EVALUATION OF THE FIXING TRADING SYSTEM IN THE SPANISH MARKET*

EVALUATION OF THE FIXING TRADING SYSTEM IN THE SPANISH MARKET* Davd Abad and Anono Ruba** WP-EC 99-7 Correspondenca a: Davd Abad: UNIVERSIDAD DE ALICANTE, Deparameno de Economía Fnancera, Campus de San Vcene del Raspeg, 0307 ALICANTE, e-mal: Gola@ua.es Edor: Insuo Valencano de Invesgacones Económcas, S.A. Prmera Edcón Dcembre 999. IVIE workng-papers offer n advance e resuls of economc researc under way n order o encourage a dscusson process before sendng em o scenfc ournals for er fnal publcaon. * Te auors would lke o express er graude o Juan Carlos Gómez Sala, Angel León, Jorge Izagurre, Mkel Tapa and Gonzalo Rubo for er valuable commens and suggesons. We are also endebed o Juan España for s compung asssance. Suppor from IVIE s graefully acknowledged. ** D- Abad and A. Ruba: Unversy of Alcane.

EVALUATION OF THE FIXING TRADING SYSTEM IN THE SPANISH MARKET Davd Abad and Anono Ruba ABSTRACT In 998 e Fxng radng sysem was mplemened n e Spans Sock Marke. I s consdered an alernave o e radonal sysem of connuous negoaon, applcable o ose socks a ave a seres of basc caracerscs n common. I represens an mporan nnovaon, e fundamenal purpose of wc s o reduce e volaly of socks and us mprove er lqudy. Te man move of s sudy s o verfy weer e mprovemens a e advocaes of e new radng sysem ave been predcng ave acually aken place, as we beleve a any nnovaon a s nroduced no e marke sould be subeced o emprcal evaluaon. To do so, e effec a s nnovaon as ad on e ndcaors of lqudy, reurns and volaly of e socks nvolved s examned, usng paramerc and nonparamerc ess and employng a meodology based on e ecnque of e even sudy. We concluded a e evdence observed seems o conradc e very expecaons a movaed e mposon of e new negoang sysem, snce a sgnfcan worsenng was observed n lqudy and reurns, wereas, on e oer and, no apparen decrease s observed n volaly. Key Words: Fxng, Lqudy, Reurns, Volaly, Tn Tradng. JEL Classfcaon: G0, G9 RESUMEN Durane 998 se mplanó en el mercado español el ssema de conraacón de valores con precos úncos en cada perodo de ause, más conocdo como ssema de negocacón fxng. Ese ssema represena una fórmula alernava al ssema radconal de negocacón connua, aplcable a aquellos valores que reúnen una sere de deermnadas caraceríscas. A su vez, consuye una mporane nnovacón cuya fnaldad es, fundamenalmene, reducr la volaldad de los íulos y meorar su lqudez. La movacón fundamenal del presene rabao consse en conrasar s se an producdo las meoras que vacnaban los mpulsores del nuevo ssema de conraacón, en la creenca de que cualquer nnovacón llevada a cabo en el mercado debe ser someda a evaluacón. Para ello, se esuda el efeco sobre ndcadores de lqudez, rendmeno y volaldad medos de los íulos a los que esa nnovacón afecó, ulzando pruebas paramércas y no paramércas y meodología basada en la écnca del even sudy. La conclusón a la que se llega en ese esudo es que la evdenca observada parece ser conrara a las expecavas que movaron la mplanacón del nuevo ssema de conraacón, pues se observa un empeorameno sgnfcavo en los nveles de lqudez y renabldad y, en cambo, no se observa dsmnucón aparene en el nvel de volaldad. Palabras Clave: Fxng, lqudez, volaldad, rendmeno, negocacón nfrecuene.

. INTRODUCTION A e nernaonal level, weer n developng markes or n ose a are already well esablsed, mporan nvesmens and nnovaons are akng place n marke mcrosrucure, enforcng e mplemenaon and evaluaon of new negoang sysems. W suc a vew, e Spans Sock Marke as recenly oulned new operaonal norms for e sock radng, n an effor o mprove s compeveness n e lg of e emergng European sngle marke. Tese reforms ave focused fundamenally on mprovng lqudy by ncreasng e ransparency and qualy n e execuon of e ransacons. In dong so, s oped a e negoang of ceran asses wll be faclaed, makng em more accessble o a wder range of nvesors. To s am, wo noveles were nroduced no our marke durng 998. Frs, a block marke was developed for wo dfferen caegores of sares, one, a agreed prces, for socks regsered n e IBEX35 ndex. Anoer one, by prces, for all sares raded on e connuous marke. Te reason for creang suc a marke was o faclae e conracng of large volumes. Ts s a grea mprovemen for bg companes and nsuonal nvesors. Secondly, e Fxng sysem, wc s e focal pon of s sudy, was nroduced. Ts new conracng mecansm was desgned o ncrease e lqudy of e less frequenly raded socks. I could be envsoned as an effor on e par of e Spans Sock Marke o faclae e radng of small and medum-szed socks. Te sysem s nended o mprove on prces formaon for less aracve values, by concenrang e conracng a us wo momens durng e sesson. I sould erefore be of grea neres, no only o fnancal economss bu o anyone wo parcpaes n marke acves, o verfy weer s ype of nvesmen n e mprovemen of radng sysems really yelds posve resuls. Includng e socks negoaed n e Fxng sysem. 3

Amud and Mendelson (997 argue a for e marke mcrosrucural mprovemens o be consdered valuable, e prces of e asses raded n e new sysem sould ncrease. In fac, any cange or novely nroduced no e marke sould be subeced o an emprcal evaluaon o deermne us ow effecve s and weer was really wor mplemenng a all. Terefore, f any grea dvergence from e predced oucome s observed, measures sould be aken o correc e msguded course. Te am of e presen sudy s o analyze e beavor of e sares raded n e Fxng sysem and measure e exen o wc e desred mprovemens ave been aceved. To do so, e effec a s nnovaon as ad on e ndcaors of lqudy, reurns and volaly of e socks a were ncluded n e Fxng sysem s analyzed. Ts sudy sould be consdered as an addon o e exensve emprcal researc wc analyzes e effecs a e announcemen and subsequen cange o an alernave radng sysem as on e sares nvolved. Among oers, Cooper e al. (985; McCowell and Sanger (986; Bandar e al. (989, and Crse and Huang (994, sand ou for er work on asses raded on e sock excange a ad prevously raded OTC. Baker and Edelman (99 analyze e case of socks ransferred from NASDAQ o NASDAQ/NMS. Amud and Mendelson (997 sudy canges n e Tel-Avv Sock Excange radng sysems and Ko and Lee (997 do e same n e case of asses raded n A and B sysems of e Korean Sock Marke. On e oer and, e move o e Fxng sysem supposes a cange of e negoang sysem from one of connuous aucon o one of call aucon. So, s sudy could also be classfed among e leraure devoed o e eorecal and emprcal analyss of e advanages and nconvenences of ese wo sysems for Ts effec on e prce concdes w e fndngs of Amud and Mendelson (986, Amud and Mendelson (99 and Brennan and Subramanyam (996, wo fnd negave correlaons beween e levels of lqudy and e expeced reurns (afer akng a seres of oer facors no accoun. 4

order-drven markes. Te man conclusons and resuls obaned from prevous sudes are summarzed n e followng epgrap. Te paper s organzed as follows: In e nex secon we descrbe ow e Fxng sysem funcons n e Spans marke. In secon ree we explan e meodology and e daa used n e sudy. In e four and ff secons, e man resuls obaned for e ndcaors of lqudy, reurns and volaly are sown. Fnally, n e sx secon, e evdence obaned n e analyss s summarzed and e man conclusons are arrved a.. THE FIXING SYSTEM From July s 998, e Spans marke as been operang w a new conracng model: Ssema de facón de precos úncos para cada perodo de ause, beer known as e Fxng Tradng Sysem. Aloug s sysem s a novely o our marke, as s precedens n oer European markes, lke Germany and France, for example. Te move bend suc a cange s o ncrease e lqudy of ose less frequenly raded socks. Perodcally, ceran asses are assgned o s negoang sysem, based on e levels of er acvy and lqudy among oer caracerscs. Tey no longer rade connuously rougou e day, bu raer, are raded exclusvely a wo fxed momens durng e day. In oer words, ere s a cange from a sysem of connuous aucon o one of call aucon. Now, from a raer general pon of vew, we sould lke o commen on e man advanages and nconvenences of e wo dfferen sysems nvolved, as glged n e leraure. Aferwards, we sall descrbe n more deal, ow e Fxng sysem funcons n e Spans marke, e approac employed for e selecon of e socks a sould be ncorporaed no e new sysem and e man prospecs olds. 5

Call Aucon vs. Connuous Aucon Under e call aucon sysem, orders accumulae for predeermned perods of me durng e radng sesson. A e end of eac of ese adusmen perods, orders are baced for execuon a a sngle prce o maxmze e number of sares negoaed. In conras, under e connuous aucon meod, orders are execued wenever submed bds and offers cross durng a radng sesson. Ts meod generaes a sngle prce for eac cross. Several eorecal and emprcal sudes ave analysed e advanages and nconvenences of e wo dfferen conracng models. Prce sably s, peraps, e mos mporan advanage of e call aucon meod over e connuous aucon. Ts greaer sably s aceved because bacng orders over me elmnaes prce flucuaons caused by e bd-ask bounce and reduces prce volaly nduced by e random sequence n wc orders and nformaon arrve. Lkewse, as radng orders accumulae over a fxed me nerval, e mpac of a sngle large order becomes less severe (Coen and Scwarz, 989. Te sysem also represens an effecve mecansm for dealng w asymmerc nformaon problems beween nformed and unnformed lqudy raders (Soll, 985. Te mposon of delays forces e nformed agens o reveal, by e locaon of er orders, e exsence of nformaon, wc, n urn, elps o reduce prce volaly 3. However, s acclamed reducon of volaly s aceved a e expense of prce dsconnuy and e lack of nformaon (Madavan, 99 wc all ranslaes no a lower level of lqudy. In conras o e call meod, e mos frequenly menoned advanage of e connuous aucon sysem s e supply of mmedacy o buyers and sellers. By allowng e mmedae execuon of ransacons, a ger degree of marke lqudy may be expeced. I would seem, erefore, a ere s a ceran volaly-lqudy rade-off n e coce beween ese wo sysems. 3 Te nformed agens ad e opon of nroducng e orders a few momens before e execuon of orders. 6

We sould pon ou a e call aucon sysem concdes w a of connuous aucon wen e nervals for accumulang orders ends owards zero, or, wc s e same ng, wen e number of negoaon momens ends owards nfny. Te wd of e nerval (or smlarly, e pre-deermned number of momens a wc orders are baced proves o be a fundamenal varable by wc o quanfy e range of e neracon beween volaly and lqudy. Tese wo negoaon sysems can be used only and, n fac, some markes allow bo meods for e same asses, aloug a dfferen momens of e day. So, n many cases call aucons are used exclusvely o deermne e socks openng prces, and en cangng o connuous aucon durng e remander of e radng sesson. Moreover, e wo sysems may be used smulaneously for dfferen socks. I s e case analyzed n s paper, e Spans Fxng radng sysem proposes a knd of call aucon meod for ceran socks w pecular caracerscs n common. Descrpon of e Fxng Sysem n e Spans marke Te new radng meod allows e admsson of orders from 9:00 ours o 6:00 ours. Te peculary s us a sares are raded a a sngle prce a wo specfc momens durng e radng sesson. Prces are frs se a :00 noon, and en agan a 6:00 ours. Ts fnal prce beng aken as e closng prce. Apar from seng e prce a maxmzes e number of sares a can be raded, e conracng uns a correspond o eac case are assgned. Te algorm and e sysem of dsrbung sares used for s purpose, are exacly e same as ose used from 989 n e openng up of e marke o all socks. Te sock selecon s done by e Comsón de Conraacón y Supervsón de la Socedad de Bolsas, S.A. (Commsson for e Recrung and Supervson of e Sock Marke, S.A. by observng e levels of acvy and lqudy of e asses 7

durng e sx prevous mons 4. Te followng varables are employed for s purpose 5 : - Daly volume n ordnary operaons. - Number of crossed operaons per day. - Average spread beween bes bd prce and bes ask prce. - Annual roaon rae: e number of conraced uns raded n ordnary operaons dvded by e number of sares admed on e marke. - Tradng frequency: Percenage of sessons durng wc e sock as been negoaed. In addon o e foregong, oer parameers a may be requred o esabls e degree of lqudy of eac asse can also be used. In e case of socks a are ncorporaed no e marke for frs me, er assgnmen o e sysem depends on er dmensons, er dffuson and oer dsngusng caracerscs, as well as on e companes marke expecaons. Te sock selecon decson s made every sx mons, excep n e case of new ncorporaons or n crcumsances n wc e marke s condons ndcae e need for an exraordnary revson. Te precursors o e Fxng sysem sugges a... s s e sysem a adaps bes o e condons of e seleced sock s lqudy and marke acvy, (Revsa de la Bolsa de Madrd, nº 67 June 998. W e new sysem a concenraon of orders s effeced a gven momens durng e radng sesson, ereby ncreasng er radng acvy and er lqudy, as well as reducng e g volaly (Revsa de la Bolsa de Madrd, nº63, February 998. Ts sysem 4 Excep n e case of e frs group of socks, for wc only e ree prevous mons were used. 5 In several sudes, e decson o cange radng sysem s volunary and s erefore aken exclusvely by e companys. Ts mples an prevous opmzaon program and prvae nformaon. Ts could cause auoselecon bas (Amud and Mendelson, 997. In s case a neress us, e decson s exogenous and s ype of bas does no exs. 8

accumulaes purcase and sellng orders for gven momens, ereby acevng equy prces beween offer and demand w lower volaly (no only nraday volaly, bu closng prce volaly, oo, w somewa lower ransacon coss and w less mpac on e prces of e orders nroduced an wen ey are negoaed n on e open marke for 7 ours a day (Revsa de la Bolsa de Madrd nº 67, June of 998. Te new sysem res o concenrae e operaors aenon a.00 ours and 6.00 ours durng eac radng sesson and so reduce e spread beween bd and ask prces, ereby reducng er volaly and ncreasng e volume of negoaon n socks nvolved. Tese obecves are muc more dffcul for ceran asses o aceve n a connuous marke of 7 ours of radng (Anuaro de la Bolsa, 998. As we can see, e promoers rus e new conracng sysem as an effecve way of elmnang e radng problems for e socks concerned, on e grounds a connuous conracng generaes g prce volaly and makes s knd of socks less aracve. So, e fac a orders and e aenon of e nvesors are concenraed on us wo momens durng e sesson, sould creae more effcen and less volale prces. I sould, n urn, generae greaer and more economc negoaon. 3. DATA AND METHODOLOGY So far, wo groups of asses ave been ransferred o e Fxng sysem, and e nex nclusons are programmed for July s, 999. Te frs group of 5 socks were announced n e Sock Marke s Operang Insrucon nº 9/998 on e 0 of June 998, and began radng n e new sysem on July s, 998. Te second group, composed of 4 asses (e mos of wc are e same a ose of e frs group was announced n e Operang Insrucon nº 30/998 on e 7 December 998. Tese socks began radng on January 4, 999. In Table, e evoluon of e frs wo seres of socks negoaed under e new radng model s dealed. 9

Table : Evoluon of e socks raded n e Fxng sysem Te followng able presens e groups of socks a canged o e Fxng sysem s GROUP SITUATION (4--999 nd GROUP (-7-998 CONTINUE DO NOT CONTINUE NEW (4--999 Bco. Andalucía (AND ( AND AFR Aforasa (AFR Bco. Alánco (ATL ATL Bco. Andalucía (AND Bco. Caslla (CAS ( - CAS* Bco. Alánco (ATL Bco. Galca (GAL ( GAL Bco. Galca (GAL Bco. Vascona (VAS ( VAS Bco. Herrero (HRR Bco. Herrero (HRR HRR Bco. Vascona (VAS Bco. Smeón (SIM - SIM** Bayer (BAY Bayer (BAY ( BAY Bco de Crédo Balear (CBL Inbesós (BES ( BES CUN Ca. Vnícola Nore España (CUN Croen Hspana (CIT - CIT** Elecra de Vesgo (VGO Commerzbank (CBK - CBK** Esaconamenos Suberráneos (HES Bco. de Crédo Balear (CBL CBL FYM Fnancera y Mnera (FYM Dmeal (DMT DMT FNZ Fnanzauo (FNZ Esaconamenos ANY HES Suberráneos (HES Grupo Anaya (ANY E.p.p..c. (EPC - EPC** Dmeal (DMT Hornos Ibércos Alba (HSB HSB Hornos Ibércos Alba (HSB Iberpsas (IBP - IBP* Inbesós (BES Indo Inernaconal (IDO - IDO* La Corporacón Baneso (LCB La Corporacón Baneso LCB (LCB Lafarge (LFG Lafarge (LFG LFG LGT Lngoes Especales (LGT Salos del Nansa (NAN NAN La Papelera Española (PAP La Papelera Española (PAP PAP Salos del Nansa (NAN Urbanzacones y Transpores (UBS - UBS** UND Unland Cemenera (UND Elecra de Vesgo (VGO VGO Volkswagen (VWG Volkswagen (VWG VWG * Wen back o e connuous marke. ** Sopped radng. ( Socks a ave undergone spls afer /7/998 and before 3// 998 Te daabase used for s sudy was compled from nformaon aken exclusvely from e frs of group of socks raded n Fxng sysem. As from e begnnng of e year 999, all socks are raded n Euros, e ncluson n e sample of e second group would ave caused a possble dsoron. Te currency cange, 0

e mnmum varaons n e prces of e asses and oer facors lnked o e new currency, could ave generaed bases and nerferences a would ave made very dffcul o solae e effecs a we ave soug o analyze. Ceran socks from e frs group of e 5 nal asses were also elmnaed (see Table. PAP, EPC and DMT were fnally excluded from e sample as ey evenually wen no lqudaon. Te SIM sock was also elmnaed, as ad prevously been radng on e oucry marke and no on e connuous marke. Fnally, CIT, CBK and UBS sopped radng n e Fxng sysem before e end of e year, so a e daa avalable was no suffcenly complee o allow e comparson of bo sysems. Te sudy, erefore, s concenraed exclusvely on ose socks a ad prevously been raded on e connuous marke, were canged o e Fxng sysem and raded unl e end of 998. For e 8 cerfcaes fnally seleced, we ave consdered a sample of daly reurns (calculaed w closng prces and correced for spls, dvdends and capal ncreases beween Ocober 997 and December 3 998. Based on s daa, afer modelng e reurn and e volaly, an analyss s done on e repercussons a e cange of negoaon sysem as ad on ese varables. For a sorer perod (from Aprl s o Sepember 30, 998 we employed e daly seres of e negoaed volumes (n peseas, and e number of daly ransacons. W s daa, and by paramerc and non-paramerc ess, we suded e effec a e new sysem as ad on lqudy. All e daa was aken from Servco de Informacón Bursál de la Bolsa de Madrd (Servce for Marke Informaon of e Madrd Sock Excange. 4. REPERCUSSIONS ON STOCK LIQUIDITY In s secon, we examne e effec a e new negoang sysem as ad on e lqudy of e sock nvolved. As Kyle (985 pons ou, e lqudy of a marke s oo wde a concep for a sngle clear defnon, as s composed of

mulple dmensons. Decdng weer a ceran even produces a sgnfcan cange on a sock lqudy or no, erefore, s a raer rsky ask, snce an unequvocal mprovemen or worsenng of s varable merely mples a s mulple dmensons vary n a ceran drecon. In s sudy, we concenered exclusvely on e radng acvy of e socks, measured by e daly volume (expressed n moneary uns and e number of daly ransacons. Anoer commonly used dmenson for measurng a marke s lqudy s e ransacon coss, measured by e spread. Even oug, we could no consder s dmenson snce e cange of sysem causes a seres of problems n e calculaon of e spread, makng a drec and relable comparson of e wo conracng sysems praccally mpossble. Te man problem s a e spreads for e Fxng Sysem socks are abnormally low. Te sandard meod employed o calculae e spread n e daabase, consss of averagng e bes bd and ask prces a eac momen, so a spreads of e sares raded n e Fxng sysem are zero mos of e me. Terefore, e spread calculaed for ose asses s an nconssen and unrelable measure of e acual ransacon coss. Concenrang exclusvely on e negoang acvy of a marke, s obvous a e ger e level of acvy, e greaer e probably of fndng a buyer/seller for e nvesors, and erefore, less me for execuon. Te advocaes of e new sysem erald an ncrease n e acvy of e socks ransferred o e Fxng sysem as a resul of a more effecve formaon of er prces and e reducon of e volaly an ey prevously ad n e connuous sysem. Te lack of relable models for esablsng e normal beavor of e varables under sudy ere (and especally for nfrequenly raded socks ndcaes e need for an adequae descrpve meodology, suppored by paramerc and nonparamerc ess. Suc ess conss of deermnng weer sgnfcan dfferences ake place n e average values of e varables beween e momen prevous o and e followng e sysem cange. To do so, dfferen equdsan perods o e even dae were used. Specfcally, e wndows employed were days 5, 0, 5, 0, 30, 40,

50 and 60 before and afer e dae of e cange 6. By consderng e perods s way, we could deec no only mmedae effecs bu e sor-erm and long-erm effecs as well. On e oer and, we observed a seven of e egeen socks a compose e sample carred ou spls sorly afer ncorporaon no e Fxng sysem. Te spl s a pracce a produces sgnfcan aleraons n e level of lqudy around e dae a s done, as Gómez Sala (999 pons ou. Ts auor fnds n s sudy of spls n e Spans marke, a raer sgnfcan ncrease n e number of a sock s ransacons on e daes mmedaely followng s spl. He fnds an rregular, oug no sgnfcan cange, n e volume on ese same daes as well. For s reason, we ave consdered bo e enre sample and a sub-sample of socks a dd no carry ou spls. I allows o gve greaer robusness o e analyss of e effec on e negoang acvy. Frs, we ave compared e number of ransacons and e volume negoaed for e ndvdual asses, for e aggregae of e 8 cerfcaes and for e asses a dd no carry ou spls. Eac of ese observaons was en normalzed, by dvdng em by e daly equvalen of e marke. In s way, e endency of e marke s consdered as e normal endency n e beavor of e varables. Te procedure also avods e possbly a any sor of seasonal beavor could bas e resuls. (Durng e Summer mons, for example, e negoang acvy decreases. Afer compleng s operaon, we compared our resuls o see f sgnfcan dfferences exsed beween average and medan for e dfferen wndows proposed. Te resuls for e varables, e relave number of ransacons and e relave volume negoaed are presened n ables.,. and 3., 3.. respecvely. 6 Te even day s aken as e frs day of e followng perods. 3

Table.. Relave number of ransacons for e sample of 8 asses In e followng able, e cange n e average and e medan n e relave number of ransacons are sown for dfferen wndows. Eac wndow s cenered on e even dae and as e duraon a appears n e frs column. Te second and rd column sow e average/medan before and afer e even dae, gven eac duraon. For e average, e dfference s sown n 4 column and s sgnfcance s evaluaed (5 column by a -es (p-value n brackes. Fnally, e 6ª and 7ª columns sow, respecvely, e number of companes a experenced decreases/ ncreases n er relave number of ransacons, w respec o e average. For e medan, e sgnfcance of e dfference beween e prevous and laer perods s measured by e non-paramerc ess of Wlcoxon, medan and Kruskal-Walls. In brackes, e p-value are sown. Fnally, e number of companes a experenced decreases and ncreases n e number of ransacons relave o er medan are gven. SAMPLE OF 8 ASSETS Wndow Before Afer Dff. ±5 days 0.56 0.4-0.84 ±0 days 0.445 0.438-0.007 ±5 days 0.40 0.400-0.00 ±0 days 0.4 0.367-0.054 ±30 days 0.43 0.58 0.68 ±40 days 0.4 0.604 0.9 ±50 days 0.45 0.55 0.6 ±60 days 0.447 0.509 0.06 AVERAGE -es [p-value] -.7-0.09 [0.97] -0.7 [0.865] -.4 [0.59].76 [0.083].59 [0.0].04 [0.044].6 [0.48] Falls Rses Before Afer Dff. 6 0.530 0.4-0.89 9 9 0.466 0.373-0.093 0 8 0.405 0.34-0.09 6 0.403 0.9-0. 6 0.403 0.36-0.04 6 0.403 0.45 0.0 6 0.4 0.380-0.03 6 0.444 0.357-0.086 MEDIAN Wlcoxon Medan Tes Tes.5 0.00 [0.0] [0.00] 0.34 0.00 [0.733] [.000] 0.95.0 [0.340] [0.73].09 3.60 [0.036] [0.058] 0.57 0.7 [0.569] [0.606].0 0.80 [0.30] [0.37] 0.5 0.64 [0.876] [0.43].78 4.80 [0.073] [0.08] K-W Tes 6.8 [0.009] 0.4 [0.705] 0.95 [0.39] 4.45 [0.034] 0.3 [0.564].04 [0.307] 0.0 [0.874] 3.0 [0.073] Falls Rses 6 7 0 8 0 8 7 6 6 6 4

Table.. Relave number of ransacons for e sample of companes. In e followng able, e cange n e average and e medan n e relave number of ransacons are sown for dfferen wndows. Eac wndow s cenered on e even dae and as e duraon a appears n e frs column. Te second and rd column sow e average/medan before and afer e even dae, gven eac duraon. For e average, e dfference s sown n 4 column and s sgnfcance s evaluaed (5 column by a -es (p-value n brackes. Fnally, e 6ª and 7ª columns sow, respecvely, e number of companes a experenced decreases/ ncreases n er relave number of ransacons, w respec o e average. For e medan, e sgnfcance of e dfference beween e prevous and laer perods s measured by e non-paramerc ess of Wlcoxon, medan and Kruskal-Walls. In brackes, e p-value are sown. Fnally, e number of companes a experenced decreases and ncreases n e number of ransacons relave o er medan are gven. SAMPLE OF ASSETS Wndow Before Afer Dff. ±5 days 0.44 0.47-0.67 ±0 days 0.38 0.63-0.65 ±5 days 0.305 0.55-0.50 ±0 days 0.30 0.54-0.66 ±30 days 0.36 0.5-0.64 ±40 days 0.303 0.49-0.54 ±50 days 0.307 0.4-0.66 ±60 days 0.33 0.39-0.93 AVERAGE -es [p-value] -8.07-4.84-6. -7.46-9.8-0.95-3.9-3.9 Falls Rses Before Afer Dff. 0 0.40 0.49-0.7 8 3 0.305 0.63-0.4 9 0.68 0.48-0.0 0 0.9 0.50-0.4 0 0.307 0.50-0.57 0 0.75 0.48-0.7 0 0.90 0.37-0.53 0 0.30 0.34-0.86 MEDIUM Wlcoxon Medan Tes Tes.50 0.00 [0.0] [0.00] 3.74 0.00 4.64 30.00 5.40 40.00 6.6 5.6 7.5 7.0 8.48 9.6 9.33.3 K-W Tes 6.8 [0.009] 4.8.77 9.7 43.87 56.48 7.0 87.8 Falls Rses 0 0 0 0 0 0 0 0 5

Table 3.. Relave negoaed volume for e enre sample. In e followng able, e cange n e average and e medan n e relave negoaed volume are sown for dfferen wndows. Eac wndow s cenered on e even dae and as e duraon a appears n e frs column. Te second and rd column sow e average/medan before and afer e even dae, gven eac duraon. For e average, e dfference s sown n 4 column and s sgnfcance s evaluaed (5 column by a -es (p-value n brackes. Fnally, e 6ª and 7ª columns sow, respecvely, e number of companes a experenced decreases/ ncreases n er relave number of ransacons, w respec o e average. For e medan, e sgnfcance of e dfference beween e prevous and laer perods s measured by e non-paramerc ess of Wlcoxon, medan and Kruskal- Walls. In brackes, e p-value are sown. Fnally, e number of companes a experenced decreases and ncreases n e number of ransacons relave o er medan are gven. SAMPLE OF 8 ASSETS AVERAGE Wndow Before Afer Dff. -es [p-valor] ±5 days 509.5 934.85-460.67 -. [0.056] ±0 days 3656.49 505.04-5.45-0.77 [0.453] ±5 days 33.39 88.60-94.79-0.84 [0.406] ±0 days 3500.54 630.7-870.37-0.83 [0.40] ±30 days 334.8 744.3-490.49-0.63 [0.53] ±40 days 803.85 374.7-49.4-0.7 [0.48] ±50 days 840.73 44.86-45.87-0.74 [0.456] ±60 days 65.97 437.9-5.06-0.43 [0.666] Falls Rses Before Afer Dff. 3 5 47.63 608.6-44.0 6 66. 809.90 48.79 6 304.58 38.6 76.68 4 4 04.98 339.45-865.53 3 5 04.98 356.30-848.68 4 4 687.99 333.55-354.44 4 4 77.8 3. -559.59 3 5 687.99 99.6-488.37 MEDIAN Wlcoxon Medan Tes Tes.08 3.60 [0.036] [0.057] 0.7 0.00 [0.473] [.000] 0.66 0.3 [0.507] [0.75].8 0.40 [0.39] [0.57].45 0.7 [0.45] [0.605].58.80 [0.3] [0.79].59 4.00 [0.009] [0.045].77 6.53 [0.005] [0.00] K-W Tes 4.8 [0.08] 0.57 [0.449] 0.47 [0.493].4 [0.34].4 [0.43].5 [0.] 6.75 [0.009] 7.73 [0.005] Falls Rses 6 6 6 4 4 3 5 5 3 5 3 5 3 6

Table 3.. Relave negoaed volume for e sub-sample. In e followng able, e cange n e average and e medan n e relave negoaed volume are sown for dfferen wndows. Eac wndow s cenered on e even dae and as e duraon a appears n e frs column. Te second and rd column sow e average/medan before and afer e even dae, gven eac duraon. For e average, e dfference s sown n 4 column and s sgnfcance s evaluaed (5 column by a -es (p-value n brackes. Fnally, e 6ª and 7ª columns sow, respecvely, e number of companes a experenced decreases/ ncreases n er relave number of ransacons, w respec o e average. For e medan, e sgnfcance of e dfference beween e prevous and laer perods s measured by e non-paramerc ess of Wlcoxon, medan and Kruskal- Walls. In brackes, e p-value are sown. Fnally, e number of companes a experenced decreases and ncreases n e number of ransacons relave o er medan are gven. SAMPLE OF ASSETS Wndow Before Afer Dff. ±5 days 438.5 555.36-3583.5 ±0 days 43.97 887.90-536.07 ±5 days 9. 055.38-866.84 ±0 days 436.03 645.38-790.65 ±30 days 337.45 398.39-939.06 ±40 days 974.4 37.7-837.07 ±50 days 859.6 359.7-499.45 ±60 days 777.65 56.83-60.8 AVERAGE -es [p-valor] -8.07 [0.] -4.84 [0.00] -6. [0.30] -7.46 [0.396] -9.8 [0.55] -0.95 [0.00] -3.9 [0.304] -3.9 [0.543] Falls Rses Before Afer Dff. 8 3 475.04 439.78-035.6 8 3 957.78 83.4-44.36 8 3 995.97 799.40-96.57 9 030.73 86.00-04.73 0 376.55 75.9-65.36 0 8.56 494.85-63.7 9 088.07 434.94-653.3 8 3 39.80 494.85-644.95 Wlcoxon Tes.08 [0.036].4 [0.].74 [0.08].85 [0.064] 3.63 4.60 5.3 5.08 MEDIAN Medan Tes 3.60 [0.057] 0.80 [0.37].0 [0.73] 3.60 [0.057] 7.06 0.00 7.04 6.3 K-W Tes 4.8 [0.08].65 [0.98] 3.0 [0.078] 3.48 [0.06] 3..4 6.37 5.87 Falls Rses 8 3 8 3 8 3 9 0 0 0 0 7

In ese ables, e resuls for e average and medan for e wo samples, are presened separaely. Te frs and second columns sow e mean values (average or medan of e aggregae (8 or asses for e dfferen perods, before and afer e cange over, respecvely. In e rd column, e dfference beween e frs wo columns s presened. Te four column sows e sgnfcance of s dfference; for e average, a -es s used and for e medan, ree non-paramerc ess were employed: e Wlcoxon (U Mann-Wney es, e C-squared es for e medan and e Kruskal-Walls es. Fnally, e las wo columns sow e number of socks a ave experenced eer a rse or a fall n er relave number of ransacons or relave volume negoaed for eac perod. For e sample of 8 socks, we observe n e number of relave ransacons a very dfferen knd of beavor beween e average and e medan. Te wo measuremens only agree wen ey ndcae a sgnfcan reducon n e number of ransacons n e wndow a corresponds o e 5 days precedng and e 5 days followng e enry of e socks no e Fxng sysem. From s perod on, mporan dfferences begn o appear beween e wo measures. Parcularly n e larger wndows, n wc ere s a cange, from sgnfcan ncreases n e number of ransacons for e average, o non-sgnfcan decreases n s varable for e medan, (excep for e 60-day wndow, wc sows a sgnfcan decrease. Te dfferen beavor of ese wo measures could be due o exreme values on ceran radng days. Tese exreme values could be e resul of e spls carred ou by some of e companes n e sample. Te resuls found n e subsample of asses a dd no spl er sares, seem o confrm s evdence. In s sub-sample, e dfference observed beween e average and e medan s que smlar. Tere s a sgnfcan reducon n e number of ransacons n every perod consdered. Ts resul suppors e evdence presened by Gómez Sala (999 n s sudy of spls n e Spans marke. To be more specfc, n comparng e wo samples, we noce a among e companes a ave eer ncreased or decreased er ransacons n any gven perod, e frms wc ncrease er number of ransacons, are generally ose a ad spl er sares. 8

W regard o e relave negoang volume, we also noe a dfferen sor of beavor beween average and medan values. In general, e averages ave muc greaer values an e medan, wc could ndcae e presence of exreme values. Te non-normaly observed n s varable, manfes n e lepokuross of e dsrbuon, (n oer words, cker als an ose of e normal dsrbuon, as been exensvely debaed n e leraure. Ts fac can no be drecly arbued o e spls, wc e smlar beavor observed beween e wo samples confrms. Once agan, s resul suppors e fndngs of Gómez Sala (999, n wc no sgnfcan canges were seen n e radng volume of e asses a ad been spl. For e sample of 8 socks, e average volume negoaed only reduces sgnfcanly n e 5-day wndow. For e remanng perods, non-sgnfcan reducons of s varable are observed. On e oer and, e medan sows sgnfcan decreases n e wndows of 5, 50 and 60 days. Te sample of asses presens smlar, f no more exreme resuls. Reducons are observed n e average volume n every sngle perod, aloug none of em s sgnfcan. W regard o e medan, e reducons become sgnfcan n almos all e wndows (excep n ose of 0 and 5 days. To renforce e accuracy of e resuls obaned a second es s done. Te new evaluaon ncluded wo nnovaons. Frs, e logarmc ransformaon was used for e varables examned n e sudy. Ts ransformaon as been wdely promoed for e analyss of e negoang acvy, snce smooens e beavor of e varables, as Ankya and Jan, (989, among oers, subsanae. Furermore, s second es measures e sgnfcance of e dfferences beween perods, n cross-secon, wc allows us o focus on e problem from oer meodologcal perspecve. Te es s based on e followng expresson, calculaed for eac asse: 9

D V V V ln M V ln ( before M afer 5,0,5,0,30,40,50,60 days. were, D, dfferenaes beween e wo perods, before and afer e cange n e varable under sudy (relave number of ransacons or relave negoang volume for e asse ; V s e mean value of e varable under sudy, for e days mmedaely precedng or followng e even, for e asse ; V M s e mean value of e varable under sudy for e days precedng or followng e even, for e marke. Once s dfference as been calculaed for eac asse and for every perod, e sgnfcance of s average and s medan s suded, n cross-secon, for e wo samples under consderaon. In Tables 4. and 4.. we presen e resuls for e relave number of ransacons and e relave volume negoaed, respecvely, for e sample and sub-sample. Te frs wo columns conan e number of companes a experence eer a rse or a fall n acvy beween e former and e laer perods. Te followng four columns presen e average and e medan of e dfferences of e socks and er sgnfcance (measured w a -es for e average and e Wlcoxon es for e medan. Te resuls are very smlar o ose obaned w e frs es. For e number of ransacons, and for e sample of 8 asses, we agan observe a grea dfference n beavour beween e average and e medan. On s aspec, bo measuremens sow a sgnfcan reducon n e number of ransacons n e 5-day wndow. For e sample of asses, a smlar beavor s observed beween average and medan. Once agan, sgnfcan reducons are observed n e number of ransacons for all e wndows. Apar from a few excepons, e companes wose negoang levels ncrease or decrease n s es are precsely e same ones a dd so n e prevous es. 0

Table 4.. Relave number of ransacons. For eac of e socks raded, e expresson ( as been calculaed. Te me nervals consdered around e even dae are presened n e frs column. Te second and rd columns reflec, respecvely, e number of socks a experenced decreases and ncreases n e number of ransacons n e pos-even perod. Te remanng columns measure, respecvely, e average, n cross-secon, of e prevously menoned magnude; s sgnfcance by e sascal es and s assocae p-value (n brackes w a -es; e medan of s magnude and fnally, s sgnfcance w e Wlcoxon non-paramerc es (sascal and p-value n brackes. SAMPLE OF 8 ASSETS SAMPLE OF ASSETS Wndow Falls Rses Average -es Wlcoxon -es Medan Falls Rses Average [p-valor] Tes [p-value] Medan ±5 days 5 3 0.63 3.78 3.09 3.4 0.444 9 0.83 [0.00] [0.00] [0..006] 0.870 ±0 days 9 9 0.08 0.50 0.6.90 0.006 8 3 0.547 [0.69] [0.54] [0.05] 0.567 ±5 days 0 8 0.08 0.4 0.48 3.05 0.09 9 0.534 [0.68] [0.63] [0.0] 0.560 ±0 days 7 0.43 0.78.04 5.0 0.97 0 0.63 [0.448] [0.96] 0.53 ±30 days 6-0.069-0.7 0.3 7.59 0.393 0 0.687 [0.790] [0.896] 0.683 ±40 days 6-0.00-0.08 0.08 0.9 0.508 0 0.705 [0.935] [0.93] 0.68 ±50 days 6 0.43 0.60 0.65 9.40 0.487 0 0.87 [0.554] [0.53] 0.847 ±60 days 6 0.6.09.00 0. 0.67 0 0.95 [0.9] [0.36] 0.934 Wlcoxon Tes.44 [0.04].00 [0.045].7 [0.09].80 [0.005].89 [0.004].89 [0.004].89 [0.004].89 [0.004] ( D ln V V ln V V ; were, D M before M represens e dfference beween e wo perods, before and afer e la varable of e afer sudy for e sock ; V s e average value of ee varable under sudy for e days before or afer e even for e sock ; V M s e mean value of e varable under sudy for e days before or afer e even

Table 4.. Relave negong volume. For eac of e negoaed asses, e expresson ( as been calculaed. Te me nervals consdered around e even dae appear n e frs column. Te second and rd columns reflec, respecvely, e number of socks a ad decreases and ncreases n er number of ransacons n e pos-even perod. Te remanng columns measure, respecvely, e average, n cross-secon of e prevously menoned magnude; e sgnfcance s evaluaed by e sascal es and e assocaed p-value (n brackes, e medum of e magnude by a -es; and fnally, e sgnfcance by e Wlcoxon non-paramerc es (sasc and p- value n brackes. SAMPLE OF 8 ASSETS Wndow Falls Rses Average -es [p-value] Medan ±5 days 3 5.4 3.7 [0.004].063 ±0 days 7 0.87 0.48 [0.639] 0.350 ±5 days 7 0.356.05 [0.309] 0.7 ±0 days 3 5 0.43.47 [0.59] 0.476 ±30 days 6 0.333.36 [0.9] 0.604 ±40 days 5 3 0.379.75 [0.098] 0.604 ±50 days 5 3 0.54.83 [0.0] 0.583 ±60 days 3 5 0.605 3.03 [0.007] 0.583 Wlcoxon Tes.65 [0.008] 0.83 [0.408].35 [0.78].74 [0.08].6 [0.06].74 [0.08].48 [0.03].48 [0.03] SAMPLE OF ASSETS Falls Rses Average -es [p-value] Medan 8 3.098.60 [0.06].5 7 4 0.73 0.33 [0.744] 0.48 8 3 0.87 0.64 [0.537] 0.536 9 0.454.7 [0.68] 0.65 9 0.686.4 [0.049] 0.67 0 0.736 3.05 [0.0] 0.75 0 0.777 3.5 [0.005] 0.66 8 3 0.8 3.33 [0.007] 0.764 Wlcoxon Tes.09 [0.037] 0.93 [0.350]. [0.66].38 [0.68].9 [0.056].7 [0.03].7 [0.006].35 [0.08] ( D ln V V M ln before V V M ; were, D represens e dfference beween e perods before and afer e varable under sudy for afer e sock ; V s e mean value of e varable under sudy for e days precedng or followng e even for e sock ; under sudy for e days before or afer e even VM s e mean value of e varable

Te resuls obaned for e negoang volume are also smlar o ose of e frs es, f no more conclusve, f we observe e grea smlary beween e average and e medan. For bo samples, (8 and asses, sgnfcan reducons n volume are seen n e exreme wndows (5, 40, 50 and 60 days. In summary, w e use of ese ess, a clear deeroraon s observed n e negoang level durng e frs days of e sock s enry no e new sysem, as measured no only by e relave number of ransacons, bu also by e relave negoaed volume. For e res of e wndows, and for e sample of 8 asses, no sgnfcan cange s seen n e number of ransacons beween e wo radng sysems, aloug s resul mg well ave been based by e presence of sock spls durng e laer perod. In fac, e sgnfcan reducon n ransacons found n e sample a excludes e spls seems o confrm s evdence. W regard o e volume, s varable beaves que smlarly n bo samples. In addon o e prevously menoned sgnfcan decrease n e 5-day wndow, sgnfcan reducons n volume are also observed over longer perods. By exenson, e resuls reveal a declne n e average negoang rae of e socks a raded on e Fxng sysem (a leas durng e frs days. Ts mples a s, a pror, n clear conradcon w e prospecve resuls predced by e advocaes of e sysem. 5. REPERCUSSIONS ON STOCK RETURNS AND VOLATILITY In s secon we sall aemp o analyze e mpac a e cange of radng sysem, from e connuous marke o e Fxng sysem, as ad on e reurns and average volaly of e sares nvolved. Ts effec s no obvous a frs sg. On e one and, e nvesors could nerpre e cange as a posve even, snce e orgnal am was o concenrae e negoang of ese cerfcaes and mprove er lqudy. Furermore, s 3

assumed a marke mcrosrucural mprovemens sould be accompaned by an ncrease n e average reurns of e sares. On e oer and, e socks cosen for e Fxng sysem ad been seleced specfcally because of er low negoang acvy and lqudy. Te cange of negoang sysem could erefore be perceved by e nvesors as a negave ndcaon from e marke abou a group of socks a, comparavely speakng, offer less aracve prospecs an oer sares radng on e connuous marke. Fnally, bo effecs mg well co-exs n a sor of radeoff, so a er ndvdual effecs on e asse reurns are no clear a all. Ter effec on volaly seems more predcable. As e negoang s lmed o a specfc nerval, and ese asses are less exposed o e flucuaons generally caused by e news releases abou e companes radng on e marke, seems only naural o expec a decrease n er average volaly. To measure e mpac on reurn and volaly for e wole group of asses, an equally weged porfolo s formed w e sares a canged sysem; e groupng of socks a compose e sample n a porfolo s, as Maloney and McCormck (98 pon ou, especally approprae wen e even dae s e same for all e socks n a sample. As approxmaon of e porfolo reurns generaed durng e perod under sudy, e average daly ndvdual asse reurn (calculaed logarmcally and adused for dvdends, rgs and spls as been used. An mporan facor s e exsence of ceran caracerscs a are common o all e socks a were subec o e sysem cange: er scarce lqudy and er low negoang level. Te marke prce of any asse raded on a fnancal marke s no ndependen of e conracng sysem o wc s subeced. Te fac a a sock scarcely rades, as mporan repercussons on e level of s sysemac rsk and s expeced reurn. Of course, s can be exended o any porfolo a s bul by e aggregaon of asses nfrequenly raded. Te n radng generaes, accordng o Sanken (987, auo-correlaons and spurous cross-correlaons a affec e rsk and reurn models, suc as e marke model and s exensons, CAPM, APT, ec. 4

As a resul s no vald o use eer e marke model specfcaon (or s exensons o esmae e expeced reurn or e abnormal reurn of s knd of socks. I would produce a measuremen error a would generae bas and nconssency n e resulng esmaes. Ts bas derves from e fac a e asse reurns s assocaed w a lower negoang level wle e marke ndex reurns s assocaed w a ger relave negoang level; creaes a measuremen error n e sock reurns wc s serally correlaed w e marke reurns. Fnancal leraure proposes several meods for correcng s problem, among e mos popular beng Dmson s (979 meod of added coeffcens, Scoles-Wllams (977 model and a of Coen e al. (983. Gven e auo-correlaon and cross-correlaons a arse from e n radng, e evaluaon of e porfolo reurns mg well be affeced. Ts effec s aken no accoun by employng an alernave o e marke model, wc nroduces a seres of lags and leads of e marke ndex as explanaory varables. In s way, e dsoron produced by e nfrequen radng s elmnaed. Ts reurn model s que smlar n essence o e meodologcal conceps of Dmson (979, and Scoles and Wllams (977, for correcng e esmaes of sysemac rsk n e marke model wen a low negoang volume s nvolved. I as already been employed n even sudes lke ose carred ou by Rcardson e al. (986, among oers. Te reurn model employed ere, erefore, s: R p q α ( p were, R s e porfolo reurns, observed a e momen ; α s e mean reurn ndependen of e porfolo s sysemac rsk; s e coeffcen of sensvy of e porfolo reurns, gven e marke reurns; s e reurn observed n e marke porfolo (refleced by e IBEX35 ndex a e momen ; s e random dsurbance; p, e number of lags; and q, e number of leads. 5

Four possble alernaves are consdered for e modelng porfolo reurns on specfcaon (: Marke model (Model I; p0, q0; Marke model lagged by one perod (Model II; p, q0; Marke model lagged by one perod and leadng by one perod (Model III; p, q v Marke model lagged by wo perods and leadng by wo perods ( Model IV; p, q. Te maemacal specfcaons of ese models can be found n Annex.. On e oer and, s usually assumed a e random error erm from expresson ( verfes e assumpons of e classcal lnear model, a s, s assumed a e dsurbance erm s serally uncorrelaed w zero mean and consan varance. Te esmae of s model by ordnary leas squares could be defecve f eeroskedascy exss and e nference calculaed on e resulng esmaes mg well be nexac, as Morgan and Morgan (987 and Connolly and McMllan (989, among oers, ave found. Te precse specfcaon of e error s mporan because, among oer reasons, e rsk premum mposed on a sock s a funcon of e condonal varance of e reurn. A more accurae alernave o e prevous specfcaon s e modelng of e condonal varance of e reurn by means of e generalzed auoregressve condonal eeroskedascy models (GARCH models. Te GARCH(p,q model was oulned by Bollerslev (986 as a generalzaon of e auoregressve condonal eeroskedascy model proposed by Engle (98. Tese knds of models dsngus beween e uncondonal varance (a s consan and saonary and e varance, a s condoned by e se of avalable nformaon, wc s varable over me. An mporan caracersc of e GARCH(p,q model s a s consdered as symmerc. In oer words, e effec on e varance of e nnovaons or marke socks s ndependen of er sgn; e evdence seems o ndcae owever, a e reurns are usually more sensve o news abou unfavorable evens an abou favorable evens. Ts fac as been aken no 6

consderaon n asymmerc volaly models, wc are, n fac, generalzaons of e GARCH model. Togeer w e GARCH(p,q, we can consder wo asymmerc models: e EGARCH model, proposed by Nelson (990, and e TGARCH model by Zakoïan (994. W regard o e modelng of e volaly of e seres of fnancal daa, e emprcal evdence avalable suppors e use of e GARCH famly of models nsead of oer specfcaons. Lamoreaux and Lasrapes (990 sae a e GARCH(, specfcaon as proven o be e deal represenaon of e volaly beavour of several economc seres. In e Spans marke, Alonso (995, fnds a e volaly of a porfolo reurns s more accuraely caracerzed from a symmercal specfcaon, as afforded by e modelng of wo regmes (Hamlon, 988 an w e asymmercal specfcaons of several oer models. Faced w s evdence, León and Mora (998 deermne a e specfcaon a bes approaces e volaly of e daly reurn of e IBEX35 s e TGARCH model, mprovng e asymmerc models and makng em even beer, n any of e cases, an e models based on symmercal specfcaons do. Faced s ambguous evdence, we consdered fve possble specfcaons for modelng e beavor of e condonal varance: A smple lnear regresson model, a group of symmercal models formed by e ARCH( and GARCH(, 7 models and fnally, a group of asymmerc models formed by e EGARCH(, and TGARCH(, models. Te maemacal specfcaons of ese models are oulned n Annex.. To denfy e bes reurn-volaly specfcaon, we refer o meods based on nformaon crera, suc as Scwarz s Informaon Crera (SIC. Ts creron s based on e expresson 8 SIC*ln (L ML /T - K ln(t/t, wc was developed by 7 Te GARCH(, model s e parsmonous represenaon of e ARCH model w a grea number of erms. 8 Were L ML s e value of e maxmun lkelood funcon (gven e dsrbuon of e random dsurbance erm valuaed by e K parameers esmaon and gven a sample of T observaons. 7

Scwarz (978 and evaluaes e relably of a gven model by e accuracy aceved and by penalzng e number of parameers employed o aceve. Consequenly, s creron allows us o locae e mos parsmonous model by coosng e one a as a greaer SIC. Ts meod as been employed n sudes amed a denfyng e bes specfcaon, for bo reurn and volaly, from a se of alernave models, suc as ose presened by León and Mora (996, or Andrés (999, among oers. Te resuls obaned on e reurn and volaly models consdered, evaluaed by e SIC, are presened n Table 5. On analyzng e resuls, a sarp ncrease s observed n e lkelood funcon n all of e models a ncorporae leads and lags n e ndependen varable as opposed o e smple marke model. Ts ncrease beng even greaer n e case of Model II. We can erefore conclude a e bes specfcaon for e reurn, followng e SIC meod and for eac of e four dfferen ways of modelng e volaly a ave been suded ere, s obvously Model II, or e marke model w a lag of one perod. Furermore, for eac of e reurn models, a sarp ncrease s agan observed n e lkelood funcon a e models of eeroskedasc condonal varance provde wen measured by e SIC, as compared o e smple lneal regresson. Ts ype of models, erefore, and more precsely e GARCH(,, seems o be e mos approprae specfcaon for modelng e volaly. I can also be observed, by means of e drec comparson of e SIC magnudes, a e bes group of models s e symmercal one (e SIC of e symmercal models s, n all of e cases, greaer an e SIC of e asymmerc models. Terefore, e srucure of symmercal volaly seems o ave a greaer descrpve capacy an e wo asymmerc generalzaons proposed. Ts resul seems o conrbue smlar evdence o a presened by Alonso (995, bu sould be consdered w cauon, gven e lmed number of models compared. Te decsve facor n e coce of a symmercal model, lke e GARCH(,, s e lack of sgnfcance 9 of 9 Sgnfcance deermned from e conssen esmaes by compung e quas-maxmum lkelood covarances and sandard errors usng e meods descrbed by Bollerslev and Wooldrdge (99. 8

e parameers a deermne e sensvy of e condonal varance o e asymmery 0 facor. Te esmaes done by maxmum lkelood, allow us o conclude, no only for e TGARCH model, bu for e EGARCH model as well, e nsensvy of e volaly o e sgn of e unexpeced evens n all of e cases suded. In Annex, e resuls of ose esmaes are presened. Table 5: SIC Magnudes. In s able we sow e value of e SIC expresson for eac of e proposed models. By columns, eac of e proposed specfcaons for modelng e reurn beavour (were p s e number of lags and q s e number of leads on e IBEX35 ndex reurns n e orgnal marke model. By lnes (readng across, eac of e fve models proposed for e sudy of e condonal volaly. Accordng o e logc of e SIC approac, e model w e ges value s e bes. Marke Model ( p0; q0 Model II (p; q0 Model III (p; q Model IV (p; q OLS 6.5390 6.683 6.6074 6.58 ARCH( 6.8348 6.997 6.977 6.8955 GARCH(, 6.8397 6.934 6.966 6.8907 TGARCH(, 6.809 6.950 6.8974 6.875 EGARCH(, 6.834 6.908 6.9035 6.8703 SIC*ln(L ML /T - K ln(t/t, were L ML s e value of e maxmum lkelood funcon (gven e dsrbuon of e random dsurbance erm evaluaed by K parameer for a sample of T observaons. We erefore conclude a e bes way o model e volaly of e porfolo reurns s by e GARCH (, specfcaon; n s sense, e evdence for e seres consdered agrees w a of Lamoreaux and Lasrapes (990. Consequenly, le us consder e reurn and volaly model defned by e marke model lagged by one perod and e GARCH (, srucure n e condonal varance of e random error erm: 0 Te sensvy of e condonal varance logarm n e case of e EGARCH model. 9

30 (0, ~ / ; ( 0, (.,.. ; / / N E E d R Ω ϕ ξ µ α (3 were, s a gaussan we nose process; s e condonal varance (volaly; µ s e mean condonal varance; ξ, e sensvy of e condonal varance o e arrval of news n e prevous perod; ϕ s e sensvy of e condonal varance o a lag; and s e random dsurbance of e model. To s specfcaon of e reurn and volaly of e porfolo, a seres of bnary varables are added n e equaon for e mean. Te purpose of s s o reflec e mean effec, durng a gven perod of me, a e even could ave on e average unexpeced reurns and e average accumulaed abnormal reurns. Te use of e dummy varable o measure and conras e mpac of a gven even on e reurn provdes, accordng o Karafa (988, smlar resuls o ose obaned w e radonal even sudy meod bu, from a meodologcal pon of vew, s more effcen an e radonal meod. Te resulng model s as follows: (0, ~ / ; ( 0, (.,.. ; / / ( ( N E E d D D R Ω ϕ ξ µ γ γ α ( 3.. 5,0,5,0,5,30,35,40,50,60,80,00,0 days. Were D (- and D ( represen dummy varables assocaed w me nervals (expressed n days of duraons. Terefore, akng e frs day of July, (e even dae as a reference, dfferen wndows of dencal duraon ( are arbrarly cosen, bo precedng and followng e even dae. Te bnary varables D (- and D ( ake a value of durng e perods (- and ( respecvely and of Te sudy done on e average acummulae abnormal reurns s no dfferen from a of e average abnormal reurns wc s sown below.

3 zero durng e remanng me nerval. In oal, 3 me nervals ave been consdered comprsed of beween 5 and 0 days. Te mpac of e cange n negoang sysem on e volaly of e asse reurns can be deermned by measurng e mpac on e average level of e condonal varance a e even could generae; erefore, followng a meodology smlar o e one prevously employed, e bnary varables D (- and D ( are nroduced no e srucure of e condonal varance of e expresson (3, analyzng e followng sysem for eac of e nervals prevously consdered: (0, ~ / ; ( 0, (.,.. ; / ( * ( * / N E E d D D R Ω γ γ ϕ ξ µ α ( 3.. Were * γ and * γ measure e sensvy of e auonomous coeffcen of e condonal varance n e me perods consdered. Te res of e varables and parameers ave prevously been defned. Te mos effcen way of dong e analyss s by esmang e effec only, on e reurn and e volaly, begnnng w us one sysem a ncorporaes e bnary varables prevously defned. Consequenly, ey ncorporae e varable dummy no e equaon for e mean and e condonal varance, w wc, e model a s fnally consdered s as follows : (0, ~ / ; ( 0, (.,.. ; / ( * ( * / ( ( N E E d D D D D R Ω γ γ ϕ ξ µ γ γ α (4 Te models (3. and (3. were esmaed separely, and e resuls were no dfferen from ose of model (4, sown below.

Model (4 was esmaed by maxmum lkelood for e daly reurns of e perod from Ocober s 997 o December 3s 998. As e aggregaon of e ndvdual reurns o form e porfolo reurns generaes e well-known eeroskedascy problem, e sandard errors were deermned from e varances and covarances marx calculaed by e quas-maxmum lkelood process descrbed by Bollerslev and Wooldrdge (99. Ts meod no only allows us o carry ou an nference on e esmaes a are conssen n e possble eeroskedascy, bu also n e possble absence of condonal normaly n e resduals of e regresson. Te esmaes carred ou for eac of e 3 me perods consdered are presened n Tables 6.. and 6.. Te clear sgnfcance of e and coeffcens can be observed for every me perod consdered, aken approxmae values of 0.8 and 0. respecvely. On e oer and, e coeffcen of e mean reurn s no sgnfcanly dfferen from zero. Te coeffcens of e condonal varance of e GARCH(, srucure are also sgnfcan. Wen we observe e γ and γ coeffcens, s clear a ere was no exraordnary mpac on e level of e mean porfolo reurns ( γˆ s negave and non-sgnfcan n any perod durng any of e perods prevous o e even dae. In e perod of approxmaely 5 days followng e negoang cange, owever, e mean porfolo reurns suffered a sysemac drop, measured by γ ˆ, wc s negave and sgnfcan. Ts fac connues for an addonal perod of approxmaely 5 days, from wc pon onwards, e drop n mean reurns dsappears and s no longer sgnfcanly dfferen from zero n any of e oer me nervals consdered. Ts fac seems o ndcae a negave reacon by e marke o e cange n e radng sysem. 3

Table 6.. Effec of e Fxng Sysem on Reurns and Volaly: Esmaon resuls. Resuls of e esmaes done for Model (4 by e maxmum lkelood meod (assumng condonally normally dsrbued errors, gven e dfferen duraons of me a appear n e frs column. Te coeffcens γ and γ ( ; reflec e sensvy of e porfolo reurns and volaly n respec of e dummy *, varables D (- and D (. Tey ake e values n e perods (- and ( respecvely and zero n e remanng me nerval. For eac nerval e esmaes of e parameers and e p-values (n brackes under e robus covarance marx esmaor gven by e meod of Bollerslev-Wooldrdge (99 are gven. COEFFICIENTS C 5 days -0.000 [0.560] 0 days -0.000 [0.495] 5 days -0.000 [0.677] 0 days -0.000 [0.5] 5 days -0.000 [0.79] 30 days -.E-05 [0.973] 35 days.9e-05 [0.96] γ 0. 0.87 0.85 0.03 0.77 0.75 0.75 0.45 0. 0.3 0.40 0.7 0.0 0. -0.003 [0.095] -0.000 [0.593] -0.00 [0.47] -0.000 [0.46] -0.000 [0.567] -0.000 [0.34] -0.000 [0.38] γ µ -0.003 [0.036] -0.004 [0.00] -0.00 [0.98] -0.00 [0.39] -0.00 [0.77] -0.00 [0.055] -0.00 [0.3].6E-05 [0.00].3E-05.5E-05.9E-05.4E-05.6E-05.9E-05 ξ ϕ * * γ γ 0.369 [0.035] 0.645 [0.00] 0.607 [0.00] 0.58 [0.007] 0.67 0.679 0.67 0.335 [0.07] 0.53 [0.07] 0.46 [0.040] 0.7 [0.086] 0.36 [0.07] 0.9 [0.09] 0.74 [0.06] -7.E-06 [0.46] -4.9E-07 [0.968] -3.6E-06 [0.68] -8.6E-06 [0.9] -7.3E-07 [0.93] -3.E-06 [0.60] -8.6E-06 [0.099] -4.9E-06 [0.558] 3.E-06 [0.79] -.3E-06 [0.838] -.E-06 [0.9] 6.E-06 [0.54] 8.E-06 [0.445] 9.7E-06 [0.404] (4 R α / µ ξ ; ϕ γ * (.. d., E( 0, γ D D ( γ * E( γ D D ( ( ; / / Ω ~ N (0, were R sows e porfolo reurns and sows e IBEX35 ndex reurns for eac observaon a me. 33

Table 6.. Effec of e Fxng Sysem on Reurn and Volaly: Esmaon resuls. Resuls of e esmaes done for Model (4 by e maxmum lkelood meod (assumng condonally normally dsrbued errors, gven e dfferen duraons of me a appear n e frs column. Te coeffcens γ and γ ( ; reflec e sensvy of e porfolo reurns and volaly n respec of e dummy *, varables D (- and D (. Tey ake e values n e perods (- and ( respecvely and zero n e remanng me nerval. For eac nerval e esmaes of e parameers and e p-values (n brackes under e robus covarance marx esmaor gven by e meod of Bollerslev-Wooldrdge (99 are gven. COEFFICIENTS C (4 40 days -.5E-05 [0.967] 50 days -4.3E-05 [0.94] 60 days -6.E-05 [0.884] 80 days -.6E-05 [0.97] 00 days -0.000 [0.585] 0 days -9.E-05 [0.897] R α / µ ξ ; ϕ γ * γ 0.76 0.7 [0.00] 0.79 0.06 0.99 0.00 (.. d., E( 0, γ D D ( γ * E( 0. 0.4 0.30 0. 0.8 0.6 γ D D ( ( ; / / Ω -0.000 [0.58] -0.000 [0.46] -0.000 [0.387] -0.000 [0.360] -0.000 [0.85] -0.000 [0.893] ~ N (0, γ µ -0.00 [0.07] -0.00 [0.344] -0.000 [0.468] -0.00 [0.5] -0.000 [0.569] -0.000 [0.380].8E-05.8E-05.8E-05.5E-05.3E-05.5E-05 [0.003] were R sows e porfolo reurns and sows e IBEX35 ndex reurns for eac observaon a me. ξ ϕ * * γ γ 0.65 0.648 0.67 0.569 [0.00] 0.603 0.60 [0.00] 0.78 [0.054] 0.68 [0.0608] 0.4 [0.] 0.64 [0.] 0.58 [0.09] 0.74 [0.08] -7.3E-06 [0.79] -7.7E-06 [0.05] -8.E-06 [0.097] -4.0E-06 [0.348] -.E-06 [0.769] -3.9E-06 [0.465] 6.8E-06 [0.5].4E-05 [0.6].4E-05 [0.078].8E-05 [0.036].5E-05 [0.06].5E-05 [0.0] 34

Observng e esmaed coeffcens γ assocaed w e dummy varables, e beavour of e mean daly volaly could also be arbued o e cange n e radng sysem. Wle γ esmaes are negave and no sgnfcan n all e cases, e γ coeffcens are posve n e maory of e cases and no sgnfcan n any, excep for e perods of 80 and 00 days followng e even, n wc case ey are posve and sgnfcan. Based on s evdence and on e meodology employed, we can conclude a e cange n negoang sysem dd no sgnfcanly aler e level of daly condonal varance of e porfolo. 6. CONCLUSIONS Our work as focused on evaluang e repercussons a e cange n radng sysem, from e connuous marke o e Fxng sysem, as ad on e negoaon levels, reurns and volaly of e socks nvolved. Te man obecve of e sudy, erefore, was o evaluae e effecs of e new radng sysem by comparng e expeced resuls (wc were e bass for e cange w e acual resuls observed, n e belef a any nnovaon or modfcaon mposed on e marke sould be emprcally evaluaed. Te evdence agans e cange can be summarzed as follows: Lqudy. In conras o e expeced ncrease n s varable by e advocaes of e Fxng sysem, s sudy as no found any ndcaons of suc an mprovemen durng e me perod examned. On e conrary, sgnfcan decreases are deeced n e negoang acvy for e perods mmedaely followng e cange.

Asse Reurns. We ave observed, for e group of socks a were subeced o e negoaon cange, an mmedae and sysemac reducon n e average reurn. Ts decrease s undoubedly relaed o e deeroraon of e lqudy, as prevously commened, wc can be nerpreed as a clear penalzaon by e nvesors of e socks a abandoned e connuous radng sysem. 3 Volaly W regard o e expeced reducon n volaly, no sgnfcan reducons ave been observed n e level of e average volaly of e reurns. On e conrary, n e longer me perods, an ncremen s acually observed n s varable. Ts evdence as been proven w e reurn-volaly model descrbed of n s paper, a s o say, w e marke model w a lag and volaly model based on e GARCH(, specfcaon. Nevereless, an exploraory sudy of ese resuls, employng e oer alernave specfcaons referred o n s work, concluded n smlar evdence. Terefore, e fundamenal concluson of s sudy, (based on e meodology employed, and for e sample me perod consdered, s a e expecaons of e advocaes of e Fxng sysem, (wc were e very move for s mplemenaon, ave no been fulflled. I s reasonable o conecure, a leas for e me beng, a e nvesors mg ave no perceved e Fxng sysem n e way as s promoers. In s way, ey obvously mg see e cange as a penalzaon mposed on ese sares by e marke. Ta s o say, as a degradaon or descen n caegory, makng ese asses less aracve. Ts evdence, erefore, could sow a dependence beween e possble success of e mcrosrucural nnovaons a can be carred ou n small markes (as e Spans example s and e correc nerpreaon by s nvesors of e causes a movae suc canges. If s s really e case, would ndcae e

necessy for a greaer nformaon effor on bealf of e promoers of e new sysem o explan er rue moves. Furermore, e evdence observed n s specfc case could ardly be an solaed rrepeble case and sould erefore be consdered n e case of any oer nnovaon or aleraon a s carred ou n e fuure. On e oer and, s mporan o bear n mnd a e mposon of e Fxng sysem supposes an mporan cange n e radng sysem for e socks nvolved. Bo sysems ave er own specal caracerscs a are valued n dfferen ways, accordng o e ype of nvesor. So a wa may be a clear advanage for one group of nvesors may well be an nconvenence for anoer. Wa s requred, erefore, s a sysem a pleases e maory of e nvesors. In e case of e Fxng sysem n e Spans marke, s possble a e cange from a sysem a offered e possbly of execung purcase and sale orders mmedaely a any momen durng a day s radng sesson o a new sysem n wc radng s only permed a wo specfc momens, as been a far oo dramac cange for a ceran group of nvesors. Te resuls observed mg well ndcae e reecon by nvesors wo were que sasfed w e way n wc e radng was prevously done. Ts would agan ndcae e need for a greaer effor, on bealf of e promoers of e Fxng sysem o explan e caracerscs and possble advanages of e new negoang sysem, on e one and, and, on e oer, e need for more careful plannng n e case of mplemenng nnovaons a mply drasc canges. Fnally, we mus pon ou a s negoang sysem s oally new n Spans marke. Terefore, aloug nally may no ave generaed e foreseen resuls, we sall ave o wa unl s sysem s oally consoldaed o arrve a defne conclusons.

ANNEXES Annex : Maemacal specfcaons of e reun and volaly models employed. Annex.: Specfcaons for porfolo reurns. Model Specfcaon General Model q p p R α Marke Model ( Model I (p0, q0 R α Model II (p; q0 R α Model III (p; q R α 3 Model IV (p; q p R α Annex.: Maemacal specfcaones for volaly. Model Specfcaon ARCH(p (Engle, 98 (0, ~ / ; ( 0, (.,.. ; ' / / p N E E d X Y Ω ξ µ α GARCH(p,q (Bollerslev, 986 (0, ~ / ; ( 0, (.,.. ; ' / / q p N E E d X Y Ω ϕ ξ µ α EGARCH(p,q (Nelson, 990 ln( (0, ~ / ; ( 0, (.,.. ; ln( ( ( ( ln( ' / / / / q p N E E d X Y Ω ϕ ϑ ξ µ α TGARCH(p,q (Zakoïan, 994 (0, ~ / ; ( 0, (.,.. ;. 0 0; ; ' / / q p N E E d caso oro en D s donde D D X Y Ω < ϕ ϑ ξ µ α

Annex : Resuls of e esmaes of e coeffcens of e condonal varance gven e dfferen specfcaons for e mean: Esmaes and sgnfcance. In e followng ables, e parameers esmaed by maxmum lkelood (assumng condonally normally dsrbued errors are sown. Among brackes, e p-values under e robus covarance marx esmaor gven by e meod of Bollerslev-Wooldrdge (99 are gven. Annex.: Coeffcens for e volaly n e Marke Model. Specfcaon µ ξ ϕ ARCH( GARCH(, EGARCH(, TGARCH(,.E-05.8E-05 [0.000] -4.846.9E-05 0.776 0.669 [0.003].005 0.633 [0.0] ϑ - - 0. [0.08] 0.58 0.09 [0.080] - 0.067 [0.60] 0.078 [0.850] Annex.: Coeffcens for e volaly n Model II ( marke model laged by one perod. Specfcaon µ ARCH( GARCH(, EGARCH(, TGARCH(,.7E-05.5E-05-3.757.5E-05 ξ ϕ 0.76 0.689 0.86 0.690 [0.00] ϑ - - 0.0 [0.04] 0.683 0.0 [0.044] - 0.35 [0.97] -0.00 [0.997]

Annex : Resuls of e esmaes of e coeffcens of e condonal varance gven e dfferen specfcaons for e mean: Esmaes and level of sgnfcance. In e followng ables, e parameers esmaed by maxmum lkelood (assumng condonally normally dsrbued errors are sown. Among brackes, e p-values under e robus covarance marx esmaor gven by e meod of Bollerslev-Wooldrdge (99 are gven. Annex.3: Coeffcens for e volaly n e model III ( marke model lagged and leadng by one perod. Specfcaon µ ARCH( GARCH(, EGARCH(, TGARCH(,.7E-05.5E-05-3.973.6E-05 ξ ϕ 0.78 [0.0000] 0.688 0.846 0.677 [0.009] ϑ - - 0.09 [0.05] 0.663 0.07 [0.055] - 0.4 [0.43] 0.08 [0.997] Annex.4: Coeffcens for e volaly n e model IV (marke model lagged and leadng by wo perods. Specfcaon µ ARCH( GARCH(, EGARCH(, TGARCH(,.5E-05.6E-05-4.048.6E-05 ξ ϕ 0.767 0.73 0.847 0.67 [0.007] ϑ - - 0.74 [0.049] 0.655 0.07 [0.055] - 0.5 [0.30] 0.70 [0.660]

REFERENCES Ankya, B.B. and P.C. Jan (989: Te Beavour of Daly Sock Marke Tradng Volume, Journal of Accounng and Economcs,, pp. 33-359. Alonso, F. (995: Te Modelzacón of e Volaly of e Spans Marke Marke, I Documen of Work Nº 9507, Bank of Span. Amud, Y. and H. Mendelson (986: Asse Prcng and Bd-Ask Spread, Journal of Fnancal Economcs, 7, pp. 3-49. Amud, Y. and H. Mendelson (99: Lqudy, Maury and e Yelds on US Tresaury Secures, Journal of Fnance, 46, pp. 4-45. Amud, Y. and H. Mendelson (997: Marke Mcro-srucure and Secury Values: Evdence from e Tel Avv Sock Excange, Journal of Fnancal Economercs, 45, pp. 365-390. Andrés, A. (999: Impac on e Marke Marke of e Expraon of e Conracs Derved from e IBEX35, Tess Nº 990, Cener of Moneary and Fnancal Sudes. Baker, H.K. and R.B. Edelman (99: Te Effecs on e Spread and Volume of Swcng o NASDAQ Naonal Marke Ssem, Fnancal Analyss Journal, enero-febrero, pp. 83-86. Bandar, A., T. Grammakos, A.K. Maka and G. Papaounnou (989: Rsk and Reurn on Newly Lsed Socks: Te Pos-Lsng Experence, Journal of Fnancal Researc, 9, pp. 3-0. Bollerslev, T. and J.M. Wooldrdge (99: Quas-Maxmum Lkelood Esmaon and Inference n Dynamc Models w Tme Varyng Covarances, Economerc Revews,, pp. 43 7. Bollerslev,T. (986: Generalzed Auoregressve Condonal Heeroskedascy, Journal of Economercs, 3, pp. 307-37. Brennan, M.J. and A. Subramanyam (996. Marke Mcrosrucure and Asse Prcng: On e Compensaon for Illqudy n Sock Reurns, Journal of Fnancal Economcs, 4, pp. 44-464. Cre, W.G. and R.D. Huang (994: Marke Srucures and Lqudy: A Transacons Daa Sudy of Excange Lsng, Journal of Fnancal Inermedaon, 3, pp. 300-36. Coen, K.J., G.A. Hawawn, S.F. Maer and R.A. Scwarz (983: Esmang and Adusng for e Inervallng-Effec Bas n Bea, Managemen Scence, 9, pp.35-48. Coen, K. J. and R.A. Scwarz (989: An Elecronc Call Markes: Lqudy, Volaly and Global Tradng, Dow Jones-Irwng, pp. 5-58.

Cooper, S.K., J.C. Gro and W.E. Avera (985: Lqudy, Excanges Lsng and Common Sock Perfomance, Journal of Economcs and Busness, 37, pp. 9-33. Connolly, R.A. and H. Mcmllan (989: Tme Condonal Varances and even sudes: Te Case of Capal Srucure Canges, Workng Paper (Marzo, Unversdad de Calforna. Dmson, E. (979: Rsk Measuremen wen Sares are Subec o Infrecuen Tradng. Commen, Journal of Fnancal Economcs, 7, pp. 97-6. Engle, R.F. (98: Auoregressve Condonal Heeroskedascy w Esmaes of Varance of U.K. Inflaon, Economerca, 50, pp. 987-007. Gómez Sala, J.C. (999: Renabldad y Lqudez alrededor de los Spls, Documeno de Trabao del Insuo Valencano de Invesgacones Económcas. Hamlon, J.D. (988: Raonal-Expecaons Economerc Analyss of Canges n Regme: An Invesgaon of e Term Srucure o Ineres Raes, Journal of Economc Dynamcs and Conrol,, pp. 385-43. Karafa, I.. (988: Usng Dummy Varables n e Even Meodology, Te Fnancal Revew, 3, pp. 35-357. Ko, K. and Lee, I. (997: Te Effecs of Secon Cange on Reurn, Volaly and Lqudy n e Korean Sock Marke, Advances n Pacfc Basn Fnancal Marke, 3, pp. 55-7. Kyle, A.S. (985: Connous Aucons and Insder Tradng, Economerca, 53, pp. 35-335. Lamoureaux, C. and W. Lasrapes (990: Perssence n Varance, Srucural Cange, and e GARCH Model, Journal of Busness and Economc Sascs, Vol:8 No., pp. 5-34. León, A. and J. I Lves (996: Modelng Condonal Heeroskedascy: Aplcaon o Sock Reurn Index Ibex35, I Documen of Work of e Insuo Valencano of Economc Invesgaons. Maloney, M. and R. Mccormck (98: A Posve Teory of Envromen Qualy Regulaon, Journal of Law and Economcs, 5, pp. 99-3. Madavan, A. (99: Tradng Mecansms n Secures Markes, Journal of Fnance, 47, pp. 607-64. Mccowell, J.J. and G.C. Sanger (986: Sock Excange Lsngs, Frm Value and Secury Marke Effcency: Te Impac of NASDAQ, Journal of Fnance and Quanave Analyss, Marzo, pp. -5. Morgan, A. and I. Morgan (987: Measuramen of Abnormal Reurns from Smalls Frms, Journal of Busness and Economc Sascs, 5, pp. -9.

Nelson, D.B. (990: Condonal Heeroskedascy n Asse Reurns: A New Approac, Economerca, 4, pp. 867-887. Resumen Anual Del SIBE (año 998. Socedad de Bolsas. Revsa de la Bolsa de Madrd nº 63 (febrero 998 Revsa de la Bolsa de Madrd nº 67 (uno 998 Rcardson, G., S. Sefck and R. Tompson (986: A Tes of Dvdend Irrelevance Usng Volume Reacons o a Cange n Dvdend Polcy, Journal of Fnancal Economcs, 7, pp. 33-334. Scoles, M. and J. Wllams (977: Esmang Beas from Nonsyncronous Daa, Journal of Fnancal Economcs, 5, pp. 309-37. Scwarz, G. (978: Esmang e Dmenson of a Model, Annals of Sascs, 6, pp. 46-464. Sanken, J. (987: Nonsyncronous Daa and e Covarance-Facor Srucure of Reurns, Journal of Fnance, 4, pp. -3. Soll, H.R. (985: Alernae Vews of Marke Makng Amud, Y.; T.S.Y. Ho; R.A. Scwarz; (eds, Marke Makng and e Cangng Srucure of e Secures Indusres (Lexngon books, pp. 607-64. Zakoïan, J.M. (994: Tresold Heeroskeasc Models, Journal of Economc Dynamcs and Conrol, 8, pp. 93-995.