**4.3 Indonesia financial link with Argentina and Turkey**

## *4.3.1 Measuring contagion with correlation*

The figure above shows that Jakarta composite index (JCI) has the highest correlation with MERVAL, namely, the Argentina exchange (0.9), and the second highest is with ISTANBUL (0.89) or Turkish stock exchange. Thus, the government of Indonesia has to pay attention to the high correlation potential with both countries. Correlation with NASDAQ (US exchange) is significant but slightly below Turkey (0.88). NASDAQ has a high and significant correlation with the three emerging market stock exchanges (Turkey, Indonesia, and Argentina) (**Figure 5** and **Table 4**).

**Table 5** shows the adjusted correlation coefficients before and during the crisis. All of the coefficients increase during the crisis, except the contagion between Indonesia and Malaysia which the stock price fell sharply. Meanwhile, the correlation between the Thai and Indonesian exchange rates and between the Thai and Malaysian exchange rates experiences a high surge during the crisis.


#### **Table 4.**

*Correlation result.*


statistically significant. In fact, there is a statistically significant decrease in the correlation coefficient between the Malaysian and Indonesian stock markets. One plausible explanation for this decline is the adoption of Malaysian capital controls in

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial…*

*BORSA (Turkey)—JCI (Indonesia) correlation 2005–2019. Source: Authors, 2019.*

in some years. Changes in the correlation between the stock market and the

*MERVAL (Argentina)—JCI (Indonesia) correlation 2005–2019. Source: Authors, 2019.*

**Table 6** shows the correlation coefficients of the stock market and exchange rate

exchange rate of Indonesia from 2017 to 2018 were not as big as when the economic crisis of 1997. This happens because after the crises, Indonesia becomes more vigilant so that various agencies were formed to predict and overcome crises. There is an increase in the correlation between Indonesian stock prices from 2017 to 2018 both with Argentina and Turkey's stock prices. Something similar also happens to the exchange rate. There is an increase in the correlation between the Indonesian exchange rate from 2017 to 2018 both with the exchange rates of Argentina and

late August and early September 1998 [24].

*DOI: http://dx.doi.org/10.5772/intechopen.92275*

Turkey (**Table 6**).

**Figure 7.**

**51**

**Figure 6.**

#### **Table 5.**

*Contagion in the currency and stock markets (adjusted correlation coefficients) in 1997 from Iriana and Sjöholm [24].*

A negative and statistically significant coefficient means that there is evidence of contagion. The increases in the correlation between the Thai and Indonesian exchange rate and between the Thai and Malaysian exchange rate are statistically significant. Therefore, the results show that difficulties in Thailand are transmitted to the Indonesian and Malaysian currency markets. There are no signs of contagion on the stock market because an increase in the correlation coefficient is not


#### **Table 6.**

*Potential contagion: correlation rises during crisis.*

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial… DOI: http://dx.doi.org/10.5772/intechopen.92275*

**Figure 6.** *BORSA (Turkey)—JCI (Indonesia) correlation 2005–2019. Source: Authors, 2019.*

statistically significant. In fact, there is a statistically significant decrease in the correlation coefficient between the Malaysian and Indonesian stock markets. One plausible explanation for this decline is the adoption of Malaysian capital controls in late August and early September 1998 [24].

**Table 6** shows the correlation coefficients of the stock market and exchange rate in some years. Changes in the correlation between the stock market and the exchange rate of Indonesia from 2017 to 2018 were not as big as when the economic crisis of 1997. This happens because after the crises, Indonesia becomes more vigilant so that various agencies were formed to predict and overcome crises. There is an increase in the correlation between Indonesian stock prices from 2017 to 2018 both with Argentina and Turkey's stock prices. Something similar also happens to the exchange rate. There is an increase in the correlation between the Indonesian exchange rate from 2017 to 2018 both with the exchange rates of Argentina and Turkey (**Table 6**).

**Figure 7.** *MERVAL (Argentina)—JCI (Indonesia) correlation 2005–2019. Source: Authors, 2019.*

A negative and statistically significant coefficient means that there is evidence of

**Stock market Exchange rate Year JCI-MERVAL JCI-Turkey Year IDR-ARS IDR-TRY** 0.338 0.779 2015 0.341 0.930 0.924 0.154 2016 0.252 0.279 0.917 0.932 2017 0.713 0.725 0.952 0.950 2018 0.841 0.826

**Correlation NASDAQ MERVAL JCI ISTANBUL BROAD\_USD**

12.578 13.295 12.280 — BROAD\_USD 0.025 0.062 0.094 0.208 1.000

**Period Stock market Exchange rate**

**Thailand-Malaysia**

Tranquil 0.25 0.26 0.08 0.02 0.12 0.14 Crisis 0.29 0.07 0.09 0.19 0.14 0.31

**Malaysia-Indonesia**

0.155 0.387 0.593 1.330 —

0.54 2.55\*\* 0.14 2.26\*\* 0.037 2.29\*\*

**Thailand-Indonesia**

**Malaysia-Indonesia** **Thailand-Malaysia**

NASDAQ 1.000

*Public Sector Crisis Management*

*Source: Authors Calculation, 2019.*

**Thailand-Indonesia**

**Table 4.** *Correlation result.*

> Statistically significant

**Table 5.**

*Sjöholm [24].*

*\*\*Significant at a 5% level. Source: Iriana and Sjöholm [24].*

*Source: Authors Calculation, 2019.*

*Potential contagion: correlation rises during crisis.*

**Table 6.**

**50**

— MERVAL 0.964 1.000

22.783 — JCI 0.878 0.901 1.000

11.436 12.995 — ISTANBUL 0.896 0.905 0.891 1.000

contagion. The increases in the correlation between the Thai and Indonesian exchange rate and between the Thai and Malaysian exchange rate are statistically significant. Therefore, the results show that difficulties in Thailand are transmitted to the Indonesian and Malaysian currency markets. There are no signs of contagion

*Contagion in the currency and stock markets (adjusted correlation coefficients) in 1997 from Iriana and*

on the stock market because an increase in the correlation coefficient is not

#### *4.3.1.1 Dynamic conditional correlation*

### *4.3.1.1.1 Stock market*

Throughout 2005–2019, the correlation between the Turkish stock market and Indonesian stock market is in the range of 0.6–0.9. The biggest decline occurs in 2007 and 2010 which reach 0.3, and the highest correlation increase occurs in 2018 (**Figure 6**). Meanwhile, **Figure 7** shows a correlation between Argentine stock market and Indonesian stock market which experience an upward trend since 2011. The upward trend between Argentina's stock market and Indonesia's stock market also happened since 2013, as well as in the periods of 2016–2018.

#### *4.3.1.1.2 Exchange rate*

The exchange rate correlations between 2005 and 2019 experience fluctuations. The correlation between lira and rupiah begins with a negative value, which is almost touching 0.5. In addition to 2005, in 2008 and 2014 lira and rupiah are also

negatively correlated. Meanwhile, the correlation between peso and rupiah experiences a negative correlation from the end of 2008 to the beginning of 2009 and at

**Correlation Argentina Indonesia Turkey 2002Q3–2008Q4** Argentina 1.000 0.036 0.125 Indonesia **0.036** 1.000 0.046 Turkey 0.125 0.046 1.000 **2009Q1 - 2018Q4** Argentina 1.000 0.207 0.088 Indonesia 0.207 1.000 0.423 Turkey 0.088 **0.423** 1.000

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial…*

**Table 7** shows the net correlation after controlling fundamental between Argentina, Indonesia, and Turkey in terms of capital markets. The results show that the highest correlation was between Indonesia and Turkey from 2009 to 2018. Meanwhile, the lowest correlation was between Indonesia and Argentina in 2002

By using monthly data and the lira exchange rate as the endogenous variables, the research will analyze the impact of weaker lira on the Indonesian economy (VAR dynamic method). **Table 8** shows that the change in lira exchange rate is

**Rupiah Interbank**

**rates**

**Net export** **LIP (production index)**

dominant in explaining changes in the rupiah exchange rate (26–37%).

1 0.031 26.635 0.096 73.269 0.000 0.000 0.000 2 0.033 35.264 0.080 63.237 0.177 0.852 0.390 3 0.033 35.947 0.216 62.291 0.174 0.895 0.476 4 0.034 37.063 0.224 61.157 0.174 0.881 0.500 5 0.034 37.077 0.263 61.073 0.174 0.909 0.504 6 0.034 37.088 0.264 61.058 0.175 0.910 0.506 7 0.034 37.092 0.266 61.049 0.175 0.913 0.505 8 0.034 37.092 0.266 61.048 0.175 0.914 0.506 9 0.034 37.092 0.266 61.048 0.175 0.914 0.506 10 0.034 37.092 0.266 61.048 0.175 0.914 0.506

**purchase**

the beginning of 2013 (**Figures 8** and **9**).

*DOI: http://dx.doi.org/10.5772/intechopen.92275*

*Net correlation in stock market after extracting fundamental.*

quarter 3 to the end of 2008.

**Period SE Lira Net foreign**

*Variance decomposition of rupiah exchange rate (level).*

*Source: Author Calculation, 2019.*

**Table 7.**

*4.3.2 VAR results*

*Source: Authors, 2019.*

**Table 8.**

**53**

**Figure 8.** *Turkey lira—Indonesia rupiah correlation 2005–2019. Source: Authors, 2019.*

**Figure 9.** *Peso Argentina—Indonesia rupiah correlation 2005–2019. Source: Authors, 2019.*

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial… DOI: http://dx.doi.org/10.5772/intechopen.92275*


### **Table 7.**

*4.3.1.1 Dynamic conditional correlation*

*Public Sector Crisis Management*

Throughout 2005–2019, the correlation between the Turkish stock market and Indonesian stock market is in the range of 0.6–0.9. The biggest decline occurs in 2007 and 2010 which reach 0.3, and the highest correlation increase occurs in 2018 (**Figure 6**). Meanwhile, **Figure 7** shows a correlation between Argentine stock market and Indonesian stock market which experience an upward trend since 2011.

The exchange rate correlations between 2005 and 2019 experience fluctuations.

The correlation between lira and rupiah begins with a negative value, which is almost touching 0.5. In addition to 2005, in 2008 and 2014 lira and rupiah are also

*Turkey lira—Indonesia rupiah correlation 2005–2019. Source: Authors, 2019.*

*Peso Argentina—Indonesia rupiah correlation 2005–2019. Source: Authors, 2019.*

The upward trend between Argentina's stock market and Indonesia's stock market also happened since 2013, as well as in the periods of 2016–2018.

*4.3.1.1.1 Stock market*

*4.3.1.1.2 Exchange rate*

**Figure 8.**

**Figure 9.**

**52**

*Net correlation in stock market after extracting fundamental.*

negatively correlated. Meanwhile, the correlation between peso and rupiah experiences a negative correlation from the end of 2008 to the beginning of 2009 and at the beginning of 2013 (**Figures 8** and **9**).

**Table 7** shows the net correlation after controlling fundamental between Argentina, Indonesia, and Turkey in terms of capital markets. The results show that the highest correlation was between Indonesia and Turkey from 2009 to 2018. Meanwhile, the lowest correlation was between Indonesia and Argentina in 2002 quarter 3 to the end of 2008.

#### *4.3.2 VAR results*

By using monthly data and the lira exchange rate as the endogenous variables, the research will analyze the impact of weaker lira on the Indonesian economy (VAR dynamic method). **Table 8** shows that the change in lira exchange rate is dominant in explaining changes in the rupiah exchange rate (26–37%).


#### **Table 8.**

*Variance decomposition of rupiah exchange rate (level).*

### *Public Sector Crisis Management*

and NFP. The decline of the lira may increase in the interbank rates, but there is a decline in NFP at the start, even though the NFP could recover after the third month. The weakening of lira also caused a decline in net exports and a manufacturing production index even though with little impact (**Figure 10**).

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial…*

sensitive to get the impact of capital flow.

*DOI: http://dx.doi.org/10.5772/intechopen.92275*

*R-square decomposition based on Shepley decomposition. Source: [27].*

**4.4 Rupiah exchange rate model results**

**4.5 Exchange rate model result**

dummy caused rupiah to depreciate) (**Table 11**).

þ *ϵ<sup>t</sup>*

(**Figure 12**).

**55**

**Figure 11.**

Claessens [27] explains that the main reasons of fragility are liquidity and investor based, while macrofundamentals only have a little explaining power except for bond (**Figure 11**). According to **Table 10**, Indonesia, Turkey, and Argentina are

Indonesian capital and financial account in the fourth quarter of 2018 showed a good performance and even had obtained the highest value since 2012. Unfortunately, this is not supported by the current account condition. The current account in the fourth quarter of 2018 continued to deteriorate compared to the previous periods. This occurs because of the global economic slowdown that is currently happening so that Indonesia's exports to several countries have decreased

*EXCH*\_*RATEt* ¼ *α*<sup>10</sup> þ *β*11*TBt*�<sup>1</sup> þ *β*12*FINANCIALt*�<sup>1</sup> þ *β*13*PRIMARY INCOMEt*�<sup>1</sup> þ *β*14*LIRAt* þ *β*15*DUMMYt* þ *β*16*AR*ð Þ1 *<sup>t</sup>* þ *ϵ<sup>t</sup>*

Based on the regression results, it can be concluded that Indonesia's trade balance and financial account have a significant impact on the exchange rate on α 0.1 with a negative coefficient (the surplus of financial account caused rupiah to appreciate). In contrast, lira and crisis dummy have a significant positive effect on exchange rates (Lira depreciation followed by rupiah depreciation, and crisis

*LOG EXCH* ð Þ \_*RATE <sup>t</sup>* ¼ *α*<sup>10</sup> þ *β*11*TBt* þ *β*12*LOG M*ð Þ2 *<sup>t</sup>* þ *β*13*DUMMYt* þ *β*14*AR*ð Þ1 *<sup>t</sup>*

Based on **Table 12**, it can be concluded that broad money (M2) and dummy variables significantly influence the exchange rate. Thus, if there is a higher amount

(16)

(17)

**Figure 10.** *IRF graph. Source: Authors, 2019.*


#### **Table 9.**

*Variance decomposition of Indonesia's industrial production index.*

The changes in the Indonesia production index in response to changes in the lira exchange rate variable are relatively small. Interbank rates, net exports, and rupiah exchange rates (apart from the production index itself) get the highest change. So we can conclude that the contagion for Turkey has hit Indonesia more on the financial market, especially exchange rate, and has a small effect on real sector activity represented by industrial production index (**Figure 10** and **Table 9**).

The weakening lira is proven to be the reason why the rupiah moves downward, but the shock would disappear after 5 months (the rupiah value returned to balance). Not only rupiah but also weaker lira also has an impact on the interbank rates *Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial… DOI: http://dx.doi.org/10.5772/intechopen.92275*

**Figure 11.**

*R-square decomposition based on Shepley decomposition. Source: [27].*

and NFP. The decline of the lira may increase in the interbank rates, but there is a decline in NFP at the start, even though the NFP could recover after the third month. The weakening of lira also caused a decline in net exports and a manufacturing production index even though with little impact (**Figure 10**).

Claessens [27] explains that the main reasons of fragility are liquidity and investor based, while macrofundamentals only have a little explaining power except for bond (**Figure 11**). According to **Table 10**, Indonesia, Turkey, and Argentina are sensitive to get the impact of capital flow.

#### **4.4 Rupiah exchange rate model results**

Indonesian capital and financial account in the fourth quarter of 2018 showed a good performance and even had obtained the highest value since 2012. Unfortunately, this is not supported by the current account condition. The current account in the fourth quarter of 2018 continued to deteriorate compared to the previous periods. This occurs because of the global economic slowdown that is currently happening so that Indonesia's exports to several countries have decreased (**Figure 12**).

#### **4.5 Exchange rate model result**

$$\begin{aligned} \text{EXCH\\_RATE}\_t &= a\_{10} + \beta\_{11} \text{TB}\_{t-1} + \beta\_{12} \text{FINANCE}\_{t-1} + \beta\_{13} \text{PRIMARY INCOMP}\_{t-1} \\ &+ \beta\_{14} \text{LIRA}\_t + \beta\_{15} \text{DUMMWY}\_t + \beta\_{16} \text{AR}(\mathbf{1})\_t + \varepsilon\_t \end{aligned} \tag{16}$$

Based on the regression results, it can be concluded that Indonesia's trade balance and financial account have a significant impact on the exchange rate on α 0.1 with a negative coefficient (the surplus of financial account caused rupiah to appreciate). In contrast, lira and crisis dummy have a significant positive effect on exchange rates (Lira depreciation followed by rupiah depreciation, and crisis dummy caused rupiah to depreciate) (**Table 11**).

$$\begin{aligned} \text{LOG}(\text{EXCH\\_RATE})\_t &= a\_{10} + \beta\_{11} \text{TB}\_l + \beta\_{12} \text{LOG}(\text{M\!2})\_t + \beta\_{13} \text{LUMM} \text{Y}\_t + \beta\_{14} \text{AR}(\text{1})\_t \\ &+ a\_l \end{aligned} \tag{17}$$

Based on **Table 12**, it can be concluded that broad money (M2) and dummy variables significantly influence the exchange rate. Thus, if there is a higher amount

The changes in the Indonesia production index in response to changes in the lira exchange rate variable are relatively small. Interbank rates, net exports, and rupiah exchange rates (apart from the production index itself) get the highest change. So we can conclude that the contagion for Turkey has hit Indonesia more on the financial market, especially exchange rate, and has a small effect on real sector activity represented by industrial production index (**Figure 10** and **Table 9**).

**Figure 10.**

*IRF graph. Source: Authors, 2019.*

*Public Sector Crisis Management*

**Period SE D**

*Source: Authors Calculation, 2019.*

**Table 9.**

**54**

**(LER\_LIRA)**

*Variance decomposition of Indonesia's industrial production index.*

**D (NFP)**

**D (LER)**

1 6080.729 0.222 1.287 0.152 1.396 3.926 93.015 2 6982.743 0.202 1.954 0.349 1.376 4.916 91.202 3 7130.040 0.223 1.866 4.273 3.866 4.797 84.973 4 7163.440 0.672 1.781 4.370 6.371 5.101 81.703 5 7169.733 0.721 1.772 4.351 6.560 5.234 81.363 6 7173.949 0.802 1.775 4.366 6.739 5.223 81.095 7 7176.103 0.811 1.779 4.367 7.014 5.250 80.778 8 7176.375 0.812 1.780 4.366 7.051 5.260 80.731 9 7176.617 0.814 1.780 4.366 7.060 5.260 80.719 10 7176.858 0.815 1.782 4.366 7.085 5.263 80.688

**D (PUAB)**

**D (NET\_EXPORT)**

**D (LIP\_INA)**

The weakening lira is proven to be the reason why the rupiah moves downward, but the shock would disappear after 5 months (the rupiah value returned to balance). Not only rupiah but also weaker lira also has an impact on the interbank rates


#### **Table 10.**

*Results of variance decompositions.*

#### **Figure 12.**

*Current account and financial account 2012Q1–2018Q4. Source: Economics and Finance Statistics, Central Bank of Indonesia.*

of money circulating in the community, then there is a decline in the rupiah exchange rate.

$$\begin{split} \text{LOG}(\text{EXCH\\_RATE})\_t &= a\_{10} + \beta\_{11} \text{FINANCE}L\_t + \beta\_{12} \text{COMPICE}\_t + + \beta\_{13} \text{LOG}(\text{M\!2})\_t \\ &+ \beta\_{14} \text{DUMMY}\_t + \beta\_{15} \text{AR}(\text{1})\_t + \varepsilon\_t \end{split}$$

$$\bf{(18)}$$

Based on the table above, it can be concluded that the commodity price variable

**Table 14** shows that financial accounts significantly influence the exchange rate at a significance level of 10% with a negative direction. The increase in financial accounts surplus can cause rupiah appreciation. The condition of the peso exchange

þ *β*14ARS*<sup>t</sup>* þ *β*15*DUMMYt* þ *β*16*AR*ð Þ1 *<sup>t</sup>* þ *ϵ<sup>t</sup>* (19)

is significant at a significance level of 5%. The impact of commodity price to exchange rate is negative; thus when there is an increase in commodity prices by 1%, the value of the rupiah will appreciate by 0.876%. On the other hand, the broad money and crisis dummy have a significant and positive coefficient, which means that if money supply increases and crisis happens, rupiah exchange rate will depre-

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial…*

*EXCH*\_*RATEt* ¼ *α*<sup>10</sup> þ *β*11*TBt* þ *β*12*FINANCIALt* þ *β*13*PRIMARYINCOMEt*�<sup>2</sup>

**Variable Coef. Prob** C 8450.149 0.000 TB (�1) �0.052 0.078\* FINANCIAL (�1) �0.016 0.095\* PRIMARY\_INCOME (�1) �0.071 0.256 LIRA 9.022.642 0.000\*\*\* DUMMY 1,084,258 0.003\*\*\* AR (1) 0.872 0.000\*\*\* Adj R<sup>2</sup> 0.968 Prob (F-stat) 0.000

*Exchange rate determination regression results – Full model with lira variables.*

*Exchange rate determination regression results – Trade balance only.*

**Variable Coef. Prob** C 7937.000 0.000 TB 0.000 0.103 LOG(M2) 0.089 0.061\* DUMMY 0.155 0.002\*\*\* AR (1) 0.787 0.000\*\*\* Adj R2 0.917 Prob (F-stat) 0.000

ciate (**Table 13**).

*DOI: http://dx.doi.org/10.5772/intechopen.92275*

*\**

*\**

**57**

**Table 12.**

*Significant at α 0.1. \*\*Significant at α 0.05. \*\*\*Significant at α 0.01. Source: Authors, 2019.*

**Table 11.**

*Significant at α 0.1. \*\*Significant at α 0.05. \*\*\*Significant at α 0.01. Source: Authors, 2019.*

*Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial… DOI: http://dx.doi.org/10.5772/intechopen.92275*

Based on the table above, it can be concluded that the commodity price variable is significant at a significance level of 5%. The impact of commodity price to exchange rate is negative; thus when there is an increase in commodity prices by 1%, the value of the rupiah will appreciate by 0.876%. On the other hand, the broad money and crisis dummy have a significant and positive coefficient, which means that if money supply increases and crisis happens, rupiah exchange rate will depreciate (**Table 13**).

$$\begin{array}{c} \text{EXCH\\_RATE}\_t = a\_{10} + \beta\_{11} \text{TB}\_t + \beta\_{12} \text{FINANCLAL}\_t + \beta\_{13} \text{PRIAMRY}\_{\text{INCOME}\_t - 2} \\ \quad + \beta\_{14} \text{ARS}\_t + \beta\_{15} \text{DUMMY}\_t + \beta\_{16} \text{AR}(\mathbf{1})\_t + \epsilon\_t \end{array} \tag{19}$$

**Table 14** shows that financial accounts significantly influence the exchange rate at a significance level of 10% with a negative direction. The increase in financial accounts surplus can cause rupiah appreciation. The condition of the peso exchange


#### **Table 11.**

*Exchange rate determination regression results – Full model with lira variables.*


#### **Table 12.**

*Exchange rate determination regression results – Trade balance only.*

of money circulating in the community, then there is a decline in the rupiah

*LOG EXCH* ð Þ \_*RATE <sup>t</sup>* ¼ *α*<sup>10</sup> þ *β*11*FINANCIALt* þ *β*12*COMPRICEt* þ þ*β*13*LOG M*ð Þ2 *<sup>t</sup>* þ *β*14*DUMMYt* þ *β*15*AR*ð Þ1 *<sup>t</sup>* þ *ϵ<sup>t</sup>*

*Current account and financial account 2012Q1–2018Q4. Source: Economics and Finance Statistics, Central*

**Country Equity Bond Bank** Turkey 0.56 0.42 0.42 Argentina 0.37 0.14 0.32 Indonesia 0.51 0.69 0.43 South Africa 0.46 0.58 0.50 Israel 0.17 0.36 �0.03 Brazil 0.58 0.52 0.46 Chile �0.06 0.15 0.19 Colombia 0.16 0.02 0.23 Mexico 0.30 0.38 0.27 Peru 0.27 0.33 0.45 Uruguay �0.09 0.44 0.02 Venezuela, Rep. Bol. �0.06 0.29 �0.18 India 0.67 0.16 0.23 China PR: Mainland 0.41 �0.08 0.57 Korea 0.49 0.27 0.43 Malaysia 0.38 0.29 0.45 Pakistan 0.90 0.40 0.12 Philippines 0.64 0.36 0.19 Thailand 0.58 0.36 0.40

(18)

exchange rate.

*Bank of Indonesia.*

**Figure 12.**

**56**

*Source: Claessens [27].*

*Results of variance decompositions.*

*Public Sector Crisis Management*

**Table 10.**


*\*\*\*Significant at α 0.01. Source: Authors, 2019.*
