An Empirical Study of China s Financial Stock Index Futures Effect on Stock Spot Market Based on CSI 300

Transcription

1 Inernaonal Journal of Mulmeda and Ubquous Engneerng, pp hp://dx.do.org/ /jmue An Emprcal Sudy of Chna s Fnancal Sock Index Fuures Effec on Sock Spo Marke Based on CSI 300 Xongbng Chen 1 and Nng Zhang 2 1 Economcs and Managemen school, Wuhan Unversy, , Wuhan, Chna 2 School of Economcs, Renmn unversy of Chna, Bejng, Chna *Correspondng auhor: Xongbng Chen (chenxongbng@homal.com) Absrac Fnance s he core of modern economy, and he capal marke as a par of he whole fnancal sysem, s he key o he developmen of a counry's economy. In hs paper, we analyze he mpac of sock ndex fuures on he sock marke by usng CSI 300 ndex. The resul shows ha he sock ndex fuures no sgnfcan effecs on he volaly of spo marke; however, here exs a co negraon relaonshp n boh long erm and shor erm. Granger causaly analyss shows ha he sock ndex fuure s no Granger cause o CSI 300, whle he CSI 300 s Granger cause sock ndex fuures. On hs bass, we pu forward relevan polcy suggesons. Keywords: Sock ndex fuures, Volaly, Fnancal markes, CSI 300, VAR model 1. Inroducon Fnance s he core of modern economy, so he fnancal developmen s he key o he developmen of a counry's economy, and he capal marke as a par of he whole fnancal sysem, s mporance n he developmen of a counry canno say oo much. Snce Chna's reform and openng up, he economy has mananed a rapd growh rend, he overall srengh of he economy and he nernaonal saus has also been sgnfcanly ncreased, n whch he connuous developmen of he fnancal sysem and mprove he susanable and sable developmen of he economy o provde adequae power [1]. As a par of he fnancal sysem, Chna's secures marke has gone hrough more han 20 years, he relevan laws and regulaons gradually perfec, he number of nvesors has been very large, he sock marke has also been pvoal. A sock ndex fuure, whch s he sock ndex fuures, s a fuures conrac wh he sock prce ndex as he subjec of he conrac. Sock ndex fuures self s no a new hng, n he early 1990s, he Uned Saes has been he frs o launch he frs sock ndex fuures conrac, he value lne compose ndex fuures conracs, from hen on, sock ndex fuures n developed counres and emergng marke counres o develop [2]. The sock marke value of he oal marke value of he world's sock marke n he forefron of our counry, he nroducon of he sock ndex fuures has been repeaedly wss and urns, unl 2010, he frs sock ndex fuures conrac named he Shangha-Shenzhen 300 ndex fuures conrac was offcally lsed on he Chnese fnancal fuures exchange. Shangha and Shenzhen 300 ndex by he CSI's from Shangha and Shenzhen Sock Exchange Marke seleced 300 A shares as a sample compled ndex consuen socks. CSI 300 sock ndex fuures s he subjec of he CSI 300 ndex fuures varees, n Aprl n he Chnese fnancal fuures exchange offcally lsed ransacons. Ths s a major reform of Chna's capal marke; he brh of fnancal dervave nsrumens such as sock ndex fuures wll be conducve o he esablshmen of a maure mul-level fnancal sysem, whch wll have a profound mpac on Chna's capal marke. Snce he Shangha and Shenzhen 300 sock ndex fuures s he subjec of he CSI 300 ndex, s very mporan o clarfy he nroducon of Shangha and Shenzhen sock ndex fuures ISSN: IJMUE Copyrgh c 2015 SERSC

2 Inernaonal Journal of Mulmeda and Ubquous Engneerng on he correspondng spo marke prces. On he one hand, wheher he nroducon of sock ndex fuures ncreased he volaly of he spo marke, on he oher hand, he relaonshp beween he sock ndex fuures and he correspondng spo marke s wha knd of, hs s he problem ha hs paper res o sudy. 2. Leraure Revew 2.1. Sock Index Fuures Sock ndex fuures frs developed n overseas maure marke, so he earles leraure s overseas scholars on hese fuures conracs lsed overseas o sudy. By comparng before and afer he nroducon of sock ndex fuures, sock marke volaly o deermne he change n he end of he nroducon of sock ndex fuures on he volaly of he spo marke produce wha knd of mpac, and analyze he reasons. In addon, ndex fuures and ndex by conacng spo marke prces, he relaonshp beween research leadng sock ndex fuures and spo, ha s, wheher here s any funcon of prce dscovery, and he causes of hs effec s. In our counry before year 2000, rare emprcal arcles n hs area, one of he few sudes of he sock ndex fuures are suck on wheher Chna should explore he developmen of fuures of such dervaves. Afer 2000, he ssue of sock ndex fuures s more and more aenon, becomng he focus of scholars. Hong (2012) poned ou he saus a he me he sock marke, he nroducon of sock ndex fuures for our polces o make a relavely smple desgn [3]. Hammoudeh (2014) developmen he heory by drawng on he experence n maure markes and emergng markes sock ndex fuures [4], ha he counry s necessary and feasble developmen of sock ndex fuures and sock ndex fuures for developmen he problems are gven recommendaons. Jason (2005) research he S&P500 ndex fuures from 1978 o 1989, confrmed ha he nroducon of sock ndex fuures o reduce he volaly n he spo marke [5]. On he conrary, here s some case sudes show ha he nroducon of sock ndex fuures o ncrease he volaly of he spo marke. Alger (2014) research S&P500 ndex of he spo found afer he nroducon of ndex fuures [6], sock volaly, manly because foregn nvesors probably compeng holdngs of US socks and ndex funds. Anonou (2005) hrough he FTSE100 ndex fuures research [7], ha fuures markes have ncreased volaly n he spo marke because he sock ndex fuures wll fnd new nformaon self, he mpac of he spo marke, hereby mprovng he flow of nformaon n he spo marke rae. Smlarly, consder he nroducon of sock ndex fuures dd no because a sgnfcan mpac on he volaly of he spo marke was confrmed mos emprcal research. Drffll (2006) sudy on Hong Kong's Hang Seng ndex fuures, sock marke volaly dd no change sgnfcanly afer he launch [8]. Chrsos (2014) sudy he dynamc relaonshp beween he S&P 500 ndex fuures and Brsh FTSE100 ndex fuures wh her respecve sock marke ndex [9], he resuls show he exsence of a long-erm relaonshp beween he wo conegraon of spo and fuures sock ndex, and boh cause and effec relaonshp, Bu mos of he me he spo prce of lead fuures prces. Francs (2006) fnd ou FTSE100 ndex wll be dvded no hree dfferen perods [10], he resuls show ha he enre perod beween he cash and fuures a he same me havng a sexual relaonshp, whch boh lead-lag relaonshps was no sgnfcan; however, for each perod, he fuures yeld leadng spo rae of reurn. Among hem, namely he Uned Kngdom durng he second major fnancal reform o 1987 before he sock marke crash, bu he leadng spo fuures, spo marke, hs s subjec o he mpac of lower ransacon coss. 408 Copyrgh c 2015 SERSC

3 Inernaonal Journal of Mulmeda and Ubquous Engneerng In addon o he above research mehods, scholars vsually compare before and afer he nroducon of he fuures spo marke rends, o sudy he mpac of sock ndex fuures on he sock marke. Donald (2005) poned ou he use of daa relaed o Hong Kong, Japan and Souh Korea before and afer he nroducon of sock ndex fuures marke [11]. The sock marke ndex rend, he spo marke volaly and radng volume rpare face of changes before and afer he nroducon of sock ndex fuures, sock marke reacon s a comparave sudy of ha ndvdual socks were he mpac of he nroducon of sock ndex fuures s no conssen, whch s before he launch of sock ndex fuures spo marke rends have a grea relaonshp Program Tradng In Chna, s a knd of ransacon mehod, he experence of he ransacon, or he observaon of he daa characerscs of a varey of radng marke. The mehod self does no necessarly have a src mahemacal logc or fnancal heory. Quanave radng s a radng sraegy ha can oban excess profs hrough modern sascal and mahemacal mehods, n he hsorcal daa o fnd ou he probably of a superor. Quanave radng s no necessarly o use a compuer o send a sgnal. Algorhm radng refers o he mehod of deermnng he execuon pah, execuon me, prce and quany of he bes radng orders hrough compuer sysem. Objecve o sudy he man algorhmc radng s o reduce ransacon cos bg mpac on he marke, mprove ransacon effcency. The range of applcaon of he algorhm s very wde, wheher s a marke maker, arbrage radng or rend rackng ransacon can reduce ransacon coss hrough he algorhm. Hgh frequency radng s a specal knd of radng, whch s quck o deal wh marke nformaon such as marke daa, news daa, economc polcy and oher fas radng. Each ransacon hgh-frequency radng prof small, bu because every day he huge radng volume, gaher a lle more, ncome s very consderable, and sably s very srong. Procedural ransacons can be used n a wde range of areas, almos all aspecs of fnancal ransacons. A presen, has a wde range of applcaons n he domesc: porfolo managemen, rsk conrol, fnancal managemen, and hgh frequency radng and so on. 1) Porfolo Managemen: porfolo managemen s a combnaon of ransacons n he managemen of he accoun. For example, he purchase of a baske of secures porfolo, or n dfferen radng producs o hedge he rsk, or n he same radng producs usng dfferen radng models o mx, ec. Procedural ransacon s beyond he lms of human energy, and can monor mulple markes n real me, so s he bes choce for porfolo managemen. 2) Rsk conrol: he process of he ransacon model n he desgn of he general wll consder he rsk conrol par, and hus n he use of he program can effecvely overcome he arfcal rade n exreme cases of rraonal behavor. Usng hsorcal daa o deec he rsk level of he model, we can draw up he correspondng rsk conrol sraegy o conrol he maxmum rsk n he process of model operaon. 3) Capal managemen: he process of he ransacon can effecvely use he hsorcal daa; can be found ha he bes combnaon of dfferen varees, dfferen radng models, can effecvely mprove he effcency of he use of funds. 4) Hgh frequency radng: hgh-frequency radng s refers o he arfcal s almos mpossble o use n a very shor me o marke a slgh change n seekng prof quanave radng. Hgh frequency radng s requred for Copyrgh c 2015 SERSC 409

4 Inernaonal Journal of Mulmeda and Ubquous Engneerng hgh speed, so ha some of he radng mechansm o se up he server se o he dsance from he exchange s very close, n order o shoren he dsance of he ransacon nsrucons hrough he cable o reach he exchange server. 5) Trend Tradng: rend radng s he mos common mode of radng n chna. In he curren domesc procedure of he ransacon has jus sared, mos of he program radng model s based on rend, bu also based on a large number of models on he bass of echncal ndcaors, so he model has a grea mprovemen n he srucure of space. 3. Model Desgn 3.1. ADF Tes Un roo es plays a very mporan role n economercs, and s he bass of co negraon analyss and Granger causaly es. The un roo es s acually saonary es. The frs sep s o deermne he order of each sequence, and he sandard un roo es s he DF es of Dckey 1979.DF es has hree forms of regresson model, n model 1; does no conan consan and lnear me rend erm: Y Y 1 In model 2, ncludes a consan erm: Y Y 1 In model 3, ncludes consan and lnear rend erms: Y Y 1 Among hem, s random error, s consan, f he receved H0: =0, ndcaes he presence of a un roo, for non-saonary sequence. Because hs mehod canno guaranee he resdual erm of he equaon s whe nose, he esmaon of he es s no unbased. Based on hs, DF es s exended o form a ADF (Augmened Dcker-Fuller) es, whch s currenly wdely used un roo es mehod, n he rgh sde of he equaon o jon he lag, so ADF es becomes Y Y 1 p 1 Y 3.2. Co-Inegraon Tes In he feld of economc research, some me seres are non-saonary, bu some lnear combnaon of hese me seres s saonary. The defnon of co negraon s as follows, f he me seres y 1,y 2, y n s d order, namely I (d), here s a vecor =( 1, 2, n ), and make as y ~ I(d-b), s he co-negraon vecor. There are wo mehods for co negraon es, one s he co-negraon es based on regresson resduals, whch s also called sngle equaon co-negraon es, ha s E-G wo sep mehod; he oher s based on he regresson coeffcen of complee nformaon co-negraon es, ha s, Johansen co-negraon es mehod. Johansen co negraon es s a mehod of regresson coeffcen based on VAR model. The VAR model s frsly proposed o solve he problem of seng up he model by Sms n The seng of he VAR model s no based on he economc heory, bu drecly on he daa self. VAR model s used o model he mulple equaons, and he model s used o model he endogenous varables n he model. In he non srucural VAR model, no dsncon beween endogenous and exogenous 410 Copyrgh c 2015 SERSC

5 Inernaonal Journal of Mulmeda and Ubquous Engneerng varables, all varables are consdered as endogenous varables. VAR (P) model, whch s composed of wo varables, s descrbed as follows: Afer he upper ype dfference: Y p 1 A y x y y 1 p 1 1 y x Among hem: p 1 A I p 1 Johansen co negraon es nvolves he characerscs of he roo locus es sasc and he maxmum egenvalue sascs, whch are derved from he egenvalues of he marx n. So he co-negraon es of he equaon s ransformed no he rank of he marx n, for characersc roo race es sascs: T r m r 1 A ln 1 If sgnfcan, o rejec he null hypohess, ha here s a leas one co-negrang vecor, hen es he sgnfcan of. So check, ge he maxmum number of co negraon vecor. 4. Emprcal Analyss 4.1. Daa Source In order o analyze he mpac of he CSI 300 sock ndex fuures on he spo marke n Shangha and Shenzhen 300 ndex fuures, hs paper selecs he Shangha and Shenzhen sock ndex fuures on, ha s, he daa from Aprl 16, 2010 o December 31, 2014, he daa comes from he Chna Secures Index Co and he Chna Fnancal fuures exchange, he sample sascs resuls are shown n Table 1. From he resuls of sascal es, boh of hem are no subjec o normal dsrbuon. From he sascal daa, he Shangha and Shenzhen 300 ndex fuures prce rend n he long-erm and he spo marke prces end o be conssen, here may exs beween he long-erm sably of he relaonshp. Table 1. Descrpve Sascs of he Spo and Fuures n CSI 300 Index ndex Mean value San.dev. Skewness Kuross J-B sascs SP FCP Sably Analyss Frs, he auocorrelaon funcon of he CSI 300 ndex and he ndex fuures are respecvely carred ou respecvely by usng he auo correlaon funcon es. Through he auocorrelaon funcon char, we found ha he Shangha and Shenzhen sock ndex fuures have a serous self correlaon. Through he correlaon graph, we observe ha he j Copyrgh c 2015 SERSC 411

6 Inernaonal Journal of Mulmeda and Ubquous Engneerng paral auocorrelaon funcon PAC, so he CSI 300 ndex and he ndex fuures sequence exs obvous auocorrelaon, s no sable, and has he characerscs of AR (P) sequence. Below, we have a ADF and PP es mehod for un roo es for he Shangha and Shenzhen 300 ndex fuures and ndex fuures and her dfference, and he es resuls are shown n he followng able: Table 2. ADF Un Roo Tes Resuls Tme Tes ype ADF es Sgnfcan level resul seres value 1% 5% 10% HS300 (c,,0) No sable HSIF (c,,0) No sable HS300 (c,0,0) sable HSIF (c,0,0) sable Table 3. PP Tes Resuls Tme Tes ype ADF es Sgnfcan level resul seres value 1% 5% 10% HS300 (c,,5) No sable HSIF (c,,4) No sable HS300 (c,0,4) sable HSIF (c,0,4) sable Through he above able, we know ha he sasc value of CSI 300 ndex and sock ndex fuures s more han he crcal value, he sequence s no sable, bu he frs order dfference s far less han he crcal value, he able shows ha he sequence has been sable Co-Inegraon Tes and Error Correcon Model Usng he leas square mehod o esablsh he regresson equaon of he CSI 300 ndex and he sock ndex fuures: HS 300 HSIF Pu no he daa, he esmaed resuls are: HS HSIF ( ) ( ) R DW Frs use E-G wo-sep co-negraon es: frs of all, hs regresson resduals do ADF un roo es, he resuls are as follows: Table 4. Resdual Un Roo Tes Varable Tes ype ADF es Sgnfcan level resul value 1% 5% 10% (0,0,1) No sable I can be seen ha he resduals of he es sasc s far less han he value of he sgnfcance level of crcal value. A he same me, he probably value of 0.000, show ha he resduals have been sable, can be expressed ~I (0), and accordng o he 412 Copyrgh c 2015 SERSC

7 Inernaonal Journal of Mulmeda and Ubquous Engneerng defnon of prevous heores, we can hnk ha he CSI 300 ndex and sock conegraon relaonshp exss beween he wo fuures seres, co-negraon vecor (I, ). Nex, we esablsh error correcon model of he ECM. HS HSIF ecm 1 ( ) ( ) ( ) 2 R DW Ecm -1 s he error correcon erm; he sze of he coeffcen of he fuures prce reflecs he devaon from he long-erm equlbrum adjusmen. The esmaed value of he coeffcen s , whch ndcaes ha he CSI 300 ndex when he shor-erm flucuaons devae from he long-erm equlbrum sae, wll be adjused o 70.5% of he adjusmen o he equlbrum sae, adjus he nensy s grea, he need o adjus he me s shorer. The coeffcen of ecm -1 s negave, and fs he reverse correcon mechansm of he error correcon model, and he model s vald hrough es VAR Model Usng Johansen co negraon es, we frs need o esablsh he VAR model o deermne he lag order. Generally speakng, you can use he followng ndcaors o deermne he resuls are as follows: Table 5. VAR Model of Hyseress Order Selecon Lag LogL LR FPE AIC SC HQ NA 1.17e * * * * * As can be seen from he above able, n addon o he SC selecon lag phase, he res of he several ndcaors have chosen o lag behnd 2. As he co negraon es s acually a model of he non bndng VAR model o ge he model, he model of he lag perod of he model s no consraned VAR model frs order dfferenal varables of he lag, so he co negraon es o choose he lag phase, he es resuls are shown n he followng able: Table 6. Co-Inegraon Tes Unresrced Conegraon Rank Tes (Trace) No. of CE(s) Egenvalue Trace Sasc Crcal Value Prob.** None * A mos 1 * Unresrced Conegraon Rank Tes (Maxmum Egenvalue) No. of CE(s) Egenvalue Max-Egen Crcal Value Prob.** None * A mos 1 * * denoes rejecon of he hypohess a he 0.05 level From he above able, we can see ha he race sasc s and he maxmum characersc value s , whch ndcaes ha here exss co-negraon Copyrgh c 2015 SERSC 413

8 Inernaonal Journal of Mulmeda and Ubquous Engneerng relaonshp beween HS300 and HSIF a he 5% confdence level, and only one conegraon equaon. The co-negraon coeffcen can be sandardzed, n whch only one co negraon relaonshp s consdered, and he co negraon equaon s obaned: Z HS HSIF Through he un roo es, Z s a sable me seres. Smlarly, he co negraon equaon can be expressed as: HS HSIF Granger Causaly Tes Based on he co-negraon relaonshp, we examne he causaly beween HSIF and HS300. Granger causaly es has wo: frs, HS300 s no a Granger HSIF, he wo s HSIF Granger HS300 s no he cause of he, he correspondng F sascs and he correspondng P values are calculaed. Due o he lag of he choce of dfferen, he es resuls may also be dfferen. The resuls are shown n Table 7 Table 7. Granger Causaly Tes H0 hypohess Lag F sascs Pvalue HSIF s no he Granger cause of HS HS300 s no he Granger cause of HSIF E-61 HSIF s no he Granger cause of HS HS300 s no he Granger cause of HSIF E-62 HSIF s no he Granger cause of HS HS300 s no he Granger cause of HSIF E-61 HSIF s no he Granger cause of HS HS300 s no he Granger cause of HSIF E-57 As can be seen from he above able, n he seleced lag perod, a he 5% level, he rejecon of he HS300 s no he cause of HSIF, acceped he HSIF s no he cause of HS300 Granger. Therefore, he sock ndex fuures s no he cause of he Shangha and Shenzhen 300 ndex, he CSI 300 ndex s he Granger reason for he sock ndex fuures. Ths shows ha he Shangha and Shenzhen 300 ndex sll deermnes he rend of he sock ndex fuures, and he general sock ndex fuures lead o he concluson of he sock ndex s dfferen. Ths emprcal ndex fuures daa and Shangha and Shenzhen sock ndex fuures are he daly closng prce, long-erm, sock ndex fuures and spo marke s hghly relevan, and he prce of sock ndex fuures s dependen on he spo marke rend, and wh he maury dae of he fuures, he prce s also naural o he spo marke. Bu n he shor erm, he prce of sock ndex fuures may be ahead of he spo marke prce, and hs need o capure he hgh frequency daa, he concluson of he hgh frequency daa wll be more relable. 5. Concluson Through quanave analyss, Merology and Inspecon vss before and afer he nroducon of sock ndex fuures, spo marke prces flucuaed. Paper seleced from sock ndex fuures marke dae o 2014 daa, he esablshmen of a vecor auo regresson model (VAR), and on hs bass o esablsh a vecor error correcon model (VECM). In order o nvesgae he order and he nformaon conen beween sock ndex fuures and spo, we preclude he use of he Granger (Granger) causaly es. Fnally, we use he mpulse response funcon and varance decomposon analyss o examne how ousde nformaon s propagaed o he Shangha and Shenzhen 300 sock ndex fuures marke and he spo marke, as well as o measure he conrbuon of a varey of nformaon on 414 Copyrgh c 2015 SERSC

9 Inernaonal Journal of Mulmeda and Ubquous Engneerng boh he prce flucuaes. Emprcal evdence shows ha CSI 300 ndex fuures volaly mpac on he spo marke s no sgnfcan. The resuls show ha afer he nroducon of sock ndex fuures, he nfluence of nerference facors effec wll soon be absorbed by he marke weakened, hereby reducng he effec of flucuaons n perssen nerference, ha means he ransmsson of nformaon more effcenly. However, he new nformaon n he sock ndex fuures marke and he spo marke and hus no passed o ncrease he volaly of he spo marke. Abou he relaonshp beween sock ndex fuures and he CSI 300 Index, wheher long erm or shor erm, here exs conegraon relaons beween hese varables. A he 5% sgnfcance level, he Sock ndex fuures s no Granger cause o CSI 300, whle he CSI 300 Index s Granger cause sock ndex fuures, ha s one way he CSI 300 Index sock ndex fuures Grand Jay reason ndex fuures prce dscovery funcon n he shor erm, bu n he longer erm, he spo marke sll plays a decsve role n he foundaon. In he shor erm, sock ndex fuures prced frs acon, he nformaon on he marke's reacon faser han he spo marke, manly due o sock ndex fuures marke lqudy, low ransacon coss, he leverage s good, wo-way rade, he mplemenaon of hgh effcency so ha new nformaon can be refleced n he rend n he marke faser. Bu n he long erm, spo marke prcng sll domnaes he marke, sock ndex fuures based on he CSI 300 Index as he underlyng, he prce depends on he prce of he spo asses, and as fuures approachng expraon dae, s prce s naurally o Convergence of he spo marke prce, sock marke, sock ndex fuures marke sll deermne he rend. References [1] R. Yang and L. Xangyang, Analyss of lnkage effecs among ndusry secors n Chna s sock marke before and afer he fnancal crss, Physcal A: Sascal Mechancs and s Applcaons, vol. 41, (2014), pp [2] H. Hongbo and S. Chen, Fnancal lberalzaon and nernaonal marke nerdependence: Evdence from Chna s sock marke n he pos-wto accesson perod, Journal of Inernaonal Fnancal Markes, Insuons and Money, vol. 33, (2014), pp [3] L. Hong, The mpac of Chna's sock marke reforms on s nernaonal sock marke lnkages, The Quarerly Revew of Economcs and Fnance, vol. 52, no. 4, (2012), pp [4] S. Hammoudeh and D. Nguyen, Dependence of sock and commody fuures markes n Chna: Implcaons for porfolo nvesmen, Emergng Markes Revew, vol. 21, (2014), pp [5] L. Jason and J. Thompson, Edgng effecveness of sock ndex fuures, European Journal of Operaonal Research, vol. 163, no.1, (2005), pp [6] B. Alger, The nfluence of bofuels, economc and fnancal facors on daly reurns of commody fuures prces, Energy Polcy, vol. 69, (2014), pp [7] A. Anonou and G. Koumos, Index fuures and posve feedback radng: evdence from major sock exchanges, Journal of Emprcal Fnance, vol. 12, no. 2, (2005), pp [8] J. Drffll and Z. Roond, Moneary polcy and fnancal sably: Wha role for he fuures marke, Journal of Fnancal Sably, vol. 2, no. 1, (2006), pp [9] F. Chrsos and E. Salvador, Calendar anomales n cash and sock ndex fuures: Inernaonal evdence, Economc Modelng, vol. 37, (2014), pp [10] I. Francs and S. Km, Mul-scale hedge rao beween he Ausralan sock and fuures markes: Evdence from wavele analyss, Journal of Mulnaonal Fnancal Managemen, vol. 16, no. 4, (2006), pp [11] L. Donald, Avalably and selemen of ndvdual sock fuures and opons expraon-day effecs: evdence from hgh-frequency daa, The Quarerly Revew of Economcs and Fnance, vol. 45, no.4, (2005), pp Copyrgh c 2015 SERSC 415

10 Inernaonal Journal of Mulmeda and Ubquous Engneerng 416 Copyrgh c 2015 SERSC