Hedging Performance of Indonesia Exchange Rae By: Eneng Nur Hasanah Fakulas Ekonomi dan Bisnis-Manajemen, Universias Islam Bandung (Unisba) E-mail: enengnurhasanah@gmail.com ABSTRACT The flucuaion of exchange rae very given he impac o he siuaion of Indonesia economic, i will give impac o he economics of Indonesia, wih he case, his paper examines he hedging raio performance by using The Consan Condiional Correlaion (CCC) of Bivariae Generalized Auoregressive Condiional Heeroscedasiciy (BGARCH). The resul of hedging raio performance of Indonesia exchange rae is very low, i means ha Indonesia almos never miigae Rupiah (IDR). Keywords: Exchange Rae, CCC, BGARCH, Hedging Performance I. INTRODUCTION 1.1. Background The flucuaion of exchange rae in Indonesia highly depend on he economic condiion in he world. Some flucuaion of exchange rae should give impac for Indonesia economics. For example, when crisis on 1997/1998, which value IDR o USD increase very high. The economics of Indonesia hreaened bankrup when he price become much higher han before and some company defaul. The flucuaion of exchange rae very given he impac o he siuaion of Indonesia economic, some impor commodiies become higher han i conribues o he increasing of inflaion in he domesic economic. Because of he high impac of he flucuaion of exchange rae o a counry especially in Indonesia, some economis suggess hedging. Hedging is used o secure he porfolio value of he asse from he marke flucuaion. The flucuaion can be he foreign exchange rae, he marke oil price, he marke of gold price, and so on which have effec for inernaional marke. Hedging is buying a conrac include he forward exchange or commodiies which he value should be increasing and he losing from he conrac or he commodiies value. 1.2. Problem Idenificaion In Indonesia, he hedging no popular as in developing marke in he world. I shown when he Cenral Bank of Indonesia (BI) encourage he companies in Indonesia o hedging of he company s asse from exchange rae flucuaion. I happens because, he exchange rae flucuaion very depends on fundamenal facor, echnical and how he governmen (on BI auhoriy) sance his happening. 1.3. Problem Formulaion Because of he hedging in Indonesia sill on he popular issue, his paper should show how hedging performance of Indonesia exchange rae (IDX) o he USD flucuaion (SGX) wih CCC BGARCH mehod han measuring he hedging performance. 34
II. LITERATURE REVIEW To explain he acual hedging behavior from he various economic agens, some scienis developed he hedging model o solving he problem. From he evaluaion, he hedging behavior shown he oal lack of reasonable of posiive model, Collins (1997). Yagani and Kamaiah (2012) invesigae he hedging effeciveness of commodiy price fuure marke in India. They calculae he opimal hedge raio using Ordinary Leas Square (OLS) regression and Error Correcion Model (ECM). The resul of his paper shown ha only 40% of hedging conrac are suiable for hedging. Then here is much difference beween wo mehods (OLS and ECM), for far and nearby mauriy periods hedging is more affecive, which has some imporan implicaion for hedging sraegy. Lee and Chien (2010) using hedging performance o invesigae he impac of sock marke liquidiy on hedging performance. They use he regression model by including sock marke liquidiy. From he empirical resul shown ha he marke liquidiy informaion useful for predicing he opimal hedge raio and enhance he hedging performance during he bear marke. Huson and Laing (2014) using a sample of 953 US firm over he period 1999-2006 o examine he relaion beween operaional hedging, financial hedging, and foreign exchange exposure. They use Jorion s wo facor model o esimae he foreign exchange exposure coefficien o each firm, They find ha when exchange rae volailiy is high as he effeciveness of financial hedging diminishes. Hou and Li (2013) assess he hedging performance of he newly esablished CSI 300 sock index fuures. From his paper, DCC model is beer wih shor hedging horizon and CCC model is beer for long hedging horizon. By comparing he ime-varying BGARCH hedging model, he CCC-GARCH model is beer han DCC for he mos hedging horizon of in-sample hedging effeciveness. III. RESEARCH METHOD On his paper, he daa ha used are Indonesia daily exchange rae (IDR) and daily USD swap for IDR. The daa for USD swap for IDR is a new issue, i begins from 2 Ocober 2013. So, wih he daa ha used is from 2 Ocober 2013 unil 30 April 2014, same wih IDR exchange rae. The mehodology in his paper is The Consan Condiional Correlaion (CCC) Bivariae GARCH (BGARCH), han i would be evaluaed using hedging performance measuremen. 3.1. The Consan Condiional Correlaion (CCC) model Beginning by Engle (1982) for ARCH model, four years laer, Bollerslev (1986) inroduced GARCH model. Then, he suggess Bivariae GARCH (BGARCH) Bollerslev (1990) wih consan over ime which named by The Consan Condiional Correlaion 35
(CCC). Then, in 2002, Engle (2002) proposed he Dynamic Condiional Correlaion (DCC) Bivariae GARCH. The Consan Condiional Correlaion (CCC) model used o evaluae he hedging performance of IDR. The CCC model and he condiional H can be wrien as: H h11, 12, 11, 0 12 11, 0 h h 1 h h21, h 22, 0 h 12 1 22, 0 h 22, Where 12 is he condiional correlaion beween spo and fuures reurns and assumed o be consan over ime. The individual condiional variances h 11, and h 22, are assumed o follow a sandard GARCH (1,1) process (Bollerslev, 1986). h h i 2, 1,2 ii, i0 i 1 i ii, 1 Then, he CCC-BGARCH model can be wrien as: 2 2 h, 1,2 ii, i 0 i 1 i, 1 i 2 Ii, 1 i, 1 i Where Ii, 1=1 if i, 1 < 0 (i=1.2) and 0 oherwise. When i3 > 0, previous negaive shocks generae higher volailiy han posiive ones. This asymmeric effec is called he leverage effec. 3.2. The Measure of Hedging Performance This paper using variance reducion o measure hedging performance. Hou and Li (2013) explain he variance reducion is calculaed as he raio of he variance of reurn of unhedged posiion minus variance of reurn of hedge posiion over he variance of reurn of unhedged posiion. Denoing S as reurn of he unhedged posiion and VH as reurn of he hedged posiion, variance reducion can be expressed as: Var VH VR 1 Var S Where Var V H and Var S denoe he variances of reurn of he hedged and unhedged posiion, respecively. Noe ha VR = 1 means ha he hedge id perfec. Then, he hedged posiion can be expressed as: V S F k H k k k Where kvh denoes reurn of hedged porfolio for k-period hedging horizon. ks S S k and k F F F k denoing k-period differencing of naural logarihms of spo and fuures prices for k-period hedging horizon, respecively. Thus, we have he hedging performance for k-period hedging horizon: 36
IV. EMPIRICAL RESULT VR k Var 1 Var V k k S H This paper repors he hedging performance using The Consan Condiional Correlaion (CCC) BGARCH model and hedging performance measuremen. The daa which used is IDR exchange rae and IDR o USD swap (SGX). Graph 4.1. IDX and SGX From he graph, i shown ha here is high differen value beween IDX and SGX. The IDX is under 100.000 poin. bu SGX above 500.000 poin. Table 4.1. Saisical Descripive of IDX and SGX IDX SGX Mean 11583.45 526515.9 Median 11574.10 526000.0 Maximum 12180.30 581500.0 Minimum 10570.80 478000.0 Sd. Dev. 317.2676 28587.62 Skewness -0.032612 0.170876 Kurosis 2.924505 1.942548 Jarque-Bera 0.052257 6.483748 Probabiliy 0.974210 0.039091 Sum 1459515. 66341000 Sum Sq. Dev. 12582343 1.02E+11 Observaions 126 126 By using AR(1) o modeling he daa o be CCC-BGARCH, he resul are: Table 4.2. CCC-BGARCH Modeling Sample: 10/14/2013 4/30/2014 Included observaions: 126 37
Toal sysem (balanced) observaions 250 Presample covariance: backcas (parameer =0.7) Convergence achieved afer 180 ieraions Coefficien Sd. Error z-saisic Prob. C(1) 11686.48 170.0820 68.71086 0.0000 C(2) 0.954763 0.018182 52.51072 0.0000 C(3) 528938.0 8840.077 59.83409 0.0000 Variance Equaion Coefficiens C(4) 2948.024 666.0400 4.426197 0.0000 C(5) 1.155532 0.254374 4.542652 0.0000 C(6) 0.068767 0.045849 1.499876 0.1336 C(7) 10043609 2411633. 4.164651 0.0000 C(8) -0.120132 0.026574-4.520593 0.0000 C(9) 0.944211 0.023691 39.85498 0.0000 C(10) -0.218204 0.130903-1.666916 0.0955 Log likelihood -2020.980 Schwarz crierion 32.72194 Avg. log likelihood -8.083920 Hannan-Quinn crier. 32.58760 Akaike info crierion 32.49568 Equaion: IDX = C(1) + [AR(1)=C(2)] R-squared 0.836907 Mean dependen var 11584.27 Adjused R- squared 0.835581 S.D. dependen var 318.4107 S.E. of regression 129.1110 Sum squared resid 2050366. Durbin-Wason sa 1.891915 Equaion: SGX = C(3) + [AR(1)=C(2)] R-squared 0.937665 Mean dependen var 526500.0 Adjused R- squared 0.937158 S.D. dependen var 28702.10 S.E. of regression 7195.136 Sum squared resid 6.37E+09 Durbin-Wason sa 1.905385 Covariance specificaion: Consan Condiional Correlaion GARCH(i) = M(i) + A1(i)*RESID(i)(-1)^2 + B1(i)*GARCH(i)(-1) COV(i,j) = R(i,j)*@SQRT(GARCH(i)*GARCH(j)) On he Table 3. The value of R-squared from he daa is high which he R-squared for IDX is0.836907 and R-squared for SGX is 0.937665. I means ha he model have fied he daa. 38
Graph 4.2. IDX and SGX Residuals On he Graph 4.2, i shown ha he residual has good condiion, i means ha he model fied wih he daa. Wih he fied model, i would ge he covariance from IDX and SGX, hen i should measuring he hedging performance for k-period hedging horizon equaion. The resul are: Table 4.3. Monhly Hedging Raio Monh Hedging Raio Ocober 2013 0.005246 November 2013 0.001871 December 2013 0.002311 January 2014 0.002178 February 2014 0.001218 March 2014 0.001786 April 2014 0.001508 Because of he limied daa, he resul shown he hedging raio jus from Ocober 2013 unil April 2014. The value of hedging raio very low which under 0.005. 0.03 0.025 0.02 0.015 0.01 0.005 0 Oc-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14 Apr-14 Graph 4.3. Indonesia Hedging Raio Performance 39
On Hou and Li (2013) paper, show he hedging raio value of Chine sock index fuures above 0.85, i means ha he marke of China sock index very liquid. Difference in Indonesia, wih he hedging raio value under 0.005 i shows ha Indonesia almos do no miigae he Rupiah (IDR). I proves he fac ha when he Dollar (USD) increase, he price of domesic needed increase oo, and in conribue he inflaion. The funcion of hedging is o decrease he volailiy of he marke value, on his case he volailiy of USD o IDR. Then from his resul, i suggess ha he Governmen of Indonesia mus concenrae o execue he hedging of IDR, hen make he hedging o secure he value of IDR and make he economics o be beer. V. CONCLUSION Indonesia on of he counry which is local economics very depend on foreign exchange rae, one of hem is Dollar (USD), wih his case, his paper invesigaes he hedging raio of Indonesia exchange rae (IDR) using The Consan Condiional Correlaion (CCC) Bivariae GARCH (BGARCH) model. The hedging raio performance shown ha Indonesia almos never do miigaing of IDR, i shown on he resul of hedging raio which very low ha is under 0.005. So, from his resul, he Governmen of Indonesia mus be concenrae o miigae he IDR. 40
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