This paper investigates the asymmetric effects of upgrade and downgrade of the sovereign credit rating on regional interdependence of seven emerging stock markets in the Asian Pacific Area. Firstly, by comparing the cross-country correlation matrices of stock market index returns on event days and none event days, we find out increases in correlations in both upgrade and downgrade rating days but the frequency of decreasing correlations is significantly higher in downgrade rating days. Secondly, with a regression analysis taking advantage of time-varying conditional correlations of each stock market index with regional market index, we discover a significant increase in the correlations of most countries because of the common information effect triggered by the upgrade rating events, while for the downgrade rating events, dominant differential information effect results in decrease in the correlations. Moreover, in terms of effects of changes on sovereign ratings from other regional countries, downgrade rating events are more influential. Lastly, we apply an Error Correction Model and discern a significant long-run effect caused by the changes on the sovereign credit ratings and significant short-run transitory effect only exists in the Thailand stock market, the source of Asian Financial Crisis, which supports the financial contagion theory.
Reinforced global economic integration and even economic regionalization not only boost international trades but offer investors a chance of allocating their assets in foreign financial markets. However, trends of financial integration and regionalization are blamed by several scholars as the source of destabilization of global financial market. Calvo and Mendoza [
On the other hand, with established system of sovereign credit ratings throughout recent decades, sovereign credit rating has been a quantified and comparable measurement of country risk, an indispensable factor in pricing assets in bond and stock markets for international portfolio investors, and thus plays a crucial role in flows of capital in global financial markets. However, mounting numbers of scholars have criticized rating agencies as the culprit of destabilizing global financial system. Ferri et al. [
On the other hand, changes of credit ratings might as well signify a wake-up call that triggers a spillover effect on other countries with unadjusted ratings. For example, by investigating impacts of rating adjustments on market premium of stock markets in unadjusted countries, Ferreira and Gama [
To sum up, major researches focus on effects of sovereign credit ratings events on risk premium on stock or bond markets, but fewer studies in their effects on financial interdependencies among different countries. However, international portfolio investors aim to take advantage of different correlations among financial markets in home countries and foreign countries to diversify systematic risks. Accordingly, effects of sovereign credit rating events on correlation among different countries should be the indispensable conference for international portfolio investors to allocate their assets. Among relative literature, Gande and Parsely [
In conclusion, for international portfolio investors prefer to invest in respective regional markets to reduce asymmetric information and sovereign credit ratings events might as well breed more significant spillover effects among regional countries because of geographical and cultural proximity and belonging to developing countries, this paper investigates effects of ratings events on Hong Kong, Taiwan India, Indonesia, South Korea, Malaysia, Philippine and Thailand on intra-regional interdependence of stock markets. This paper applies the analysis of cross-country correlation matrices of returns in negative ratings events days and none-event days, but furtherly includes studies of positive ratings event days so as to reveal an asymmetric effect of ratings changes on return correlations. Secondly, we also introduce dynamic conditional correlations and apply an error correction model as Chiang et al. [
The rest of this paper is organized as follows. Section II outlines the data and model designed used in our analysis. Section III presents mainly empirical results, and I concluded this paper by summarizing major findings in section VI.
Based on categorization of emerging markets in Asian Pacific area from Christopher et al. [
From
We choose S & P Foreign Currency Long Term Rating to construct sovereign rating variable, and its readiness is as follows. Firstly, Kaminsky and Schimukle
Mean | Max | Min | Variance | Kurtosis | Skewness | |
---|---|---|---|---|---|---|
HSI (Hong Kong) | 0.0004% | 5.8225% | −3.9512% | 0.0072 | 7.7065 | −0.0268 |
JCI (Indonesia) | 0.0133% | 4.9904% | −5.5294% | 0.0069 | 10.5388 | −0.2666 |
SENSEX 30 (India) | 0.0027% | 6.9444% | −5.1287% | 0.0070 | 8.8748 | −0.0039 |
KOSPI(South Korea) | −0.0015% | 3.5444% | −5.3711% | 0.0077 | 6.7142 | −0.3291 |
KLCI (Malaysia) | −0.0059% | 8.7986% | −10.4897% | 0.0062 | 48.0950 | −0.2277 |
PSI (Philippine) | 0.0022% | 7.0258% | −4.2318% | 0.0064 | 12.9464 | 0.3160 |
SET (Thailand) | −0.0093% | 4.5935% | −6.9762% | 0.0072 | 9.5131 | −0.0925 |
Regional Index | 0.0003% | 2.8455% | −3.4443% | 0.0045 | 8.4611 | −0.4225 |
[
As
In line with Gande and Parsely [
Firstly, we open a window of [−60, −21] with respect to positive (negative) rating event date for any country and randomly chose one trading return from seven countries and form a 7 × 38 ( 7 × 25 ) return matrix for none event dates, and then calculate relative correlation matrices before positive (negative) ratings events. Above procedure will be repeatedly conducted for 10000 times and finally we obtain 10000 correlation matrices for none event days with respect to either positive or negative rating event dates. Thereafter, we implement following two tests.
Firstly, in order to discern the difference in correlation matrices of ratings event and none rating event date, we apply Jennrich [
Secondly, in terms of whether correlations increase or decrease on rating event date, we compare correlation coefficients in correlation matrices in each randomization to that of ranting event dates, and count the number of correlations on none events dates above or below those on rating event dates, and cal- culate net-increase proportion and net-decline proportion. And the comparison
of those two proportions will shed lights on distinguishing the direction of changes in correlations on rating event dates.
Model of Calculating Dynamic Conditional Correlation
Engle [
r i , t = α i + β i r t-1 US + e i , t (1)
r j , t = α j + β j r t − 1 US + e j , t (2)
e t = ( e i , t e j , t ) ~ N ( o , H t ) H t = [ h i i , t h i j , t h j i , t h j j , t ] (3)
in which r i , t is daily return of each stock market index, r j , t is the daily return of regional market index. This model is formulated according to Christopher et al. [
h i i , t = γ 0 , i i + γ 1 , i i e i i , t − 1 2 + γ 2 , i i h i i , t − 1
h j j , t = γ 0 , j j + γ 1 , j j e j j , t − 1 2 + γ 2 , j j h j j , t − 1
h i j , t = γ 0 , i j + γ 1 , i j e i j , t − 1 2 + γ 2 , i j h i j , t − 1
And the time-varying conditional correlations are calculated as below.
ρ i j , t = h i j , t h i i , t h j j , t
Summary of description of time-varying conditional correlations between 7 emerging stock markets and regional markets is listed in
Effects of Sovereign Rating Events on Dynamic Conditional Correlation
As public signals, will changes on sovereign ratings influence decisions of international portfolio investors so as to trigger a structural change on interdependences of stock markets in Asian Pacific area? In order to answer this question, we formulate indicate variables proposed by Chiang et al. [
Mean | Max | Min | Variance | Kurtosis | Skewness | |
---|---|---|---|---|---|---|
HSI (Hong Kong) | 0.6800 | 0.8693 | 0.2632 | 0.1048 | 3.4943 | −0.6862 |
JCI (Indonesia) | 0.6358 | 0.9026 | 0.1981 | 0.1283 | 3.0571 | −0.6913 |
SENSEX30 (India) | 0.5575 | 0.7413 | −0.1485 | 0.1293 | 8.0836 | −1.7157 |
KOSPI (South Korea) | 0.6058 | 0.8129 | −0.2248 | 0.1339 | 7.0901 | −1.5040 |
KLCI (Malaysia) | 0.5436 | 0.8714 | 0.1099 | 0.1106 | 2.9025 | −0.1233 |
PSI (Philippine) | 0.5010 | 0.7132 | 0.2459 | 0.0629 | 2.9814 | −0.2282 |
SET (Thailand) | 0.6335 | 0.8626 | 0.2196 | 0.0788 | 4.0060 | −0.6007 |
the upgrade and downgrade rating events and indicate variables which display ratings change events in other countries in this region.
Firstly, for each country i, we open a window at time T as
T s = { T − s , T , T + s } ,and s = { − 1 , 0 , 1 } , we define I i ( T s ) given by Equation (4) as a indicate function measuring changes on ratings, in which Δ v is the difference values that mentioned previously..
I i ( T s ) = { Δ v , ( t = T s ) 0 , ( t ≠ T s ) (4)
According to the sign of the value of Δ v , we can distinguish a upgrade indicate variable I i ( T s ) upgrade and downgrade indicate variable I i ( T s ) downgrade as follows; and we assign an absolute value on I i t ( T s ) for convenience of discussing marginal effect of negative ratings events on dynamic condition correlations.
I i t ( T s ) upgrade = { | I i t ( T s ) | , I i t ( T s ) > 0 , 0 , I i t ( T s ) ≤ 0 (5)
I i t ( T s ) downgrade = { | I i t ( T s ) | , I i t ( T s ) < 0 , 0 , I i t ( T s ) ≥ 0 (6)
Considering possible impacts of abroad rating events on dynamic conditional correlations, we additionally formulate I region , t ( T s ) upgrade , I region , t ( T s ) downgrade given by Equation of (7) and (8).
I region , t ( T s ) upgrade = ∑ i = 1 7 I i t ( T s ) upgrade − I i t ( T s ) upgrade (7)
I region , t ( T s ) downgrade = ∑ i = 1 7 I i t ( T s ) downgrade − I i t ( T s ) downgrade (8)
Finally we estimate marginal effect of rating events on dynamic conditional correlations by an OLS regression specified as Equation (9),and estimated coe- fficients β 1 , i ( β 2 , i ) displays marginal effects of upgrade (downgrade) rating events, if β 1 , i > 0 ( β 2 , i > 0 ),it shows that upgrade (downgrade) rating will increase the dynamic conditional correlation; if β 1 , i < 0 ( β 2 , i < 0 ), it means that upgrade (downgrade) ratings will decrease crease the dynamic conditional correlation. Similarly, γ 1 , i ( γ 2 , i ) estimates marginal effect of foreign rating events in this region on dynamic conditional events, if γ 1 , i > 0 ( γ 2 , i < 0 ) , it means foreign upgrade (downgrade) rating will increase the dynamic conditional correlation; while if γ 1 , i > 0 ( γ 2 , i < 0 ) , it means foreign upgrade (downgrade) rating will decrease the dynamic conditional correlation.
ρ i j , t = α + β 1 , i I i , t ( T s ) upgrade + β 2 , i I i , t ( T s ) downgrade + γ 1 , i I region , t ( T s ) upgrade + γ 2 , i I region , t ( T s ) downgrade + ε i j (9)
Short-and long-term effect of Sovereign Ratings on Dynamic Conditional Correlation
Although previous discussion on marginal effect of indicate variables that measure rating events sheds light on whether sovereign ratings events cause a structural change on interdependence of stock markets, it fails to discern whether such effect is transitory (short-term effect) or permanent (long-term effect). Similar to Christopher et al. [
ρ i j , t = α 0 i + α 1 i Rating i , t + ε i , t Δ ρ i j , t = β 0 , i + β 1 , i Δ Rating i , t + β 2 , i ε i , t − 1 + β 3 , i VIX t + u i , t (10)
In this model Rating i , t is time series of Foreign Currency Long Term Ratings of country i, VIX t is the Volatility Index of S & P 500 Index provided by Chicago Board of Option Exchange. Different from Christopher et al. [
Gande and Parsely [
Applying Jennrichchi-square test for the equality of correlation matrix on none-event days and event days in each random selection, we obtain 10000 chi- square test statistics. With a threshold value of 32.67 for the significance at the level 5% under the degree of freedom of 21, we apply a right tail test of the mean and median of Jennrich chi-square statistics and relative t statistic and z statistic are listed in the
From
Furthermore,
Above analysis displays that correlation of stock markets in regional market changes significantly on the day of rating adjustments. While such analysis on
Mean Test | ||
---|---|---|
T statistics | Upgrade Rating | Downgrade Rating |
283.90*** | 38.25*** | |
Median Test | ||
Z statistics | Upgrade Rating | Downgrade Rating |
99.95*** | 27.19*** |
***denote significance at the 1% level.
Upgrade Rating | Downgrade Rating | |
---|---|---|
Net-increase proportion | 93.50% | 79.43% |
Net-decrease proportion | 6.50% | 20.57% |
the static correlation fails to enlighten us whether rating adjustment could impose structural changes on correlation and whether such effect is permanent or transitory. Those questions will be disentangled by empirical results based on the dynamic conditional correlation of stock markets with respective of regional markets in the following section.
Firstly, coefficient of I i ( T s ) downgrade is significant at the 1% level in all models, which implies that when rating of one country is downgraded its correlation of stock market index with respective regional market index has been changed significantly. While coefficient of I i ( T s ) u p g r a d e is only significant at the 1% level for Indonesia and at the 10% level for India and South Korea.Furthermore, if we only consider the coefficient of I i ( T s ) u p g r a d e which is significant at the level 1%, we find out that the upgrade rating event increases the correlations of stock market index in Indonesia with respective regional market index.
constant | |||||
---|---|---|---|---|---|
HSI (Hong Kong) | −0.0328 | −0.0815*** | −0.0017 | 0.0220*** | 0.6806*** |
(−0.7386) | (−20.7841) | (−0.4031) | (4.4826) | (287.8014) | |
JCI | 0.0255*** | −0.0356*** | −0.0039 | 0.0191** | 0.6363*** |
(Indonesia) | (2.9833) | (−11.9005) | (−0.4995) | (2.4598) | 219.8271 |
SENSEX30 | 0.0092* | 0.0369*** | −0.0199* | 0.0383*** | 0.5586*** |
(India) | (1.7684) | (5.8993) | (−1.6912) | (6.7488) | (190.8986) |
KOSPI | −0.0407* | 0.0459*** | 0.0105 | 0.0166* | 0.6064*** |
(South Korea) | (−1.6707) | (3.8326) | (0.9598) | (1.6591) | (201.6000) |
KLCI | 0.0300 | −0.1121*** | −0.0230* | −0.0039 | 0.5437*** |
(Malaysia) | (1.1222) | (−5.4777) | (−1.8713) | (−0.2538) | (220.8740) |
PSI | −0.0180 | −0.0857*** | −0.0007 | 0.0000 | 0.5009*** |
(Philippine) | (−0.6440) | (−6.9142) | (−0.1000) | (−0.0019) | (358.4257) |
SET | −0.0493 | 0.0384*** | 0.0036 | −0.0012 | 0.6336*** |
(Thailand) | (−1.4581) | (5.9414) | (0.7583) | (−0.2293) | (361.6326) |
Note: This table displays OLS regression results of, ρ i j , t = α + β 1 , i I i , t ( T s ) u p g r a d e + β 2 , i I i , t ( T s ) d o w n g r a d e + γ 1 , i I r e g i o n , t ( T s ) u p g r a d e + γ 2 , i I r e g i o n , t ( T s ) d o w n g r a d e + ε i j is the dynamic conditional correlation of return of stock market index of country i with respective of that of regional market index, I i ( T s ) u p g r a d e ( I i ( T s ) d o w n g r a d e ) is the indication variables defining the upgrade (downgrade) rating event of country i; I i , r e g i o n ( T s ) u p g r a d e ( I i , r e g i o n ( T s ) d o w n g r a d e ) is the indication variable denoting foreign upgrade (downgrade) rating events in this region for country i. All t statistics in the parentheses below the coefficients are adjusted by New-West estimation with one lagged term. ***, **, *denote significance at 1%, 5% and 10% level.
On the contrary, the sign of significant coefficients of I i ( T s ) downgrade is complicated, indicating different spillover effects of rating events on stock markets co-movements. Four of seven significant coefficients are significantly negative, displaying a general effect of downgrade rating event decreasing stock markets co-movements in the Asian Pacific area. On other words, investors are inclined to regard downgrade rating events in this area as the specific information of risk of adjusted countries instead of a signal of the deteriorated economic and investment conditions of the whole regional markets. Consequently, international investors might withdraw capital from the downgraded country to the neighboring country so as to weaken the stock markets co-movement. While for the downgrade rating event of Thailand, positive coefficient of I i ( T s ) downgrade demonstrates that co-movement of Thailand stock market with reginal market is intensified when negative rating event happens. For downgrade events of Thailand are clustered in the financial crisis in 1997 and the Thailand is the original sou- rce of this crisis, negative rating events trigger a financial contagion effect so as to deter the capital out of the regional markets, which intensify the co-move- ment of stock markets in this region.
In terms of the effect of neighboring rating events on stock markets co-mo- vement, significance of estimated coefficient of I i , region ( T s ) upgrade and I i , region ( T s ) downgrade reveals that neighboring downgrade rating events are more likely to introduce structure changes on correlations of stock market index with respective of regional market index. In details, for neighboring upgrade rating events, only the correlations of Indonesia and Malaysia stock markets with respective of regional market index is significant, but the effect is only significant at the 10% level. While facing the neighboring downgrade rating events, co-movements with regional markets is significant positive at the level of 1% for Hong Kong and India stock markets, at the level of 5% for the Indonesia stock market and at the level of 10% for South Korea stock markets.
Regression results of Error Correction Models in
More specifically, α 1 in models of Hong Kong, Indonesia, Malaysia and Thailand is significantly positive and is positive in the model of Philippine though insignificant, but is negative in the Model of India and South Korea. Above results indicate, in general, when positive rating events happen, investors not only treasure it as a signal of the improvement of economic and investment condition of adjusted countries but an indicator of the uprising of the whole region so as to increase the investment in the whole region and promote co- movement of stock markets; however, when a negative rating happen, investor prefer to regard it as a specific signal of deteriorated credit condition of adjusted country and reallocate weights of the international investment portfolio by withdrawing the capital from downgraded country to neighboring countries so as to weaken the co-movement of stock markets. Above findings in terms of long-term effect are consistent with Christopher et al. [
coint_test | |||||||
---|---|---|---|---|---|---|---|
HSI (Hong Kong) | 0.0852*** | 0.0352*** | −0.0012*** | 0.0025 | −0.0128*** | 0.0001** | −4.8887*** |
(4.8808) | (35.7615) | (−2.5819) | (1.0858) | (−3.3065) | (2.5469) | ||
JCI | −0.1326*** | 0.0767*** | −0.0014* | −0.0058 | −0.0187*** | 0.0001* | −6.3073*** |
(Indonesia) | (−4.4583) | (26.5222) | (−1.7486) | (−0.9252) | (−5.3569) | (1.8782) | |
SENSEX30 | 0.5890*** | −0.0042*** | −0.0007* | 0.0005 | −0.0063*** | 0.0000** | −5.0152*** |
(India) | (92.4974) | (−4.4882) | (−1.8440) | (1.0156) | (−4.7652) | (2.2457) | |
KOSPI | 0.8689*** | −0.0183*** | −0.0014** | 0.0033 | −0.0129*** | 0.0001*** | −5.7283*** |
(South Korea) | (34.4788) | (−10.2512) | (−2.1282) | (0.9855) | (−5.2456) | (2.6221) | |
KLCI | 0.2773*** | 0.0193*** | −0.0033*** | −0.0040 | −0.0293*** | 0.0002*** | −7.5807*** |
(Malaysia) | (12.1227) | (11.6152) | (−3.5795) | (−0.8991) | (−6.9609) | (3.5538) | |
PSI | 0.4798*** | 0.0023 | 0.0004 | −0.0050 | −0.0450*** | −0.0000 | −9.3198*** |
(Philippine) | (32.8765) | (1.463) | −0.5715 | (−0.4835) | (−9.9864) | (−0.5831) | |
SET | 0.5759*** | 0.0046*** | −0.0034*** | 0.0251*** | −0.0677*** | 0.0002*** | −11.5384*** |
(Thailand) | (32.4591) | (3.3268) | (−3.2438) | (4.4559) | (−10.9694) | (3.2778) |
Note: This table regression result of Error Correction Model given by: ρ i j , t = α 0 i + α 1 i Rating i , t + ε i , t Δ ρ i j , t = β 0 , i + β 1 , i Δ Rating i , t + β 2 , i ε i , t − 1 + β 3 , i VIX t + u i , t ρ i j , t is the dynamic conditional correlation of return of stock market index of country i with respective of that of regional market index, Rating i , t is the rating series of country I, VIX t is the Volatility Index of S&P 500 Index provided by Chicago Board of Option Exchange. All t statistics in the parentheses below the coefficients are adjusted by New-West estimation with one lagged term. coint_test is statistics of the CADF test in terms of ρ i j , t and Rating i , t with lagged 1 term5, ***, **, *denote significance at 1%, 5% and 10% level.
In terms of the short-term effect, estimated coefficient β 1 is only significant in the model of Thailand, and the significance is at 1% level. With a closer look on the adjustments of rating on Thailand, we find that all downgrade rating events happened during the Asian Crisis and upgrade rating events happened right after the crisis. As the trigger of the Asian Financial Crisis, downgrade rating event for Thailand triggers financial contagion in this area while upgrade rating event signifies economic condition of the whole region claw out of slump since the crisis, so as to increase the co-movement of stock markets. In addition, coefficient of VIX is positively significant as we expected. Increased VIX indicates that investors have higher risk aversion and prone to diversify their asset into neighboring markets when economic and investment condition is uprising in the home country, but withdraw the capital from the whole region when downgrade event happens.
This paper originally investigates how co-movements of stock market index with respective to regional market index react to both upgrade and downgrade sovereign credit rating events, and reveals that those effects are asymmetric in terms of both static and dynamic interdependence of stock markets in a fairly spanned sample period. Namely, we find out increases in correlations in both upgrade and downgrade rating day and the effect is more profound for upgrade rating events, but the frequency of decreasing correlations is significantly higher in downgrade rating days. As for asymmetric effects on dynamic correlations, we discover a significant increase in the correlations of most countries because of the common information effect triggered by the upgrade rating events, while for the downgrade rating events, dominant differential information effects result in decrease in the correlations. Moreover, we discern a significant long-run effect of sovereign credit rating on correlations, and correlations, similar to the previous test, increase because of common information effect when ratings are upgraded and decrease because of differential information effect when ratings are decreased.
Our original empirical findings demonstrates that, because of the existed asymmetric effects, both investors’ decision of reallocating their portfolios in regional markets and regulators’ management of shock to the stock markets caused by domestic as well as foreign rating events should be adjusted and tailed differently. However, due to data availability, we do not investigate the effect of credit outlook information which is argued by Christopher et al. [
Ni, B. (2017) Do Changes on Sovereign Credit Rating Have Impacts on the Interdependence of Stock Markets in the Asian-Pacific Emerging Markets? Modern Economy, 8, 351-367. https://doi.org/10.4236/me.2017.83025