Monetary Policy Response On Exchange Rate Volatility In Indonesia

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Current issue

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JOURNAL OF CONTEMPORARY ECONOMIC AND

BUSINESS ISSUES

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Volume 1, No. 2, 2014 Print ISSN 1857-9094 online ISSN 1857-9108

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CONTENTS

Author Title

Hyrije Abazl-Alili

Page

Milan Eliskovskl

Ferry Syarifuddin, Noer Azam Achsani, Oedi Budiman Hakim, TonlBakhtiar

Matevz Obrecht

David Widdowson

Neda Popovska-Kamnar

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U DC: 334. 72: 005.342) : 303.025( 4-11 )

The determinants of capital buffer in the macedonian banking sector (/ArticleContents/JCEBl/JCEBl_2/spisanie Milan Eliskovski.pdf)

UDC: 336.71: 658.14) :303.025(497.7)"2003/2013"

Monetary policy response on exchange rate volatility in Indonesia (/ArticleContentslJCEBl/JCEB1_2/spisanie Ferry Syanfuddin.pdQ UDC: 339.743: (338.23:336.74(594)

Would internalisation of external costs change cost-competitiveness of different energy sources? (/ArticleContents/JCEBVJCEB1_2/spisanie Matevu017E Obrecht pdf)

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The use of electronic money and its impact on monetary policy (/ArticleContents/JCEBllJCE81_2/spisanie Neda Popovska-Kamnar.pdf) UDC: 336.74:004.031.4):338.23:336.74(4)

19-33

35-54

5 5 -66

67-77

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MONETARY POLICY RESPONSE ON EXCHANGE RATE

VOLATILITY IN INDONESIA

Ferry Syarifuddin

1

Noer Azam Achsani, Ph0

2

Dedi Budiman Hakim, Ph0

3

Toni Bakhtiar, Ph0

4

Abstract

High fluctuation of exchange rate in short honzon is obviously making economic activity

more risky as uncertainty rises. As it is not good for the economy, then there should be a

systematic and measured policy to mitigate the foreign exchange fluctuations and to minimize the fluctuations. as well as to drive it to its fundamental value. In this part this research measures how persistent the exchange rate fluctuation m Indonesia 1s. and how are thus the central banks able to perform appropriate monetary policy. especially in determining their policy interest rate. or to make foreign exchange intervention to stabilize the exchange rate. In this study, USDllDR volatility is investigated usmg TGARCH approach. The result reveals that, USDllDR volatility in Indonesia is obviously persistent. This study also presents the outcomes of effectiveness of policy response by the Central Bank. Foreign-exchange sale interventions by the Central Bank lead to a slight USDllDR decrease Whatever Bank of Indonesia's efforts to exert a stabilizing effect of foreign exchange interventions, the results are inconclusive.

Keywords: exchange rates. foreign-exchange intervention. GARCH

JEL Classification: F31 . E52. CSB

Introduction

Following global economic integration . rapid growth of international trade and capital flows development push exchange rate transactions to grow even more as financial markets also have been playing an increasing role in funding the international trade and foreign exchange speculation. As a consequence. it is noted that daily foreign-exchange turnover

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is exponentially increasing followed by higher volatihty. This 1s supported by Kocenda and Valachy (2006) that confirm volatility increases under a less tight floating regime than pegged regime Using TARCH approach, they claim asymmetric decreasing effects of news on exchange rate volatility, as well as contemporaneous impact of the interest differential. This 1s also supported by K6bor and Szekely (2004). Using Markov regime-switching model (low volatile regime and high volatile regime). they confirm that volatility is lower in lowly volatile periods. Meanwhile, Buli (2005) arrives at an opposite conclusion that financial liberalization significantly contributes to the stability of the exchange rates in all four countries examined. Meanwhile a study by Kocenda (1998) finds that the Czech exchange rate is less volatile with a wider fluctuation band in EU countnes.

Over the last few decades, many countries have been adopting floating exchange rate regime. As a consequence, these respected countries have been facing wide fluctuations of their exchange rates as capital flows freely coming in or out the countnes. Nevertheless. free floating exchange rate will be needed by independent monetary authority to conduct independent monetary pohcy. Floating exchange-rate has also been regarded as an automatic stabilizer which is able in some cases to rebalance the unbalanced economy. However. many countries usually are reluctant to allow their currencies to fluctuate because of the potential for sharp exchange rate movements to exacerbate inflationary pressures and financial sector vulnerabilities (Calvo and Reinhart. 2000). Foreign exchange rates can move to undesired long-run fundamental levels (overshoot phenomenon) in the short horizon which is not good for domestic economy if 1t persists.

Besides a free foreign exchange market mechanism. foreign-exchange regime adopted plays a significant role in the dynamics of the foreign-exchange rates. The failure of Bretton Woods exchange rate system followed by free floating exchange rate, has led to significant fluctuation in both real and nominal exchange rates. Exchange-rate fluctuation has widened since then. The liberalization of capital flows and the associated intensification of cross-border financial transactions also appear to have amplified the volatility of exchange rates and the misalignment movement of exchange rate from its fundamental value. The increase in exchange rate volatility and misalignment is widely believed to have detrimental effects on international trade and capital flows, thus having a negative impact on domestic economy.

The exchange-rate fluctuation has a close relationship with the supply and demand of foreign exchange which is ignited by economic fundamentals such as inflation rates, productivity, real interest rates, consumer preference, government trade policy, and market sentiments such as news about future market fundamentals and traders' opinion about future exchange rates. In the short run horizon, foreign-exchange transactions are dominated by transfers of assets (bank accounts. government securities) that respond to differences in real interest rates and to shifting expectations of future exchange rates. Over the medium run, exchange rates are governed by cyclical factors such as cyclical fluctuations in economic activity. Over the long-run, foreign-exchange transactions are dominated by flows of goods, services, and investment capital, which respond to forces such as inflations rates, investment profitability, consumer taste, productivity, and government trade policy (Carbaugh, 2013).

Indonesia is among the countries that adopted a free floating exchange rate regime. By freely floating exchange rate and its position as a small open economy, Indonesia' exchange rate movements are strongly influenced by capital flows and net export's proceeds. Although Indonesia adopted ITF, it does not mean that rapid exchange-rate fluctuation 1s desirable.

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-Hence, existence of central bank optimal monetary response (1.e. foreign exchange intervention) is needed to mitigate the exchange-rate fluctuations and dnve the exchange rate for the long-term equ1l1brium exchange rate. For example, Bank Indonesia applied various monetary policies including exchange-rate intervention policy to stabilize USO/ IDR and drive 1t to its fundamental value. Hence, even though the IDR performed worse between 2011 and 2013. Rupiah experienced the lowest volatility when compared to the other currencies of regional economies.

Since Indonesia has adopted a floating exchange rate regime in the wake of the 1997 economic crisis, the value of the rupiah continues to be volatile and determined by fundamental and speculative forces. If exchange rate fluctuations are not anticipated, increasing exchange rate volat1hty may lead risk-averse agents to reduce their international trading activities (Chit, Rizov, and Willenbockel. 2010). As a small open economy, sometimes the Rupiah movements overshoot 1n a short honzon ignited by market sentiments. This cond1t1on 1s deteriorated when macroeconomic indicators announcements show unfavorable development. Countering this, Bank Indonesia as the monetary authority, has had exchange-rate interventions in order to prevent sharp currency movements. These efforts should be made in order to avoid domestic price fluctuations, as well as to promote export competitiveness through the maintenance of a low, stable exchange rate. In this regard, Bank Indonesia's foreign-exchange intervention has been to enter the foreign-exchange market so as to stabilize market expectations, calm disorderly market, and limit unwarranted short term exchange rate movements because of temporary shocks (Warj1yo, 2005).

Exchange rate is determined by supply and demand of foreign exchange in a free foreign exchange market that causes the exchange rate movements. i.e. to appreciate or depreciate. The exchange rate might be very volatile in a short period. Indeed, exchange rates can fluctuate by several percentage points even during a single day. Exchange rate volatility is commonly accepted to have a negative effect on domestic economy passed through international trade and capital flows. The fluctuations are ignited directly through uncertainty and adjustment costs, and indirectly through its effect on the allocation of resources and government policies. Investors rely on favorable economic environment to invest and avoid an uncertain climate. Exchange-rate volatility worsens the economic climate as it is interpreted as uncertainty; it becomes a key input to many investment decisions and portfolio design. The first Basie Accord in 1996 suggests financial risk management as a tool to forecast volatility as compulsory exercise for many financial institutions around the world. Indeed, monetary and financial authorities often rely on market estimates of volatility as an indicator of the vulnerability of financial markets and the economy.

Discussing exchange-rate volatility which is characterized by mild and volatile periods 1s as interesting and important when measuring it. Two proposed processes to measure volatility are presented in autoregressive conditional heteroscedasticity (ARCH) model developed by Engle (1982) and general autoregressive conditional heteroscedasticity (GARCH) model developed by Bollerslev (1986). These models have been proven to provide a good fit for many exchange rate series in literature, allowing volatility shocks to persist over time by imposing autoregressive structure on the conditional variance. This persistence is consistent with periods of relative volatility and tranquility in returns and it is employed to explain the non-normalities in exchange rate series. As it is crucial to observe and investigate the underlying reasons for volatility as well as measuring it, this dissertation tries to measure and explain the volatility in the USD/IDR exchange rate utilizing TGARCH model.

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The study is organized as follows Section 1 provides the background of the research. Section 2 briefly discusses the theory and empirical evidences of volat1llty of exchange rate, including an econometric framework for measuring exchange rate volat1l1ty Section 3 provides the applications of exchange-rate volatility models to Indonesian case. Section 4 offers conclusions

The volatility of financial time series data : the theory and empirical

evidences

It 1s commonly admitted that rapid volatility in financial time series data, is caused by the presence of statistically significant correlations between observations. Financial data generally characterized by heteroscedastic1ty in the data showing time-varying volattltty. Further explanation can be found in Ozturk (2006), Harris and Sollis (2003). Meanwhile, as ouUined by Brooks (2002). conventional time series model such as:

is also unable to explain a number of important features common to much financial data, including.

•

Leptokurtosis

-

that 1s, the tendency for financial asset returns to have distributions that exhibit fat tails and ･ｸ｣ｾｳｳ＠ peakedness at the mean.

• Volatility clustering or volatility pooling - the tendency for volatility in financial markets to appear 1n bunches. Thus large returns (of either sign) are expected to follow large returns, and small returns (of either sign) to follow small returns.

•

Leverage effects

-

the tendency for volatility to rise more following a large price fall than following a price rise of the same magnitude.

As heteroscedasticity has been commonly existing in financial time series return data, Engle (1982) proposes a model in which a variance to be modeled and therefore instead of considering heteroscedasticity as a problem to be corrected. the conditional variance of a time series is a function of past shocks. Generally, financial price changes deviate consistently from log normality when the normality is considered as unconditional distribution of the series will be non-normal. There are more very large changes and (consequently) more very small ones than a lognormal distribution call. Brooks (2002) argued that the phenomenon called "fat tails or leptokurtic" as excess kurtosis. This fact explains that the lognormal diffusion model fails because of price overshoot occasionally from one level to

another without trading at the prices in between. He also explains the characteristic known as volatility clustenng, as shown by the fact that large changes of the data tend to follow large changes. of either sign, and small changes tend to follow small changes. Studies by Mussa (1979), Friedman and Vandersteel (1982), and Hsieh (1988) find that means and variances of daily USO exchange rate series change over time. Meanwhile, study by Hsieh (1989) finds that GARCH (1, 1) and EGARCH (1, 1) are able to overcome conditional heteroscedast1c1ty problem from daily exchange-rate movements in 5 countries examined. Moreover, he claims that EGARCH is fitted to the data slightly better than standard GAR CH using a variety of diagnostic checks. Furthermore, studies by Bollerslev (1987), Hsieh (1989) and Baillie and Bollerslev (1989b) conclude GARCH (1.1) provide good description for most exchange rate series under free floating exchange rate regime.

\fonnar;. pulic.1 rnpume 1111

ｴＧＮ｜､ｴＱＱＱＱｾｴＧ＠

rate 11>/ati/m· 111 ltt.lu11,•sia

-Oz turk (2006); 8a1llte and Bollerslev (1 989a). Pagan and Schwert ( 1990) find that time series appear to have unit roots and warn against non stationarity problem Positive (negative) correlation between consecutive price changes lowers (raises) measured volattltty relative to the true value. They also find that there is trade-off between increasing accuracy by using daily data with a larger number of observations and losing accuracy because of the relatively greater effect of transitory phenomena on daily prices. There is an assumption that, volatiltty persistence, which 1s highly significant in daily data, weakens as the frequency of data decreases. On the other, Drost and N1jman (1993) find opposite conclusion on their study. They insist that the structure of the volatility does not depend on frequency of the data used. Therefore, he argues that different frequent data such as hourly, daily, or monthly intervals in the data would result in the same volatility. However, important findings by Diebold (1988), Baillie and Bollerslev (1989b) show that conditional heteroscedast1c1ty disappears if the sampling time interval increases to infinity.

Measuring volatility: ARCHIGARCH model

As outlined by Brooks (2002), most financial time series data exh1b1t periods of unusually large volatility followed by periods of relative tranquility which called heteroscedasllc1ty phenomenon. To measure exchange-rate volatility within this phenomenon, the most popular time-varying volatility model or non-linear model is widely used such as Arch model. This model was pioneered by Engle (1982).

In this part of this dissertation , the exchange rate volatility is investigated within the ARCH family models. Threshold GARCH (TGARCH) is chosen to model the exchange rate volatility clustering where large changes in returns are likely to be followed by further large changes in addition to include leverage effect. GARCH itself estimates conditional volatility by forming a weighted average of a long term average (the constant term), from information about the volatility observed in the previous period (the ARCH term), and the forecasted variance from the last period (the GARCH term). In order to estimate models from the ARCH family, maximum likelihood is employed to find the most likely values of the parameters given the actual data.

Therefore, to explain the fluctuation/volatility of the exchange rate in Indonesia TGARCH approach is implemented. TGARCH model is relevant to be applied in exchange-rate model as it assumed that downward movements in the market are less volatile than upward movements of the same magnitude (a kind of irreversibility) a TGARCH model can be specified. The 'T' stands for threshold - a TGARCH model is a GARCH model with a dummy variable which is equal to 0 or 1 depending on the sign of ｵｲ Ｍ ｾ ﾷ＠ A TGARCH (1, 1) model is specified as:

Foreign exchange volatility: the empirical literature

As this rising exchange-rate volatility would hamper domestic economy through international trade performance and inflation through its power of pass-through , measuring exchange rate volatility has been an important area of research as supported by empirical study

- f't'rn \'rnr1J11dd111, \ ol'r . l:a111 . le Jm1111. /)(•c/1 !111d1111<111 l/aJ.1111, fom /JoJ..'111111

Bakhromov (2011 ), Boar (2010). and further study by Fountas and Bredin ( 1998). They find evidence that exchange-rate volat1hty has a negative effect on real export in the short run on in the respective countries examined. While

rn

the long run, its influence 1s insignificant. A study by Musyok1 , Pokhanyal , Pundo (2012) in Kenya. concludes that real exchange rate volatility has a negative impact on economic growth in Kenya economy. S1m1lar research is elaborated by Dell'Ariccia ( 1999) investigating the effect of exchange rate volatility on bilateral trade flows. and further investigation of the impact of exchange rate volatil,ty on the volume of bilateral exports 1s done by Baum, Caglayan, Ozkan(2004). and Choudhry (2005), applying the GARCH model for measuring volatility. Further study by Musyok1, Pokhariyal, Pundo (2012) also concludes that excessive exchange-rate volatility in Kenya generally exhibited an appreciation and volatility trend that undermines Kenya's International trade competitiveness. With respect on exchange rate volatility, a study by Fountas and Bredin (1997) also found evidence that in the short run the exchange rate volatility and the associated uncertainty has a negative effect on real export, while in the long run it is insignificant. On the other hand, opposite conclusion is found in the study by Mahmood , Ehsanullah. and Ahmed (2011) in Pakistan. They indicate the presence of positive impact of exchange rate volatility on GDP. growth rate and trade openness. Meanwhile, he finds negative impact of exchange rate volatility on foreign direct investment. Further study for G-7 countries by Abdur and Chowdhury (1997) find result indicating the exchange-rate volatility has a significant negative impact on the volume in each respective country. This implies that exchange-rate uncertainty as a response to excessive exchange-rate volatility, reduces their activities, changes prices. and shifts sources of demand and supply.

The magnitude of exchange rate dynamics within countries depends not only on macroeconomic conditions but also on the exchange-rate regime chosen by them. Most countries prefer price stability and therefore avoid rapid change of their exchange rate as literature largely confirms a negative relationship between exchange-rate volatility and economic growth. Actually, there are several reasons why a country chooses a specified exchange-rate regime or even prefers adopting a fixed exchange rate regime. One of them is mainly based on its own interest to protect their domestic interest even though other countries maz feel unhappy with the choice. Some of them think that tighter exchange-rate regime is safer to ensure domestic price stability environment. This 1s confirmed by Baxter and Stockman (1989). While Ghosh, Gulde, and Wolf (2003) also support that reason as pegged regimes is no worse than that of floating regimes. Supported by overshooting theorem By Dornbush, they test for speed of convergence within Purchasing Power Parity (PPP) theorem and their findings confirmed a positive significant coefficient for exchange rate volatility, meaning that the higher the exchange rate volatility, the stickier the prices are.

With regard to monetary policy, central banks will continue to intervene in foreign-exchange markets by trading directly or indirectly to mitigate the high exchange-rate volatility. Orlowski (2003) examines the impact of monetary policies on exchange rate risk premiums and inflation in the Czech Republic, Hungary, Poland, and Slovakia . Republic Poland and Hungary confirm the success of these countries. in towering inflation, rather than exchange rate volatility. Meanwhile, Dominguez ( 1998) suggests that official purchases of the dollar increase the exchange rate volatility. He also examines the effects of US, German and Japanese monetary and intervention policies on dollar-mark and dollar-yen exchange rate volatility over the 1977-1994 periods, which confirm that the presence of a central bank 1n the foreign exchange market influences volatility. Another study employing FIGARCH model by Beine, Benassy-Quere and Lecourt (1999), finds that traditional GARCH estimations

,\/(Jfll'ltJ0 coli<,1 rnpu111 c c>ll n1/11111ec · rdft ' lcl/(lfi/ifl 111 /11Jo11ni11 -tend to underestimate the effects of central bank interventions

With regard to the effectiveness of interventions . studies in Turkey by Domac and Mendoza (2002) Agcaer (2003). ｇｵＱｭ｡ｲｾ･ｳ＠ and Karacadag (2004) and Akinc1. Culha Oz1_{ale , and}

Sahinbeyoglu (2005a) and (2005b), employing EGARH model. suggest that overall central bank auctions have reduced the conditional variance. However, when the impact of auctions 1s studied separately. the reduction of volatility 1s a result of sales and purchase operations which do not seem to have statistically significant effect on volatility of exchange rate. The interesting finding also confirms that the overnight interest rate has a negative effect on exchange rate volatility. Furthermore, Agcaer (2003) finds that the presence of the central bank matters, and foreign exchange auctions and direct interventions have a favorable impact on both the level and volatility of exchange rates However opposite result are found by Domac and Mendoza (2002). They find that purchases have a positive effect on the level of exchange rates, while sales have no such significant effect.

On the other hand, Guimaraes and Karacadag (2004), reveal that neither foreign exchange sales nor purchases appear to be significant 1n affecting exchange rate level. When variance equations are examined, only the foreign exchange sales are found to be reducing volalthty in the short-term, but increasing it in the long-term. Other study by Akinc1, Culha. Ozlale. and Sahinbeyoglu (2005a) and (2005b) using probit analysis and Granger causality tests to analyze the main motivation of CBRT interventions, finds that. there is two-way causality between sale interventions and volatility.

Application of foreign-exchange volatility model to Indonesia

Measure of volatility

As previously explained, unlike real sector data, heteroscedastic1ty exists in many financial series including foreign exchange which do not have constant mean and variance (where). It also exhibits phases of relative tranquility followed by periods of high volatility. An econometric model that can encounter this problem in time-series models is the autoregressive conditional heteroscedasticity (ARCH) or (GARCH).Utilizing TGARCH models, this study tries to measure and explain the volatility in the USD/IDR exchange rate level.

Regarding the dependent variable, i.e .. the volatility of exchange rates, this dissertation employs the Threshold General Autoregressive Conditional Heteroskedasticity (TGARCH) model. This model comprises a leverage term that allows for the asymmetric effects of good and bad news. The general TGARCH (p,q) model is specified as·

p Q

re

=

a

0

+

I

a;r1 _,

+

I

b1Ec-i; Ee - N(O,

al)

1=1 i=O

p q

a?=

w

+

L

a;el- 1

+

LJ31<1c

2-1

+

(dc-1El- 1

l= l i=l

- Ff!rl\' Swm111dd111, \ oa. 1:11111 . l l lis11111. L>t!.lt Jt11,/11111111 I lak1m. /um /111V1tu11 Where

r,

the exchange rate change over t\vo consecutive trading days

ｯｾ＠

,

the cond1ltonal variance that is a functron Of not only the previous realizations Of E,, but

'

also the prevrous cond1t1onal variances and the leverage term

The leverage term in this model 1s the dummy variable d

1_1 which equals 1 1n the case of a

negative shock (£,_₁S 0) and 0 in the case of a positive shock (t-1 > 0). Thus, the positive value of the coefficient ｾ＠ indicates an increased conditional variance by E21-1 in the case of negative shocks or news that occur at time t-1 . while the negative value of coefficient indicates a decreased conditional variance The additional restriction

i. ,

iP

"

₁ is a sufficient and necessary condition for stability of the cond1t1onal variance. .

The most appropriate ARMA(P,Q) model of the exchange rate return is estimated using the Box-Jenkins methodology (Box and Jenkins, 1976) in order to get a properly specified model and correctly conditioned volatility. Then the LJung-Box Q-test is applied to test squared residuals of the ARMA(P,Q) model for the presence of conditional heteroskedasticity (lJung and Box , 1978). The next step is to identify the orders of the TGARCH(p,q) process by experimenting with different orders p and q, estimating the whole ARMA(P,Q)-TARCH (p,q) model is estimated using the maximum likelihood estimation where the log-likelihood function has the form

Finally, the standardized residuals are diagnosed by applying the LJung-Box Q-test and the LM test for the presence of an ARCH process (Eng le, 1982). If the estimated model is a correct one, then these residuals should be white noise and no further GARCH process should be present.

Overview of the data

A broad consensus has emerged that nominal exchange rates over the free float penod are best described as non stationary, or specifically I ( 1 ), type processes· see e.g. Baillie and Bollerslev (1989b). Therefore in this empirical study, exchange rate series is calculated as the daily difference in the logarithm form:

Dlfx

=

lnfx, - lnfx, _1

::!

fxr ) -l

=\.tx1-1

The foreign exchange rate data (fx) are the daily market foreign exchange closing rate for 1 USO. Data consist of daily prices from 3 January 2008 and 31 December 2013. for a total of 1,564 observations excluding weekends and holidays. In fact, the sampled period offers a clear picture as 1t includes both appreciation and depreciation periods, and both selling and purchasing foreign-exchange interventions by 81.

Graphical illustration of the data in Figure 1 displays volatility clustering which means that there are periods of high and low variance.

l/1 >111•r,1n pvlin ft 1po111t' 1111 t \dum.;c· full ' 10/atilit\ 111 /11do11ni<1

-Figure 1. Volatility in USO/IDR series (daily return) R_SPOT

6

4

2

0

-G

.. a ..- '..._._.,.__ __ , ... " - r - " r--· ｾｾｾｾ＠ ＭｾｾｾＭ ﾷ＠ ｾｾｾＮＮＭＮ＠

11 UI IV I 11 tll IV I 11 Ill IV I II llt IV I II fU IV I ti IU IV

2008 2009 2010 201 I 201:.? 2013

Source: Author's calculations

Before modeling time series data . it is important to check the stat1onanty of data. In order to test the stationarity of the series three different unit root tests. ( 1) the Augmented Dickey-F uller (AODickey-F) test with optimal lag length determined by both the Schwarz Info Criterion and Aka1ke Info Criterion . (2) the Phillips-Perron (PP) test. and (3) the Kwiatkowski-Ph1ll1ps-Schmidt-Shin (KPSS) test are employed. While the ADF and

PP

test stat1st1cs test the null hypothesis that exchange rate return series contains a unit root. KPSS statistics test the null hypothesis that series is stationary. The tests are repeated w ith constant term and with constant and trend terms. Table

1

displays the results of the tests and all tests indicate the stationarity of the return of the foreign exchange rate series denoted with

R

_Spot₁1(0).

Table 1. Unit root tests Null J lypothesi!>: R SPOT ha:. a unil

root

Exogenous: Consl:int

ｾＱｧ＠ Length: 0 ( Automallc - ba,,.:J on SIC. m:ixlag

=

ｾＳ＠

1-ｾＱ｡ＱｩｳＱｩ｣＠ Prob •

Augmented -47.25723 0.000 1

Dickcy-Ｑ ﾷｵ Dickcy-ＱDickcy-Ｑ｣ｲDickcy-Ｑ｣ＺｾDickcy-Ｑ＠

statistic

Source: Author's calculations

unit rool Exogcm111'. ｃｯｮｾｴＮＱＱＱＱ＠

H::mJ,, iJ1h 19 (:\c\\ cy-\\'c,l autom:111c) u:.ing J);mlcu l;cmcl

,\Jj 1-s1::11 Prob ..

Null I ｬｾｬＧｬ＼ｬｬｨ｣ＺＬ ｩｳＺ＠ R SPOT

ｨ｡ｾ＠ :i unil root Exug.:m111>. ｃＧｯｮｾ ｴＮ ｭｴ＠

Ba11J,,jJ1h. 19 Ｈｲ｜＼ＺＱ｜ ＮＺｾﾭ

\\' c't :1utom.111c) u,111g Bartlcll kcmcl

LMStat

Philips- -46.63928 0.000 1 K1\ iatko\\Ski· 0.29534

Prn·on

Philips-S.:hmidt-lc:st Shin tc5t ｾ ｴ｡ｴｩ ｾｴｴＮＺ＠

ｾｬＴＱ ｴｩｾＱｩ｣＠

Additionally, Table 3 reports the ljung-Box-Pierce a statistics of autocorrelation of the deviations and the squared deviations of exchange rate series from its sample mean. Ljung-Box-Pierce a statistics carries out the Breusch-Godfrey Lagrange multiplier test for high-order serial correlation. While the a-statistic of the deviations are employed to detect autocorrelation. a-statistic of the squared deviations (a2) , are employed to test the volatility clustering or ARCH effects. For the exchange rate series, the statistics are calculated for lags up to 24 days. According to the results, there is a serial correlation and

0

2 _statistic

displays strong evidence of ARCH effect. The null hypothesis of the test is that there is no

[image:20.642.32.583.86.654.2]

- Fern· S111r11111/tl111 \ v1·1 l:t1111. 1, 11.'''"'· l>edt H11d11n.111

ｊＯＬＱｾｭＱＮ＠

lu111 /J,u,ltt1111

serial correlation in the residuals up to the spec1f1ed order. Re1ection of the null hypothesis

1mphes volatility clustering 1n the series

Impact of Central Bank's Foreign Exchange Interventions

Generally, the literature investigating Indonesia experiences focused on the effectiveness

of the Bank Indonesia (Bl) interventions in general Differently, in this study the impact of

the Bank Indonesia's sale of foreign-exchange intervention (D_INT _Sell_Spot) and other possible relevant variable such as NDF rate and real interest rate differential (RIRD) and foreign-exchange turn over (GTOV _FX_ TOTAL), will be examined. Therefore, the following model 1s proposed to model mean of the exchange ra te returns and cond1t1onal volatility as follows:

llsp<Hi

=

a0

+

｡ Ｑ ｒ ＮｾＱＬＰ ｲＱ｟

Ｑ＠

+

aiRNoF,

+

a3RIRDc

+

a4GTOVFXror,1L,

+

｡ｾｄｬＮｈＧＮｦｲｬｬｳｰｯｲＬ＠

+

r, (1)

Eclnc-1-N(O.h, )

For TGARCH (1, 1)

h,

=

bo

+

b1Rspot1- 1

+

b2RNDF1

+

b3R/RD,

+

b1GTOVnror11L,

+

｢ｳｄＱｎｔＧｓｾｬｬｳ ｰ＼＾ｴＱ＠

+

ac/. ,

+

flhc -1 (2)

+

Ｈ､Ｌ ｟ ＬﾣｾＭＱ＠

where b₀ a,

P

> 0 dan a +

P

< 1

Table 2. Mean equation and pre-estimation test results Dependent Venable R_SPOT

Method least SQuares Dale 07115/1 4 Time 14 16

Sample (ad1usted) 1/04/200812/31/2013 Included obseMbons 1563 after ad1ustments

Yariable Coellioent Std Error t-Stabsbc

c

-0.073351 0 054676 ·1 341570 R_SPOT(-1) -0.306758 0 022883 ·13 40573 R_NOF 0402657 0019795 20 34148 RIRP 0 009802 0 009116 1.075332 INT_SEll_SPOT 4 41E·10 1 12E·10 3 938418 GTOV _FX_ TOT AL -0 001270 0 000603 -2.108006 R-squared 0 250244 Mean dependent var /ld1usted R-sQuared 0.247836 S O dependent 1.er SE of regression 0.597249 AAa1ke 1nro criterion Sum squared res1d 555 3921 Schwarz cntenon log likelihood ·1409 192 Hannan -Quinn enter

ｆＭｳｴ｡ｨｾ｣＠ 103 9351 Durbin-Watson stal Prob(F-statisbc) 0000000

Source. Author's Calculations

44

I

JCl:BI. Vol.I ＼ｾＰＱＴＩ＠ No.2. pp. 35 · 5-1 [image:21.642.28.570.100.791.2]

.\ftl/ll'hll I f'O/tC\ I 1'1(><110<' t111 < \t'/ltlllf!<' rtlfc' I ,1/c11ifl11 Ill /JitfVlll')ill

-Table 3. Q Statistics of deviations and squared deviations

ｏ ｾ＠ 0111!1.i-u r.m . 1• ' '

S • ""P'• 1.0l."200112131110 1) ...0...0•4 oot•"9-.on1 \$4)

0,1 Y • 1te pt'ODe.C.J.te 1 ｾＱＱＮＱｕｏ＠ JOt 1 ､ｾ･ＧｦＧ＠ <!1 •2t eto1 0t

ｾｴｏｃｏｮＧＢ＠ eWC\ P•l"I•• co,..-•• • •o-"' N; PN::

i:

1 0 \) •O 1l

l 001 00)

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.

OOH 0047

ｾ＠ ＰＰ ｾ＠ 0000

6 OOCM 0011

1 OOH 0079

• -004 ·003

•

oue 0 . , .

1 ·006 00•

1 000 ·OO l

000 -003

0036 0026

0002 ·000 0002 ＰＰＱｾ＠

0 017 0002

I .ooo OOt:t

ＰＰ ｾ＠ 0020

006 ·00•

o ｯｾ｡＠ 00)9

ｯｯｾ｡＠ 001'

000 0010

-003 ·003

·OOt .0 0)

Source. Author's calculat1ons

OrA 0111sn • Time 14 ie Sample 1'<)312008 12/31/2013

ｾＮａＮ｡＠ oc•etWt.(lf'li• tS63

Ait0e0trel•t.100 Par11a1 ConelabOt'I >G r>K.

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>taoa 0000

)tffJ 0000 5 0 178 0()6.4

:n 171 0000 e 0210 0100

7 0 11!1 0028 a 0 137 0012 II 0 110 0010 0 Oll6 0001 0211 0152

)I 'U 0000

)I ｾ ｴ＠ 0000

41 lU 0000

ＺｉｑＬＮｾ＠ 0000

H » l 0000

12 07) 0000 u 079 0000

0 " ' 0010

, , 10& 0000 0 Ille 0072 0 148 OOJ.4 0 124 -001 0 Ollll -0 00 0 190 0104

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... ＲＳｾ＠ 0000 •• 239 0000

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••010 0000 1 0205 00611

9• 101 0000 I 0213 0054

98 S• l 0000 2 0 101 0011 2 0 146 0021 2 0 084 -006 2 0073 -002 2 00611 -000 10•91 0000

104 " ' 0000

tOOOt 0000

10104 0000

o.s ....

1&:l ｾ＠...

30500 3"14 :e 401 211 4501111 5?021 56074 59042 60983 82• 07 ＸｾＰＲ＠ 7287• 787 •• 821 ge 1143 07 Ml 32 '1834 g8403 10505 10018 1131 8 113$4 11'88 11511•

Figure 2 . Error terms from the OLS estimation of mean equation

ｆＮｳｬＺＧＮＡｾＮ［＠

･ｴｳﾷｒｾＮＺ｡Ｎｾ＠

12Sl.'S ｐｲＭｾ＠ ｒＱＱｾＱ＠

Ｑｾｾ＠ H °"5,wJ)

Proo 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000

o • • f " • • .,. • o • • • • o • • o • • o • • o • o • o o • • • • o • o o • ' r I O • • • • '" I • r t • • t • • I O • I 0 0 t • °' t • •

• I t . . . I V ' f " . . . I V ' • U l l t I - t " IH ,... I . , 111 I V ' f 1 1 ttl '"""

, ... _. .• ＭＭﾷﾷﾷﾷﾷﾷﾷﾷﾷﾷｾ＠ l

Source: Author's calculations

The results of standard OLS estimation of equation ( 1) are reported m Table 2, with the test statistics applied to estimated error terms. As can be seen from the graphical representation of estimated errors (Figure 2), although addition of explanatory variables to the model relatively loosens the clustering, the ARCH effect in senes is obvious. Moreover, Ljung-Box serial correlation tests show sign of autocorrelation and the test p-values of 02 _{shown in the} Table

3

are all zero, resoundingly rejecting the "no ARCH" hypothesis. As for the ARCH LM test for absence of conditional heteroscedasticity, it is highly significant at any level. [image:22.642.24.577.160.672.2]

[image:23.623.13.568.29.686.2]

- r1•rr1· Srnrt/11dtlm .\ Ut' I • I :c1111 '• h\11111. Dt'tll /foc/11111111 /

ｬ ｡ ｾＯＱＱＱ Ｌ＠

fo111 /JaJ..l1t1ttr

Table 4. Results of model 2 (with-without FX intervention by the Central Bank)

ｃ･ｾｮ｣･ｯＱ＠ v.i"ao e i< Si'CT

Merioa leas1 S<lvares

Dace 071151 H Time u 35

C'tf:t,.,;!c"I ｖＬＮＮｾ Ｇ Ｘ＠ R SPOT

Metf\od J..\.. >.A CH ｴｍ ｾ ｲｑＮＬＬＮ ｬｩ ｊＡｊｴｬ＠ Norm• 0 t viet ... ton

o.·. 0111S, 14 r.m. "4 26

S<1mol• (•.S1u•l•d) l'Oll2008 1'2 ,"3t1"20tl

tnc:iv-oeo 00••"410"• ts.61 •rtet • 0;1.1• tm•nl1 Con..,.rgcl'IC. t tn••'40 •"9t 39 tMt• ton t

ＢＧｾｾ｡ｴｴｬ＠ 107?00I

Ptet•mP • •ntf\U b•ta.Utl tP.i11• m • •1 I 0 7J

Samele 1ao1us1ea) 1-0712008 12131'1013 lndudf<l ooseM!>Ons 1 ｾＲ＠ 1r.e1aO!uS1men1S

ｃｯｮ ｾ ｲ［･ｴ｣･＠ acr"td a'lt1 10 •ll!•aaons

!AA ｂ｡ｾ｣｡ｳｬ＠ l<l"2008 CAR:Ct4. _{C(12rCAACH, 1)} c,9; . cno•·RESt0t- 11•2. Ct t t ;•A£S•O.: • t•Z'tRES•O, , , .. o • •

Ya:lal>•t Coel!ioen1 Sio Enor ｾｓｬＮｬｬｓｉｃ＠ Proo V.tt.ID• • CoeMoent $t0 [not l S LIUlC ProD

1 174687 OH03 c 0006ll& ＰＰＰＲＰ｜ｾ＠ l •'2191 ＰＰＱ ｾ＠

c 0007601 0006471 R SPOTll l 0 006846 00152•• S&r.7197 00000

R SPOTl·I) 029508-« 0 025348 1164130 00000 R "'OF Oel90H 0013576 81 06029 00000

R_NOF 0 598S66 0 020733 2887040 00000 f:UAP 00014S2 0 000.lS . ) ))9619 00008 INT SElL SPOI 211E tt I 20E· 1' I USlU OOSZ9

RIRP .0 001031 0001126 .0915022 03603 GTOV FA TO TAL. -0000$9• 0000Ul • 907US 00000

GTOV rx TOTAL

.o

0013 14 0000520 -2 525501 00117 ARm 0074 7tU 00ll810 2 lS032l 001 . .

AA(!) -0 141327 0028883 -' 892204 00000 MAlll .011601> OOISHI S6 2)$)1 00000

LWIJ .() 8-«9609 0 0207 14 ·'1 01658 00000 V• n•nct EQu•bon

R·SQuared 0 385542 M?an d1penden1 var 0018762 _{RESIOI 11"2} c 0 001834 _{0 374907} 0 000276 _{0 02698•} 680))82 _{13 t93• S} 00000 ₀₀₀₀₀

A01usle<l R-squareo 0383171 S 0 dependenl var 0688860 RES10(· 11"2' CAESJ0( II< .oonau 0 Ol• S02 -2 2SS34 ) 0 024 '

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Figure 3. Conditional standard deviation of

TGARCH

4

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0

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2008

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2009

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2010

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Results of the Model 2 (volatility), estimated with TGARCH (1, 1) are displayed in Table 4. According to the results of the model, the results confirm IRP theory, which suggests that an increase in real interest rate parity (RIRP) causes appreciation of the domestic currency. Furthermore. foreign exchange sale interventions lead return of exchange rate to decrease. On the other hand, foreign-exchange turn-over (GTOV _FX_ TOTAL) has a negative impact on exchange-rate return significantly. It means that higher foreign exchange transactions (in value) will make the domestic exchange market more efficient. Besides, foreign-exchange sale intervention is estimated to be negative and statistically significant, which

'

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11/1 t '\t'h<lll!it' ru/I' n1/uttfif\ Ill /11JVllOICI -can be interpreted as. an increase in foreign-exchange sale intervention causes Indonesian

Rupiah Return to decrease. However. the results of Bank Indonesia's efforts to exert a

stab1l1Z1ng effect of foreign exchange interventions are 1nconclus1ve Bank Indonesia's policy

response rn mrnim1zrng exchange rate volatility cannot be said to be effective as 1t cannot reduce the volatility of USD/IDR (rn term of reduced conditional variance).

On the other hand. the NDF return has a pos1t1ve impact on the on-shore exchange rate return When the impact on return of exchange rate 1s inveshgated as expected, R-NOF 1s estimated to be positive and statistically significant, which can be interpreted as an increase

rn return of NOF causes Indonesian Rupiah return to increase for more. Therefore, it is

important for the central bank to introduce a new policy called JISDOR (Jakarta Interbank Spot Dollar Rate) to reduce the role of NDF 1n driving on-shore exchange rate.

The coeff1c1ents on all four terms 1n the cond1llonal variance equation are highly statistically significant. Also. as 1s typical of TGARCH model estimates for financial asset returns data, the sum of the coefficients on the lagged squared error and lagged conditional variance is very close to unity. This implies that shocks to the conditional will be highly persistent. This can be seen by considenng the equations for forecasting future values of the conditional variance using a TGARCH model given in a subsequent section. A large sum of these coefficients will lead future forecast of the variance to be high for a protracted period. The conditional variance

coefficients are also as one would expect. The vanance intercept term

·c·

is very small, while

the coefficient on the lagged conditional vanance ('GARCH') is smaller at 0. 7 4 when the Central

Bank enters the domestic FX market. The core of this leverage term is the dummy variable d,_₁

that equals 1 m the case of a negative shock (£1-15 0) and

O

in the case of a positive shock (t-1

> 0). Thus, the positive value of the coefficient (indicates an increased conditional variance by

£21-1 in the case of negative shocks or news that occur at time t-1, while the negative value of

coefficient indicates a decreased conditional variance. In this case, the negative value of the

coefficient (indicates a decreased conditional variance by £21-1 m the case of negative shocks or

news that occurs at time t-1.

Table 5. Q, Q,2, and ARCH LM test statistics of TGARCH model

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In Indonesia's case, this research is of the view that exchange rate movement does not always reflect the economic fundamental Thus. foreign exchange intervention is applied 1n order that the exchange rate is consistent with the overall Objective of achieving pnce stability and supporting financial system stability Exchange rate overshooting occurs as previously mentioned because of a number of factors. such as excessive capital nows. speculative intention of foreign-exchange market players, and the microstructure conditions of the market and ignited by sudden external shocks impact on the domestic financial market. Thus, as stated above. the Objective of foreign exchange intervention 1s to mitigate the exchange-rate volatility as well as to drive the exchange rate along its fundamental path. Furthermore. beyond of that the foreign-exchange intervention implementations are consistent with achieving the inflation target and supporting financial stability.

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However. it is regarded that the effectiveness of intervention in influencing exchange rate expectations is more difficult to assess. since the exchange rate is more susceptible to news developments and market reactions to them. In general, when market reactions are not excessive, foreign-exchange supply and demand in the market would be in equihbnum. Therefore exchange-rate intervention by a central bank may be more effective in influencing both the spot and forward exchange markets if 1t is used to deal with any remaining excess demand or supply in the market. As shown in figure 5, foreign exchange tum-over in Indonesia 1s relatively shallow and dominated by spot transactions. This shallow turn-over is vulnerable to any external shocks or negative news. When news and market reactions are erratic. excess supply or demand of foreign exchange tends to widen, and the exchange rate movements tend to diverge from the central bank's view on where the fundamental exchange rate path should be. An example is what happened when negative rumors such as widening current account deficits, increasing fuel subsidy burdens related to fiscal unsustainability, and worries about foreign exchange liquidity in the domestic market, the spread between offshore (NDF-Non deliver forward) and onshore forward rates widened at that period (Figure 4). At the same time, the aggregate turnover in regional NDFs of Asian countries has risen, particularly in Renmimbi Yuan China. Asian NDFs tend to correlate positively among them and respond similarly to movements of major currencies. Fortunately, the spreads have diminished in recent years as central banks have performed several actions to mitigate the problem by ensuring foreign-exchange adequacy as well as by performing intensively moral suasion.

Figure 4. Exchange rate : Onshore vs NDFFigure 5 volume of FX transactions

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-The thinness of domestic foreign-exchange market makes exchange rate more volatile when external shocks exist. This shallow foreign-exchange market let the banks heavily dependent on the central bank absorb any excess supply in the market (during current account surplus and/or large capital inflow periods) and supply any excess demand in the market (dunng current account deficit and/or capital outflow periods). Therefore, Bank Indonesia ensures the adequacy of foreign exchange reserves daily as it may increase the cred1bihty and effectiveness of intervention policy. To increase the supply of foreign exchange 1n the market, Bl releases a regulalton requiring all exporting/importing companies to repatriate their foreign exchange receipts from exports and offshore borrowing to domestic banks. In addition , a holding period for investment rn the central bank's bill is implemented to limits on short-term offshore borrowing . Another pohcy has also been directed toward deepening the domestic foreign exchange market by including offering of foreign exchange term deposits. and t