Monetary Policy Response On Exchange Rate Volatility In Indonesia

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Editorial board
Editorial Boud
Vesna Bucevska. Edft0t-ln.Chlel
a.11ana Tashema - K1lkashl1eva. S.creury

Albeno Munoz Glrcia. Un1versid1d Carlos 111 de Madrid, Spain
• Andre)a Pudhar. Unlverslf'( of Mlrlbor. Faeully or Organlzauonal Sciences. Slovenia
• Bozidar Cerovlc Un1vers11y or Belgrade. Facuuy of Economics. Sert.a

• Btanlto R11uta. Un1vers.f'(of Belgrade. Facully of Economics. Setbta
• Edvard lijan. University of Rljeka. Facully of Manbme Srud1es. Slovenia
• Franas Ille. Jntemabonal Urnvers11y of Monaco. Monaco
• i.-ai Hashl Business School Statlordshire Un1vers•ly Leek Rd • Um!ed Kingdom

• Ivan Milenko-.ic. U111vet$11'( of of Pnsbna Faculf'( of Economics. Kosovsl.

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Source: Author's calculations
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 (a 2) , 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
JCtBI. \bl.I (2014) :>.o 2. pp 35 - .5.t I

43

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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 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
-0.306758
0402657
0 009802
4 41E·10
-0 001270

0 054676
0 022883
0019795
0 009116
1 12E·10
0 000603

·1 341570
·13 40573
20 34148
1.075332
3 938418
-2.108006

R_SPOT(-1)
R_NOF
RIRP
INT_SEll_SPOT
GTOV_FX_ TOTAL
R-squared
/ld1usted R-sQuared
SE of regression
Sum squared res1d
log likelihood
fMウエ。ィセ」@

Prob(F-statisbc)

0 250244
0.247836
0.597249
555 3921
·1409 192
103 9351
0000000

Source. Author's Calculations

44

IJCl:BI. Vol.I \セPQTI@

No.2. pp. 35 · 5-1

Mean dependent var
S O dependent 1.er
AAa1ke 1nro criterion
Schwarz cntenon
Hannan -Quinn enter
Durbin-Watson stal

Prob
0 1799
0.0000
00000
0.2824
00001
0 0352
0 018866
0 688651
1 810867
1 831421
1 818508
2 269315

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