**3. Research method**

#### **3.1 VAR and OLS method**

The research uses VAR and dynamic conditional correlation (DCC) methods to assess the three important financial and external variables including contagion, exchange rate, and financial volatility of Indonesia triggered by global turbulence and Argentina-Turkey crisis in 2018. Data used in this research are the monthly data from 2004 to 2018. The VAR method has also been conducted by Marcel Fratzscher [16] to examine the impact of exchange rate crisis and its transmission (**Table 3**).

The VAR model can be expressed in Eq. (1):

$$Y\_t = a\_{\text{1i}} + \sum \beta\_{\text{1i}} Y\_{t-n} + \sum \gamma\_{\text{1i}} X\_{t-n} + \epsilon\_{\text{1t}} \tag{1}$$

**3.2 DCC method**

GARCH model is defined as:

of the correlation matrix.

where

**45**

time of univariate GARCH with ffiffiffiffiffiffi

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

*Dt* ¼

*P*1

∝*ipr* 2 *it*�*<sup>p</sup>* <sup>þ</sup><sup>X</sup> *Q*1

*p*¼1

*hi*,*<sup>t</sup>* <sup>¼</sup> *<sup>ω</sup><sup>i</sup>* <sup>þ</sup><sup>X</sup>

*Rt* ¼

Therefore, *Rt* is structured as follows:

The DCC representation was introduced by Engle [26] to capture the empirically observed dynamic contemporaneous correlations of asset returns. This is the latest method that allows simultaneous variant modeling and conditional correlation from several series. The estimation consists of two steps. First, we estimate the conditional variance of each variable using the univariate autoregressive conditional heteroscedasticity (ARCH) procedure. Second, we use the standard regression residues obtained in the first step to model conditional correlations that vary over time. The essence of this model is the covariance matrix (*Ht*), which is compiled into a diagonal matrix from standard conditional deviation (*Dt*) and matrix correlation containing the conditional correlations (*Rt*). In the DCC GARCH model, both the *Dt* and *Rt* models are designed to be time-varying. The DCC

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

where *Dt* is a diagonal matrix k x k of a standard deviation which has a different

p on the diagonal *i*

⋯ 0 ⋱ ⋮ ⋱ 0 0 ffiffiffiffiffiffi *hi*,*<sup>t</sup>* p

> ⋯ *P*1*n*,*<sup>t</sup>* ⋯ *P*2*n*,*<sup>t</sup>* ⋱ ⋮ ⋱ *Pn*�1,*n*,*<sup>t</sup>*

*Pn*�1,*n*,*<sup>t</sup>* 1

*Rt* <sup>¼</sup> *<sup>Q</sup>* <sup>∗</sup> �<sup>1</sup> *<sup>t</sup> QtQ* <sup>∗</sup> �<sup>1</sup> *<sup>t</sup>* (7)

*<sup>t</sup>*�<sup>1</sup> þ *bQt*�<sup>18</sup> (8)

*hi*,*<sup>t</sup>*

ffiffiffiffiffiffi *hi*,*<sup>t</sup>* p 0

> 0 ffiffiffiffiffiffi *hi*,*<sup>t</sup>* p

> > *q*¼1

1 *P*12,*<sup>t</sup> P*13,*<sup>t</sup>*

*P*12,*<sup>t</sup>* 1 *P*13,*<sup>t</sup>*

*P*13,*<sup>t</sup> P*23,*<sup>t</sup>* 1 ⋮ ⋮⋱

*P*1*n*,*<sup>t</sup> P*2*n*,*<sup>t</sup>* ⋯

*Qt* <sup>¼</sup> ð Þ <sup>1</sup> � *<sup>a</sup>* � *<sup>b</sup> <sup>Q</sup>* <sup>þ</sup> <sup>∝</sup>*ε<sup>t</sup>*�<sup>1</sup>*ε<sup>T</sup>*

To ensure positive definite *Ht*, *Rt* must be positive too.

Two requirements must be considered when specifying the form of *Rt*:

1.*Ht* must be positively defined because it is in the form of a covariance matrix.

2.All elements in the correlation matrix *Rt* must be equal to or less than one.

⋮ ⋱ 0 ⋯

*Ht* ¼ *DtRtDt* (3)

*βiqhit*�*<sup>q</sup>*fori ¼ 1*;* 2*;* 3*;* … *;* k (5)

*th* and *Rt* is the time variation

(4)

(6)

where Yt is the rupiah exchange rate against US Dollar in year t; Yt-n is the rupiah exchange rate against the US dollar in year t-n; Xt-n is the economic condition of Indonesia in year t-n; *α* is the constants; and *ϵ* is the error.

The model can be estimated using the ordinary least squares (OLS) method separately. The OLS method is widely used to estimate the linear regression parameter model. The OLS model used according to Gujarati [25] is as follows:

$$Y = \beta\_0 + \underbrace{\Sigma}\_t = \mathbf{1} \dots p \,\beta\_t \mathbf{X}\_t + \varepsilon \tag{2}$$

where *Y* is the dependent variable, *β<sup>0</sup>* is the intercept of the model, *X <sup>t</sup>* corresponds to the t explanatory variable of the model (j = 1 to p), and e is the random error with expectation 0 and variance σ<sup>2</sup> .


**Table 3.** *Data operational and sources.* *Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial… DOI: http://dx.doi.org/10.5772/intechopen.92275*

### **3.2 DCC method**

*Yt* <sup>¼</sup> *<sup>α</sup>*1*<sup>i</sup>* <sup>þ</sup>X*β*1*iYt*�*<sup>n</sup>* <sup>þ</sup>X*γ*1*iXt*�*<sup>n</sup>* <sup>þ</sup> *<sup>ϵ</sup>*1*<sup>t</sup>* (1)

*<sup>t</sup>* <sup>¼</sup> <sup>1</sup> … *<sup>p</sup> <sup>β</sup>tXt* <sup>þ</sup> *<sup>ε</sup>* (2)

Bloomberg database

Bank Indonesia

of Statistics, Central Bank

Central Bank of Indonesia

External sector statistics, Central Bank of Indonesia

External sector statistics, Central Bank of Indonesia

Central Bank of Indonesia

0 = before tapering off

Fund

of Statistics

of Indonesia

where Yt is the rupiah exchange rate against US Dollar in year t; Yt-n is the rupiah exchange rate against the US dollar in year t-n; Xt-n is the economic condi-

The model can be estimated using the ordinary least squares (OLS) method separately. The OLS method is widely used to estimate the linear regression parameter model. The OLS model used according to Gujarati [25] is as follows:

where *Y* is the dependent variable, *β<sup>0</sup>* is the intercept of the model, *X <sup>t</sup>* corresponds to the t explanatory variable of the model (j = 1 to p), and e is the random

.

8. NET\_EXPORT Net exports Indonesia Central Bureau

9. LIP\_INA Production index Indonesia Central Bureau

10. TB Balance of trade External sector statistics,

of foreign assets and the foreign ownership of

13. M2 Broad money Monetary sector statistics,

14. COMPRICE Commodity price index International Monetary

15. DUMMY Dummy tapering off 1 = after tapering off 2013

dividends from investments in other countries and net remittance flows from

**No. Variables Descriptions Data sources**

2. FDI Foreign direct investment CEIC database 3. LER\_LIRA Lira exchange rate to US dollar Bloomberg database 4. NFP Net foreign purchase Bloomberg database 5. LER Indonesia rupiah exchange rate to US dollar Bloomberg database 6. ARS Argentina peso exchange rate to US dollar Bloomberg database 7. PUAB Interbank rates External sector statistics,

1. Stock price Stock price in ISTANBUL MERVAL, Jakarta, NASDAQ

11. FINANCIAL Financial accounts. The domestic ownership

domestic assets

migrant workers

12. PRIMARY\_INCOME The net flow of profits, interests, and

16. AR *Autoregressive*

*Source: Authors, 2019.*

*Data operational and sources.*

**Table 3.**

**44**

tion of Indonesia in year t-n; *α* is the constants; and *ϵ* is the error.

*Y* ¼ *β*<sup>0</sup> þ *Σ*

error with expectation 0 and variance σ<sup>2</sup>

*Public Sector Crisis Management*

The DCC representation was introduced by Engle [26] to capture the empirically observed dynamic contemporaneous correlations of asset returns. This is the latest method that allows simultaneous variant modeling and conditional correlation from several series. The estimation consists of two steps. First, we estimate the conditional variance of each variable using the univariate autoregressive conditional heteroscedasticity (ARCH) procedure. Second, we use the standard regression residues obtained in the first step to model conditional correlations that vary over time.

The essence of this model is the covariance matrix (*Ht*), which is compiled into a diagonal matrix from standard conditional deviation (*Dt*) and matrix correlation containing the conditional correlations (*Rt*). In the DCC GARCH model, both the *Dt* and *Rt* models are designed to be time-varying. The DCC GARCH model is defined as:

$$H\_t = D\_t R\_t D\_t \tag{3}$$

where *Dt* is a diagonal matrix k x k of a standard deviation which has a different time of univariate GARCH with ffiffiffiffiffiffi *hi*,*<sup>t</sup>* p on the diagonal *i th* and *Rt* is the time variation of the correlation matrix.

$$D\_t = \begin{bmatrix} \sqrt{h\_{i,t}} & 0 & \cdots & 0 \\ 0 & \sqrt{h\_{i,t}} & \ddots & \vdots \\ \vdots & \ddots & \ddots & 0 \\ 0 & \cdots & 0 & \sqrt{h\_{i,t}} \end{bmatrix} \tag{4}$$

where

$$h\_{i,t} = o i\_i + \sum\_{p=1}^{P\_1} \alpha\_{ip} r\_{i-p}^2 + \sum\_{q=1}^{Q1} \beta\_{iq} h\_{it-q} \text{fori} = 1,2,3,\dots,\text{k} \tag{5}$$

$$R\_t = \begin{bmatrix} \mathbf{1} & P\_{12,t} & P\_{13,t} & \dots & P\_{1n,t} \\\\ P\_{12,t} & \mathbf{1} & P\_{13,t} & \dots & P\_{2n,t} \\\\ P\_{13,t} & P\_{23,t} & \mathbf{1} & \ddots & \vdots \\\\ \vdots & \vdots & \ddots & \ddots & P\_{n-1,n,t} \\\\ P\_{1n,t} & P\_{2n,t} & \dots & P\_{n-1,n,t} & \mathbf{1} \end{bmatrix} \tag{6}$$

Two requirements must be considered when specifying the form of *Rt*:


Therefore, *Rt* is structured as follows:

$$R\_t = Q\_t^{\*-1} Q\_t Q\_t^{\*-1} \tag{7}$$

$$Q\_t = (1 - a - b)\overline{Q} + \varkappa \epsilon\_{t-1} \varepsilon\_{t-1}^T + bQ\_{t-18} \tag{8}$$

where parameters a and b are scalars, *Q* is an unconditional covariance of standardized residues produced from univariate GARCH equations, and *Q* <sup>∗</sup> *<sup>t</sup>* is a diagonal matrix consisting of square roots of diagonal elements *Qt*:

$$\mathbf{Q}\_{t}^{\*} = \begin{bmatrix} \sqrt{q\_{i,t}} & \mathbf{0} & \dots & \mathbf{0} \\ \mathbf{0} & \sqrt{q\_{i,t}} & \ddots & \vdots \\ \vdots & \ddots & \ddots & \mathbf{0} \\ & \mathbf{0} & \dots & \mathbf{0} \\ & \mathbf{0} & \dots & \sqrt{q\_{i,t}} \end{bmatrix} \tag{9}$$

substitute inflation rate. We also clean up the stock return from the international market sentiment. Hence, following Fratzscher [16], we use trading volumeweighted average of S&P 500, FTSE 100, and Nikkei return as explanatory.

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

Normally, the exchange rate correlation already represents the interdependence

<sup>þ</sup> *<sup>β</sup>*2*d inf arg*,*<sup>t</sup>* � *inf us*,*<sup>t</sup>*

Variables within the model are coming from various sources. Our dependent variable is coming from the IFS. As in Engel (2002), we use the end-of-quarter nominal exchange rate. Other explanatories including money supply, consumer price index (CPI), and real GDP are obtained from the OECD database. As we compared the variables for each country with those of the United States, we have to ensure that the variables are in the same unit. Hence, money supply, consumer price index, and real GDP are used in growth. For money supply and GDP, we use quarterly growth. However, for CPI growth, we have to use the annual inflation rate instead of quarterly inflation rate for Indonesia, Turkey, and the United States, since we have to use interpolated annual GDP deflator as a proxy for change in price level for Argentina due to the unavailability of quarterly CPI data

For interest rate, ideally, we use policy rates for each country. While we use policy rate for Argentina and the United States, policy rate data is not available for Turkey and incomplete for Indonesia since Indonesia changed its policy rate from BI rate to BI7DRR (7-day reverse repo rate) in 2016; hence, we use money market

<sup>þ</sup> *<sup>β</sup>*3*d r*ð Þ *idn*,*<sup>t</sup>* � *rus*,*<sup>t</sup>*

<sup>þ</sup> *<sup>β</sup>*3*d r*ð Þ *tur*,*<sup>t</sup>* � *rus*,*<sup>t</sup>*

<sup>þ</sup> *<sup>β</sup>*3*d rarg*,*<sup>t</sup>* � *rus*,*<sup>t</sup>*

(13)

(14)

(15)

of stock market between countries. However, it might be overestimated due to fundamentals. Therefore, we perform an estimation to obtain fundamental factorfree exchange rate for country *i* in period *t* (*εi*,*t*Þ. To ensure stationarity, we run the

*3.3.2 Net correlation for exchange rate*

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

regression in first-difference form as in Eqs. (13)–(15):

þ *β*4*d yidn*,*<sup>t</sup>* � *yus*,*<sup>t</sup>*

þ *β*4*d ytur*,*<sup>t</sup>* � *yus*,*<sup>t</sup>*

þ *β*4*d yarg*,*<sup>t</sup>* � *yus*,*<sup>t</sup>*

*Exrate*: exchange rate in USD country *i* period *t. m*: money supply growth country *i* period *t. inf* : inflation rate country *i* period *t. r*: interest rate country *i* period *t. y*: real GDP market period *t.*

<sup>¼</sup> *<sup>α</sup>* <sup>þ</sup> *<sup>β</sup>*1*d marg*,*<sup>t</sup>* � *mus*,*<sup>t</sup>*

*d Exratearg*,*<sup>t</sup>*

Note:

for Argentina.

**47**

rate for both countries.

*d Exrate* ð Þ¼ *idn*,*<sup>t</sup> α* þ *β*1*d m*ð *idn*,*<sup>t</sup>* � *mus*,*t*Þ þ *β*2*d inf idn*,*<sup>t</sup>* � *inf us*,*<sup>t</sup>*

*d Exrate* ð Þ¼ *tur*,*<sup>t</sup> α* þ *β*1*d m*ð *tur*,*<sup>t</sup>* � *mus*,*<sup>t</sup>*Þ þ *β*2*d inf tur*,*<sup>t</sup>* � *inf us*,*<sup>t</sup>*

<sup>þ</sup> *<sup>ε</sup>tur*,*<sup>t</sup>*

<sup>þ</sup> *<sup>ε</sup>arg*,*<sup>t</sup>*

<sup>þ</sup> *<sup>ε</sup>idn*,*<sup>t</sup>*

The typical element*Rt* is *<sup>ρ</sup>ijt* <sup>¼</sup> *qijt* ffiffiffiffiffiffi *qiiqjj* <sup>p</sup> , and the matrix*Rt* will be a positive/constant. The K asset covariance matrix *Ht* is thus definite/constant and can be written as *Ht* ¼ *DtRtDt*.

#### **3.3 Net correlation after controlling fundamental**

#### *3.3.1 Net correlation for stock market*

Normally, stock market correlation already represents the interdependence of stock market between countries. However, it might be overestimated due to fundamentals. Therefore, we perform an estimation to obtain a fundamental factorfree stock return for country *i* in period *t* (*ε<sup>i</sup>*,*<sup>t</sup>*Þ. The estimation is in Eqs. (10)–(12):

$$\text{Stock}\_{\text{indo},t} = a + \beta\_1 \text{Balance}\_{\text{indo},t} + \beta\_2 \text{Int}\_{\text{indo},t} + \beta\_3 \text{Inf}\_{\text{indo},t} + \beta\_4 \text{World}\_t + \varepsilon\_{\text{indo},t} \tag{10}$$

$$\text{Stock}\_{\text{avg},t} = a + \beta\_1 \text{Balance}\_{\text{avg},t} + \beta\_2 \text{Int}\_{\text{avg},t} + \beta\_3 \text{Inf}\_{\text{avg},t} + \beta\_4 \text{World}\_t + \varepsilon\_{\text{arg},tt} \tag{11}$$

$$\text{Stock}\_{\text{tur},t} = a + \beta\_1 \text{Balance}\_{\text{tur},t} + \beta\_2 \text{Int}\_{\text{tur},t} + \beta\_3 \text{Inf}\_{\text{tur},t} + \beta\_4 \text{World}\_t + \varepsilon\_{\text{tur},t} \tag{12}$$

Note:

*Stock*: stock market returns country *i* period *t. Balance*: trade balance country *i* period *t. Int*: interest rate country *i* period *t. Inf*: inflation rate country *i* period *t. World*: world stock market period *t.*

To perform the estimation, we use several variables retrieved from various sources. We obtain the stock market index data for each country from Yahoo Finance. The data are available in monthly; hence, we calculate month-on-month (m-o-m) stock return from the data and then calculate the quarterly average from the obtained m-o-m return. We calculate the trade balance by subtracting the export value from the import value within period *t* in the US dollar denomination. These data are obtained from the International Financial Statistics (IFS). For interest rate, ideally, we use policy rates for each country. However, the data is not available for Turkey and incomplete for Indonesia because Indonesia changed its policy rate from BI rate to BI7DRR (7-day reverse repo rate) in 2016 and thus not comparable with the interest rate in the period earlier than 2016.

We obtain a year-on-year (y-o-y) quarterly inflation rate, which is publicly available from the Organization for Economic Co-operation and Development (OECD) database. Unfortunately, Argentina does not report monthly inflation rate before 2018. The annual inflation rate for Argentina is also not reported. As a result, we decided to interpolate the annual GDP deflator into quarterly deflator to

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

substitute inflation rate. We also clean up the stock return from the international market sentiment. Hence, following Fratzscher [16], we use trading volumeweighted average of S&P 500, FTSE 100, and Nikkei return as explanatory.

#### *3.3.2 Net correlation for exchange rate*

where parameters a and b are scalars, *Q* is an unconditional covariance of standardized residues produced from univariate GARCH equations, and *Q* <sup>∗</sup>

> 0 ffiffiffiffiffiffi *qi*,*t* p

⋯ 0 ⋱ ⋮ ⋱ 0 0 ffiffiffiffiffiffi *qi*,*t* p

p , and the matrix*Rt* will be a positive/constant. The

⋮ ⋱ 0 ⋯

K asset covariance matrix *Ht* is thus definite/constant and can be written as *Ht* ¼ *DtRtDt*.

Normally, stock market correlation already represents the interdependence of stock market between countries. However, it might be overestimated due to fundamentals. Therefore, we perform an estimation to obtain a fundamental factorfree stock return for country *i* in period *t* (*ε<sup>i</sup>*,*<sup>t</sup>*Þ. The estimation is in Eqs. (10)–(12):

*Stockindo*,*<sup>t</sup>* ¼ *α* þ *β*1*Balanceindo*,*<sup>t</sup>* þ *β*2*Intindo*,*<sup>t</sup>* þ *β*3*Infindo*,*<sup>t</sup>* þ *β*4*Worldt* þ *εindo*,*<sup>t</sup>* (10) *Stockarg*,*<sup>t</sup>* ¼ *α* þ *β*1*Balancearg*,*<sup>t</sup>* þ *β*2*Intarg*,*<sup>t</sup>* þ *β*3*Inf arg*,*<sup>t</sup>* þ *β*4*Worldt* þ *εarg*,*tt* (11) *Stocktur*,,*<sup>t</sup>* ¼ *α* þ *β*1*Balancetur*,*<sup>t</sup>* þ *β*2*Inttur*,*<sup>t</sup>* þ *β*3*Inftur*,*<sup>t</sup>* þ *β*4*Worldt* þ *εtur*,*<sup>t</sup>* (12)

To perform the estimation, we use several variables retrieved from various sources. We obtain the stock market index data for each country from Yahoo Finance. The data are available in monthly; hence, we calculate month-on-month (m-o-m) stock return from the data and then calculate the quarterly average from the obtained m-o-m return. We calculate the trade balance by subtracting the export value from the import value within period *t* in the US dollar denomination. These data are obtained from the International Financial Statistics (IFS). For interest rate, ideally, we use policy rates for each country. However, the data is not available for Turkey and incomplete for Indonesia because Indonesia changed its policy rate from BI rate to BI7DRR (7-day reverse repo rate) in 2016 and thus not

We obtain a year-on-year (y-o-y) quarterly inflation rate, which is publicly available from the Organization for Economic Co-operation and Development (OECD) database. Unfortunately, Argentina does not report monthly inflation rate before 2018. The annual inflation rate for Argentina is also not reported. As a result,

we decided to interpolate the annual GDP deflator into quarterly deflator to

comparable with the interest rate in the period earlier than 2016.

diagonal matrix consisting of square roots of diagonal elements *Qt*:

ffiffiffiffiffiffi *qi*,*t* p 0

ffiffiffiffiffiffi *qiiqjj*

*Q* <sup>∗</sup> *t* ¼

**3.3 Net correlation after controlling fundamental**

*Stock*: stock market returns country *i* period *t. Balance*: trade balance country *i* period *t. Int*: interest rate country *i* period *t. Inf*: inflation rate country *i* period *t. World*: world stock market period *t.*

The typical element*Rt* is *<sup>ρ</sup>ijt* <sup>¼</sup> *qijt*

*Public Sector Crisis Management*

*3.3.1 Net correlation for stock market*

Note:

**46**

*<sup>t</sup>* is a

(9)

Normally, the exchange rate correlation already represents the interdependence of stock market between countries. However, it might be overestimated due to fundamentals. Therefore, we perform an estimation to obtain fundamental factorfree exchange rate for country *i* in period *t* (*εi*,*t*Þ. To ensure stationarity, we run the regression in first-difference form as in Eqs. (13)–(15):

$$\begin{split}d(\operatorname{Extrate}\_{\operatorname{idn},t}) &= \alpha + \beta\_1 d(m\_{\operatorname{idn},t} - m\_{\operatorname{us},t}) + \beta\_2 d \left(\inf\_{\operatorname{idn},t} - \inf\_{\operatorname{us},t}\right) + \beta\_3 d(r\_{\operatorname{idn},t} - r\_{\operatorname{us},t}) \\ &+ \beta\_4 d \left(y\_{\operatorname{idn},t} - y\_{\operatorname{us},t}\right) + \varepsilon\_{\operatorname{idn},t} \end{split}$$

(13)

(14)

(15)

$$\begin{split}d(\text{Extrate}\_{\text{tur},t}) &= \alpha + \beta\_1 d(m\_{\text{tur},t} - m\_{\text{us},t}) + \beta\_2 d \left(\text{inf}\_{\text{tur},t} - \text{inf}\_{\text{uv},t}\right) + \beta\_3 d(r\_{\text{tur},t} - r\_{\text{us},t}) \\ &+ \beta\_4 d \left(y\_{\text{tur},t} - y\_{\text{us},t}\right) + \varepsilon\_{\text{tur},t} \end{split}$$

$$\begin{split}d\left(\text{Exrate}\_{\text{arg},t}\right) &= a + \beta\_1 d \left(m\_{\text{arg},t} - m\_{\text{us},t}\right) + \beta\_2 d \left(\text{inf}\_{\text{arg},t} - \text{inf}\_{\text{us},t}\right) + \beta\_3 d \left(r\_{\text{arg},t} - r\_{\text{us},t}\right) \\ &+ \beta\_4 d \left(y\_{\text{arg},t} - y\_{\text{us},t}\right) + \varepsilon\_{\text{arg},t} \end{split}$$

Note:

*Exrate*: exchange rate in USD country *i* period *t. m*: money supply growth country *i* period *t. inf* : inflation rate country *i* period *t. r*: interest rate country *i* period *t. y*: real GDP market period *t.*

Variables within the model are coming from various sources. Our dependent variable is coming from the IFS. As in Engel (2002), we use the end-of-quarter nominal exchange rate. Other explanatories including money supply, consumer price index (CPI), and real GDP are obtained from the OECD database. As we compared the variables for each country with those of the United States, we have to ensure that the variables are in the same unit. Hence, money supply, consumer price index, and real GDP are used in growth. For money supply and GDP, we use quarterly growth. However, for CPI growth, we have to use the annual inflation rate instead of quarterly inflation rate for Indonesia, Turkey, and the United States, since we have to use interpolated annual GDP deflator as a proxy for change in price level for Argentina due to the unavailability of quarterly CPI data for Argentina.

For interest rate, ideally, we use policy rates for each country. While we use policy rate for Argentina and the United States, policy rate data is not available for Turkey and incomplete for Indonesia since Indonesia changed its policy rate from BI rate to BI7DRR (7-day reverse repo rate) in 2016; hence, we use money market rate for both countries.

### **4. Result and analysis**

#### **4.1 Indonesia trade link with Argentina and Turkey**

The following figures summarize the share of Indonesian export and import to and from Turkey and Argentina, as well as with the United States as a benchmark. **4.2 Indonesia FDI link with Argentina and Turkey**

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

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

*Share of foreign direct investment to Indonesia. Source: CEIC database.*

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**

**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.

*Stock price index graph (United States,Turkey, Argentina, Indonesia). Source: Authors, 2019.*

*4.3.1 Measuring contagion with correlation*

and **Table 4**).

**Figure 5.**

**49**

**Figure 4.**

Argentina is also important.

Based on **Figure 4**, the share of FDI from Turkey to Indonesia is from 0 to 0.749%. The range is higher than from Argentina, which is only 0–0.001%. The FDI trend from the United States is declining, while those from China are increasing. It is also necessary to be aware of the decline in FDI flows from countries affected by the Turkey and Argentina crises (direct and indirect). Besides that, exploring the FDI flows between China and Turkey, China and Argentina, the United States and Turkey, the United States and Argentina, Europe and Turkey, and Europe and

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

The United States is one of the Indonesia's main trade partners, besides China. Indonesia's export share to the United States has a declining trend. Even so, the value is higher than the value of the US product imports into Indonesia. In 2018, the value of Indonesia's exports to the United States was USD 18,439,760.7 thousand, while the value of imports from the United States to Indonesia reached USD 10,176,226.6 thousand.

Indonesian exports to Argentina and Turkey tend to be stable. **Figure 2** shows that in the last 3 years, imports from Argentina were higher than imports from Turkey and the Philippines. Even the value of Argentine imports to Indonesia is higher than Indonesian exports to Argentina. The Indonesian Ministry of Trade data from 2014 to June 2019 shows that the trade balance resulting from Indonesia's trade activities was negative throughout the period (**Figure 3**).

**Figure 2.** *Share of Indonesia export to several countries. Source: CEIC database.*

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