLVW, E-IVOLEW, E- IVOLVW, and MVOL to find the time trend. We
clearly observe downward trends for both E-IVOLEW, E- IVOLVW, and MVOL, but neither IVOLEW
nor IVOLVW seem to have trends. The Figure 1 shows that both E-IVOLEW and E- IVOLVW are nearly
four times higher than their correspondent IVOL over the study period. However, all five volatility
series are at their peak in the third quarter of 2008, which coincides with the global financial crisis.
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
13
Table 6. The predictabilities of the Fama-French four-factor model versus the Carhart fourfactor model in E-IVOL-sorted portfolios
Adjusted R2
ARCH
B-G LM
Panel A: Fama-French three-factor model with EW portfolio returns
High IVOL
0.0011
1.1814
−1.0046
0.2824
t-statistics
0.2211 24.0914
−7.7707
2.3208
Medium IVOL
−0.0011
1.0859
−0.7860
0.1301
t-statistics
−0.3907 38.6382 −10.6086
1.8655
Low IVOL
−0.0026
1.0023
−0.6288
0.1198
t-statistics
−1.3811 52.6620 −12.5311
2.5367
0.8855
NA
0.9500
NA
0.9721
NA
0.4302
NA
0.0348
NA
0.7466
NA
0.0944
NA
0.4818
NA
0.2117
NA
Panel B: Fama-French three-factor model with VW portfolio returns
High IVOL
0.0034
1.1238
−0.0402
0.1215
t-statistics
0.5463 18.0985
−0.2455
0.7883
Medium IVOL
0.0008
1.0883
−0.0625
0.0930
t-statistics
0.2344 30.0430
−0.6545
1.0350
Low IVOL
−0.0016
0.8949
−0.0009 −0.017
t-statistics
−0.7512 41.8596
−0.0162 −0.324
0.8027
NA
0.9183
NA
0.9566
NA
0.7196
NA
0.4109
NA
0.5448
NA
0.5840
NA
0.6135
NA
0.5020
NA
w
Adjusted R2
ARCH
B-G LM
Panel C: Carhart four-factor model with EW portfolio returns
High IVOL
0.0010
1.1622
−1.0188
0.2282
t-statistics
0.2041 22.2681
−7.8467
1.7325
Medium IVOL −0.0012
1.0684
−0.7990
0.0807
t-statistics
−0.4231 36.1275 −10.8596
1.0813
Low IVOL
−0.0026
1.0091
−0.6238
0.1390
t-statistics
−1.3649 49.7570 −12.3623
2.7156
−0.1140
−1.0716
−0.1039
−1.7226
0.0403
0.9743
0.8858
NA
0.9513
NA
0.9721
NA
0.4605
NA
0.0065
NA
0.7091
NA
0.0908
NA
0.4291
NA
0.2667
NA
Panel D: Carhart four-factor model with VW
High IVOL
0.0032
1.0899
t-statistics
0.5265 16.6072
Medium IVOL
0.0008
1.0848
t-statistics
0.2287 27.9459
Low IVOL
−0.0015
0.9084
t-statistics
−0.7328 40.3916
−0.2010
−1.5025
−0.0209
−0.2637
0.0796
1.7356
0.8058
NA
0.9173
NA
0.9577
NA
0.9069
NA
0.3769
NA
0.7549
NA
0.7760
NA
0.6276
NA
0.5275
NA
Downloaded by [Ji Wu] at 02:04 09 December 2015
Intercept
Intercept
b
b
s
s
h
h
portfolio returns
−0.0652
0.0258
−0.3995
0.1559
−0.0651
0.0831
−0.6741
0.8485
0.0090
0.0206
0.1608
0.3638
Notes: We test the explanatory power of the Fama-French three-factor model as RP(t)—RF(t) = a + b[RM(t)—RF(t)]+
sSMB(t) + hHML(t) and the Carhart (1997) four-factor model as RP(t)—RF(t) = a + b[RM(t)—RF(t)]+ sSMB(t) + hHML
(t) + wWML(t) using Vietnamese stock returns. At the beginning of every month we sort stocks into tertiles based on
E-IVOL: high E-IVOL, medium E-IVOL, and low E-IVOL. E-IVOL of each firm is computed by EGARCH (3, 1)
model by using lagged thirty monthly returns. T-statistics are reported in a separate row. ARCH represents the
autoregressive conditional heteroskedasticity test with p-value; B-G LM is Breusch-Godfrey serial correlation Lagrange
multiplier test with p-value.
Finally, there is a major decline in E- IVOLVW after 2011, which indicates that big stocks are becoming
more stable than in the past.
To accurately investigate the trend of the five volatility series, we estimate the deterministic time
trend model for each series using the equation
IVOLt ¼ b0 þ b1 t þ εt ;
(7)
where IVOL represents IVOLEW, IVOLVW, E-IVOLEW, E- IVOLVW, and MVOL, and t is time. The
estimated time trend b1 parameter and its t-statistic are reported in Table 7. The results in Penal A
show that there are strong negative trends for both IVOLVW and E- IVOLVW; there is a marginal
Downloaded by [Ji Wu] at 02:04 09 December 2015
14
K. FANG ET AL.
Figure 1. Monthly volatility series of the Vietnamese stock market.
Figure 1 plots the trends of five volatility series. IVOLEW and IVOLVW are average equal- and value-weighted aggregate
realized idiosyncratic volatilities; E-IVOLEW and E-IVOLVW are aggregate conditional idiosyncratic volatilities. MVOL is
the value-weighted market volatility. The IVOL of each firm is estimated at the beginning of every month as the standard
deviation of the residuals of the Fama-French three-factor model using daily data for the previous twenty-two trading
days. E-IVOL is estimated by EGARCH (3, 1) model by using lagged thirty monthly returns. MVOL is the standard
deviation of daily value-weighted market returns for the past twenty-two trading days ending on the last trading day of
the month. The sample period is August 2007–December 2014.
significant negative trend of E- IVOLEW, but no trend for IVOLEW over the testing period. This
implies that large-sized stocks become less volatile, and the levels of volatility for small firms are not
increased. Furthermore, the result also shows a negative trend for the MVOL over the sample period.
To hedge the global financial crisis and the liberalization effect of the VSM, we conduct a robustness
test for all five volatility series over 2009–2014. Results in Panel B of Table 7 are the same as results
in Panel A, except more significant. For example, the E- IVOLEW shows a significant downward trend
after 2009.
However, Bunzel and Vogelsang (2005) indicate that the use of a standard t-statistic often rejects
the null hypotheses of no trend when errors in the trend regression are persistent. Thus, Bunzel and
Vogelsang (2005) develop the t-dan test, which has better power than standard t-statistic while
retaining its good size properties. The corresponding t-dan test statistic is also reported in Table 7.
We find that the results are mostly the same as what we have previously reported, except the
marginal negative trend of E-IVOLEW turns statistically insignificant for both testing periods under
the t-dan test.
Therefore, IVOLEW and E-IVOLEW have flat trends over the study period while market volatility
(MVOL) appears to have trended downward. This implies that average firm-level correlations have
decreased over the study period leading to increased benefits from diversification. Since our testing
period coincided with the global financial crisis and the liberalization of the VSM, we expected an
increasing trend of MVOL, but instead we observed an opposite trend.5 Our explanations are as
follows. First, the Vietnamese stock exchange committee imposed a price-limit policy under which the
price of any individual firm’s stock was allowed to change only between 2 percent and 10 percent in
2008. The price-limit policy can help to reduce speculative activities and market volatilities. For
example, when the Chinese stock exchange imposed a price-limit policy on its stock market in 1996,
MVOL was reduced dramatically (Nartea, Wu, and Liu 2013). Second, the VSM began its liberalization on April 15, 2009, and the entry of foreign investors helped reduce the market volatility. For
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
15
Table 7. Time trend of the three volatility series in the Vietnamese stock market
Downloaded by [Ji Wu] at 02:04 09 December 2015
IVOLEW
IVOLVW
E-IVOLEW
E-IVOLVW
MVOL
Panel A: Full sample
Panel B: Sub-sample
2007:08 – 2014:12
2009:01 – 2014:12
Time Trend
t-stat
t-dan statistics
Time Trend
t-stat
t-dan statistics
5.73E-06
-5.5E-05
-8.6E-05
-0.0005
-0.0002
0.4843
-4.2976
-3.3485
-10.8432
-5.2195
0.3065
-2.2595
-1.2168
-5.1636
-3.4470
6.71E-06
-4.97E-05
-6.36E-05
-4.87E-04
-0.0002
0.3741
-3.0893
-1.7614
-9.0453
-4.1312
0.2278
-1.9848
-0.4859
-4.7076
-3.3320
Notes: This table presents a time series trend analysis for each volatility series in the Vietnamese stock market. We
perform a simple trend testing model VOLt = b0 + b1Time+ εt, where VOL represents IVOLEW, IVOLVW, E-IVOLEW,
E-IVOLVW, and MVOL, and t is time. The IVOL of each firm is computed at the beginning of every month as the
standard deviation of the residuals of the Fama-French three-factor model using daily data for the previous twenty-two
trading days. E-IVOL of each firm is computed by EGARCH (3, 1) model by using lagged thirty monthly returns. MVOL
is the standard deviation of daily value-weighted market returns for the past twenty-two trading days, ending on the last
trading day of every month. We report the estimated time trend b1 parameter and its t-statistic and t-dan for the full
sample and subsample periods.
example, Nartea, Wu, and Liu (2013) report that when the Chinese stock market allowed foreign
investors in, there was a decreased trend for aggregate MVOL. De Santis and İmrohoroǧlu (1997) also
suggest the process of liberalization in emerging markets helps decrease the volatility in their capital
markets. Thus, we argue that the reduction of aggregate MVOL in the VSM might be due to market
liberalization.
In summary, our results indicate a decreasing correlation among stocks, so investors benefit from
portfolio diversification (Xu and Malkeil 2003). In other words, the number of stocks needed in a
particular portfolio is less than has been considered necessary in the past to achieve a certain level of
diversification.
Conclusion
In this research, we investigate the roles of idiosyncratic volatility, conditional idiosyncratic volatility,
and multifactor asset pricing models in pricing the Vietnamese stocks, from both the aggregate
portfolio level and firm level. First, our results show neither IVOL nor E-IVOL has a significant
relationship with one-month-ahead stock returns in the VSM. Results are robustness after controlling
for the effect of the 2008 financial crisis and a group of control variables, such as size, BM, short-term
reversal, and momentum. Second, we find that both the Fama-French three-factor model and the
Carhart (1997) four-factor model could well explain the portfolio returns in the VSM. The coefficients
of market β play extremely important roles in explaining portfolio returns in both models. We
emphasize that systematic risk plays a more important role than idiosyncratic risk after the liberalization and privatization in the VSM. Finally, we find flat trends of both EW IVOL and E-IVOL and
decreased trends of market volatility as well as VW IVOL (E-IVOL) in the VSM.
Our results imply that investors in the VSM stock market cannot arbitrage from IVOL trading
strategy. Investors are better off holding a mean-variance portfolio since they hardly beat the market in
the VSM. Furthermore, investors actually could increase the benefit from portfolio diversifications.
We shed light on future research on testing how the liberalization and privatization affect the riskreturn trade-off in the emerging stock markets. To investigate how the process of liberalization reduces
emerging stock market volatility and the role of foreign investors in the emerging markets after the
16
K. FANG ET AL.
liberalization would also be interesting. Finally, to further examine our research hypotheses in all
emerging stock markets would add significant meaning to the literature.
Downloaded by [Ji Wu] at 02:04 09 December 2015
Notes
1. A description of how to construct the adapted Fama-French three-factor model and the correlation of the
three-factor matrix are introduced in Appendix A.
2. We also report levels of both IVOL and E-IVOL for each year. Results show that the levels of both IVOL
and E-IVOL are quite stable. Results are reported in Table 1.
3. Other independent variables are defined in Appendix B.
4. For both Tables 5 and 6, we also add a factor HIVMLIV in either the Fama-French three-factor model or the
Carhart (1997) four-factor model; HIVMLIV is the difference between the high- and low-IVOL (E-IVOL)
portfolios. None of coefficients of the HIVMLIV are statistically significant regardless of how we weight the
IVOL- (E-IVOL-) sorted portfolios. We do not report results here, but results are available upon request.
5. We also regress the market volatility by numbers of stocks listed on the Vietnamese stock market. There is no
significant relationship between market volatility and the number of stocks listed on the VSM. Results suggest that the
declining trend of market volatility is not because of increased numbers of listed stocks. Results are available upon
request.
Acknowledgements
The authors thank the editor Ali M. Kutan and three anonymous referees for valuable comments. We
thank Xinyan Fan, who provided distinguished research assistance.
Funding
Kuangnan Fang acknowledges the financial support of National Natural Science Foundation of China
(71471152, 71201139, and 71303200), and National Social Science Foundation of China
(13&ZD148). Ji Wu acknowledges the financial support of the Fundamental Research Funds for the
Central Universities (Grant No. 0155zk1010), National Social Science Foundation of China
(14BJY207), and National Natural Science Foundation of China (71572160).
References
Ang, A., R. J. Hodrick, Y. Xing, and X. Zhang. 2006. The cross section of volatility and expected returns. The Journal of
Finance 61:259–99. doi:10.1111/j.1540-6261.2006.00836.x.
Ang, A., R. J. Hodrick, Y. Xing, and X. Zhang. 2009. High idiosyncratic volatility and low returns: International and further U.S.
evidence. Journal of Financial Economics 91:1–23. doi:10.1016/j.jfineco.2007.12.005.
Bae, K., W. Bailey, and C. Mao. 2006. Stock market liberalization and the information environment. Journal of International
Money and Finance 25 (3):404–28. doi:10.1016/j.jimonfin.2006.01.004.
Bali, T. G., and N. Cakici. 2008. Idiosyncratic volatility and the cross-section of expected returns. Journal of Financial &
Quantitative Analysis 43 (1):29–58. doi:10.1017/S002210900000274X.
Banz, R. W. 1981. The relationship between return and market value of common stocks. Journal of Financial Economics 9:3–18.
Bekaert, G., and C. Harvey. 2000. Foreign speculators and emerging equity markets. The Journal of Finance 55:565–613.
doi:10.1111/jofi.2000.55.issue-2.
Bekaert, G., R. Hodrick, and X. Zhang. 2012. Aggregate idiosyncratic volatility. Journal of Financial & Quantitative Analysis
V47 (6):1155–85. doi:10.1017/S0022109012000543.
Bennett, J. A., R. W. Sias, and L. T. Starks. 2003. Greener pastures and the impact of dynamic institutional preferences. Review
of Financial Studies 16 (4):1203–38. doi:10.1093/rfs/hhg040.
Brandt, M. W., A. Brav, J. Graham, and A. Kumar. 2010. The idiosyncratic volatility puzzle: Time trend or speculative episodes?
Review of Financial Studies 23 (2):863–99. doi:10.1093/rfs/hhp087.
Bunzel, Helle, and T.J. Vogelsang. 2005. Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an
Application to the Prebisch-Singer Hypothesis. Journal of Business & Economic Statistics 23:381–94.
Campbell, J. Y., M. Lettau, B. G. Malkiel, and Y. Xu. 2001. Have individual stocks become more volatile? An empirical
exploration of idiosyncratic risk. The Journal of Finance 56:1–43. doi:10.1111/jofi.2001.56.issue-1.
Cao, C., T. Simin, and J. Zhao. 2008. Can growth options explain the trend in idiosyncratic risk? Review of Financial Studies
21:2599–633. doi:10.1093/rfs/hhl039.
Carhart, M. 1997. On persistence in mutual fund performance. The Journal of Finance 52:57–82. doi:10.1111/j.1540-6261.1997.
tb03808.x.
Downloaded by [Ji Wu] at 02:04 09 December 2015
THE PATTERN OF RISK-RETURN IN A LIBERALIZED STOCK MARKET
17
Chen, C. R., and Y. S. Huang. 2009. Economic freedom, equity performance and market volatility. International Journal of
Accounting & Information Management 17 (2):189–97. doi:10.1108/18347640911001221.
De Santis, G., and S. İmrohoroǧlu. 1997. Stock returns and volatility in emerging financial markets. Journal of International
Money and Finance 16 (4):561–79. doi:10.1016/S0261-5606(97)00020-X.
Diamonte, R., J. Liew, and R. Stevens. 1996. Political risk in emerging and developed markets. Financial Analysts Journal 52
(3):71–76. doi:10.2469/faj.v52.n3.1998.
Drew, M. E., and M. Veeraraghavan. 2002. A closer look at the size and value premium in emerging markets: Evidence from the
Kuala Lumpur Stock Exchange. Asian Economic Journal 16:337–51. doi:10.1111/asej.2002.16.issue-4.
Fama, E., and K. French. 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33:3–
56. doi:10.1016/0304-405X(93)90023-5.
Fama, E. F., and K. R. French. 1998. Value versus growth: The international evidence. Journal of Finance 53 (6):1975–99.
doi:10.1111/0022-1082.00080
Fink, J., K. W. Fink, G. Grullon, and J. P. Weston. 2009. What drove the increase in idiosyncratic volatility during the internet
boom? Journal of Financial & Quantitative Analysis V45 (5):1253–78.
Fu, F. 2009. Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics 91:24–37.
doi:10.1016/j.jfineco.2008.02.003.
Goetzmann, W., and A. Kumar. 2008. Equity Portfolio Diversification. Review of Finance 12:433–63. doi:10.1093/rof/rfn005
Hamao, Y., J. Mei, and Y. Xu. 2003. Idiosyncratic Risk and Creative Destruction in Japan. NBER Working Paper. National
Bureau of Economic Research, Cambridge, MA. doi:10.3386/w9642
Hou, K., and R. K. Loh (2014). Have we solved idiosyncratic volatility puzzle? Charles A. Dice Center Working Paper, No.
2012-28; Fisher College of Business Working Paper No. 2012-03-028. Available at SSRN: http://papers.ssrn.com/sol3/papers.
cfm?abstract_id=2190976
Huang, B., J. Wald, and R. Martell. 2013. Financial market liberalization and the pricing of idiosyncratic risk. Emerging Markets
Review 17:44–59. doi:10.1016/j.ememar.2013.08.005.
Irvine, R. J., and J. Pontiff. 2009. Idiosyncratic return volatility, cash flows, and product market competition. Review of Financial
Studies 22:1149–77. doi:10.1093/rfs/hhn039.
Jegadeesh, N., and S. Titman. 1993. Returns to buying winners and selling losers: Implication for stock market efficiency.
Journal of Finance 48(1):65–91
Lee, J., and C. Wei. 2012. Types of shares and idiosyncratic risk. Emerging Markets Finance and Trade V48 (0):68–95.
doi:10.2753/REE1540-496X4805S305.
Levy, H. 1978. Equilibrium in an imperfect market: A constraint on the number of securities in a portfolio. American Economic
Review 68:643–58.
Malkiel, B. G., and Y. Xu (2006) Idiosyncratic risk and security returns. Princeton University & The University of Texas at
Dallas, Richardson.
Malkiel, B. G., and Y. Xu. 1997. Risk and return revisited. The Journal of Portfolio Management 23(Spring):9–14. doi:10.3905/
jpm.1997.409608.
Merton, R. 1987. A simple model of capital market equilibrium with incomplete information. The Journal of Finance 42:483–
510. doi:10.1111/j.1540-6261.1987.tb04565.x.
Nartea, G. V., B. Ward, and L. Yao. 2011. Idiosyncratic volatility and cross-sectional stock returns in Southeast Asian stock
markets. Accounting & Finance 51 (4):1031–54. doi:10.1111/j.1467-629X.2010.00384.x.
Nartea, G., and J. Wu. 2013. Is there a volatility effect in the Hong Kong stock market? Pacific-Basin Finance Journal 25:119–
35. doi:10.1016/j.pacfin.2013.07.004.
Nartea, G. V., J. Wu, and Z. T. Liu. 2013. Does idiosyncratic volatility matter in emerging markets? Evidence from China.
Journal of International Financial Markets, Institutions, & Money 27:137–60. doi:10.1016/j.intfin.2013.09.002.
Newey, W. K., and K. D. West. 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent
covariance matrix. Econometrica 55 (3):703–08. doi:10.2307/1913610.
Perotti, E., and P. Oijen. 2001. Privatization, political risk and stock market development in emerging economies. Journal of
International Money and Finance 20:43–69. doi:10.1016/S0261-5606(00)00032-2.
Pukthuanthong-Le, K., and N. Visaltanachoti. 2009. Idiosyncratic volatility and stock returns: A cross country analysis. Applied
Financial Economics 19:1269–81. doi:10.1080/09603100802534297.
Rosenberg, B., K. Reid, and R. Lantein. 1985. Persuasive evidence of market inefficiency. Journal of Portfolio Management
11:9–17.
Sharpe, W. F. 1964. Capital asset prices: A Theory of Market Equilibrium under conditions of risk. Journal of Finance 19
(3):425–42.
Xu, Y., and B.G. Malkiel. 2003. Investigating the behaviour of Idiosyncratic volatility. Journal of Business 76:613–44
18
K. FANG ET AL.
Appendix A
Construction of adopted Fama-French three factors
Downloaded by [Ji Wu] at 02:04 09 December 2015
Market
Panel A. Descriptive statistics for the three factors
Average return
−0.0153
t-Statistic
−0.1614
Minimum
−0.3218
25th percentile
−0.0589
Median
−0.0219
75th percentile
0.0345
Maximum
0.2447
Skewness
−0.5052
Kurtosis
4.9959
Panel B. Correlation matrix for the three factors
Market
1.0000
SMB
0.1657
HML
−0.0297
WML
−0.3279
SMB
HML
WML
−0.0047
−0.0901
−0.2874
−0.0295
0.0023
0.0253
0.0976
−1.9880
11.6618
0.0111
0.2556
−0.0840
−0.0153
0.0095
0.0397
0.1584
0.5709
3.9605
−0.0038
−0.0756
−0.1284
−0.0342
0.0001
0.0252
0.1192
−0.2249
3.1501
1.0000
−0.5081
0.0549
1.0000
−0.3568
1.0000
Notes: Numbers are Pearson product moment correlation coefficients. We follow Ang et al.’s (2009) method to construct
the adapted Fama-French three factors for the Vietnamese stock markets. The market factor in month t is a value-weighted
average market return across all sample stocks. The SMB and HML factors are constructed separately using equalweighted portfolios formed on either Size (market capitalization) or BM. To form the factor of SMB, we use the median
point to sort stocks into two portfolios, big- and small-sized portfolios, and then SMB is the return of the upper half less
the return of the lower half of all firms ranked in ascending order according to Size. To form the factor of HML, we use
lagged six-month BM as breakpoints, which are the top thirty-third and sixty-seventh percentiles. The HML factor is the
difference of average return between the two top 33.33 percent high-BM portfolios and the two bottom 33.33 percent lowBM portfolios. WML is defined as momentum factor, which is the difference of average return between the past elevenmonth winner stock portfolio and loser stock portfolio. Ang et al. (2009) indicate that there is no statistical difference
between using this method to construct the adopted Fama-French three-factor model for small stock markets and using the
original Fama-French three-factor model.
Appendix B
Definitions and descriptions of control variables
Variable
Size (market capitalization)
BM
MOM
Short-term reversal (REV)
Definition
Explanation
Size is measured as the firm’s
Small firms tend to have higher returns than big
market capitalization at the end of
firms (Banz 1981)
month t.
BM is the firm’s book -to-market
High-BM firms outperform low-BM firms
ratio six months prior (i.e., at the
(Rosenberg et al. 1985)
end of t-6).
The momentum at time t is the
Stocks that perform well (or poorly) in the past
stock’s eleven-month past returns
three to twelve months continue to perform
lagged one month
well (or poorly) in the succeeding three to
twelve months (Jegadeesh and Titman 1993)
The REV is the firm’s last- month’s Past winner stocks will change to loser stocks in
return
the following month (Jegadeesh and Titman
1993)