We examined the potential relationships between changes in the money supplies of Korea and the United States and volatility of the Korean stock market using the GARCH, GJR-GARCH, and EGARCH models. We did not identify any such relationships, implying that changes in money supply do not influence the flow of information to the market. However, we found that the asymmetric effect of bad news on volatility was higher when contemporaneous changes in Korean and US money supply variables were included in the models. This indicates that changes in money supply did not affect Korean stock volatility directly. Finally, the results based on a variance model indicated that the money supply of the two countries had no effect on the Korean stock market. This formal study suggests that there is no significant forecasting power of past changes in money supply. Although stock returns and volatility are not directly affected by changes in the money supply, the influence of supply on macroeconomic activity should not be disregarded.
Financial economists are very interested in whether money supplies affect stock returns, and various studies have established that they do [
However, few papers have analyzed the effects of supply changes on stock return volatility. Of those that have, most have analyzed relationships using a VAR model and the Granger causality test.
In the present study, we investigated whether changes in money supply, as a proxy for information flow, can be used to improve predictions of volatility. For this purpose, we examined the relationships between changes in money supply and stock volatility for the domestic economy of Korea. Changes in monetary policy in major countries such as the United States that provide liquidity in the global financial market will have a negative or positive impact on other countries’ financial markets through foreign trading. Thus, we also examined relationships between the US money supply and the Korean stock market.
For the empirical analysis, we considered monthly data regarding the money supplies of the United States and Korea and Korean stock returns. We estimated and analyzed the relationships between return volatility and mo- ney supply using the GARCH, Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), and exponential GARCH (EGARCH) models.
The remainder of this paper is organized as follows. A literature review is presented in Section 2. Section 3 presents the data and descriptive statistics. Section 4 presents the methodology of the study. The empirical results are discussed in Section 5. Section 6 concludes the paper.
The potential effects of money supply on markets have long been debated. Some empirical studies have shown that stock returns are affected by changes in the money supply. Homa and Jaffee [
In contrast, other empirical studies have reported that past changes in money supply have no significant forecasting power. Rozeff [
For the empirical analysis of the present study, we used monthly Korean stock market price index data, the Korean money supply (M1, M2, Lf), and the United States money supply (M1, M2, M3). We used data from January 1980 to June 2013. These data were obtained from the Korea Bank and the Board of Governors of the Federal Reserve System. Monthly index returns were calculated in terms of percentage logarithmic change, based on the following formulae:
where
Tables 1-3 summarize the descriptive statistics for stock market returns and money supply. The mean returns
Returns | |
---|---|
Mean | 0.7082 |
Median | 0.7237 |
Maximum | 39.3162 |
Minimum | −29.9747 |
Std. Dev. | 7.1689 |
Skewness | 0.2143 |
Kurtosis | 6.3010 |
Jarque-Bera | 185.14 [0.0000]*** |
Notes: Jarque-Bera (J-B) is the test statistic for the null hypothesis of normality in sample returns distribution. Significance levels: ***1%.
M1 | M2 | Lf | |
---|---|---|---|
Mean | 1.2133 | 1.3221 | 1.1534 |
Median | 1.2117 | 1.1968 | 1.0489 |
Maximum | 10.3803 | 5.4031 | 4.4047 |
Minimum | −11.1521 | −1.4100 | −0.5789 |
Std. Dev. | 2.3206 | 1.1002 | 0.7745 |
Skewness | −0.5683 | 0.6050 | 0.5436 |
Kurtosis | 7.3579 | 3.6527 | 3.6613 |
Jarque-Bera | 338.90 [0.0000]*** | 31.59 [0.0000]*** | 22.20 [0.0000]*** |
Notes: Jarque-Bera (J-B) is the test statistic for the null hypothesis of normality in sample returns distribution. Significance levels: ***1%, **5%, *1%.
M1 | M2 | M3 | |
---|---|---|---|
Mean | 0.4682 | 0.4905 | 0.5532 |
Median | 0.4233 | 0.4763 | 0.5540 |
Maximum | 5.9297 | 2.7625 | 2.0181 |
Minimum | −3.2562 | −0.8031 | −0.4295 |
Std. Dev. | 0.8353 | 0.3803 | 0.3767 |
Skewness | 1.5996 | 1.4719 | 0.2463 |
Kurtosis | 12.8138 | 9.6668 | 3.4505 |
Jarque-Bera | 1780.23 [0.0000]*** | 887.42 [0.0000]*** | 5.81 [0.0546]* |
Notes: Jarque-Bera (J-B) is the test statistic for the null hypothesis of normality in sample returns distribution. Significance levels: ***1%, **5%, *10%.
and changes in the Korean money supply and the US money supply were positive. The kurtosis was positive for monthly stock returns and each money supply, and greater than 3. Returns skewness and each change in money supply skewness were positive, except the change in Korean money supply M1. Seasonally adjusted data were used to measure the money supply. Applying the Jarque-Bera (J-B) test for normality rejected the null hypothesis of normality for returns and money supply.
We tested the stationarity of returns and trading volume series, for which the most common test is the unit root test. To test for a unit root, we used both the augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test.
We used the GARCH model, proposed by Bollerslev [
where
shocks will have a larger effect on volatility than positive shocks. In Equation (6), the conditional variance is log-linear, which has several advantages over the pure GARCH specification. First, regardless of the magnitude of
ADF | PP | |
---|---|---|
Returns | −17.4094 [0.0000]*** | −17.4007 [0.0000]*** |
Korean Money supply (M1) | −19.3145 [0.0000]*** | −19.4475 [0.0000]*** |
Korean Money supply (M2) | −5.0643 [0.0002]*** | −23.9493 [0.0000]*** |
Korean Money supply (Lf) | −4.1804 [0.0053]*** | −18.7636 [0.0000]*** |
US Money supply (M1) | −4.4228 [0.0022]*** | −19.2469 [0.0000]*** |
US Money supply (M2) | −5.9434 [0.0000]*** | −12.7542 [0.0000]*** |
US Money supply (M3) | −2.3307 [0.4156] | −10.6707 [0.0000]*** |
Note: The critical value for the ADF and PP tests are −3.9611 and −3.4323 at the 1% significance level, respectively. Significance levels: ***1%, **5%, *10%; ADF, augmented Dickey-Fuller test; PP, Phillips-Perron test.
of using
To test the effects of money supply on the volatility of stock returns, the following models were used:
If change in money supply is considered a proxy for information arrival, then it is expected that
Tables 6-11 present the results when contemporaneous change in money supply is included in the conditional variance equation. The coefficient of change in Korean money supply
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
0.8629 (0.3262)*** | 0.7601 (0.3307)** | 0.7116 (0.3357)** | |
0.1135 (0.0535)** | 0.1093 (0.0547)** | 0.1361 (0.0540)** | |
2.4668 (1.3537)* | 3.2005 (1.5880)** | 0.0533 (0.1167) | |
0.1617 (0.0583)*** | 0.0997 (0.0643) | 0.3239 (0.0922)*** | |
0.7962 (0.0665)*** | 0.7683 (0.0787)*** | 0.9188 (0.0358)*** | |
0.1505 (0.0915)* | −0.0947 (0.0497)* | ||
23.113 [0.454] | 23.900 [0.409] | 26.353 [0.284] | |
ARCH (10) | 1.1696 [0.310] | 1.0715 [0.383] | 1.2675 [0.247] |
Note: Standard errors are in parentheses and p-values are in brackets. The Ljung-Box
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
0.8703 (0.3250)*** | 0.7661 (0.3290)** | 0.6994 (0.3346)** | |
0.1148 (0.0535)** | 0.1092 (0.0547)** | 0.1350 (0.0542)** | |
2.1368 (1.3351) | 2.8596 (1.5481)* | 0.0488 (0.1172) | |
0.1645 (0.0594)*** | 0.0974 (0.0653) | 0.3262 (0.0932)*** | |
0.7910 (11.4342)*** | 0.7605 (0.0814)*** | 0.9168 (0.0368)*** | |
0.1613 (0.0941)* | −0.0986 (0.0506)* | ||
0.3639 (0.4364) | 0.4556 (0.4242) | 0.0082 (0.0145) | |
23.357 [0.440] | 23.719 [0.420] | 25.818 [0.310] | |
ARCH (10) | 1.149 [0.323] | 0.987 [0.453] | 1.223 [0.274] |
Note: See
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
0.8741 (0.3272)*** | 0.7607 (0.3307)** | 0.7089 (0.3383)** | |
0.1124 (0.0537)** | 0.1060 (0.0548)* | 0.1352 (0.0542)** | |
1.6289 (1.4686) | 1.7806 (1.5119) | 0.0151 (0.1133) | |
0.1604 (0.0558)*** | 0.0878 (0.0649) | 0.3139 (0.0946)*** | |
0.7983 (0.0654)*** | 0.7736 (0.0744)*** | 0.9221 (0.0343)*** | |
0.1671 (0.0879)* | −0.0995 (0.0486)** | ||
0.5869 (0.7046) | 1.0019 (0.7786) | 0.0249 (0.0222) | |
22.781 [0.474] | 22.652 [0.481] | 24.797 [0.361] | |
ARCH (10) | 1.160 [0.316] | 1.004 [0.439] | 1.212 [0.281] |
Note: See
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
1.0033 (0.4039)** | 0.7815 (0.4010)* | 0.6478 (0.4198) | |
0.1129 (0.0601)* | 0.1017 (0.0611)* | 0.1351 (0.0588)** | |
1.4231 (1.9252) | 1.2187 (1.7356) | 0.0735 (0.1208) | |
0.1584 (0.0665)** | 0.0468 (0.0597) | 0.2741 (0.0958)*** | |
0.7724 (0.8147)*** | 0.7673 (0.0809)*** | 0.9147 (0.0358)*** | |
0.2294 (0.0913)** | −0.1587 (0.0506)*** | ||
2.4065 (1.7847) | 2.8038 (1.6046)* | 0.0358 (0.0307) | |
25.017 [0.349] | 20.893 [0.588] | 23.825 [0.414] | |
ARCH (10) | 1.439 [0.162] | 0.9399 [0.496] | 1.2062 [0.286] |
Note: See
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
0.8629 (0.3265)*** | 0.7589 (0.3305)** | 0.7341 (0.3369)** | |
0.1135 (0.0536)** | 0.1093 (0.0547)** | 0.1359 (0.0545)** | |
2.4826 (1.5498) | 3.1537 (1.7729)* | 0.0728 (0.1287) | |
0.1616 (0.0584)*** | 0.0998 (0.0646) | 0.3201 (0.0897)*** | |
0.7962 (0.0668)*** | 0.7685 (0.0790)*** | 0.9173 (0.0371)*** | |
0.1509 (0.0917) | −0.0921 (0.0501)* | ||
−0.0082 (0.8775) | 0.0652 (1.0374) | −0.0242 (0.0332) | |
23.114 [0.454] | 23.878 [0.411] | 25.654 [0.317] | |
ARCH(10) | 1.169 [0.310] | 1.070 [0.384] | 1.249 [0.257] |
Note: See
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
0.8758 (0.3236)*** | 0.7717 (0.3297)** | 0.7146 (0.3348)** | |
0.1136 (0.0536)** | 0.1096 (0.0547)** | 0.1357 (0.0540)*** | |
1.4508 (1.9144) | 2.1484 (2.1945) | 0.0078 (0.1348) | |
0.1656 (0.0587)*** | 0.1016 (0.0655) | 0.3294 (0.0950)*** | |
0.7987 (0.0660)*** | 0.7737 (0.0770)*** | 0.9202 (0.0363)*** | |
0.1481 (0.0897)* | −0.0945 (0.049)* | ||
1.5809 (2.3713) | 1.6235 (2.7148) | 0.0716 (0.0908) | |
22.776 [0.474] | 23.539 [0.430] | 25.962 [0.303] | |
ARCH (10) | 1.168 [0.311] | 1.052 [0.399] | 1.224 [0.273] |
Note: See
GARCH | GJR-GARCH | EGARCH | |
---|---|---|---|
0.8815 (0.3970)** | 0.7867 (0.4071)* | 0.7884 (0.4067)* | |
0.1309 (0.0600)** | 0.1262 (0.0619)** | 0.1567 (0.0618)** | |
1.9587 (2.4112) | 2.3997 (2.5660) | −0.0226 (0.1331) | |
0.1425 (0.0564)** | 0.0823 (0.0641) | 0.3129 (0.1016)*** | |
0.8276 (0.0644)*** | 0.8075 (0.0786)*** | 0.9325 (0.0370)*** | |
0.1344 (0.0929) | −0.0863 (0.0549) | ||
0.1690 (2.4208) | 0.6239 (2.6037) | 0.0696 (0.0713) | |
22.608 [0.484] | 22.332 [0.500] | 24.003 [0.404] | |
ARCH (10) | 1.380 [0.188] | 1.193 [0.294] | 1.2777 [0.242] |
Note: See
Tables 9-11 show the same test for the US money supply. Like the change in Korean money supply, the coefficient of change in the US supply
We examined the relationships between changes in the money supplies of Korea and the United States and stock returns using the GARCH, GJR-GARCH, and EGARCH models. Our important findings are as follows. First, stock returns exhibited strong volatility, persistence, and asymmetry. Second, the inclusion of contemporaneous change variables on the Korean and US money supplies in all GARCH class models did not explain Korean stock return volatility. Third, an asymmetric effect of bad news on volatility existed when such contemporaneous changes were included in the volatility models. Changes in supply did not affect Korean stock returns directly. Finally, neither supply had any effect on the volatility of Korean stock returns. Although stock returns and volatility are not directly affected by changes in money supply, the influence of money supply on macroeconomic activity should not be ignored.
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A5B6053791).