This study used the data sample of some developed and Asian emerging countries including the U.S., Japan, China, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Taiwan, Thailand, and Vietnam. We collected the daily data of stock markets for these countries from DataStream for the period from January 2005 to July 2021, with a total of 2754 daily observations.The indices are converted to a daily rate of return follow (6), which are defined as the natural logarithmic returns in two consecutive trading days:
𝑟𝑡 = ln(𝑝𝑡) − ln(𝑝𝑡−1) = ln(𝑝𝑝𝑡
𝑡−1 ) (6)
Where 𝑟𝑡 is the daily log return, 𝑝𝑡 and 𝑝𝑡−1 are the daily adjusted closing price of each stock indices at time t and t-1.
Due to the different time zone, the US market closes later than the other Asian stock markets; therefore, a shock in the US stock market during day t will not be reflected in the
Asian emerging stock markets until day t+1. Thus, the appropriate pairing is time t-1 for the US and time t for the Asian markets.
The plots for the daily log returns fluctuate around a zero mean (see Figure 1). Each of all series appears to show the signs of ARCH effects in that the amplitude of the returns varies over time.
Volatility clustering – the periods of high volatility alternate periods of low volatility – can be observed (large and small swings tend to cluster, see Figure 1). Abusing the terminology slightly, it could be started that “volatility is autocorrelated”. Observing the time series data set of returns, we see that there exists heteroskedasticity in the model. However, we cannot determine whether this is enough to warrant consideration.
Figure 1: The daily returns of stock indices
Source: Datastream
Table 1. Descriptive statistics of returns in the full period (from January 1, 2005 through July 6, 2021
Country USA Japan China India Indonesia Korea Malaysia Pakistan Philippines Taiwan Thailand Vietnam
Observations 2754 2754 2754 2754 2754 2754 2754 2754 2754 2754 2754 2754
Mean 0.0006 0.0003 0.0002 0.0005 0.0003 0.0002 0.0001 0.0005 0.0003 0.0002 0.0001 0.0003
Median 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.001 0.001 0.001 0.000 0.001
Maximum 0.110 0.132 0.089 0.160 0.097 0.083 0.034 0.083 0.094 0.057 0.106 0.047
Minimum -0.128 -0.121 -0.097 -0.116 -0.110 -0.112 -0.100 -0.071 -0.143 -0.067 -0.161 -0.065
Std.Dev. 0.013 0.015 0.017 0.014 0.013 0.012 0.007 0.013 0.013 0.011 0.012 0.014
Skewness -0.522 -0.544 -0.519 0.115 -0.762 -1.008 -1.523 -0.407 -0.815 -0.721 -1.407 -0.326
Kurtosis 15.837 8.739 4.309 11.787 8.787 9.242 16.075 3.601 10.361 4.476 19.527 1.876
Jarque Bera Test
28955 (0.00)
8916.5 (0.00)
2259.7 (0.00)
15978 (0.00)
9143 (0.00)
10286 (0.00)
30769 (0.00)
1567.6 (0.00)
12647 (0.00)
2543.9 (0.00)
44735 (0.00)
454.29 (0.00)
LB Q-statistics
86.73 (0.00)
47.526 (0.00)
26.526 (0.00)
49.537 (0.00)
43.603 (0.00)
23.915 (0.01)
21.866 (0.01)
66.873 (0.00)
25.673 (0.00)
15.816 (0.09)
18.956 (0.04)
85.493 (0.00)
ArchTest (p.value) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Source: Datastream
4.2. Summary Statistics
Descriptive characteristics for the daily stock index returns are presented in Table 1.
Table 1 shows that the average daily returns are positive in full period from 2005 to 2021 but negligibly small compared to the sample standard deviation. The average return of the U.S.
stock market (0.0006) is the highest among the selected countries. The average return of the Thailand and Malaysia stock markets are 0.001 which are the lowest returns, compared to other countries. In terms of the daily standard deviation, which represents the risk or volatility of the returns in the stock markets, we see that the standard deviation of the Chinese stock market returns is 0.017, being the highest volatility. In contrast, Malaysian stock market returns has the lowest standard deviation (0.007) among the stock indices.
The returns series display similar statistical properties as far as the third and fourth moments are concerned. More specifically, as skewness of all the return series is negative, which is evidence for overall negative performance, and asymmetry tail extending out to the left and is referred to as “skewed to the left” in given returns. As we know, skewness is a measure of symmetry, which is equal to zero for normal distribution. The kurtosis values are a measure of the fatness of peaked-ness of the tails of the distribution and distribution of data around the mean. Both the assets show evidence of fat tails (leptokurtic), since the kurtosis exceeds 3 (the normal value), implying that the distribution of these returns has a thicker tail than the normal distribution. Therefore, the standard deviation of all markets implies that the
“risk” is underestimated when kurtosis is higher and skewness is negative.
We check whether sample data have the skewness and kurtosis matching a normal distribution through the Jarque–Bera test. As a rule, this test is applied before using methods of parametric statistics which require distribution normality, like GARCH models. The non- normal distribution of data is also supported via the Jarque–Bera statistics, which rejects the null hypothesis of normality at 1% significance level. This means used data in our sample departing from normal distribution. In order to deal with this problem, in this paper, Student's- t distributions will be used.
Furthermore, the Ljung-Box (LB) Q statistics for daily stock returns of both assets are highly significant at five-percent level indicate the presence of serial correlations. The p-value of ArchTest (for the Portmanteau Q and the Lagrange Multiplier test statistic) shown in the last row are all zero to both places, resoundingly rejecting the “no ARCH” hypothesis.
Table 2: Descriptive statistics of returns during Global Financial Crisis (from January 1, 2005 through December 31, 2009)
Notes: Pre: Pre-period of Global financial crisis (from January 1, 2005 to August 31, 2007). In: Period during Global financial crisis (from September 1, 2007 to December 31, 2009)
Source: Datastream
Country USA Japan China India Indonesia Korea Malaysia Pakistan Philippines Taiwan Thailand Vietnam
Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In
Observations 461 391 461 391 461 391 461 391 461 391 461 391 461 391 461 391 461 391 461 391 461 391 461 391
Mean 0.0003 0.0002 0.0006 -0.0005 0.002 -0.0008 0.0015 0.0001 0.0013 -0.0007 0.0008 -0.0012 0.0003 -0.0005 0.0006 -0.0002 0.0008 -0.0003 0.0004 -0.001 -0.0001 -0.0004 0.002 -0.0016 Median 0.001 0.001 0.000 0.000 0.003 0.001 0.002 0.001 0.002 0.000 0.002 0.001 0.001 0.000 0.002 0.000 0.001 0.000 0.001 0.001 0.000 0.000 0.001 -0.001 Maximum 0.024 0.110 0.033 0.132 0.079 0.089 0.067 0.160 0.053 0.073 0.034 0.060 0.026 0.034 0.058 0.083 0.094 0.071 0.029 0.057 0.106 0.062 0.047 0.046 Minimum -0.030 -0.095 -0.039 -0.121 -0.097 -0.085 -0.070 -0.116 -0.065 -0.110 -0.072 -0.112 -0.036 -0.100 -0.060 -0.051 -0.062 -0.057 -0.047 -0.067 -0.161 -0.072 -0.050 -0.048 Std.Dev. 0.007 0.021 0.011 0.024 0.018 0.026 0.014 0.024 0.013 0.020 0.012 0.020 0.007 0.011 0.017 0.017 0.013 0.017 0.010 0.018 0.014 0.017 0.017 0.021 Skewness -0.339 0.060 -0.188 -0.338 -0.778 -0.067 -0.453 0.484 -0.782 -0.813 -0.970 -0.979 -0.801 -1.888 -0.344 -0.093 0.367 -0.023 -0.902 -0.436 -2.610 -0.461 -0.090 -0.010 Kurtosis 1.884 4.918 0.789 5.293 4.265 1.109 3.179 5.733 4.009 4.854 4.010 4.440 4.608 15.114 0.864 2.087 5.795 1.714 3.337 1.013 46.081 1.964 0.950 -0.490
Table 3: Descriptive statistics of returns during Covid-19 pandemic (from January 1, 2013 through July 6, 2020)
Country USA Japan China India Indonesia Korea Malaysia Pakistan Philippines Taiwan Thailand Vietnam
Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In Pre In
Observations 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 1132 243 Mean 0.0006 0.0015 0.0005 0.0004 0.0001 0.0002 0.0005 0.0009 0.0003 -0.0006 0.0003 0.0012 0.0001 -0.0002 0.0006 0.0009 0.0004 -0.0012 0.0005 0.001 0.0003 -0.0002 0.0005 0.001 Median 0.001 0.002 0.001 0.000 0.000 0.001 0.001 0.002 0.001 0.000 0.000 0.002 0.000 0.000 0.000 0.002 0.001 0.000 0.001 0.002 0.000 0.000 0.001 0.002 Maximum 0.048 0.089 0.074 0.069 0.065 0.032 0.052 0.086 0.045 0.097 0.029 0.083 0.022 0.033 0.044 0.047 0.055 0.072 0.035 0.050 0.045 0.065 0.038 0.038 Minimum -0.042 -0.128 -0.083 -0.063 -0.092 -0.082 -0.061 -0.085 -0.041 -0.068 -0.045 -0.088 -0.032 -0.054 -0.048 -0.071 -0.070 -0.143 -0.065 -0.060 -0.048 -0.114 -0.061 -0.065 Std.Dev. 0.008 0.018 0.013 0.015 0.015 0.014 0.009 0.017 0.009 0.016 0.008 0.015 0.006 0.011 0.010 0.013 0.011 0.021 0.008 0.012 0.008 0.016 0.010 0.014 Skewness -0.518 -1.488 -0.443 -0.247 -0.906 -1.132 -0.335 -0.826 -0.387 0.211 -0.397 -0.532 -0.475 -1.014 -0.216 -1.310 -0.612 -2.022 -0.913 -0.725 -0.098 -2.202 -0.816 -1.539 Kurtosis 4.281 15.151 5.020 3.815 6.391 5.232 3.772 7.850 2.756 8.064 2.277 8.718 2.808 4.648 2.195 7.116 4.705 12.903 6.812 4.967 3.640 14.692 4.019 4.775
Notes: Pre:Pre-period of Covid-19 pandemic (from January 1, 2013 to December 31, 2019). In: Period during Covid-19 pandemic (from January 1, 2020 to July 6, 2021)
Source: Datastream
Table 2 provides information about the descriptive statistics in the period of Global Financial Crisis. In the pre-crisis period, it can be clearly seen that mean returns of all markets are positive (except for Thailand). In which, China and Vietnam markets had the highest returns (0.002). Regarding the daily standard deviation, the lowest level belongs to the U.S.
and Japan, which are developed markets, implied such markets have lower volatility in returns than others. The higher value in the standard deviation of the emerging markets suggests that such financial markets are more volatile than the developed markets.
The Global Financial Crisis from 2007-2009, stemmed from the collapse of the subprime mortgage market in the U.S. market, had spread throughout the global financial system, including emerging markets. Due to this major shock, 9 of the 10 emerging financial markets in our sample experienced a sharp decrease in mean levels with large negative returns (see Table 2). Specifically, Taiwan and Korea markets had lowest returns (-0.001 and -0.0012, respectively). Associated with decreased returns, the risks in these financial markets, represented by standard deviations, also increase significantly in comparison with the relatively stable period (pre-crisis period). Of which, China has the largest market volatility (0.026). These features raise the question whether there is a financial contagion during a crisis period from the U.S to emerging financial markets. This will be discussed in section 4 by the DCC-GARCH, which is used to test for a financial contagion.
The Global Financial Crisis seems more severely affected the global financial markets, compared to the Covid-19 pandemic. The reason would be that since Covid-19 has been declared by WTO, the country’s governments around the world have immediately provided incentives in order to hinder the negative impact of COVID-19 pandemic. For example, the Federal Reserve stepped in with a broad array of actions to limit the economic damage from the pandemic: near-Zero Interest Rates; supporting Financial Market Functioning;
encouraging Banks to Lend; supporting Households and Consumers; supporting State and Municipal Borrowing;cushioning U.S. Money Markets from International Pressures.
It is noticeable that the selected indices have diversity in returns and volatility during Covid-19 pandemic period (see Table 3). India, Korea, Pakistan experiences a considerable increase in returns, whereas almost all remaining markets witness a dramatical decrease in returns. Four out of ten emerging markets (Indonesia, Malaysia, Philippines, Thailand) suffered negative returns, in which, Indonesia experienced a significant decrease in mean returns, from a positive level (0.0003) to a large negative return (-0.0006). According to(Prasidya, 2020), in 2019, the foreign ownership within the Indonesia capital market was at 44.29 percent and domestic ownership accounted for 55.71 percent. Foreign investors also have made up 36 percent of the total value traded on the Indonesia Stock Exchange while domestic investors made up 64 percent. Amid the prolonged COVID-19 pandemic, the Indonesian stock market has endured a selling spree by foreign investors as unfavorable the global economic and political situations. There are about 20 trillion rupiah of foreign funds
coming out of the Indonesian stock exchange throughout 2020, especially as a result of massive sales in the last 3 months. Another noteworthy statistic in this table is that, all markets showed higher risk with a substantial increase in standard deviation level compared to pre-period of Covid-19. Both skewness and kurtosis have the same feature as aforementioned analysis in the whole period.