Do Futures and Options trading increase stock market volatility?

Transcription

1 Do Fuures and Opions rading increase sock marke volailiy? Dr. Premalaa Shenbagaraman Absrac The obecive of his sudy is o assess he impac of inroducing index fuures and opions conracs on he volailiy of he underlying sock index in India. Numerous sudies on he effecs of fuures and opions lising on he underlying cash marke volailiy have been done in he developed markes. The empirical evidence is mixed and mos sugges ha he inroducion of derivaives do no desabilize he underlying marke. The sudies also show ha he inroducion of derivaive conracs improves liquidiy and reduces informaional asymmeries in he marke. In he lae nineies, many emerging and ransiion economies have inroduced derivaive conracs, raising ineresing issues unique o hese markes. Emerging sock markes operae in very differen economic, poliical, echnological and social environmens han markes in developed counries like he USA or he UK. This paper explores he impac of he inroducion of derivaive rading on cash marke volailiy using daa on sock index fuures and opions conracs raded on he S & P CNX Nify (India). The resuls sugges ha fuures and opions rading have no led o a change in he volailiy of he underlying sock index, bu he naure of volailiy seems o have changed pos-fuures. We also examine wheher greaer fuures rading aciviy (volume and open ineres) is associaed wih greaer spo marke volailiy. We find no evidence of any link beween rading aciviy variables in he fuures marke and spo marke volailiy. The resuls of his sudy are especially imporan o sock exchange officials and regulaors in designing rading mechanisms and conrac specificaions for derivaive conracs, hereby enhancing heir value as risk managemen ools CFA, Deparmen of Finance, Clemson Universiy, Clemson, USA. The views expressed and he approach suggesed are of he auhors and no necessarily of NSE.

2 I. Inroducion In he las decade, many emerging and ransiion economies have sared inroducing derivaive conracs. As was he case when commodiy fuures were firs inroduced on he Chicago Board of Trade in 86, policymakers and regulaors in hese markes are concerned abou he impac of fuures on he underlying cash marke. One of he reasons for his concern is he belief ha fuures rading aracs speculaors who hen desabilize spo prices. This concern is eviden in he following excerp from an aricle by John Suar Mill (87): The safey and cheapness of communicaions, which enable a deficiency in one place o be, supplied from he surplus of anoher render he flucuaions of prices much less exreme han formerly. This effec is much promoed by he exisence of speculaive merchan. Speculaors, herefore, have a highly useful office in he economy of sociey. Since fuures encourage speculaion, he debae on he impac of speculaors inensified when fuures conracs were firs inroduced for rading; beginning wih commodiy fuures and moving on o financial fuures and recenly fuures on weaher and elecriciy. However, his radiional favorable view owards he economic benefis of speculaive aciviy has no always been accepable o regulaors. For example, fuures rading was blamed by some for he sock marke crash of 987 in he USA, hereby warraning more regulaion. However before furher regulaion in inroduced, i is essenial o deermine wheher in fac here is a causal link beween he inroducion of fuures and spo marke volailiy. I herefore becomes imperaive ha we seek answers o quesions like: Wha is he impac of derivaives upon marke efficiency and liquidiy of he underlying cash marke? To wha exen do derivaives desabilize he financial sysem, and how should hese risks be addressed? Can he resuls from sudies of developed markes be exended o emerging markes? This paper seeks o conribue o he exising lieraure in many ways. This is he firs sudy o examine he impac of financial derivaives inroducion on cash marke volailiy in an emerging marke, India. Furher, his sudy improves upon he mehodology used in prior sudies by using a framework ha allows for generalized auo-regressive condiional heeroskedasiciy (GARCH) i.e., i explicily models he volailiy process over ime, raher han using esimaed sandard deviaions o measure volailiy. This esimaion echnique enables us o explore he link beween informaion/news arrival in he marke and is effec on cash marke volailiy. The sudy also looks a he linkages in ongoing rading aciviy in he fuures marke wih he underlying spo marke volailiy by decomposing rading volume and open ineres ino an expeced componen and an unexpeced (surprise) componen. Finally his is he firs sudy o our knowledge ha looks a he

3 effecs of boh sock index fuures inroducion as well as sock index opions inroducion on he underlying cash marke volailiy. The resuls of his sudy are crucial o invesors, sock exchange officials and regulaors. Derivaives play a very imporan role in he price discovery process and in compleing he marke. Their role in risk managemen for insiuional invesors and muual fund managers need hardly be overemphasized. This role as a ool for risk managemen clearly assumes ha derivaives rading do no increase marke volailiy and risk. The resuls of his sudy will hrow some ligh on he effecs of derivaive inroducion on he efficiency and volailiy of he underlying cash markes. The sudy is organized as follows. Secion II discusses he heoreical debae and summarizes he empirical lieraure on derivaive lising effecs, Secion III deails he model and he economeric mehodology used in his sudy, Secion IV oulines he daa used and discusses he main resuls of he model and finally Secion V concludes he sudy and presens direcions for fuure research. II. Theoreical foundaions and survey of he empirical lieraure. The inroducion of equiy index fuures markes enables raders o ransac large volumes a much lower ransacion coss relaive o he cash marke. The consequence of his increase in order flow o fuures markes is unresolved on boh a heoreical and an empirical fron. Sein (987) develops a model in which prices are deermined by he ineracion beween hedgers and informed speculaors. In his model, opening a fuures marke has wo effecs; (). The fuures marke improves risk sharing and herefore reduces price volailiy, and (). If he speculaors observe a noisy bu informaive signal, he hedgers reac o he noise in he speculaive rades, producing an increase in volailiy. In conras, models developed by Danhine (978) argue ha he fuures markes improve marke deph and reduce volailiy because he cos o informed raders of responding o mispricing is reduced. Froo and Perold(99) exend Kyle s(98) model o show ha marke deph is increased by more rapid disseminaion of marke-wide informaion and he presence of marke makers in he fuures marke in addiion o he cash marke. Ross (989) assumes ha here exiss an economy ha is devoid of arbirage and proceeds o provide a condiion under which he noarbirage siuaion will be susained. I implies ha he variance of he price change will be equal o he rae of informaion flow. The implicaion of his is ha he volailiy of he asse price will increase as he rae of informaion flow increases. Thus, if fuures increase he flow of informaion, han in he absence of arbirage opporuniy, he volailiy of he spo price mus change. Overall, he heoreical work on fuures lising effecs offer no consensus on he size and he direcion of he change in volailiy. We herefore need o urn o he empirical lieraure on evidence relaing o he volailiy effecs of lising index fuures and opions. 3

4 The firs sock index fuures conrac inroduced in he world was he Value line conrac, inroduced by he Kansas Ciy Board of Trade in 98 in he USA. Since hen we have seem numerous markes all over he world launching new derivaive conracs every year. Following he inroducion of derivaive conracs in developed markes like he US and UK, researchers have sough o analyze he impac of derivaives inroducion on he volailiy and efficiency of he underlying cash marke. The empirical evidence is however quie mixed. Mos sudies summarize ha he inroducion of derivaives does no desabilize he underlying marke; eiher here is no effec or perhaps only a very small decline in volailiy. The impac however, seems o vary depending on he ime period sudied and he counry sudied. For example, in a sudy of counries, Gulen and Mayhew () find ha fuures rading is associaed wih increased volailiy in he Unied Saes and Japan. In some counries, here is no robus, significan effec, and in many ohers, volailiy is lower afer fuures have been inroduced. Nahan Associaes (974) was he firs o sudy he impac of lising opions on he Chicago Board of Exchange. He repored ha he inroducion of opions seemed o have helped sabilize rading in he underlying socks. This resul has been suppored by Skinner (989) and also by oher auhors for he UK, Canada, Swizerland and Sweden. More recen work by Lamoureux and Pannikah (994), Freund, McCann and Webb (994) and Bollen (998) have found ha he direcion of he volailiy effec is no consisen over ime. Afer 987, he residual variance of boh opioned socks and socks in a mached conrol group increased a he ime of he opion lising. This migh be inerpreed in wo ways; viz. perhaps he lising has no rue impac on volailiy and here is some common unknown facor ha is driving he magniude of he idiosyncraic risk for differen socks. Or perhaps, here are spill over effecs associaed wih lising opions for some socks, such ha he dynamics of oher socks also changes (Deemple and Jorion, 99, and Cao 999). In looking a he effec on liquidiy, Nahan Associaes (974) found ha he rading volume did no change wih opion inroducion. However, laer sudies like Kumar, Sarin and Shasri (99) have found ha he volume in he underlying sock does increase afer he inroducion of sock opions. Sudies have also found ha afer he inroducion of opions, prices end o reflec new informaion more quickly, bid-ask spreads narrow, and he adverse selecion componen of he spread becomes smaller. Relaively few auhors have sudied he impac of sock index opions lising on volailiy in he cash marke. Evidence repored by Charah, Kamah, Chakornpipa and Ramchander (99) indicaes ha S&P sock index opions rading had a sabilizing effec on he For a deailed summary of his lieraure, see surveys by Hodges (99), Damodaran and Subrahmanyam (99), Sucliffe (997) and Mayhew (999). 4

5 underlying sock index. Sudies of volailiy effecs of individual equiy opions have also repored mixed resuls; some find ha volailiy is unchanged, while some repor a small decrease in volailiy. Only one paper Wei, Poon and Zee (997) repor an increase in volailiy for opions on OTC socks in he USA. However no consensus resul emerges, which probably a resul of differen daa and ime-periods sudied, as also he inheren endogenously of he opion lising decision. III. Model and Mehodology One of he key assumpions of he ordinary regression model is ha he errors have he same variance hroughou he sample. This is also called he homoscedasiciy model. If he error variance is no consan, he daa are said o be heeroscedasic. Since ordinary leas-squares regression assumes consan error variance, heeroscedasiciy causes he OLS esimaes o be inefficien. Models ha ake ino accoun he changing variance can make more efficien use of he daa. There are several approaches o dealing wih heeroscedasiciy. If he error variance a differen imes is known, weighed regression is a good mehod. If, as is usually he case, he error variance is unknown and mus be esimaed from he daa, one can model he changing error variance. In he pas, sudies of volailiy have used consruced volailiy measures like esimaed sandard deviaions, rolling sandard deviaions, ec, o discern he effec of fuures inroducion. These sudies implicily assume ha price changes in spo markes are serially uncorrelaed and homoscedasic. However, findings of heeroskedasiciy in sock reurns are well documened (Mandelbro 963), Fama (96), Bollerslev (986). Thus he observed differences in variances from models assuming homoscedasiciy may simply be due o he effec of reurn dependence and no necessarily due o fuures inroducion. The GARCH model assumes condiional heeroscedasiciy, wih homoscedasic uncondiional error variance. Tha is, he model assumes ha he changes in variance are a funcion of he realizaions of preceding errors and ha hese changes represen emporary and random deparures from a consan uncondiional variance, as migh be he case when using daily daa. The advanage of a GARCH model is ha i capures he endency in financial daa for volailiy clusering. I herefore enables us o make he connecion beween informaion and volailiy explici, since any change in he rae of informaion arrival o he marke will change he volailiy in he marke. Thus, unless informaion remains consan, which is hardly he case, volailiy mus be ime varying, even on a daily basis. A model wih errors ha follow a GARCH (p,q) process is represened as follows: In a recen working paper, Mayhew and Mihow () explicily model he exchanges opion lising choice using a logi model o accoun for his endogeniey.

6 Y h = a = + a X + ε, ε Ψ ~ N(, h ) + p i= ε i + q = β h Equaion a and b where Equaion a is he condiional mean equaion and b is he condiional variance equaion. In sudying he links beween informaion, cash marke volailiy and derivaives rading, wo issues are ineresing. Firs, how he iniial inroducion of derivaive conracs impac cash marke volailiy. Second, wheher he exisence of fuures rading affecs daily volailiy in he cash marke. To address he firs issue, we inroduce a dummy variable ino he condiional variance equaion. Equaion () hus becomes: Y h = a + a X + ε, ε Ψ ~ N(, h ) = + p i= ε i + q = β h Equaion DF where DF is a dummy variable aking he value of before fuures inroducion and afer. If he coefficien on he Dummy is saisically significan hen he inroducion of fuures has an impac on he spo marke volailiy. To address he second issue, we divide he sample ino he pre-fuures and pos- fuures sub-sample and a GARCH model is esimaed separaely for each sub-sample. This allows us o compare he naure of volailiy before and afer he onse of fuures rading. Furher, we also incorporae he conrac volume and open ineres in he fuures marke in he condiional variance equaion in he pos-fuures sub sample. The impac of sock index fuures and opion conrac inroducion in he Indian marke is examined using a univariae GARCH (,) model 3. The ime series of daily reurns on he S&P CNX Nify Index is modeled as a univariae GARCH process. Following Pagan and Schwer (99) and Engle and Ng (993), we need o remove from he ime series any predicabiliy associaed wih lagged world reurns and/or day of he week effecs. Furher, we need o conrol for he effec of marke wide facors, since we are ineresed in isolaing he unique impac of he inroducion of he fuures/opions conracs. Forunaely for he Indian sock marke we have anoher index, he Nify Junior, which comprises socks for which no fuures conracs are raded. As such, i serves as a perfec conrol variable for us o isolae marke wide facors and hereby concenrae on he residual volailiy in he Nify as a direc resul of he inroducion of he index derivaive conracs. We herefore inroduce he reurn on he Nify Junior index as an addiional independen variable. The following condiional mean equaion is esimaed: 3 Alernaive GARCH models were esimaed, he GJR-GARCH, EGARCH AND TGARCH, bu we find he GARCH (,) model o provide he bes fi for he daa in his sudy. 6

7 nify, = + Rsp, + Rnifyunior, + = R DAY + u Equaion 3 where R nify, is he daily reurn on he S&P CNX Nify Index calculaed as he firs difference of he log of he index, R sp, is he lagged S&P index reurn, and DAY are day-of-he-week dummy variables for Tuesday o Friday. The lagged S&P index reurn is used as an independen variable o remove he effecs of worldwide price movemens on he volailiy of he Nify Index reurn. For example, if he Indian marke is influenced by US markes, his will be refleced hrough he lagged S&P reurn. In GARCH, he residuals { u } from Equaion 3 are assumed o be disribued N (,h ) condiional volailiy h is given by he following equaion: where he h = γ ε h 3D Equaion 4 where D is a dummy variable ha akes on a value of zero before he opions/fuures were inroduced and a value of one afer. A significan posiive value for γ 3would indicae ha derivaives inroducion increases he volailiy of he underlying index. Secion IV. Daa and Resuls Daily closing prices for he period h Oc 99 o 3 s Dec for he SNX Nify and he Nify Junior were obained from he CD-ROMs provided by NSE and he NSE websie. Daa on Nify fuures conrac volume and open ineres were downloaded from he NSE websie. Daa on he S&P index were obained from Reuers Inc. All esimaions in his sudy are done using SAS. The SNX Nify is an index of socks raded on he Naional Sock Exchange and represens approximaely % of he oal marke capializaion of he marke. Nify Junior is an index of he nex mos liquid socks. The firs index fuure in India was inroduced on he SNX Nify on June,. The firs index opions conrac was inroduced on 4h June,. Table provides summary saisics for he Nify and Nify Junior indices. All reurns are calculaed as he firs difference of he log of he index daily close price and Char graphs he reurns on he Nify index over ime. As seen in Table, he overall sample has 8 ime series observaions. The mean reurn on he Nify is.3% per day wih a sandard deviaion of.67% per day. The mean daily reurn on he Nify Junior is.7% wih a sandard deviaion of.9%. If we divide he sample period ino pre-fuures vs. pos-fuures using he June, cuoff dae, he mean daily reurn on he Nify is a posiive.9% before and a negaive.44% afer he fuures 7

8 was inroduced. A similar paern in Nify Junior reurns is also apparen. The average daily sandard deviaion for he Nify reurn pre-fuures is.79% and.4% pos-fuures. However, he daily sandard deviaion for he Nify Junior, for which no index fuures were raded, pre-fuures is % and pos fuures is.7%. A very similar paern emerges when one examines he pre-opions and pos-opion sub-sample means and sandard deviaions. As saed in he previous secion, i is imporan o remove marke-wide influences on Nify reurns, if we are o isolae he impac of fuures inroducion. In order o do his we need a proxy ha is no associaed wih any fuures conrac, and ye capures marke-wide influences in India. For example, informaion news releases relaing o economic condiions like, inflaion raes, growh forecass, exchange raes, ec are likely o affec he whole marke. I is necessary o remove he effecs for all hese facors on price volailiy. Since he Nify Junior has no fuures conracs raded on i, we use i as a proxy o capure marke-wide informaion effecs. Following Pagan and Schwer (99) and Engle and Ng (993), we also need o remove from he ime series any predicabiliy associaed wih lagged world marke reurns and day-of-he-week effecs. The lagged reurn on he S&P index is used as a proxy for he world marke reurn o remove any worldwide price movemens on volailiy in he Nify reurn. We inroduce day of he week dummies for Tuesday o Friday. Table 3 examines he Nify reurns for he presence of any ARCH/GARCH effecs and finds ha here exiss subsanial ARCH effecs in he residuals and herefore a model ha accouns for hese effecs would describe he daa beer. Having demonsraed he need o use some ype of GARCH model o model he Nify reurns, we conduced ess o see which form of he GARCH model fis he reurns daa bes. We esed he GARCH (,) model, he EGARCH model of Nelson (99), he GARCH model wih - disribuion and he GJR-GARCH model of Glosen, Jagannahan and Runkle (993). We find ha he GARCH (,) and he EGARCH model boh seem o fi he daa beer han he GJR-GARCH and he TGARCH models. However, forecasing he muli-period error variance is easier in he GARCH (,) model relaive o he EGARCH model, and hence in he ineres of pracicaliy, we use he GARCH (,) model in his sudy. As menioned earlier, in order o esimae he impac of he inroducion of he fuures and opions conracs, we inroduce a Dummy variable in he condiional volailiy equaion. A significan posiive co-efficien would indicae and increase in volailiy, a significan negaive coefficien would indicae a decrease in volailiy. The resuls of he esimaion for he impac of fuures inroducion are presened in Table 4. The coefficien on he fuures dummyγ 3, is no significanly differen from zero, indicaing no impac on volailiy. There appears o be significan day-of-he-week effecs as evidenced by he coefficiens on he dummies for Tuesday and Friday. γ can be viewed as a news 8

9 coefficien, wih a higher value implying ha recen news has a greaer impac on price changes. I relaes o he impac of yeserday s news on oday s price changes. In conras, γ reflecs he impac of old news', i.e. i is picking up he impac of prior news on yeserdays variance and as such indicaed he level of persisence in he informaion effec on volailiy. Table presens he resuls of he model wih an Opions dummy. Index opions were inroduced on June 4 h,. The Dummy-Opions is zero before and on/afer June 4 h. The inroducion of opions has had no saisically discernable effec on spo marke volailiy. The resuls hus far sugges ha he inroducion of fuures and opions has had no effec on spo marke volailiy, a leas none ha is saisically significan. However, in realiy, one migh expec a lo of uncerainy in he marke leading up o he inroducion of he derivaive conracs, which our cu-off daes are unable o capure in he model. Table presens some basic saisics on he means and sandard deviaions of he reurns for he six monhs leading up o he inroducion of he fuures conracs in June. The sandard deviaion of nify reurns up unil Dec 999 was.7%. Beween Jan and June, he sandard deviaion rose o.% and hen afer June dropped back o.4%. Ineresingly, a similar paer emerges for he Nify Junior reurns, even hough no underlying fuures conracs were being inroduced for socks in his index. This was also an exremely volailiy period in world sock markes, especially he US sock markes. The increase in volailiy in he Indian marke migh have been a consequence of increased volailiy in he US markes. This effec is picked up by he lagged reurn on he S&P index in our model. In conclusion, we find lile evidence ha he spo marke volailiy changed significanly as a resul of fuures or opions inroducion. Char plos he GARCH model prediced condiional error sandard deviaion over ime. Clearly, he model is able o capure he emporary increase in he volailiy leading up o he inroducion of he fuures conracs in he firs six monhs of. Furher, one can see ha if we ignore his 6 monh period, he volailiy has no changed much before and afer he fuures inroducion. Char 3 depics he acual Nify reurns and he Model prediced reurns. A casual observaion of his graph shows ha he model does a decen ob of capuring he ime varying condiional volailiy in he Nify reurns and hereby increases he efficiency wih which our model parameers are esimaed. I is ineresing o explore furher wheher he naure of he GARCH process was alered as a resul of he fuures inroducion. We herefore esimae he GARCH model separaely for he prefuures and he pos-fuures period separaely. Table 6 presens he resuls of his esimaion. The firs poin o noe in comparing he resuls before and afer fuures inroducion is ha he onse of fuures rading has alered he naure of he volailiy. Before fuures, he Arch and he GARCH 9

10 effecs are significan, suggesing ha boh recen news and old news had a lingering impac on spo volailiy. The resuls also show he presence of day-of-he-week effecs for Tuesday and Friday. Afer he fuures inroducion, he day-of-he-weeks effecs are no longer saisically significan. Also he coefficien on he GARCH variable is no longer significan, suggesing ha old news has no impac on oday s spo price changes. However our sample period pos fuures is fairly small, only 97 observaions, so we mus rea hese resuls wih some cauion. The resuls are similar when we analyze he GARCH effecs pre and pos opions inroducion in Table 7. We have hus far, esed wheher here appears o be any srucural change in he underlying spo marke volailiy a he ime of fuures and opions inroducion. I is ineresing o see if here has been any srucural change in he mean equaion pre and pos fuures/opions inroducion. In order o es for parameer sabiliy in he mean equaion, assuming consan uncondiional variance, we conduc a Chow es for srucural change. The Chow es is a formal es o evaluae he sabiliy of he regression coefficiens. The sample is divided ino wo pars a he specified break-poin, and he fi of he model in he wo pars is compared o es wheher boh sub samples are consisen wih he same model. The Null Hypohesis is ha he coefficiens in boh sub-samples are equal, condiional on he same error variance. Under he Null, he Chow es saisic has an F-disribuion wih K and (n+n-k) degrees of freedom where k is he number of coefficiens. Using June, as our firs break poin for fuures inroducion, he value of he F-sa (7, 66) df is 3.63 and is highly significan a he % level. This suggess ha he coefficiens are no he same before and afer fuures inroducion. Using June 3, as our breakpoin for opions inroducion, he F-sa (7, 66) df is. and we are unable o reec he null ha he coefficiens are he same. Now we es o see if here is any relaionship, afer he fuures are inroduced, beween he level of fuures rading aciviy and he volailiy of he spo marke reurn. We follow Bessembinder and Sequin (99) and using an ARIMA (p,q) model, decompose he ime series of he fuures rading volume and open ineres ino expeced and unexpeced componens. The expeced componen represens a hreshold level (or average) of fuures rading, and he unexpeced componen picks up any sudden increase in rading volume as a resul of unexpeced price changes. Bessembinder and Sequin find ha spo marke volailiy in he US marke is posiively relaed o he unexpeced componens of volume and open ineres, and negaively relaed o he expeced componen, suggesing an increase in volailiy due o unexpeced informaion, bu an oherwise sabilizing influence of fuures rading aciviy. Using an ARIMA (,) model for he conracs volume and an ARIMA (,) model for he Open Ineres, we decompose each series ino an expeced and an unexpeced componen. We hen inser hese componens as addiional variables in he condiional variance equaion:

11 h = 7 γ h ε 3D 4CONTex CONTunex 6OIex OIunex The resuls of his esimaion are presened in Table 8. None of he coefficiens on he rading aciviy variables are saisically significan. This however, may be an arifac of he raher low sample size in he pos fuures period. As more daa becomes available, i will be ineresing o reesimae his model o evaluae he impac of coninuing rading aciviy in he fuures and/or opions marke on he underlying spo marke. Also, in decomposing he volume indicaor variables, no adusmen was made o remove any seasonal effecs like conrac expiry monhs, ec. An ineresing opic for furher research would be o see if adusing for his seasonaliy will have a significan impac on he decomposiion of he permanen and emporary componens of rading aciviy. V. Conclusion In his sudy, we have examined he effecs of he inroducion of he Nify fuures and opions conracs on he underlying spo marke volailiy using a model ha capures he heeroskedasiciy in reurns ha characerize sock marke reurns. The resuls indicae ha derivaives inroducion has had no significan impac on spo marke volailiy. This resul is robus o differen model specificaions. 4 However, fuures inroducion seems o have changed he sensiiviy of nify reurns o he S&P reurns. Also, he day-of-he-week effecs seem o have dissipaed afer fuures inroducion. We hen esimaed he model separaely for he pre and pos fuures period and find ha he naure of he GARCH process has changed afer he inroducion of he fuures rading. Pre-fuures, he effec of informaion was persisen over ime, i.e. a shock o oday s volailiy due o some informaion ha arrived in he marke oday, has an effec on omorrow s volailiy and he volailiy for days o come. Afer fuures conracs sared rading he persisence has disappeared. Thus any shock o volailiy oday has no effec on omorrow s volailiy or on volailiy in he fuure. This migh sugges increased marke efficiency, since all informaion is incorporaed ino prices immediaely. However, we prefer o rea our resuls here wih cauion since we are esimaing he GARCH model wih only wo and a half years of daa. Nex, using a procedure inspired by Bessembinder and Sequin (99), we find ha afer he inroducion of fuures rading, we are unable o pick up any link beween he volume of fuures conracs raded and he volailiy in he spo marke. As more daa becomes available, i will be ineresing o explore his link once more. 4 In he ineres of breviy, he esimaion resuls of he various GARCH specificaions are no presened. All he models showed no effec of fuures or opions inroducion on spo marke volailiy.

12 I is imporan o emphasize ha alhough we have sough o analyze he impac of he inroducion of fuures/opions on spo marke volailiy, in realiy he lising of index derivaive conracs is hardly an exogenous even. The lising is usually preceded by many decisions made by regulaors and sock exchange officials, who in urn may be reacing o world developmens. Furher, i is quie possible ha he inroducion of fuures and opions has differen impac on spo volailiy depending on he rading mechanisms, conrac designs and regulaory environmens. This migh explain he raher mixed resuls reached by researchers in differen markes. Furher research needs o explore he relaionship beween hese facors and he naure of spo marke volailiy before and afer derivaives rading began. As more daa becomes available in he Indian marke, such a sudy would be immensely beneficial o invesors, insiuional raders and regulaors alike. Furher, i should be noed ha a relaively long ime series, is required o obain reliable GARCH parameer esimaes. For he model esimaed over he enire sample period, Oc 99-Dec, his migh no be a problem. However in our esimaions for he pos fuures period, clearly his is affecs he reliabiliy of our esimaes. Unforunaely, he only soluion is paience and persisence. In summary, we find lile evidence ha he inroducion of new sock index fuures or opions conracs in emerging markes like India will desabilize sock markes. On he conrary, i appears ha he sock markes become more efficien and informaion is incorporaed ino prices a lo faser. Engle and Mezrich (99) sugges using a leas eigh years of daily daa for proper GARCH esimaion.

13 References Bollen, Nicolas P.B., 998, A noe on he impac of opions on sock reurn volailiy, Journal of Banking and Finance v: 8-9. Bollerslev,. 986, Generalized Auoregressive Condiional Heeroscedasiciy, Journal of Economerics 3, Bessembinder, Hendrik and Paul J. Seguin, 99, Fuures rading aciviy and sock price volailiy, Journal of finance 47, -34. Cao, H.Henry, 999, The effec of derivaive asses on informaion in informaion acquisiion and price behavior in a raional expecaions equilibrium, Review of Financial Sudies v n: Charah, A., R. Kamah, R. Chakornpipa and S. Ramchander., 99, Lead-lag associaions beween opion rading and cash marke volailiy, Applied Financial Economics (6), Damodaran, Aswah and Mari G. Subrahmanyam, 99, The Effecs of Derivaive Securiies on he Markes for he underlying asses in he Unied Saes: A Survey, Financial Markes, Insiuions and Insrumens (), -. Danhine, J., 978, Informaion, fuures prices, and sabilizing speculaion, Journal of Economic Theory 7, Deemple, Jerome and Philippe Jorion, 99, Opion Lising and Sock Reurns, Journal of Banking and Finance v4: Engle, Rober and Vicor Ng, 993, Measuring and Tesing he Impac of News on Volailiy, Journal of Finance 48, Engle,Rober and Joseph Mezrich,99, Grappling wih GARCH, Risk, 8, -7. Fama, E.F., 96, The behavior of sock marke prices, Journal of Business 38, 34-. Freund, Seven, P. Douglas McCann and Gwendolyn P. Webb, 994, A Regression Analysis of he Effecs of opion inroducion on sock variances, Journal of Derivaives v: -38. Froo, K.A., and A.F. Perold, 99, New rading pracices and shor-run marke efficiency, WP MIT. Glosen, Lawrence R., Ravi Jagannahan and David E. Rundle, 993, On he Relaion beween he Expeced Value and he volailiy of he Nominal Excess Reurn on Socks, Journal of Finance 48, Gulen, Huseyin and Sewar Mayhew, 999, The Dynamics of Inernaional Sock Index Reurns, Working paper, Universiy of Georgia. Gulen, Huseyin and Sewar Mayhew,, Sock Index Fuures Trading and Volailiy in Inernaional equiy markes, Working paper, Universiy of Georgia. Hodges, Sewar, 99, Do Derivaive Insrumens Increase Marke volailiy?, Opions: Recen Advances in Theory and Pracice vii (chaper ), Sewar Hodges, ed., Mancheser Universiy Press. 3

14 Kumar, Raman, Aulya Sarin and Kuldeep shasri, 99, The impac of he lising of index opions on he underlying socks, Pacific,-Basin Finance Journal 3, Kyle,A.S., 98, Coninuous aucions and insider rading, Economerica 3, Lamoureux, Chrisopher G. and Sunil K. Panikkah, 994, Variaions in Sock Reurns: Asymmeries and oher paerns, working paper. Mandelbro,B., 963, The variaion of cerain speculaive prices, Journal of Business 36, Mayhew, Sewar,, The Impac of Derivaives on Cash Markes: Wha have we learned?, Working paper, Universiy of Georgia Mayhew, Sewar and Vassil Mihov,, Anoher Look a opion lising effecs, Working Paper, Purdue Universiy. Mill,J.S., 87, Principles of poliical economy II, 7 h ed. (Longmans, Green, Reader and Dyer). Nahan Associaes, 974, Review of Iniial Trading Experience a he Chicago Board Opions Exchange. Nelson,D., 99, Condiional Heeroscedasiciy in Asse reurns: A New Approach, Economerica 9, Pagan, A. and G.W.Schwer, 99, Alernaive Models for Condiional Sock Volailiy, Journal of Economerics 4, Ross, S.A., 989, Informaion and volailiy: The no-arbirage maringale approach o iming and resoluion irrelevancy, Journal of Finance 44, -7. Skinner, Douglas J., 989, Opions markes and sock reurn volailiy, Journal of Financial Economics v3: Soresu, Sorin M., 999, The effec of opions on sock prices: , Journal of finance, forhcoming. Sein,J.C., 987, Informaional exernaliies and welfare-reducing speculaion, Journal of Poliical Economy 9, 3-4. Sucliffe,C., 997, Sock Index Fuures: Theories and Inernaional evidence, nd ed., Inernaional Thomson Business Press. Wei,P., P.S. Poon and S.Zee, 997, he effec of opion lising on bid-ask spreads, Price volailiy and rading aciviy of he underlying OTC socks, Review of Quaniaive Finance and Accouning 9(),

15 CHART : Reurn on he SNX Nify reurn rnify JAN9 JAN96 JAN97 JAN98 JAN99 JAN JAN JAN JAN3 d CHART : Esimaed error sandard deviaion from he GARCH (,) model Uncondiional error sandard deviaion=.9 SHAT JAN9 JAN96 JAN97 JAN98 JAN99 JAN JAN JAN JAN3 d

16 CHART 3: Model forecass of reurns compared o acual reurns rnify JAN9 JAN96 JAN97 JAN98 JAN99 JAN JAN JAN JAN3 Red : Prediced reurns Yellow: Acual reurns 6

17 Table : Descripive Saisics Means and sandard deviaions of firs differences of he log of he Nify and he Nify Junior daily price indices, Oc 99 o Dec Period NOB Nify Nify Junior Mean Sd.Deviaion Mean Sd.Deviaion Pre-Fuures Pos-Fuures Pre-Opions Pos-Opions Fuures conracs were inroduced on June, and Opions conracs on June 4,. Table : Descripive Saisics Means and sandard deviaions of Index reurns for sub-periods Period NOB Nify Nify Junior Mean Sd.Deviaion Mean Sd.Deviaion Jan-Jun Jun Table 3: Q and LM Tess for ARCH Disurbances in Nify Reurn Order Q Pr > Q LM Pr > LM.8488 <..849 < < < < < < < < < < < < < < < < < < < < < < <. 7

18 Table 4: Esimaes of he GARCH(,) model wih Fuures dummy nify, = + Rnifyunior, + Rsp, + = h = γ ε h 3D R DAY + where D is a dummy variable ha akes a value of afer June h and before. u Inercep -.6 * -.69 NifyJunr reurn.736 * 77. Lagged S&P.38 * 7. Dummy-Tue.4 * Dummy-Wed..69 Dummy-Thur.8.36 Dummy-Fri.7*.7 6 γ Arch. * 4.3 γ Arch.3*.4 γ Garch.9 * γ Dummy-Fuures.. 3 * Saisically significan a he % level. Toal R-square=.674 N=67 Uncondiional variance=.847 8

19 Table : Esimaes of he GARCH(,) model wih Opions dummy nify, = + Rnifyunior, + Rsp, + = h = γ ε h 3D R DAY + where D is a dummy variable ha akes a value of afer June 4 h and before. u Inercep -.6 * -.69 NifyJunr reurn.7 * Lagged S&P.37 * 6.94 Dummy-Tue.4 * Dummy-Wed..7 Dummy-Thur.8.36 Dummy-Fri.7*.73 6 γ Arch. * 3.9 γ Arch.33*.4 γ Garch.97 * 68. γ Dummy-Opions.. 3 * Saisically significan a he % level. Toal R-square=.674 N=67 Uncondiional variance=

20 Table 6: Esimaes of he GARCH(,) model before and afer fuures inroducion. nify, = + Rnifyunior, + Rsp, + = = γ ε h R DAY + h u BEFORE AFTER Esimae -sa Esimae -sa Inercep -.48 * Nify unior reurn.8649 * * 34.7 Lagged S&P.338 * * 3.49 Dummy-Tue.89 * Dummy-Wed Dummy-Thur Dummy-Fri.9* γ Arch. * * 6.36 γ Arch.768* γ Garch.96 * 6... Toal R-square N Uncondiional variance.97.7 * Saisically significan a he % level. Chow es: F=3.63 Pr>F=.7 Df=7, 66

21 Table 7: Esimaes of he GARCH(,) model before and afer opions inroducion. nify, = + Rnifyunior, + Rsp, + = = γ ε h R DAY + h u BEFORE AFTER Esimae -sa Esimae -sa Inercep -.3 * NifyJunr reurn.79 * * 6.4 Lagged S&P.7 * *.73 Dummy-Tue.68 * Dummy-Wed Dummy-Thur Dummy-Fri.* γ Arch. * * 3.6 γ Arch.77* γ Garch.94 * Toal R-square N Uncondiional variance.94.9 * Saisically significan a he % level. Chow es: F=. Pr>F=.969 Df=7,66

22 Table 8: Esimaes of he AUGMENTED GARCH (,) model afer fuures inroducion. nify, = + Rnifyunior, + Rsp, + = = γ ε h R DAY + h u Esimae -sa Inercep NifyJunr reurn.99 * 33.4 Lagged S&P.76 *.83 Dummy-Tue Dummy-Wed.4.9 Dummy-Thur.8.39 Dummy-Fri γ Arch.6 *. γ Arch.9*.64 γ Garch.88. γ Con-expeced γ 4 Con-unexpeced γ OI-expeced.. γ OI-unexpeced.. 6 Toal R-square.643 N 94 Uncondiional variance.69 * Saisically significan a he % level. Con=change in he log of he oal number of conracs raded for all expiry for he nify fuures. OI=change in he log of he open ineres for all expiry horizons for nify fuures conracs. An ARIMA (, ) is used o decompose conracs series ino expeced and unexpeced componens. An ARIMA (, ) model is used o decompose he OI series ino expeced and unexpeced componens.