Return performance, leverage effect, and volatility spillover in Islamic stock indices evidence from DJIMI, FTSEGII and KLSI

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1 Reurn erformance, leverage effec, and volaly sllover n Islamc sock ndces evdence from DJIMI, FTSEGII and KLSI AUTHORS ARTICLE INFO JOURNAL Mohamed Albay Rub Ahmad Mohamed Albay and Rub Ahmad (011). Reurn erformance, leverage effec, and volaly sllover n Islamc sock ndces evdence from DJIMI, FTSEGII and KLSI. Invesmen Managemen and Fnancal Innovaons (oen-access), 8(3) "Invesmen Managemen and Fnancal Innovaons (oen-access)" NUMBER OF REFERENCES 0 NUMBER OF FIGURES 0 NUMBER OF TABLES 0 busnessersecves.org

2 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 Mohamed Albay (Malaysa), Rub Ahmad (Malaysa) Reurn erformance, leverage effec, and volaly sllover n Islamc sock ndces evdence from DJIMI, FTSEGII and KLSI Absrac Emrcal sudes on sock reurns and volaly have no made serous aem o examne hese wo ssues on he conex of Islamc sock marke ndexes. Ths aer, herefore, nvesgaes he behavor of reurns and volaly of hree Islamc sock marke ndces DJIMI, FTSEGII, and KLSI ha are lsed n he USA, he Uned Kngdom, and Malaysa resecvely. Our aer examnes four man ssues: (1) wheher here s a dfference n reurns among hese Islamc sock marke ndces; () wheher here s a rsk remum n each sock exchange; (3) wheher hese ndces face he leverage effec rsk and lasly; (4) wheher here s a volaly sllover among hese hree Islamc sock marke ndces. The emrcal nvesgaon s conduced by means of he GARCH model (GARCH-M) usng daly daa coverng he erod from January 1999 unl Ocober 007. No only our resuls show no sgnfcan dfference n her reurns, rsk remum s found o be absen n each Islamc sock ndex. Whle KLSE reors no leverage effec, DJIMI and FTSEGII ndcae oherwse. Fnally, based on EGARCH and TARCH models here s a sllover from DJIMI and FTSEGII oward KLSI bu no vce versa. Keywords: Islamc ndex, volaly, GARCH, Sllover, DJIMI, FTSEGII, KLSI. JEL Classfcaon: G10, G11, G1, G15. Inroducon Over he las weny years here has been a connuous develomen n he convenonal bankng and fnance o roduce an Islamc counerar o caer for Muslm oulaon around he globe. One of hese develomens s he naon of Islamc sock ndces. An Islamc sock ndex measures he erformance of a ceran baske of secures and hese secures are ermssble for he Muslm o nves. The hree oular Islamc sock marke ndces are Fnancal Tmes Sock Exchange Global Islamc ndex (FTSEGII) of he London Sock Marke, Dow Jones Islamc Marke Index (DJIMI) of he New York Sock Exchange and lasly, Kuala Lumur Syarah Index (KLSI) of he Bursa Malaysa nroduced beween January 1998 and December Smlar o convenonal sock ndces, hese Islamc sock ndces are desgned o monor he erformance of some secors of he fnancal markes, whch he nvesmen follows closely o he enes of Islam. DJIMI and FTSEGII cover wde range of counres and socks whle KLSI covers only local lsed socks. Pas sudes have concenraed on he erformance of hese hree ndces agans her convenonal counerars. Theorecally, he value of any nvesmen s deermned by he resen value of he nvesmen s execed fuure cash flows. Subseuenly, a raonal nvesor maxmzes hs uly by maxmzng hs wealh and mnmzng rsk. A raonal nvesor who wans o maxmze hs uly wll choose he hghes ossble reurn for a gven level of rsk ha can be acheved by consrucng a well- Mohamed Albay, Rub Ahmad, 011. dversfed orfolo. Ths ales o all orfolo nvesmen decsons ncludng screened nvesmen funds such as he Islamc Muual Funds. Gven ha no all socks lsed on he sock exchanges are ermssble for he Muslms o nves, every fund manager of Islamc Muual Funds has o oban he aroval from hs comany s Sharah Board before urchasng any new shares. The srcer screenng crera n screened nvesmen as observed n he Islamc Muual Funds have been argued as one of he reasons why screened nvesmen n general brngs lower execed reurn han unscreened nvesmen (Rudd, 1981; Teer, 1991; Johnson and Neave, 1996; and Langben and Posner, 1980). The low dversfcaon benefs by screened nvesmen resuled o n hgher orfolo rsk. On o of ha, screened nvesmen s also erceved o ncur hgh admnsraon and monorng coss. Followng he work by Abdul Rahm, Ahmad and Ahmad (009) ha exlores he volaly of Islamc ndces n Malaysa and Indonesa, n hs aer we examne he sock reurns and volales n hree Islamc sock marke ndces namely, FTSEGII, DJIMI and KLSI. Ths sudy s dfferen from Abdul Rahm e al. (009) sudy s four folds. Frs, hs sudy uses hree dfferen sock marke ndces whle Abdul Rahm e al. (009) s sudyng wo closely relaed markes Malaysa and Indonesa. Second dfference s ha Indonesan Islamc marke ndex s raher small. I conans 30 lsed comanes whle DJIMI and FTSEGII ndces conan more han 1000 lsed comanes from many counres. Thrd, KLSI s ls comanes from Malaysa whle FTSE- GII and DJIMI nclude local and nernaonal frms from dfferen counres and regons. Forh, he Islamc sock ndces n hese hree markes have ds- 161

3 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 ncve screenng crera. Havng dfferen screenng crera mgh lead o dfference n reurns. Therefore, he frs ueson of hs sudy s wheher here s a sgnfcan dfference beween he hree Islamc sock marke ndces. Besdes comarng her reurns and volaly, we also examne he leverage effec of a fall n he secury rces lsed n DJIMI, FTSEGII and KLSI. Accordng o Black (1976), volales and asse reurns can be negavely correlaed and hs relaonsh s oularly known as he leverage effec. Brooks (008) exlans ha leverage effec haens when a fall n he rce of a frm s sock causes he frm s deb o euy rao o ncrease. When he large declne n he euy rce s no mached by he declne n he value of deb, he frm s deb o euy rao wll rse ogeher wh he fnancal rsk of he frm s nvesors. Because of he hgher rsk, nvesors would exec he volaly of he sock reurn o rse also. Cheung and Ng (199), Poon and Taylor (199), Koumos (1996) Koumos and Booh (1995), Booh, Markanen and Tse (1997) found ha here s a sgnfcan leverage effec and bad news (.e., decrease n sock rces) seem o have a greaer nfluence on sock rces han good news (.e., ncrease n sock rce). If he Islamc ndces screen hgh deb o euy rao frms such as DJIMI and FTSEGII hen hey should mnmze he leverage effec comared o KLSI whch does no have any screenng ac agans deb o euy rao. Ths s because a comany havng a hgher han he benchmark deb o euy rao s excluded from he DJI- MI and FTSEGII. Ulrch and Marzban (008) ha boh Islamc and convenonal fnance agree ha lower deb s beer han hgher deb because lower deb s nerreed as a osve nvesmen sgnal. Boh DJIMI and FTSEGII have a screenng crera based on he level of deb. Boh ndces elmnae frms ha have deb raos exceedng 33%. However, KLSI does no have any crera agans deb rao. Based on hs reasonng, we osulae ha leverage effec o be romnen n KLSI bu no n DJIMI and FTSEGII. In addon o ha, he Islamc ndces ha yeld low reurns are execed o have hgher rsk and wll no be comensaed for he exra rsk ncur by screenng. Ths sudy also examnes wheher he ncluson of deb rao screen makes any dfference. Fnally, Kouoms (1996) srongly suggess ha sudes nvesgang he nformaon ransmsson n he frs momen and second momen can be done based on reurns and volaly, resecvely. In addon o examnng he sock marke ndces volaly, hs sudy analyzes wheher here s nformaon ransmsson from KLSI o DJIMI and FTSEGII and vce versa. The nformaon ransmsson from one marke o anoher has been wdely reored. Bu maory of hese sudes are based on develoed markes only (Anonou, Pesceo and Volars, 003; Baur and Jung, 006; Caorale, Ps and Sagnolo, 006; Kouoms, 1996; and Kasbhala, Sewar, Sen and Malndreos, 006). Only few sudes examne he emergng markes (Daly, 003; Lamba and Ochere, 001; Shachmurove, 005; and Soydemr, 000). Our aer herefore examnes four man ssues: (1) wheher here s a dfference n reurns among hese Islamc sock marke ndces, () wheher here s a rsk remum n each sock exchange, (3) wheher hese ndces face he leverage effec rsk; and lasly, (4) s here a volaly sllover among hese hree Islamc sock marke ndces. The emrcal nvesgaon s conduced by means of he GARCH model (GARCH-M) usng daly daa coverng he erod from January 1999 unl Ocober 007. No only our resuls show no sgnfcan dfference n her reurns, rsk remum s found o be absen n each Islamc sock ndex. Whle KLSE reors no leverage effec, DJIMI and FTSEGII ndcae oherwse. Fnally, based on EGARCH and TARCH models for KLSI here s sllover from DJIMI and FTSEGII oward KLSI bu no vce versa. The remander of he aer s organzed as follows. Secon 1 oulnes he leraure revew whle secon dscusses he daa and mehodology emloyed. Secon 3 analyzes he resuls and fnally, he las secon hghlghs he maor conclusons and mlcaons of he sudy. 1. Leraure revew The nvesgaon of volaly s a romnen ssue n fnancal me seres analyss. Many aers have been wren usng dfferen mehodology and varable o nvesgae dfferen ssues abou volaly. Ths secon wll revew some of hese sudes. Yalama and Sevl (008) emloyed seven dfferen GARCH models o sudy he sock marke volaly n 11 dfferen markes usng daly daa from 1995 o 007. They found ha he bes model o exlan marke volaly dffer from one marke o he oher. Meanwhle, Yeh and Lee (000) nvesgaed he resonse of nvesors o unexeced reurns and he nformaon ransmsson n Chna, Hong Kong and Tawan sock markes. Usng GARCH model o analyze he asymmerc reacon of reurn volaly o good and bad news, hey found ha he mac of bad news of volaly s greaer han he mac of good news n Tawan and Hong Kong bu no n Chna. Koulakos, Paasyrooulos and Molyneux (006) nvesgaed wheher he here s a relaon- 16

4 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 sh beween volaly and sock reurns n 8 develoed markes. Usng weekly daa and mlemenng GARCH-M and EGARCH-M, hey found ha here s a relaonsh beween rsk and reurns n he GARCH- M model for he UK. Lao and Q (008) usng daly daa comared he rsk and reurn n NYSE comose ndex and Shangha sock ndex (SSI). They used ARCH, GARCH, TARCH, and EGARCH on boh markes and found ha he bes model ha f SSI was EGARCH whle TARCH was he bes f for NYSE comose ndex. In addon, hey found ha here s leverage effec n NYSE comose ndex bu no n SSI. Moreover, hey found ha SSI volaly causes NYSE comose ndex bu no vce versa. A recen sudy by Abdul Rahm e al. (009) uses develong counres sock marke daa. They analyze he nformaon ransmsson n boh reurn and volaly beween Jakara Islamc ndex (JII) and Kuala Lumur Syarah ndex. They reor ha here s nformaon ransmsson ha flows from KLSI o JII. However, he wo sock ndces are no hghly correlaed. The low correlaon could be because hese wo sock exchanges do no cross ls. Tesng for leverage effec n boh markes also roved nsgnfcan. The undreconaly n he ransmsson mgh be due o KLSI s hgher marke caalzaon gven ha he number of shares ncluded n KLSI s weny mes greaer han JII. Caorale e al. (006) examned he nerrelaonshs among he US, Euroean and Jaanese markes wh he Souh Eas Asan markes by usng hree bvarae GARCH-BEKK models. Ther fndngs show ha Souh Eas Asan volaly deends osvely on shocks from Euroean markes and Jaanese markes. Rashd and Ahmad (008) evaluaed he erformance of lnear and non-lnear model of volaly n Karach Sock Exchange (KSE) usng daly daa from 001 o 007. They found ha GARCH-M s beer han EGARCH n exlanng he volaly n KSE. In addon, hey found ha here s rsk remum or relaonsh beween rsk and reurns n GARCH-M model. Regardng leverage effec n EGRACH, was found ha here s a leverage effec n KSE. Ozun (007) examned he effec of develoed sock markes on he reurns of emergng markes usng daly daa from 00 o 006 and EGARCH model for volaly. The emergng markes used are Brazl and Turkey and he develoed markes are Jaan, he UK, France, Germany and he US. I was found ha Brazl s affeced by he lagged reurns of all he markes exce he US whle France, he US and Jaan, affeced Turkey reurn. In erm of leverage effec boh ndces have leverage effec. Kova (008) nvesgaed he leverage effec as well as he rsk remum n he Macedonan Sock Exchange usng daly daa from 005 o 007. I was found ha rsk remum effec, s sascally weakly sgnfcan n all models wh a negave sgn ndcang ha as reurns ncrease rsk decreases. Smlarly, n erms of leverage effec was found ha leverage effec s weakly sgnfcan. Based on he above sudes, hs aer ulzes he models from he GARCH famly. GARCH-M EGARCH-M and TARCH-M are used o es he rsk remum, he mean and volaly sllover, and leverage effec n hese hree sock marke ndces. The dealed exlanaon of he mehodology used s dscussed n he nex secon.. Daa and mehodology Rosly (005) ndcaed ha here are four man mehods of screenng. The frs mehod s roducon aroach where he acves of he comany are he focus of he screenng. The second mehod s he caal srucure aroach where he modes of fnance of he comany wll be under Sharah screenng. The hrd mehod s he ncome aroach where he ncome of he comany s scrunzed. The las mehod s he asse aroach where comany s asses are o be screened. Mos of he Islamc ndces do no follow a sngle mehod bu a mxure of almos all of hem. The dfference s only n he exen of he focus. Some ndces focus more on ncome and roducon bu mgh be flexble n modes of fnance. Ohers mgh emhass more on he roducon han on ncome. Unlke he revous sudes, hs aer examnes he reurns and volaly of hree Islamc sock marke ndces n hree dfferen counres, he US, he UK and Malaysa. Whle he DJMI and FTSE screened ndces follow he same screenng crera, KLSI n Malaysa follows dfferen screenng crera. DJ and FTSE screened ndces focus more on he ncome aroach han he acvy aroach whle KLSI end o gve greaer wegh on he acves of he comany raher han her ncomes. Therefore, s no surrsng ha DJ Islamc marke ndex and FTSE Islamc Global ndex follow he same se of screenng crera 1. The frs creron s ha he comany s rmary busness mus be ermssble accordng o Islamc laws. Therefore, comanes ha engage n gamblng, alcohol, armamens, obacco, ornograhy, or ork are excluded from he ls. Second creron s ha he comany mus mee secfc fnancal consrans ha nclude a deb rao of eual or less han 33%, accoun recevables euals or less han 45% for FTSEGII and 33% for DJIMI. Fnally, he comany s neres ncome mus be less han 5% for FTSEGII and 33% for DJIMI of s oal revenue. On he oher hand, he screenng crera for Malaysa s KLSI excludes comanes ha have non-ermssble 1 h:// 163

5 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 acves under Islamc laws such as gamblng, gamng, alcohol, neres, ec. For comanes wh acves comrsng boh ermssble and non-ermssble elemens, he Syarah Advsory Councl (SAC) consders wo addonal crera. Frs, he ublc erceon or mage of he comany mus be good. Second, he core acves of he comany are moran and consdered benefcal o Muslms and he counry, and he nonermssble elemen s very small and nvolves maers such as common lgh, cusom and he rghs of he non-muslm communy. To deermne he olerable level of mxed conrbuons from ermssble and non-ermssble acves, he SAC has esablshed several benchmarks based on reasonng from ualfed Syarah scholars. If he conrbuons from nonermssble acves exceed he benchmark, he comany s classfed as non-syarah comlan 1. Tme seres daa usually exhb hree man characerscs. Frs, hey exhb volaly cluserng or volaly oolng. In oher words, erods of hgh volaly s followed by erods of hgh volaly and he same ales for erods of low volaly. Second, her dsrbuon s leokuross, whch mean ha he dsrbuon s fa-aled. Thrd characersc s he leverage effec. The leverage effec s he fac ha bad news affecs reurns more han good news. In oher words, changes n he rces end o be negavely correlaed wh changes n volaly. Therefore modelng such seres needs o be exended usng oher models. The frs wo characerscs have been successfully modeled usng ARCH (Auoregressve Condonal Heeroscedascy) by Engle (198) and GARCH (Generalzed Auoregressve Condonal Heeroscedascy) develoed by Bollerslev (1986). The dea of ARCH and GARCH s o model he varance of he error erm from he mean euaon on he revous suared error erms. If he mean euaon s as follows. Y X, (1) 1 where Y s he deenden varable or reurns n hs case, X s he ndeenden varable and s he error erm and and 1 are he coeffcens. The error erm ~ N 0, s assumed o have zero mean and a consan varance or homoscedasc. However, s unlkely n he fnancal me seres ha he varance of he error erm be homoscedasc. Ignorng he fac ha he varance of he error erm s heeroskedasc wll resul n eher over/under 1 (1) The fve-ercen benchmark s used o assess he level of mxed conrbuons from he acves ha are clearly rohbed such as Rba, gamblng, luor and ork. () The 10-ercen benchmark s used o assess he level of mxed conrbuons from he acves ha nvolve he elemen of common lgh whch s a rohbed elemen affecng mos eole and dffcul o avod. (3) The 5-ercen benchmark s used o assess he level of mxed conrbuons from he acves ha are generally ermssble accordng o Syarah and have an elemen of benef o he ublc, bu here are oher elemens ha may affec he Syarah saus of hese acves. esmaon of he sandard error and herefore bas nferences. To overcome hs roblem ARCH model s used. The arch model s as follows:, () 1 1 where s he condonal varance, 1 s he lagged erm of he suared error erm from he mean euaon, and and are he coeffcens. Ths model ndcaes ha he varance of he error erm s deenden on he lagged suared error erm. Such model s referred o as ARCH (), where ndcaes he lag order of he suared error erm n he varance euaon. Alhough ARCH model s caable of elmnang he heeroscedascy n he mean euaon, sll has some drawbacks ha led o he develomen of GARCH model. GARCH model was develoed by Bollerslev (1986) who ndcaed ha a GARCH model wh smaller number of erms can erform as well as or even beer han ARCH model wh many lags. The dea of he GARCH model s smly o nclude he lagged value of he varance n he varance euaon. The GARCH model s as follows:, (3) The frs erm n he rgh hand sde s he ARCH erm exlaned earler, whle he second erm s he lagged varance ha s GARCH. Ths model s referred o as GARCH (,) where () s he lagged ARCH erm and () s he GARCH lagged erm. The above model ndcae ha s he long-erm average varance, s he nformaon abou he volaly n he revous erod, and he bea s he coeffcen of he lagged condonal varance. Alhough GARCH model s beer han ARCH secfcaon snce s more arsmonous and less lkely o breach he non-negave consran s sll does no accoun for he leverage effec n he aaren n fnancal me seres and does no allow for any drec feedback beween he condonal varance and he condonal mean. Anoher exenson of GARCH by Engle, Llen and Robns (1987) s GARCH-M where eher he sandard devaon or he varance s ncluded n he mean euaon n order o es wheher here s a rsk remum or a radeoff beween rsk and reurns. Ths model s reresened as follows: Y, (4) 0 1X 1 where Y s he deenden varable or reurns n hs case, X s he ndeenden varable, s he condonal varance or he rsk remum, and s he error erm and 0, 1 and 1 are he coeffcens. The 164

6 GARCH-M model allows me-varyng volaly o be relaed o execed reurns. An ncrease n rsk, gven by he condonal sandard devaon leads o a rse n he mean reurn. The value of gves he ncrease n reurns needed o comensae for a gve ncrease n rsk. Therefore, s a measure of rsk averson. One of he roblems n GARCH s ha reas any shocks o he volaly as symmercal. Tha s good news and bad news has he same effec. One of he mehods used o overcome hese ssues n GARCH s Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 asymmerc GARCH. However, was argued by revous sudes such as Black (1976), Chrse (198), Engle and Ng (1993) ha volaly resonds asymmercally o news esecally bad news. Therefore, asymmerc GARCH s develoed o overcome hs roblem. Two man models deal wh asymmerc nformaon EGARCH (Exonenal GARCH) and TARCH (Threshold GARCH). Nelson (1991) develoed he followng euaon o rea he asymmery n he volaly: log log. (5) The lef-hand sde s he log of he condonal varance. Ths mles ha he leverage effec s exonenal, raher han uadrac, and ha forecass of he condonal varance are guaraneed o be nonnegave. The resence of leverage effecs can be esed by he hyohess ha < 0. Whle TARCH model was nroduced by Zakoan (1994) and Glosen, Jagannahan and Runkle (1993). Ths model s desgned o es wheher here s asymmerc mac of news and wheher here s a leverage effec. The secfcaon of he TARCH model s as follows: d 1, (6) where d -1 = 1 f 1 < 0 and 0 oherwse. In hs model, good news 1 1, <, and bad news s ( 1 < 0), have dfferen mac on he condonal varance whereby good news has he mac of, whle bad news has he mac of, for he leverage effec f > 0 here s leverage effec on he oher hand f 0 hen he news mac s asymmerc. Therefore, bad news causes more volaly n he marke hen good news. In hs aer, he EGARCH and TARCH are used o es wheher here s any leverage effec n he hree screened marke. Tha s wh here s an asymmery n nformaon. The daa used for hs sudy wll cover hree Islamc ndces namely, DJIMI, FTSEGII, and KLSI. The erod of he sudy sar from Arl 1999 o November 007 on daly bass. Reurns are calculaed r usng he comounded reurn formula. The calculaon s done as follows: P, R ln, (7) P, 1 where R s he reurn for ndex a me, P, s he rce for ndex a me and P, 1 s he rce of ndex a me 1. Therefore, four euaons wll be esed here o answer hs aer uesons. Frs euaon s he mean reurns euaon where each marke reurns wll be regressed on s own lag and he oher wo marke reurns lags. Second euaon s a GARCH-M (1,1) o es wheher here s any rade off beween rsk and reurns and he effec of he volaly of each ndex on self. The hrd and forh euaons are wo dfferen mehods of es he leverage effec n each sock marke ndces. The euaon s as follows: 0 rdjimi, 1 1 KLSI DJIMI,, (8) DJIMI, 1, (9) DJIMI, 1 1 KLSI DJIMI, DJIMI, log 1 KLSI DJIMI, 1, log (10) DJIMI, 1 1d 1 1 KLSI DJIMI, 1, (11)

7 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 Euaon (8) s he reurn euaon where r s he daly reurn for DJIMI regressed on s lagged, s he varance of DJIMI ndex, whch reresen he rsk and reurn rade off, and s he error erm. Euaon (9) s he varance euaon where s he condonal varance, s he lagged erm of he suared error erm from he mean euaon, s he lagged condonal varance, and,, and, are he coeffcens as n euaon (3). Euaons (10) and (11) are EGARCH and TARCH models ha are used n hs sudy. The same four euaons wll be run for each marke. 3. Resuls and analyss Fgure 1 shows he reurns of he hree ndces. From he reurn grahs, s clear ha he mean reurns are consan, however he varance change overme for hese ndces. I s evden ha volaly ends o cluser,.e., changes n volaly wheher bg or small ends o erss. I s evden ha DJIMI and FTSEGII moves ogeher almos durng he whole erod of he sudy whch exlans he srong or almos erfec correlaon. I also shows ha here was a lo of volaly beween 1999 and 003. On he oher hand, KLSI seems no relcae he movemen on hose wo ndces however n erm of volaly has he same erod of hgher volaly as hose wo ndces. Fg. 1. Plo of closng rces and reurns for DJIMI, FTSEGII and KLSI 166

8 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 Fgure los hsogram of reurns for each marke ndex agans he normal dsrbuon. I shows ha varous reurns fall beyond four sandard devaons whch s unlkly n normal dsrbuon. Ths knd of dsrbuon s called o have heavy als. The dsrbuon of he reurns n hese markes show ha s also leokurc or has hghes eak. A uanle-uanle (Q-Q) lo on he oher hand s a ool o check wheher wo dsrbuons are he same,.e, normal dsrbuon agans he seres dsbuon. If boh dsrbuons are smlar, he lo s assumed o be lnear. In hs Fgure, boh dsrbuns aear o be dfferen. The reurns devae from he srngh lne and hs confrms he heavy als and hgh eakedness characersc of he reurns. Table 1 dslays he descrve roeres of he reurns of DJIMI, FTSEGII, and KLSI from Arl 1999 o Ocober 007. Toal observaons n hs sudy are 8 observaons. The mean reurns of he hree ndces are osve. The KLSI has he hghes reurn of (1.8% annually) whle DJIMI (5.8% annually) and FTSEGII (5.1% annually) have lower reurns a and , resecvely. In erm of volaly, KLSI has he lowes volaly followed by FTSEGII and fnally he Fg.. Normalzed reurns dsrbuon and Q-Q lo hghes volaly s DJIMI. Alhough he fnancal heory ndcaes ha hgher volaly mus be comensaed by hgher reurns hs s no he case n hese hree ndces. KLSI has he hghes reurns bu he lowes volaly. DJIMI seems o earn lower reurn han KLSI. However, he former reors hgher volaly. The reurns of all he hree ndces are negavely skewed and leokurc. Ths ndcaes ha her reurns are asymmerc. In addon, he hree ndces are no normally dsrbued based 167

9 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 on J-B es of normaly. Meanwhle, he Lung-Box auocorrelaon es on reurns and reurns suared a 10 lags. I ndcaes ha lnear and non-lnear deendences exs n he frs and second momen. Lnear deendency mgh be exlaned as marke neffcency (Koumos, 1996; Koumos and Booh, 1995; and Kova, 008). On he oher hand, nonlnear deendency mgh ndcae he resence of GARCH effec (Kova, 008). Table 1. Descrve sascs of DJIMI, FTSEGII and KLSI reurns DJIMI FTSEGII KLSI Mean Sd. dev Skewness Kuross Jarue-Bera 38* 350* 515* LB (10) 63.97* 57.19* 89.97* LB (10) * 65.18* * Noe: * Sgnfcan a 1 %. Table shows he correlaon coeffcen or he uncondonal correlaon beween he hree ndces reurns. The correlaon beween DJIMI and FTSE- GII s he hghes reachng almos one whch ndcae erfec correlaon. However, he correlaon beween KLSI and each ndex s abou 0.13 ha ndcae very weak bu osve and sgnfcan relaonsh. Ths low correlaon beween DJIMI and FTSEGII can be an ndcaon ha hese ndces movemens do no affec KLSI. Ths s mgh be because DJIMI, FTSEGII are n develoed markes, whle KLSI s n a develong marke. Anoher reason could be ha DJIMI and FTSEGII mgh have many frms ha are cross-lsed n boh ndces whle KLSI does no have hs characersc. Ths low correlaon beween KLSI and boh DJIMI and FTSEGII can be useful n erm of dversfcaon by nvesors. Table. Smle correlaon coeffcen for he reurns of DJIMI, FTSEGII and KLSI Varable FTSEGII KLSI DJIMI 0.983* 0.133* FTSEGII * KLSI Noe: * Sgnfcan a 1 %. Table 3 dslays he resuls of he dfference n mean reurns -es. The resul n all cases ndcaes ha here s no dfference n mean reurns among he hree ndces. Table 3. T-es for dfference n mean reurns Reurns T-es value DJIMI and FTSEGII DJIMI and KLSI KLSI and FTSEGII Table 4 reors he resuls of Augmened Dckey fuller (ADF) es. The urose of hs es s o fnd ou wheher hese seres are saonary by esng he null hyohess ha he seres have un roo. From he resuls, s clear ha all he sock markes reurns are saonary n he mean bu no n he varance. Table 4. ADF un roo es KLSI DJIMI FTSEGII None * * * Trend & nerce * * -41.1* Inerce * * * Noe: * Sgnfcan a 1%. Table 5 reors he resuls of hree esmaons, GARCH-M, EGARCH-M, and TARCH-M as secfed n euaons (9), (10) and (11). These hree esmaons models were done for KLSI, DJIMI, and FTSEGII. Snce DJIMI and FTSEGII have almos a erfec correlaon beween hem, he esmaons below were done n wo markes relaonsh (.e., KLSI wh DJIMI whou FTSEGII and KLSI wh FTSEGII whou DJIMI) raher han hree markes o avod basness n he resuls. In he reurns euaon of KLSI wh DJIMI and KLSI wh FTSEGII, s evden ha KLSI s affeced osvely by s own one-day lag, one-day lag of DJIMI and one-day lag of FTSEGII. Ths resul ndcaes ha here s a sllover n reurns from DJIMI and FTSEGII on KLSI. In addon, he coeffcen of he rsk reurns rade off () s no sgnfcan n any of he hree models. In he varance euaon, he coeffcen 1 and 1 are osve and sgnfcan n all he hree esmaons ndcang ha KLSI curren volaly s affeced by s as volaly. The coeffcen 1, whch s suosed o es he asymmery n he marke, s no sgnfcan n any of he models ndcang ha here s no leverage effec. Moreover, he coeffcen measurng he sllover from DJIMI o KLSI and from FTSEGII o KLSI are sgnfcan n he GARCH-M model onng o he fac ha here s sllover from DJIMI and FTSEGII owards KLSI. In oher words, here s nformaon ransmsson from DJIMI and FTSEGII volales o KLSI volaly. The half-lfe 1, whch measure he erod akes a shock o decay no he fuure, for GARCH- M effec s 17.9 days for KLSI wh DJIMI and 18.4 days for KLSI wh FSEGII, resecvely. I s clear ha akes longer for he shock n volaly o dsaear n he KLSI wh FTSEGII esmaon han n KLSI wh DJIMI esmaon. To deermne he bes model among he hree models he log lkelhood crera s used. From he able s clear ha 1 Half-lfe = In(0.5)/In( 1+ 1 ). 168

10 GARCH-M model s he bes f where log lkelhood s he mnmum. For all he models, an ARCH es was done o es for heeroscedascy n he Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 hree models. The resuls of ARCH n lag 1 and 10 sugges ha here s no roblem of heeroscedascy. Table 5. Parameer esmaes of fng GARCH (1,1), EGARCH and TARCH for KLSI from Coeffcen GARCH-M EGARCH TARCH C FTSEGII (-1) 0.04* 0.197* 0.04* DJIMI (-1) 0.194* 0.19* 0.195* KLSI (-1) 0.158* 0.157* 0.161* 0.161* 0.160* 0.159* 0.010* 0.011** * * 0.011* 0.011* 1 (ARCH) 0.095* 0.095* 0.06* 0.00* 0.078* 0.079* 1 (GARCH) 0.894* 0.893* 0.977* 0.978* 0.89* 0.891* DJIMI o KLSI ( ) -0.07** *** -0.04*** FTSEGII o KLSI ( ) -0.08** *** Log lkelhood ARCH (1) ARCH (10) Noe: *, ** and *** sgnfcan a 1%, 5%, and 10% resecvely. KLSI s he deendan varable. Table 6 reors he resuls for he esmaon of DJIMI on KLSI. In he reurns euaon, he coeffcen s no sgnfcan ndcang ha here s no rsk remum n DJIMI. On he oher hand, s clear ha DJIMI s affeced osvely by s own lag and negavely by KLSI lagged reurns n GARCH-M model only. In he varance euaon, he coeffcens for ARCH are sgnfcan n he frs wo esmaons whle GARCH coeffcen s sgnfcan n all he models esmaed. The coeffcen 1 n EGARCH and TARCH models s negave and osve resecvely, and sgnfcan mlyng ha here s a leverage effec and asymmery of news. Ths means ha bad news has a greaer effec on volaly han good news. The sllover effec coeffcen from KLSI o DJIMI s no sgnfcan n all he models ndcang ha here s no ransmsson of nformaon from KLSI volaly o DJIMI volaly. The half-lfe n hs case s 10.8 days for half of he shock o dsaear no he fuure. GARCH-M s he bes f based on log lkelhood crera. ARCH dagnosc es for he heeroscedascy ndcae ha n lag 1 and 10 here s no roblem of heeroscedascy. Table 6. Parameer esmaes of fng GARCH (1,1), EGARCH and TARCH for DJIMI from DJIMI GARCH-M (1,1) EGARCH TARCH C KLSI (-1) *** DJIMI(-1) 0.147* 0.147* 0.150* 0.005** * 0.006* 1 (ARCH) 0.053* 0.09* (GARCH) 0.94* 0.991* 0.949* ** 0.08* KLSI o DJIMI ( 1) Log lkelhood ARCH (1) ARCH (10) Noe: *, **, *** sgnfcan a 1%, 5%, and 10% resecvely. DJIMI s he deendan varable Table 7 reors he resuls for he esmaon of FTSEGII on KLSI. In he reurn euaon for here s no rsk remum n hs marke. In addon, FTSEGII curren reurn s affeced osvely and sgnfcanly by s own lagged reurns and negavely by one lag of KLSI n he frs model only. In he varance euaon, he coeffcens for ARCH are sgnfcan n he frs wo esmaons whle GARCH coeffcens are sgnfcan n all he models esmaed. In addon, he leverage effec coeffcen n he EGARCH and TARCH models s sgnfcan. I s negave n he EGARCH and osve n he TARCH model. Ths ndcaes ha here s a leverage effec and bad news has hgher mac han good news on he ndex volaly. The sllover effec from KLSI o FTSEGII s no sgnfcan n any of he models, whch ndcae ha here s no nformaon ransformaon from KLSI volaly owards FTSEGII volaly. The half-lfe n hs case s 11. days for half of he shock o dsaear n he fuure. Based on he log lkelhood crera s clear ha GARCH-M model s he bes model. ARCH dagnosc es for he heeroscedascy ndcae ha n lag 1 and 10 here s no roblem of heeroscedascy. 169

11 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, 011 Table 7. Parameer esmaes of fng GARCH (1,1), EGARCH and TARCH for FTSEGII from FTSEGII GARCH-M (1,1) EGARCH TARCH C KLSI(-1) *** FTSEGII(-1) 0.149* 0.150* 0.151* 0.005** * 0.005* 1 (ARCH) 0.054* 0.078* (GARCH) 0.941* 0.989* 0.953* * 0.091* KLSI o FTSEGII ( 1) Log lkelhood ARCH (1) ARCH (10) Noe: *, ** and *** sgnfcan a 1%, 5%, and 10% resecvely. FTSEGII s he deendan varable. To summarze, from he above models s clear ha none of he markes has rsk-reurns rade off. In oher words, here s no relaonsh beween he sock reurns of any of hese markes and her volaly. All he ndces are affeced osvely by her own lagged reurns. In addon lagged reurns of DJIMI and FTSEGII are affecng KLSI reurns osvely ndcang nformaon ransformaon from hese markes no KLSI. On he oher hand, KLSI has a negave one-lagged effec on boh DJIMI and FTSEGII n he GARCH-M model only. The varance euaons ndcae ha he coeffcen of 1 and 1 sgnfcan and osve n mos of he cases ndcang ha as flucuaons has osve nfluence on he fuure volaly. In addon, 1 s bg and sgnfcan ndcang ha reurns has long-erm memory or he flucuaons are erssen. Moreover, here s leverage effec n DJIMI and FTSEGII only bu no n KLSI. The leverage effec ndcaes ha hese markes become volale when here s a large decrease n he rces (.e., bad news). When rces of a sock fall hs causes deb o euy rao o ncrease leadng shareholder o erceve ha hs sock s more rsky. Ths s somehow erlexng. Boh DJIMI and FTSEGII have src screenng crera regardng deb rao, whch mus no exceed 33%, References whle KLSI does no have any screen agans deb rao. In addon, here s asymmerc effec of news n hese DJIMI and FTSEGII snce 1 0. Therefore, bad news has sronger mac han good news n DJIMI and FTSEGII bu no KLSI. Lasly, n erms of sllover or nformaon ransmsson, s clear ha here s evden sllover from KLSI o boh DJIMI and FTSEGII bu no vce versa. Ths means ha here a ransmsson of nformaon from KLSI o DJIMI and FTSEGII markes. Therefore, volaly n KLSI affecs DJIMI and FTSEGII bu no vce versa. Concluson Our resuls sugges ha here s no sgnfcan dfference n sock marke reurns beween he hree Islamc sock marke ndces, KLSI, DJIMI, and FTSEGII. Therefore nvesng n any of hem wll yeld he same reurns. In addon, was found ha here s no rsk remum n any of he hree markes. Moreover, our resuls show ha here s leverage effec rsk n he case of DJIMI and FTSEGII bu no KLSI. These wo Islamc sock marke ndces seem o be affeced more by bad news han good news, whch could be due o her larger marke caalzaon han KLSI. Moreover, DJIMI and FTSEGII are nernaonal ndces whle KLSI s a local ndex. In addon, here s asymmerc mac of news on volaly, whch means ha bad news has a greaer effec on volales han good news. Based on he half-lfe values he marke ha revers o mean faser s DJIMI followed by FTSEGII and lasly KLSI. I means ha KLSI ake longer me o rever o mean or for any shock n volaly o decay. Ths could be because boh DJIMI and FTSEGII ncludes secures from dfferen counres and have a larger number of socks hen KLSI whch ncludes local socks and s smaller comared o DJIMI and FTSEGII. Lasly, here s nformaon ransmsson DJIMI and FTSE- GII from oward KLSI bu no vce versa. Ths mgh be a resul of cross lsng of some secures n KLSI a DJIMI and FTSEGII bu no vce versa. 1. Abdul Rahm, F., Ahmad, N. & Ahmad, I. (009). Informaon ransmsson beween Islamc sock ndces n Souh Eas Asa, Inernaonal Journal of Islamc and Mddle Easern fnance and Managemen, (1), Anonou, A., Pesceo, G. & Volars, A. (003). Modelng nernaonal rce relaonshs and nerdeendences beween he sock ndex and sock ndex fuures of hree EU counres: a mulvarae analyss, Journal of Busness Fnance & Accounng, 30 (5/6), Baur, D. & Jung, R.C. (006). Reurn and volaly lnkages beween he US and he German sock marke, Journal of Inernaonal Money and Fnance, 5 (4), Black, F. (1976). Sudes n sock rce volaly changes, Paer resened a he 1976 Meeng of he Busness and Economc Sascs Secon. 5. Bollerslev, T. (1986). Generalzed auoregressve condonal heeroskedascy, Journal of Economerc, 31 (4),

12 Invesmen Managemen and Fnancal Innovaons, Volume 8, Issue 3, Booh, G., Markanen, T. & Tse, Y. (1997). Prce and volaly sllover n Scandnavan sock markes, Journal of Bankng and Fnance, 1, Brooks, C. (008). Inroducory Economercs for Fnance (nd Ed.), New York: Cambrdge Unversy Press. 8. Caorale, G.M., Ps N. & Sagnolo, N. (006). Volaly ransmsson and fnancal crses, Journal of Economcs and Fnance, 30 (3), Cheung, Y. & Ng, L. (199). Sock rce dynamcs and frm sze: an emrcal nvesgaon, Journal of Fnance, 47, Chrse, A. (198). The Sochasc Behavor of Common Sock Varances, Journal of Fnancal Economcs, 10, Daly, K.J. (003). Souheas Asan sock marke lnkages evdence from re- and os Ocober 1997, Asean Econmcs Bullen, 0 (1), Engle, R. (198). A general aroach o Lagrange muller model dagnoscs, Journal of Economercs, 0 (1), Engle, R.F., Llen, D.M. & Robns, R.P. (1987). Esmang Tme Varyng Rsk Prema n he Term Srucure: The ARCH-M Model, Economerca, 55 (), Engle, R.F. & Ng, V.K. (1993). Measurng and Tesng he Imac of News on Volaly, Journal of Fnance, 48, Glosen, L.R., R. Jagannahan & Runkle, D.E. (1993). On he Relaon beween he Execed Value and he Volaly of he Nomnal Excess Reurn on Socks, Journal of Fnance, 48 (5), Johnson, L. & Neave, E. (1996). Effcency and effecveness of Islamc fnancng: he cos of orhodoxy, Workng Paer, No. 96-6, Queen s School of Busness, Queen s Unversy, Canada. 17. Kasbhala, K.M., Sewar, D., Sen, S. & Malndreos, J. (006). Are daly sock rce ndces n he maor Euroean euy markes conegraed? Tess and evdence, Amercan Economs, 50 (1), Koulakos, A., Paasyrooulos, N. & Molyneux, P. (006). More evdence on he relaonsh beween sock rce reurns and voally: a noe, Inernaonal Research Journal of Fnance and Economcs, 1, Koumos, G. & Booh, G. (1995). Asymmerc volaly ransmsson n Inernaonal sock marke, Journal of Inernaonal Money and Fnance, 14, Koumos, G. (1996). Modelng he dynamc nerdeendence of maor Euroean sock markes, Journal of Busness Fnance & Accounng, 3 (7), Kova, Z. (008). Forecasng volaly: Evdence from he Macedonan sock exchange, Inernaonal Research Journal of Fnance and Economcs, 18, Lamba, A.S. & Ochere, I. (001). An analyss of he dynamc relaonshs beween he Souh Afrcan euy marke and maor world euy markes, Mulnaonal Fnance Journal, 5 (3), Langben, J.H. & Posner, R.A. (1980). Socal nvesng and he law of russ, Mchgan Law Rewev, 79 (1), Lao, X. & Q, G. (008). Analyss and Comarson of ARCH effecs for Shangha comose ndex and NYSE comose ndex, Inernaonal Journal of Busness and Managemen, 3 (1), Nelson, D.B. (1991). Condonal Heeroskedascy n Asse Reurns: A New Aroach, Economerca, 59 (), Ozun, A. (007). Are he reacons of emergng euy markes o he volaly n advanced marke smlar? Comarave evdence from Brazl and Turkey, Inernaonal Research Journal of Fnance and Economcs, 9, Poon, S. & Taylor, S. (199). Sock reurns and volaly: An emrcal of UK sock marke, Journal of Bankng and Fnance, 16, Rashd, A. & Ahmad, S. (008). Predcng sock reurns volaly: an evaluaon of lnear vs. nonlnear mehods, Inernaonal Research Journal of Fnance and Economcs, 0, Rosly, S.A. (005). Crcal ssues on Islamc bankng and fnancal markes (1s Ed.). Kuala Lumur: Dnamas. 30. Rudd, A. (1981). Socal resonsbly and orfolo erformance, Calforna Managemen Revew, 3 (4), Soydemr, G. (000). Inernaonal ransmsson mechansm of sock marke movemens: evdence from emergng euy markes, Journal of Forecasng, 19 (3), Teer, J.A. (1991, May 13). The cos of socal crera, Pensons and Invesmen, Ulrch, D. & Marzban, S. (008). Revew and analyss of curren Syarah-comlan euy screenng racces, Inernaonal Journal of Islamc and Mddle Easern Fnance and Managemen, 1(4), Shachmurove, Y. (005). Dynamc lnkages among he emergng Mddle Easern and he Uned Saes sock markes, Inernaonal Journal of Busness and Managemen, 10 (1), Yalama, A. & Sevl, G. (008). Forecasng world sock markes volaly, Inernaonal Research Journal of Fnance and Economcs, 15, Yeh, Y.-H. & Lee, T.-S. (000). The neracon and volaly asymmery of unexeced reurns n he greaer Chna sock markes, Global Fnance Journal, 11, Zakoan, J. (1994). Threshold Heeroskedasc models, Journal of Economc Dynamcs and Conrol, 18,