THE IMPACT OF SHORT SALE RESTRICTIONS ON STOCK VOLATILITY: EVIDENCE FROM TAIWAN

The Inernaional Journal of Business and Finance Research Volume 5 Number 4 2011 THE IMPACT OF SHORT SALE RESTRICTIONS ON STOCK VOLATILITY: EVIDENCE FROM TAIWAN Shih Yung Wei, Universiy of Science and Technology, Taiwan Jack J. W. Yang, Universiy of Science and Technology, Taiwan ABSTRACT Governmens implemen policies o sabilize sock markes in imes of financial crisis. The mos common inervenion is o forbid shor sales. For insance, around he financial crisis of 2008, eleven governmens announced resricions on naked shor sales in heir sock markes. In ligh of he Greek credi crisis in 2010, Germany also disallowed naked shor sales. Opinions were widely divided regarding he appropriaeness of governmen o inerfere in markes. This paper sudies he influence of volailiy asymmeries caused by he Taiwanese governmen s naked shor sale resricions. Inraday daa is used o analyze he issue by way of EGARCH models. We find he high liquidiy associaed wih large socks increases asymmeric volailiy. However, asymmeric volailiy of middle and small sized socks decreases around he naked shor sale ban. JEL: C22; C58; G18; KEYWORDS: Asymmeric volailiy, Informaion exposure, Naked shor sale, Firm size, EGARCH INTRODUCTION T he Greek deb crisis in 2010 caused a variey of financial concerns. In an aemp o sabilize he financial siuaion, he German Federal Financial Supervisory Auhoriy forbid he naked shor sale for Euro zone bonds, and shoring some financial socks. The rule came ino effec on May 19, 2010 unil March 31, 2011. In 2008 global financial crisis resuled in global sock marke collapse ogeher, I also made every governmen adop all kinds of policies o sabilize he sock marke, such as limiing or banning he naked shor sale in Taiwan, Korea, Belgium, Holland, Canada, German, Ireland, England, America, Ausralia, and Russia; pausing o rade in Russia, Korea, and Brazil; sopping o rade in Russia, Ukraine, Kuwai and Indonesia. Limiing or banning naked shor sales was he policy nearly every governmen carried ou o sabilize is sock marke. The effeciveness of his approach is debaed. This paper researches he effecs of banning naked shor sales in he Taiwan sock marke afer he financial crisis of 2008. In he pas, mos research has been based on he closing prices, hereby ignoring changes in inraday volailiy. In his paper, we advance he analysis o include inraday volailiy. We conclude ha he banning naked shor sales increases reurn volailiy for large firms, bu i can decrease he reurn volailiy for he middle and small sized firms. This paper is organized ino five pars. The following secion conains a lieraure review abou shor sale resricions and asymmeric volailiy. The hird par presens he daa and mehodology which is based on EGARCH Analysis o assess he influence of shor sale bans on inraday daa asymmeric volailiy in he sock marke. The fourh par presens he empirical resuls, which reveals ha inerfering policies and he banning naked shor sales have cerain influences on inraday asymmeric volailiy. The paper closes wih a summary and some concluding commens. LITERATURE REVIEW Limiing or banning naked shor sales was a common policy o provide sabiliy o he sock markes 89

S. Y. Wei & J. J. W.Yang IJBFR Vol. 5 No. 4 2011 around he recessions of 2008. However, he success of his approach remains debaable. Woolridge and Dickinson (1994) sudied he relaionship beween sock prices and securiies lending. They found ha securiies lending couldn collapse sock prices, hose who raded in securiies lending were no able o earn super-normal reurn, and i provided liquidiy. Fros and Savarino (1988) found invesmen limi resricions could no only help o reduce he esimaed error, bu also improve porfolio reurns. Ho (1996) sudied he Singapore sock marke, from 1985 o 1986. He found ha by forbidding naked shor sales affeced volailiy. He used uncondiional flucuaion and condiional flucuaion in his ess and found sricly limiing naked shor sales would increase he volailiy of he sock marke. Hong and Sein (2003) derived a model for he heerogeneous expecaions and used limied naked shor sales o explain why sock prices showed negaive skewness. In oher words, sock price declines were an excess volailiy phenomena. Dieher, Lee, and Werner(2009)examined 2,485 socks, 1,352 from he NYSE and 1,133 from he NASDAQ. They explored how naked shor sales affec liquidiy, volailiy and he effecs of marke qualiy. While shor-selling aciviy increased boh for NYSE and NASDAQ-lised Pilo socks, reurns and volailiy a he daily level were unaffeced. NYSE-lised Pilo socks experience more symmeric rading paerns and a sligh increase in spreads and inraday volailiy afer he suspension while here was a smaller effec on marke qualiy for NASDAQ-lised Pilo socks. Chelley-Seeley and Seeley (1996), Laopodis (1997), Hu e al. (1997), and Yang (2000) discovered he exisence of asymmeric volailiy. The phenomenon of asymmeric volailiy refers o a siuaion when new informaion causes price change. When new informaion is posiive, fuure price volailiy is smaller. When new informaion is negaive, fuure price volailiy is greaer. Black (1976) firs found ha curren reurns had a negaive correlaion wih fuure volailiy. Chrisie (1982) and Schwer (1990) laer found he same resuls. Liau & Yang (2008) argued ha asymmeric mean reversion and volailiy reflec he fac ha invesors reac more srongly o bad news han o good news, confirming he volailiy of asymmery. This paper researches naked shor sale bans in he Taiwan sock marke afer he global financial crisis in 2008 which affeced he degree of volailiy. Based on he above sudies, we can assume ha when new informaion resuls in falling sock prices, he financial leverage of companies will rise. In oher words, he risk of holding a sock increases, and fuure reurns will be more volaile. On he oher hand, when new informaion causes sock price o rise, he financial leverage of companies will decrease, and flucuaion of fuure reurns will be less volaile. This phenomenon is called he leverage effec. Wheher asymmeric volailiy of sock reurns is caused by leverage effecs is sill no conclusive. Senana & Wadhwani (1992) on he oher hand assume he asymmeric volailiy phenomenon was due o herding behaviors by rader. Lo and MacKinlay (1987) argued ha asymmeric volailiy resuled from non-synchronous rading. In he empirical model, when dealing wih high-frequency financial daa, Engle (1982) esablished he Auoregressive Condiional Heroskedasiciy Model (ARCH) o solve self-relaive and heroskedasiciy problems. Bollerslev (1986) exended his work o he GARCH model (generalized ARCH) o describe he phenomenon of volailiy clusering of reurns. However, he GARCH model canno disinguish differences in volailiy beween posiive and negaive informaion (he phenomenon of he violabiliy asymmeries). Nelson (1991) developed he exponenial GARCH model (EGARCH) o disinguish his difference. Campbell and Henschel (1992) disribued he asymmeric volailiy by he quadraic GARCH model (QGARCH). Laer, Engle & Ng (1993) compared hese wo models, finding he EGARCH model had a beer disribuion, and Hafner (1998) proved wih empirical daa ha he EGARCH model was beer a disribuing he volailiy of high-frequency daa. The EGARCH model is widely applied o high-frequency daa so his research uses he EGARCH model o discuss he asymmeric volailiy of sock reurns. 90

The Inernaional Journal of Business and Finance Research Volume 5 Number 4 2011 Duffee (1995) uilized he daily reurn square roo of he sum o consruc he esimaed volailiy, o sudy he relaions beween reurn and volailiy of individual socks, and reurn and volailiy of he aggregae marke. He found posiive relaions beween reurn and volailiy of individual socks was he primary reason why he sock price fell, reurn volailiy rose. The relaionship was sronger for small firms. Kun and Levine (1996) analyzed he developmen of sock markes. They found posiive relaions beween developmen of sock markes and financial agencies, banks and non-banks. He discovered ha large scale markes have he low volailiy properies. DATA AND METHODOLOGY Daa and Descripive Saisics Afer he global financial crisis in 2008, he Taiwan sock marke inroduced he upick rule on Sepember 22, 2008. Laer, naked shor sales were banned on Ocober 1, 2008. The upick rule ban was lifed on January 5, 2009. The policy express in Table 1. Table 1: The dae of Banned he naked shor sale in he Taiwan Sock Marke Order Sar End Even 1 2005/5/16 2008/9/21 Besides he composiion sock of Tai 50 and Tai mid-cap 100 upick rule 2 2008/9/22 2008/9/30 All sock Upick rule 3 2008/10/1 2008/12/31 banned he naked shor sale 4 2009/1/5 Besides he composiion sock of Tai 50 and Tai mid-cap 100 upick rule This able presens he period of Banned he naked shor sale in he Taiwan Sock Marke This paper sudies he influence of banning naked shor sales has for he asymmeric volailiy of he sock marke pre-period, in he period, and pos-period in Taiwan markes. Skinner (1989) found ha a minimum of five hundred observaions is necessary in ensuring reliable esimaes wih he EGARCH model. Thus we adop inraday daa for each 30 minues of he TAI 50 and TAI mid-cap 100 indices before, in and afer banning naked shors sale as our daa. Daa were obained from he Taiwan Sock Marke Exchange. Because he Taiwan sock marke doesn have a small-cap index, we assume he paern of he TAIEX weighed average index. Firs, we calculae he marke value. The base period is December 28, 2004. The index of he base period is 6000. The index on December 28, 2004 is show in Table 2. The small cap index mode, is compued using Eq. 1. marke value he value of he consiuen sock of he TAI 50 and TAI mid cap100 I * 6000 (1) s he value of he base period Where he base period is December, 28, 2004, and he marke value o reduce he value of he composiion sock of he TAI 50 and TAI mid-cap 100 is 5283103 housands. Table 2: The Index and Marke Value index Marke Value TAIEX weighed average index 4521.5 13541728 Tai 50 index 6000.6 6271838 TAI mid-cap 100 index 6053.2 1986787 TAI small-cap 6000.0 5283103 This able presens he index and marke value in 2004/12/28 91

S. Y. Wei & J. J. W.Yang IJBFR Vol. 5 No. 4 2011 Figure 1 plos he 30 min. sock price movemens for he four indexes. The reurn for each marke are calculaed as he percen logarihmic difference in he 30 min sock index, i.e., P R = ln 100,where P 1 R,P,P -1 sand for he marke reurn and price for each 30 min, respecively; ln is he coninuous compounding facor. Reurn descripive saisics of hree subperiods are exhibied on Table 3. The skewness saisics indicae ha all reurn series are eiher negaively or posiively skewed. The excess kurosis saisics sugges deparure form normaliy, ha is, all series are highly lepokuric. Hence, he Jarque-Brea saisics rejecs he normaliy for each reurn series. The Augmened Dickey-Fuller (ADF) and Phillips and Perron (PP) uni roo ess revel all he series are saionary. Figure 1: Taiwan Sock Index of he 30 min 8000 TAI 50 TAIEX TAI Mid-cap 100 TAI Small-cap 7000 6000 5000 4000 3000 2000 Dae 8/4/2008 9/15/2008 10/29/2008 12/10/2008 1/21/2009 3/13/2009 Table 3: Number of Observaions index Sar End Obs. 1 Pre- 2008/6/23 2008/9/21 576 2 In 2008/10/1 2008/12/31 585 3 Pos 2009/1/1 2009/3/31 513 This able presens he observaion for hree sub-periods This research used reurn series o analyze asymmeric volailiy. The Ljung-Box (LB) saisics for 12 lags applied o residuals and squared residuals indicae significan linear/nonlinear dependence exis. If he Q saisic of Ljung-Box reurn series is significan, i shows he auocorrelaion phenomenon exiss in his series. Tha is o say, if he Ljung-Box of he square of reurn series Q saisic is significan, i indcaes he series variance exiss for he auocorrelaion phenomenon. This implies his series conains he heeroskedasiciy phenomenon. The ess of LB(12) LB 2 (12) shown in Table 4 show ha mos reurn series and he square of reurn series all conain he auocorrelaion phenomenon. As such, he analysis models should conssider auoregression (AR), and condiional herocedesiciy (CH). The mean equaion of he GARCH Family Model can resolve auocorrelaion series, and is variance equaion allows he variance o be decided by 92

The Inernaional Journal of Business and Finance Research Volume 5 Number 4 2011 he pre-variance and disurbance erm. So he exisence of condiional herocedesiciy is accepable. In order o explain he phenomenon, i is opimal o adop he GARCH Family Models. Table 4: Descripive Saisics of Three Index Each 30min Sock Reurn in Four Sample Periods EVENT PERIOD μ σ S K JB LB(12) LB 2 (12) ADF PP Per- -0.04 0.70 0.43 14.15 3002.02 38.14 *** 104.28 *** -21.86 *** -21.84 *** TAI 50 In- -0.04 1.02-0.83 12.89 2450.64 20.59 ** 86.33 *** -24.14 *** -24.14 *** Pos- 0.02 0.64 0.30 16.92 4148.83 13.62 23.09 ** -23.97 *** -23.94 *** Per- -0.07 0.81-0.63 14.93 3453.07 37.74 *** 115.81 *** -21.75 *** -21.68 *** TAI Mid-cap In- 100-0.04 1.04-0.73 10.90 1574.11 9.67 88.58 *** -23.54 *** -23.54 *** Pos- 0.04 0.65 0.32 12.44 1913.89 14.84 39.60 *** -24.47 *** -24.40 *** Per- -0.05 0.73-0.42 19.10 6238.80 35.96 *** 145.70 *** -25.07 *** -25.05 *** TAI Small-cap In- -0.02 0.87-0.61 11.19 1671.30 5.92 137.94 *** -24.61 *** -24.61 *** Pos- 0.03 0.57 0.49 10.29 1155.59 19.70 ** 22.61 ** -25.19 *** -25.09 *** Noes:*, ** and*** denoe significance a he.1,.05 and.01 level, respecively. μ and σ are sample mean and sandard deviaion; S and K are measures for skewness and excess kurosis. JB represens Jarque-Bera saisics, esing for normaliy. LB(12) is are he Ljung-Box es saisics esing for auocorrelaion in he residuals and squared residuals up o he welfh lags, which is disribued as χ 2 wih degree of freedom equal o he number of lags. ADF and PP sand for he augmened Dickey-Fuller and Phillips-Perron uni roo ess. The criical values of ADF and PP a he.05 and.01 level are -2.86 and -3.43, respecively. The auoregressive process is required in describing linear dependen series. We adop he Akaike informaion crierion (AIC) o deermine he order of he AR(p) and he smalles value of AIC is chosen. As shown in Table 5, he resul is show ha AR(1) is adoped for all indexes and periods. The Asymmeric Volailiy Model The diagnosics of higher order auoregressive condiional heeroskedasiciy and volailiy clusering sugges ha a GARCH-class model would be appropriae. Neverheless, ordinary GARCH models do no disinguish differenial impacs of good and bad news on volailiy. To examine he asymmeric responses of volailiy o posiive and negaive innovaions, he EGARCH model developed by Nelson (1991) is employed. As suggesed by Bollerslev, Chou, and Kroner(1992) o use a model ha is as parsimonious as possible, we adop he EGARCH(1,1) model. The model is described as follows: 2 R I ~ f µ, σ (2) ln 1 0 ( ) R = β + β R i= 1 2 2 ( σ ) = α + α ( z E[ z ] + δz ) + f ln( σ ) (4) p 0 i 1 1 + ε 1 1 1 1 (3) Equaion 2 expresses he condiional reurn R a ime, given he informaion se I a ime -1. Wih he condiional densiy funcion f( ), R has he condiional mean μ =E(R I -1 ) and condiional variance σ 2 =E(ε 2 I -1 ), where ε represens he innovaion a ime, i.e., ε = R - μ. Eq. 3 describes he p auoregressive process of order p for he sock reurns, wih β ir 1 capuring he auocorrelaion. As described in he previous secion, he order of AR(p) is decided based on he Akaike informaion crierion. The seleced order for each marke in each period is presened in Table 4. The process of condiional variance is expressed by Eq. 4, where he logarihm of he condiional variance is modeled as an asymmeric funcion of las period s sandardized innovaion, z -1, and he logarihm of las period s condiional variance. i= 1 93

S. Y. Wei & J. J. W.Yang IJBFR Vol. 5 No. 4 2011 Table 5: Values of AIC EVENT PERIOD Values of AIC 1 2 3 4 5 6 TAI 50 TAI Mid-cap 100 TAI Small-cap Noe: bold number represens he minimum value. Per- 2.1179 2.1224 2.1262 2.1292 2.1303 2.1349 In- 2.8792 2.8807 2.8839 2.8873 2.8901 2.8933 Pos- 1.9372 1.9397 1.9415 1.9395 1.9434 1.9471 Per- 2.4066 2.4118 2.4121 2.4128 2.4098 2.4147 In- 2.9193 2.9194 2.9228 2.9262 2.9292 2.9324 Pos- 1.9920 1.9954 1.9981 1.9943 1.9979 2.0011 Per- 2.2130 2.2181 2.2211 2.2237 2.2259 2.2285 In- 2.5566 2.5574 2.5603 2.5635 2.5663 2.5689 Pos- 1.7111 1.7133 1.7170 1.7163 1.7195 1.7218 The sandardized innovaion, z is defined as ε /σ 2 such ha a posiive z implies an unexpeced increase in sock reurns whereas a negaive z implies an unexpeced decrease. Thus he second erm in Eq. 3 allows condiional variance process o respond asymmerically o rises and falls in sock price. Specifically, he erm z -E z -1 represens he size effec of he innovaion, ha is, providing α 1 is posiive, a pas innovaion hen a posiive (negaive) impac on ln(σ 2 ) when he magniude of z -1 is larger (smaller) han is expeced value. The erm δz -1 on he oher hand capures he sign effec; ha is, when he coefficien δ is significanly negaive (posiive), hen negaive (posiive) innovaion increases volailiy more han does a posiive (negaive) innovaion of he same magniude. In essence, o examine he presence of asymmeric volailiy is presen, he impac of posiive innovaion on ln(σ 2 ) is equal oα 1 (1- δ ) z -1 and he impac of a negaive innovaion is α 1 (1+ δ ) z -1. Given he daa for he reurn series R, esimaes of he parameers in Eq. 3 and Eq. 4 (namely β 0, β 1, α 0, α 1, δ, ψ) can be derived by maximizing he log-likelihood of he reurns over he sample period. Diagnosic es for appropriaeness of he models are performed on he sandardized residuals and squared residuals via Ljung-Box es and Lagrange muliplier es. Specifically, he Ljung-Box es applied o he sandardized residuals ess for remaining serial correlaion in he mean equaion, whereas he Ljung_Box es as well as he Lagrange muliplier es applied o he squared sandardized residuals checks he specificaion of he variance equaion. EMPIRICAL RESULTS This paper mainly examines banning naked shor selling in he Taiwan Sock Marke. The paper uses inraday daa for each 30 minues, and applies hem o he above EGARCH(1,1) Model. Table 6 shows he resuling analysis. Firs, we divide he daa ino hree sub-periods he pre-period, in he period, and he pos-period, o assess wheher he samples in Taiwan Sock Marke have he Asymmeric Volailiy phenomenon in each sage. This paper compares he difference of he asymmeric volailiy for he hree periods and discusses if δ value differs dramaically among he hree sub-periods. Asymmeric volailiy exiss when volailiies caused by posiive informaion and negaive informaion are in differen ranges. When δ is negaive, negaive informaion will increase fuure volailiy more han ha of posiive informaion. Likewise, if δ is posiive, posiive informaion will increase fuure volailiy more han ha of negaive informaion. For example TAI Mid-cap 100 banning shor sale period, he EGARCH(1,1) Model esimae of he resul would be as follows: 94

The Inernaional Journal of Business and Finance Research Volume 5 Number 4 2011 R = -0.0066 + 0.0333R ln -1 + ε 2 2 ( σ ) = 0.0677 + 0.1478( z - E[ z ]- 0.0559z )- 0.9404ln( σ ) -1-1 -1 If he -1 period conains negaive informaion o make ε-1 become negaive, z -1 =ε -1 /σ 2-1 should be negaive, hen z -1 in each uni will make ln(σ 2 ) increase 0.1478(1-0.0559) in he nex period ( period ), i is equal o 0.1473. On he conrary, if here is posiive informaion, each -1 period will conain posiive informaion, hen each z -1 will make ln(σ 2 ) increase 0.1478(1+0.0559) in he nex period, which is equal o 0.1561. So, compared o he posiive and negaive informaion, he volailiy caused by he former will be 1.0597 imes by he laer. Tha is o say he higher he absolue value of δ become, he more volaile he asymmeric volailiy will be. In oher word, he degree of asymmery can be measured by (1+ δ )/(1- δ ) (Koumos and Saidi (1995)). Because he degree of asymmery is measured by (1+ δ )/(1- δ ) and a higher absolue value of δ implies a higher degree of asymmery, we can simply compare he absolue values of δ beween he hree sub-periods o examine wheher is a change in he exen of asymmery. Table 6: Maximum Likehood Esimaes of he EARCH -1 Even TAI 50 TAI Mid-cap 100 TAI Small-cap Period Pre In Pos Pre In Pos Pre In Pos AR(p) AR(1) AR(1) AR(1) AR(1) AR(1) AR(1) AR(1) AR(1) AR(1) β 0-0.0036 0.0431 0.0282-0.0626-0.0066 0.0306-0.0258 0.0081 0.0314 (0.7969) (0.0000) *** (0.0317) ** (0.0028) *** (0.6335) (0.0671) * (0.0930) * (0.5704) (0.0185) ** β 1 0.0334 0.0342-0.0086 0.0969 0.0333-0.0514 0.0201 0.0136-0.0424 (0.1233) (0.0000) *** (0.6560) (0.0002) *** (0.0023) *** (0.0617) * (0.5322) (0.3338) (0.1832) α 0 0.0077-0.0139-0.3031-0.0225-0.0677-0.3219-1.1968-0.0260-1.5035 (0.4360) (0.8447) (0.1325) (0.2402) (0.7386) (0.1078) (0.0004) *** (0.1594) (0.0002) *** α 1-0.0379-0.0098-0.0799 0.0077 0.1478-0.0295 0.4985 0.0301 0.4468 (0.0271) ** (0.9249) ** (0.3695) ** (0.7787) ** (0.0016) ** (0.7451) ** (0.0000) ** (0.2431) ** (0.0030) ** Δ -0.0935 0.1599-0.0838-0.0876-0.0559-0.1177 0.0572-0.0484-0.1843 (0.0000) *** (0.0943) * (0.2137) (0.0005) *** (0.0425) ** (0.0813) * (0.4184) (0.0133) ** (0.0674) * Ψ 0.9866 0.6452 0.6936 0.9742-0.9404 0.6691 0.0599 0.9926 0.0710 (0.0000) *** (0.0270) *** (0.0001) *** (0.0000) *** (0.0000) *** (0.0002) *** (0.8541) (0.0000) *** (0.7920) Log L -469.3761-643.7005-368.3761-574.0872-710.3047-422.2639-489.3824-609.6951-346.1007 LB(12) 11.6940 23.5770 13.3000 21.7180 8.5151 13.9660 30.2410 5.6073 18.0450 LB 2 (12) 18.5520 79.0390 22.7030 39.1540 38.2890 36.4190 74.2730 76.4490 23.0960 LM(6) 4.0819 4.3606 3.8951 3.4458 4.0335 4.0316 2.0894 9.1180 6.2808 1 + δ 1.2063 1.3806 1.1829 1.1920 1.1185 1.2669 1.1213 1.1017 1.4518 1 δ Noes: ** and*** denoe significance a he.05 and.01 level, respecively. Numbers in parenheses are sandard errors. As he order of AR(p) is differen for each even, o save space he esimaes of he condiional mean equaions are shown. LB(12) and LB 2 (12) are he Ljung-Box es saisics esing for auocorrelaion in he sandardized residuals and sandardized squared residuals for he EGARCH model up o he welfh lags, which is disribued as χ 2 wih degree of freedom equal o he number of lags. LM(6) represens he Lagrange muliplier es saisics examining wheher he sandardized residuals exhibi addiional ARCH up o he sixh lags, which is disribued as χ 2 wih degree of freedom equal o he order. (1+ δ )/(1- δ ) measures he degree of asymmery. This paper found i wasn significan for he Tai 50 pre-period and Tai Small cap pos-period. However, he ohers were significan, and he maximum and minimum of δ were 0.1843, and 0.0484, respecively, showing ha he asymmeric volailiy exised in he inraday daa. We compared he pre-period o he pos-period. Excep he volailiy change ((1+ δ )/(1- δ )) of he TAI 50 was he bigges in he period of 95

S. Y. Wei & J. J. W.Yang IJBFR Vol. 5 No. 4 2011 he banning naked shor selling (1.2063,1.3806,11829, respecively), he ohers (TAI Mid-cap 100, TAI Small-cap) significanly decreased in he period of he naked shor selling ban (1.1920 1.1185), (1.1213 1.1017)). Upon lifing he naked shor selling ban, he asymmeric volailiy was rose significanly (1.1213 1.1017), (1.1017 1.4518)), as is shown in Table 7. Table 7: T-Tes of δ for he Period Even Period Obs. δ significance σ -value TAI 50 TAI Mid-cap 100 TAI Small-cap pre 576-0.0935 *** 0.0160 in 585 0.1599 * 0.0956 pre 576-0.0935 *** 0.0160 pos 513-0.0838 0.0674 in 585 0.1599 * 0.0160 pos 513-0.0838 *** 0.0674 pre 576-0.0876 *** 0.0252 in 585 0.0016 ** 0.0276 pre 576-0.0876 *** 0.0252 pos 513-0.1177 * 0.0676 in 585 0.0016 ** 0.0276 pos 513-0.1177 * 0.0676 pre 576 0.0572 0.0706 in 585-0.0484 ** 0.0196 pre 576 0.0572 0.0706 pos 513-0.1843 * 0.1008 16.5655 *** -3.1883 *** -24.9726 *** -55.4353 *** 9.5356 *** 36.3741 *** -2.8673 *** 23.8302 *** Noe : *, **and *** denoe significance a he.1,.05 and.01 level in 585-0.0484 ** 0.0196 pos 513-0.1843 * 0.1008 30.0479 *** SUMMARY AND CONCLUSIONS Asymmeric volailiy has received more research aenion recenly, bu research on inraday volailiy is limied. This paper uses he EGARCH model o research asymmeric volailiy. The Taiwan sock marke resriced shor sales for hree monhs, so his paper uses 30 minues inraday daa o research inraday volailiy. Mos researchers adoped he las rade price of each day o sudy asymmeric volailiy. However, ignoring inraday volailiy resuled in differen conclusions. This paper is based on inraday daa o analyze asymmeric volailiy and long-run and shor-run effecs. Based on our research, we concluded ha inraday asymmeric volailiy also exiss. The policy of banning shor selling was mainly o preven invesors excessively panic moods from making unreasonable decisions. This paper found banning naked shor selling was effecive a decreasing asymmeric volailiy for mid-cap and small-cap socks. I was no effecive for decreasing asymmeric volailiy for large firms. On he conrary, he asymmeric volailiy was increased. We argue i is easy o acquire informaion of large firms so ha he invesor analyze raionally. The policy resuled in increasing he asymmeric volailiy of he sock marke. This sudy suppors he findings of Hogan, Melvin (1994) and Tse and Tsui (1997). The lemma of heerogeneous expecaions is ha he 96

The Inernaional Journal of Business and Finance Research Volume 5 Number 4 2011 heerogeneous expecaion would be more serious if governmen inerference was expanded or increased. However, i was no easy o acquire he informaion of mid-cap and small scale of firms. The resuls were similar o hose of Greenwald and Sein (1991) ha researched he America sock marke. They find he inerposed policy provides he opporuniy o calm invesors, reduced rading noise, and volailiy. We close he paper wih a fall for more research o more fully undersand he affecs of inraday asymmeric volailiy. REFERENCES Black, F. (1976). Sudies in Sock Price Volailiy Changes, Proceedings of he 1976 Business Meeing of he Business Economic Saisics Secion, American Saisical Associaion, 177-81. Bollerslev T., Chou R. and Kroner K. (1992). ARCH Modeling in Finance: A Review of he Theory and Empirical Evidence, Journal of Economerics, 51: 5-59. Campbell J., and Henschel L. (1992). No News is Good News: An Asymmeric Model of Changing Volailiy in Sock Reurns, Journal of Financial Economics, 31: 281-318. Chelley-Seeley P. L. and Seeley J. M. (1996). Volailiy, Leverage and Firm size: The U.K. Evidence, Mancheser School of Economic & Social Sudies, 64: 83-103. Chrisie, A. A. (1982). The Sochasic Behavior of Common Sock Variances: Value, Leverage and Ineres Rae Effecs, Journal of Financial Economics, 10: 407-432. Demirgüc-Kun, A. and R. Levine (1996). Sock marke developmen and financial inermediaries: sylized facs, The World Bank Economic Review,10(2):291-321 Duffee, Gregory R. (1995) Sock reurns and volailiy, Journal of Financial Economics 37, 399 420. Engle R. F. (1982). Auoregressive Condiional Heeroskedasiciy wih Esimaes of he Variance of Unied Kingdom Inflaion, Economerica, 50: 987-1007. Dieher, Karl, Kuan Hui Lee, and Ingrid Werner (2009). I s SHO Time! Shor Sale Price-Tess and Marke Qualiy, The Journal of Finance, Vol. 64, No. 1., pp. 37-73.. Engle R. F., and Ng V. K. (1993). Measuring and Tesing he Impac of News on Volailiy, Journal of Finance, 48: 1749-78. Fros, P. A., J. E. Savarino (1988). For beer performance: Consrain porfolio weighs, Journal of Porfolio Managemen. 15 29-34. Greenwald B., and Sein J. (1991). Transacional Risk, Marke Crashes, and he Role of Circui Breakers, Journal of Business, Vol. 64: 443-462. Hafner C. M. (1998). Esimaing High-frequency Foreign Exchange Rae Volailiy wih Nonparameric ARCH Models, Journal of Saisical Planning and Inference, 68: 247-69. Hogan Jr. K. C., and Melvin M. T. (1994). Sources of Meeor Showers and Hea Waves in he Foreign Exchange Marke, Journal of Inernaional Economics, 37: 239-47. Hong, H., Sein, J. (2003). Differences of Opinion, Shor-sales Consrains, and Marke Crashes, 97

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