The Impact of News on Measures of Undiversifiable. Risk: Evidence from the UK Stock Market *

Transcription

1 The Impac of News on easures of Undiversifiable Risk: Evidence from he UK ock arke Chris Brooks IA Cenre Deparmen of Economics Universiy of Reading Ólan T. Henry Deparmen of Economics Universiy of elbourne Absrac The usual measure of he undiversifiable risk of a porfolio is is bea. Recen research has allowed bea esimaes o vary over ime ofen based on symmeric mulivariae GARCH models. There is however widespread evidence in he lieraure ha he volailiies of asse reurns in paricular hose from sock markes show evidence of an asymmeric response o good and bad news. Using UK equiy index daa his paper considers he impac of news on ime varying measures of bea. The resuls sugges ha bea depends on wo sources of news - news abou he marke and news abou he secor. The asymmeric effec in bea is consisen across all secors considered. Recen research provides conflicing evidence as o wheher abnormaliies in equiy reurns are a resul of changes in expeced reurns in an efficien marke or an over-reacion o new informaion. The evidence in his paper suggess ha such abnormaliies may occur as a resul of changes in expeced reurn caused by ime-variaion and asymmery in bea. JEL Codes: Keywords: G2 G5 ock Index ulivariae Asymmeric GARCH News Impac urfaces Condiional Bea urfaces. Iniial work on his paper ook place while he second auhor was on sudy leave a he IA Cenre Deparmen of Economics The Universiy of Reading. All esimaion was on a 366.H. Penium 3 PC; he News impac surfaces were graphed using a GAU Rouine wrien by he firs auhor and ichalis Ioannides. The daa and esimaion rouines are available upon reques from he corresponding auhor. The responsibiliy for any errors or omissions lies solely wih he auhors. Corresponding auhor: Deparmen of Economics The Universiy of elbourne Parkville Vicoria 352 Ausralia. Tel: ( Fax: ( olhenry@cupid.ecom.unimelb.edu.au

2 . Inroducion There is widespread evidence ha he volailiy of equiy reurns is higher in bull markes han in bear markes. One poenial explanaion for such asymmery in variance is he so-called leverage effec of Black (976 and Chrisie (982. As equiy values fall he weigh aached o deb in a firm s capial srucure rises ceeris paribus. This induces equiy holders who bear he residual risk of he firm o perceive he sream of fuure income accruing o heir porfolios as being relaively more risky. An alernaive view is provided by he 'volailiy-feedback' hypohesis. Assuming consan dividends if expeced reurns increase when sock price volailiy increases hen sock prices should fall when volailiy rises. Pagan and chwer (99 Nelson (99 Campbell and Henschel (992 Engle and Ng (993 Glosen Jagannahan and Runkle (993 and Henry (998 iner alia provide evidence of asymmery in equiy reurn volailiy using univariae GARCH models. Kroner and Ng (995 Braun Nelson and unnier (995 Henry and harma (999 and Engle and Cho (999 iner alia use mulivariae GARCH models o capure ime-variaion and asymmery in he variancecovariance srucure of asse reurns. uch ime-variaion and asymmery in volailiy may be used o explain a imevarying and asymmeric bea. A risk averse invesor will rade off higher levels of expeced reurn for higher levels of risk. If he risk premium is increasing in volailiy and if bea is an adequae measure of he sensiiviy o risk hen ime-variaion and asymmery in he variance-covariance srucure of reurns may lead o ime-variaion and asymmery in bea. Recen research by Braun Nelson and unnier (995 hereafer BN explores ime variaion and asymmery in bea using a bivariae EGARCH model. Engle and Cho (999 hereafer EC exend he BN paper in wo main direcions. Firs EC consider he differing roles of marke- and asse-specific shocks. This is imporan since a series of negaive reurns caused by marke or asse-specific shocks may lead o an increase in bea. econd EC use daily daa on individual firms raher han he aggregaed daa used by BN. 2

3 Our approach differs markedly from ha of boh BN and EC. In paricular we use a linear as opposed o an exponenial mulivariae GARCH model o disinguish beween he role of idiosyncraic and marke shocks in deermining poenial asymmery in bea. The exponenial GARCH approach of BN does no readily admi negaive covariance esimaes and moreover he EGARCH form appears o dramaically oversae he response of he condiional variance o a negaive shock - see Engle and Ng (993 and Henry (998 iner alia. Our approach allows for a (poenially negaive ime varying and asymmeric covariance beween he risky asse and marke porfolio while guaraneeing a posiive definie variance-covariance marix. oreover we define he Condiional Bea urface an exension of he News Impac urface concep of Ng and Kroner (995. Using his approach i is possible o produce a graphical represenaion of he impac of idiosyncraic and markewide shocks upon esimaes of bea. We also employ indicaor dummy regressions o idenify sources of he observed asymmery in he esimaed bea series. The remainder of he paper develops as follows. ecion 2 oulines he sraegy employed for modelling he ime-variaion and asymmery in bea while secion 3 describes he daa and presens he empirical resuls. The saisical properies of he esimaed bea series are repored in secion 4. The final secion of he paper provides a summary and some concluding commens. 2. odelling Time Variaion and Asymmery in Bea The saic Capial Asse Pricing odel (CAP predics ha he expeced reurn o invesing in a risky asse or porfolio E( R should equal r f he risk free rae of reurn plus a risk premium. The risk premium is deermined by a price of risk he exceped reurn on he marke porfolio in excess of r f and a quaniy of risk known as he bea of asse β. The saic CAP may be wrien as E ( R rf [ E( R rf ] β ( 3

4 4 where 2 / σ σ β. By definiion β so porfolios wih a bea greaer han uniy are seen as being relaively risky. Esimaes of β may be obained from OL esimaes of he slope coefficien in i u R b b R (2 I has long been recognised ha he volailiy of asse reurns is clusered. Thus he assumpion of consan variance (le alone covariance underlying he esimaion of (2 mus be regarded as enuous. Bollerslev Engle and Wooldridge (988 Braun Nelson and unier (995 and Engle and Cho (999 iner alia repor evidence of ime variaion in β based upon he GARCH class of models. Braun Nelson and unnier (995 and Engle and Cho (999 use he bivariae EGARCH approach specifying he condiional mean equaions as h R R h R. β (3 β he measure of undiversifiable risk associaed wih indusry secor is defined as: ] [ ] [ 2 R E R R E β (4 where [.] E denoes he expecaion a ime -. The model is compleed by he equaions defining he ime series behaviour of and β h h [ ] [ ] [ ] ( ( ln( ln( ( ln( ln( m m g g h h g h h ξ ξ ξ ξ β ξ ξ β λ λ γ ϖ φ ϖ λ γ ϖ φ ϖ (5 where and are conemporaneously uncorrelaed i.i.d. processes wih ero mean and uni variance and [ ] ( i i I i E g for i. As noed by Braun e al. (995 he bivariae EGARCH (5 implies some srong assumpions. Firs he model does no allow for feedback as would be he case if

5 5 and ln( ln( β h h followed a VARA process. econd he model assumes a linear auoregressive process for β. Third alhough he model allows for leverage effecs i does so in an ad-hoc fashion. In conras o Braun Nelson and unnier (995 and Engle and Cho (999 our approach allows for feedback beween he condiional means and variances of R and R. Furhermore we make no formal assumpions as o he ime series process underlying β. We assume a VARA process for he reurns and model he ime variaion in he variancecovariance marix using a linear as opposed o an exponenial GARCH model. The mulivariae GARCH approach allows he researcher o examine he effecs of shocks o he enire variance-covariance marix. Thus he effec of a shock o R on he covariance beween R and R may be inferred direcly from he parameer esimaes. oreover he condiional variance-covariance marix may be parameerised o be ime varying and asymmeric. Given he role of covariances in asse pricing and financial risk managemen correc specificaion of he variance-covariance srucure is of paramoun. For example he condiional covariance may be used in he calculaion of prices for opions involving more han one underlying asse (such as rainbow opions and is vial o he calculaion of minimum capial risk requiremens. Boh variance and covariance esimaes may be used in he calculaion of he measure of undiversifiable risk from he Capial Asse Pricing odel. I follows ha if he variance and/or covariance erms are ime-varying (and asymmeric he CAP β is also likely o be ime-varying (and asymmeric. The condiional mean equaions of he model are specified in our sudy as a Vecor Auoregressive oving Average (VARA which may be wrien as: Θ Θ Θ Θ Θ Γ Γ Γ Γ Γ Θ Γ k k k k k j i j j j j k n k k j m j j R R Y Y Y ( ( ( ( ( ( ( ( ; ; ; ; ε ε ε µ µ µ ε ε µ (6 where and denoe he marke and secor respecively.

6 Under he assumpion ε Ω ~ ( H where ε represens he innovaion vecor in (6 and defining h as vec(h where vec is he operaor ha sacks he columns of a marix he bivariae VARA(mn GARCH( vec model may be wrien where vec h ( H h h C Avec( ε Bh h ε (7 c C c c ; a a a A 2 a22 a23 ; 22 a a a a b B b b 2 3 b b b b b b Resricing he marices A and B o be diagonal gives he model proposed by Bollerslev Engle and Wooldridge (988 where each elemen of he condiional variancecovariance marix H depends on pas values of iself and pas values of ε ε. There are 2 free parameers in he condiional variance-covariance srucure of he bivariae GARCH( vec model (7 o be esimaed subjec o he requiremen ha H be posiive definie for all values of ε in he sample. The difficuly of checking le alone imposing such a resricion led Engle and Kroner (995 o propose he BEKK parameerisaion H C ε ε (8 C AH A B B The BEKK parameerisaion requires esimaion of only free parameers in he condiional variance-covariance srucure and guaranees H posiive definie. I is imporan o noe ha he BEKK and vec models imply ha only he magniude of pas reurn innovaions is imporan in deermining curren condiional variances and covariances. This assumpion of symmeric ime-varying variance-covariance marices mus be considered enuous given he exising body of evidence documening he asymmeric response of equiy volailiy o posiive and negaive innovaions of equal magniude (see Engle and Ng 993 Glosen Jagannahan and Runkle 993 and Kroner and Ng 996 iner alia. 6

7 Defining min{ ε } ξ for j marke secor he BEKK model in (8 may be j exended o allow for asymmeric responses as H C ε ξ (9 C AH A B ε B Dξ D where C B c b b 2 c c 2 22 b b 2 22 ; ; A D a a 2 d d 2 a a 2 22 d d 2 22 ; and 2 2 ξ ξ 2 ( ξ The symmeric BEKK model (8 is given as a special case of (9 for d for all values of i j i and j. Given esimaes of H he condiional covariance beween he reurn o he marke porfolio R and he reurn o he individual secor R and he variance of reurn o he marke porfolio H i is possible o calculae a ime varying esimae of β he measure of undiversifiable risk associaed wih indusry secor as: H E [ R R ] β ( 2 H E [ R ] where E [.] denoes he expecaion a ime -. Kroner and Ng (996 analyse he asymmeric properies of ime-varying covariance marix models idenifying hree possible forms of asymmeric behaviour. Firs he covariance marix displays own variance asymmery if ( h h ( R is affeced by he sign of he innovaion in R ( R R he condiional variance of. econd he covariance marix displays cross variance asymmery if he condiional variance of ( R affeced by he sign of he innovaion in ( R R R is. Finally if he covariance of reurns h is sensiive o he sign of he innovaion in reurn for eiher porfolio he model is said o display covariance asymmery. 7

8 The innovaion in prices from ime - o ime P ε represens changes in P informaion available o he marke (ceeris paribus. Kroner and Ng (996 rea such innovaions as a collecive measure of news arriving o marke j beween he close of rade on period - and he close of rade on period. Kroner and Ng (996 define he relaionship beween innovaions in reurn and he condiional variance-covariance srucure as he news impac surface a mulivariae form of he news impac curve of Engle and Ng (993. By consrucion he model allows β he measure of undiversifiable risk associaed wih indusry secor o respond asymmerically o news abou he marke porfolio and/or news abou secor. 3. Daa Descripions and Empirical Resuls Weekly UK equiy index daa for he period //965 o /2/999 was obained from Daasream Inernaional. The FT-All hares index was used as a proxy for he marke porfolio. The paper considers six Indusry secor reurn indices namely Basic Indusries (BAICUK Toal Financials (TOTLFUK Healhcare (HLTHCUK Publishing (PUBLUK Reail (RTAILUK and Real Esae (RLETUK. In all cases he daa was in accumulaion index form and was ransformed ino coninuously compounded reurns for secor i as R i ln( Pi / Pi (2 ummary saisics for he daa are presened in able. As one migh anicipae he daa display evidence of exreme non-normaliy. In only one case Healhcare is he degree of skewness no saisically significan. In all cases he daa display srong evidence of excess kurosis. Columns and 2 of Figure display he index and reurns daa respecively. Visual inspecion of he graph of he reurns daa suggess ha here is srong volailiy clusering. A Ljung-Box es on he squared reurn daa suggess ha here is srong evidence of Auoregressive Condiional Heeroscedasiciy (ARCH in he daa. The final column of able 8

9 displays saic esimaes of undiversifiable risk obained from OL esimaion of (2. The range of esimaes runs from.93 for Healh Care o.79 for Reailing. The Akaike and chwar Informaion crieria were used o deermine he lag order of he VARA model (6. In all cases he resriced VARA(2 given as (2 was deemed opimal: Y µ 2 j Γ Y j j Θ ε ε Y R R µ ; µ ; Γ µ j Γ Γ ( j ( i j Γ Γ ( j ( j ; Θ k ( Θ k ( Θ k ε ; ε ε (2 aximum likelihood echniques were used o obain esimaes of parameers for equaions (9 and (2 assuming a uden s- disribuion wih unknown degrees of freedom for he errors. The parameer esimaes for he condiional mean and variance equaions are displayed in Tables 2a and 2b respecively. hocks o volailiy appear highly persisen. Esimaes of he main diagonal elemens of A are in general close o uniy. There is srong evidence of own variance cross variance and covariance asymmery in he daa. This is highlighed by he significance of he parameers in he D marix. The insignificance of he off-diagonal elemens in he B marix suggess ha he majoriy of imporan volailiy spillovers from he marke o he secor are associaed wih negaive realisaions of R. Wih he excepion of he financial secor he models all pass he usual Ljung-Box es for serial correlaion in he sandardised and squared sandardised residuals displayed in able 3. Figures 2-7 display he variance and covariance news impac surfaces for he esimaes of he ulivariae GARCH model displayed in Table 2. Following Engle and Ng (993 and Ng and Kroner (996 each surface is evaluaed in he region [ ] ε j 55 for j arke ecor holding informaion a ime - and before consan. There are relaively few exreme ouliers in he daa which suggess ha some cauion should be exercised in inerpreing he news impac surfaces for larger absolue values of ε j. Despie his cavea he asymmery in variance and covariance is clear from each figure. The sign and magniude 9

10 of idiosyncraic and marke shocks have clearly differing impacs on elemens of H. In he cases of he basic indusries reail and healhcare secors a marke-wide shock has a bigger impac on subsequen volailiy han an idiosyncraic shock of he same sie. In fac an idiosyncraic shock has virually no effec on volailiy since ha par of he surface on he firs diagram is fla. On he oher hand in he cases of he financial and real esae secors idiosyncraic socks have a much sronger role o play. Holding informaion a ime - and before consan and evaluaing β in he range [ ] ε j 55 for j arke ecor as before yields he response of he measure of undiversifiable risk o news. The fourh panel of figures 2-7 graphs he response of β o news using he esimaes displayed in Table 2. Again he asymmery in response o marke and idiosyncraic shocks is clear. 4. Properies of he ˆβ series The hird column of Figure plos he esimaed ˆβ. The ime variaion of he measure of undiversifiable risk across each secor is eviden. Table 3 presens descripive saisics for he ˆβ series. The mos volaile of he ˆβ series is associaed wih he healhcare indusry. Here he ˆβ ranges from a minimum of.53 o a maximum of 2.9. In erms of he average value of ˆβ reailing appears o be he riskies secor wih a ˆβ.5 indicaing ha reailing has higher risk han he marke porfolio which has β by definiion. The averages of he ˆβ compare favourably wih he saic esimaes presened in able. On he basis of a sequence of Dickey-Fuller uni roo ess he ˆβ series appear saionary.

11 Wha facors underlay he observed asymmery in ˆβ? EC argue ha shocks o he marke and idiosyncraic shocks deermine asymmeric effecs in ˆβ. This logic underlies he News Impac urface ha we propose for ˆβ depiced in Figures 2 o 6. To idenify negaive reurns o he marke le I represen an indicaor variable which akes he value of uniy when R he reurn o he marke porfolio is negaive and ero oherwise. imilarly in order o idenify he magniude of negaive marke reurns le R I R. imilar variables may be defined o idenify negaive reurn innovaions and he corresponding magniudes for each individual secor. Consider he OL regression ˆ β φ φ I φ R φ I φ R φ C φ C u ( where C I R and C I R represen dummy variables designed o capure he secor reurn when he marke reurn is negaive ( C and he marke reurn when he secor reurn is negaive ( C. The resuls from esimaion of (3 are displayed in able 4. Periods of negaive reurns o he marke only significanly affec ˆβ for he healh and publishing secors in boh cases leading o a fall in he value of he measure of undiversifiable risk. However large negaive innovaions o he marke porfolio uniformly lead o an increase in ˆβ across all secors considered. There is no paern of correlaion beween a negaive reurn o he secor and changes in ˆβ. imilarly C and C do no appear o significanly affec esimaes of sysemaic risk.

12 5. ummary and Conclusions Recen research provides conflicing evidence as o wheher abnormaliies in equiy reurns are a resul of changes in expeced reurns in an efficien marke or an over-reacion o new informaion in a marke ha is inefficien. De Bond and Thaler (985 Chopra Lakonishok and Rier (992 and Jegadeesh and Timan (993 iner alia conclude ha he reurn o a porfolio formed by buying socks which have suffered capial losses (losers in he pas and selling socks which have experienced capial gains (winners in he pas has a higher average reurn ha prediced by he CAP. All hree sudies conclude ha such overreacion is inconsisen wih efficiency since such conrarian sraegies should no consisenly earn excess reurns. On he oher hand Chan (988 and Ball and Kohari (989 argue ha he ime variaion in expeced reurn due o ime-variaion in bea can explain he success of he losers porfolio. The sudies find ha here exiss predicive asymmery in he response of he condiional bea o large posiive and negaive innovaions. Braun Nelson and unier (995 find weak evidence of asymmery in bea bu conclude ha i is no sufficien o explain he over-reacion o informaion or mean reversion in sock prices. Engle and Cho (999 argue ha his lack of evidence of asymmery in bea is due o sock price aggregraion and lack of cross-secional variaion in he monhly daa used by Braun Nelson and unier (995. Engle and Cho (999 argue ha he use of daily daa on individual socks makes he deecion of asymmery an easier ask. This paper employs weekly daa on indusry secors from he UK equiy marke o examine he impac of news on ime-varying measures of bea. The use of weekly daa on secors of he marke should overcome he poenial price aggregaion problems associaed wih lower frequency daa and mainain sufficien cross-secional variaion o deec ime variaion and asymmery in bea. Treaing prices innovaions as a collecive measure of news arriving o he marke beween ime and ime he resuls sugges ha ime-variaion in bea depends on wo sources of news - news abou he marke and news abou he secor. However he asymmeric 2

13 response of bea o news appears relaed only o large negaive innovaions o he marke. Bad news abou each individual secor does no appear o significanly affec he measure of undiversifiable risk. The asymmeric effec in bea is consisen across all secors considered. Given he magniude of he asymmery idenified in bea he evidence in his paper suggess ha abnormaliies such as mean reversion in sock prices may occur as a resul of changes in expeced reurn caused by ime-variaion and asymmery in bea raher han as a by-produc of marke inefficiency. Foonoes We also considered GARCH- versions of (6. However on he basis of Wald and LR ess he VARA-GARCH was chosen as he opimal condiional daa characerisaion. 3

14 References Akaike H. (974 New look a saisical model idenificaion I.E.E.E Transacions on Auomaic Conrol AC Ball R. and Kohari.P. (989 Non-saionary expeced reurns: Implicaions for ess of marke efficiency and serial correlaion in reurns Journal of Financial Economics Black F. (976 udies in price volailiy changes Proceedings of he 976 eeing of he Business and Economics aisics ecion American aisical Associaion Bollerslev T. Engle R.F. and Wooldridge J.. (988 A capial asse pricing model wih ime-varying covariances Journal of Poliical Economy Braun P.A. Nelson D.B. and unier A.. (995 Good News Bad News Volailiy and Beas Journal of Finance Brooks C. and Henry Ó.T.J. (999 Linear and non-linear ransmission of equiy reurn volailiy: Evidence from he U Japan and Ausralia forhcoming Economic odelling Campbell J. and Henschel L. (992 No news is good news: An asymmeric model of changing volailiy in sock reurns Journal of Financial Economics Chrisie A. (982 The sochasic behaviour of common sock variance: Value leverage and ineres rae effecs Journal of Financial Economics Chopra N. Lakonishok J. and Rier J. (992 easuring abnormal reurns: Do socks overreac? Journal of Financial Economics DeBond W. and Thaler R. (985 Does he sock marke overreac? The Journal of Finance Engle R.F. (982 Auoregressive condiional heeroscedasiciy wih esimaes of he variance of Unied Kingdom inflaion Economerica

15 Engle R.F. and Cho Y-H (999 Time Varying Beas and Asymmeric Effecs of News: Empirical Analysis of Blue Chip ocks NBER Working Paper No 733 Engle R.F and Kroner K. (995 ulivariae simulaneous generalied ARCH Economeric Theory Engle R.F. and Ng V. (993 easuring and esing he impac of news on volailiy Journal of Finance Glosen L.R. Jagannahan R. and Runkle D. (993 On he relaion beween he expeced value and he volailiy of he nominal excess reurn on socks Journal of Finance Henry Ó.T. (998 odelling he Asymmery of ock arke Volailiy Applied Financial Economics Henry Ó.T. and harma J.. (999 Asymmeric Condiional Volailiy and Firm ie: Evidence from Ausralian Equiy Porfolios Ausralian Economic Papers Jegadeesh N. and Timan. (993 Reurns o buying winners and selling losers: Implicaions for sock marke efficiency Journal of Finance Kroner K.F. and Ng V.K. (996 ulivariae GARCH odelling of Asse Reurns Papers and Proceedings of he American aisical Associaion Business and Economics ecion Pagan A.R. and chwer G.W. (99 Alernaive odels for Condiional ock Volailiy Journal of Economerics chwar G. (978 Esimaing he dimensions of a model Annals of aisics

16 Tables and Figures Table : ummary saisics for he reurns daa eries ean Variance kew E.K. ρ Q(5 Q 2 (5 β FTALL BAIC (.2 TOTLF [.9] (. HLTH [.29] [.] (.22 PUBL (.6 RTAIL [.35] (.8 RLET (.2 Noes o Table : arginal significance levels displayed as [.] sandard errors displayed as (.. kew measures he hird momen of he disribuion and repors he marginal significance of a es for ero skewness. E.K. repors he excess kurosis of he reurn disribuion and he associaed marginal significance level for he es of ero excess kurosis. The firs order auocorrelaion coefficien is ρ. Q(5 and Q 2 (5 are Ljung-Box ess for fifh order serial correlaion in he reurns and he squared reurns respecively. Boh ess are disribued as χ 2 (5 under he null. β is he OL esimae of he measure of undiversifiable risk. 6

17 ( µ.338 (.27 ( Γ (.3 ( Γ.3 2 (.9 ( Γ.45 (.2 ( Γ.2 2 (.5 ( Θ.252 (.53 ( µ.99 (.29 ( Γ -. (.32 ( Γ.3 2 (.5 ( Γ.4 (.48 ( Γ.3 2 (.7 ( Θ.4 (.38 Table 2a: Condiional ean Esimaes BAIC TOTLF HLTH PUBL RTAIL RLET.244 (.2 -. (.9.44 (.9.43 (.9.74 ( (..268 (.22.4 (..2 (..29 (..9 (.9.24 (..224 ( (.2.44 (.4.5 (.2.62 (. -.8 ( (.5.3 (.9.88 ( (.3.4 (.4.38 (.4 Noes o able 2a: andard errors displayed as (..293 ( (.4.27 (.2.38 (. -. (.2. ( ( (.7.22 (.6.53 ( (.3 -. ( ( (.8.5 (.4.35 ( ( ( ( (.9.64 ( ( (.5.39 ( ( (.6.83 (.3.3 (..24 (..29 (.5.22 (.45.2 (.9.3 (.7. (.3.9 ( (.2 7

18 Table 2b: Condiional Variance Esimaes BAIC TOTLF HLTH PUBL RTAIL RLET c.362 (.6.22 ( ( (.6.34 ( (.62 c 2.35 ( ( ( (.93.3 ( (.42 c (.3.35 ( ( (.8.3 (.7.26 (.5 a.955 ( (.8.93 ( (.3.94 (..99 (.22 a 2.3 (.5.6 ( ( ( ( (.24 a 2 -. ( (.9.32 (.6.38 ( (.7. (.7 a ( (..957 ( (.46.7 ( (.6 b.86 ( ( (.5.38 ( ( (.46 b ( ( ( (.7.9 (.39.3 (.44 b 2.28 ( ( (.3.37 ( ( (.33 b (.43.2 ( ( ( ( (.35 d.456 ( ( ( ( ( (.98 d ( ( ( ( ( (.92 d ( ( (.7.89 ( ( (.64 d (..357 ( ( ( ( (.63 Noes o Table 2b: andard errors displayed as (. 8

19 Table 2c: Residual Diagnosics BAIC TOTLF HLTH PUBL RTAIL RLET η ( ( ( ( ( (.254 Log L Q( [.] 2.34 [.33] [.2].324 [.67] 2.39 [.3].48 [.43] Q 2 (5.767 [.979].764 [.979].69 [.957].383 [.926].844 [.87].536 [.99] Q( [.235] [.79].6 [.75] 3.38 [.679] 3.53 [.692] [.23] Q 2 (5.78 [.978] [.7] [.84].987 [.964] [.47].479 [.63] Noes o Table 2c: andard errors displayed as (.. arginal significance levels displayed as [.].η represens he degrees of freedom parameer esimaed from he sudens- densiy. Q(5 i and Q 2 (5 i represen Ljung Box ess for serial dependence in he sandardised residuals and heir corresponding squares for iarke ecor 9

20 Table 3: Descripive aisics for ˆβ BAIC TOTLF HLTH PUBL RTAIL RLET ean Variance kew -.2 [.976] EK.83.6 [.883] [.3] -.43 [.] in ax ADF Noes o Table 3: arginal significance levels displayed as [.]. kew measures he hird momen of he disribuion and repors he marginal significance of a es for ero skewness. E.K. repors he excess kurosis of he disribuion and he associaed marginal significance level for he es of ero excess kurosis. ADF is an Augmened Dickey-Fuller (98 es for a uni roo in ˆβ The 5% criical value for he ADF es is

21 Table 4: ources of Asymmery in ˆβ BAIC TOTLF HLTH PUBL RTAIL RLET φ.994 ( ( ( (.3.32 ( (.6 φ 2.8 (.7.2 ( (. -.8 (.6.3 (. -. (.2 φ 3.55 (.7.52 (..9 (.8.7 (.5.55 (.9.3 (.9 φ ( ( (. -.2 ( ( (. φ 5.2 ( (. -.2 ( ( ( (.6 φ ( (. -.4 ( ( ( (.8 φ ( (. -.5 ( ( ( (.6 L Noes o Table 4: arginal significance levels displayed as [.]. andard errors displayed as (.. denoes significance a he 5% level. 2

22 7 Basic Indusries Index 5 Basic Indusries Reurn.36 Basic Indusries Bea Toal Financials Index 24 Toal Financials Reurn.35 Toal Financials Bea Healh Care Index 3 Healh Care Reurn 2.25 Healh Care Bea Publishing Index 24 Publishing Reurn.6 Publishing Bea Reail Index 24 Reail Reurn.5 Reail Bea Real Esae Index 32 Real Esae Reurn.6 Real Esae Bea Figure : ecor index secor reurn and esimaed secor bea 22

23 Figure 2: News Impac urfaces for Basic Indusries 23

24 Figure 3: News Impac urfaces for Toal Financial 24

25 Figure 4: News Impac urfaces for Healhcare 25

26 Figure 5: News Impac urfaces for Publishing 26

27 Figure 6: News Impac urfaces for Reail 27

28 Figure 7: News Impac urfaces for Real Esae 28