Shock and Volatility Spillovers Among Equity Sectors of the Gulf Arab Stock Markets

Transcription

1 Shock nd Voltility Spillovers Among Equity Sectors of the Gulf Arb Stock Mrkets Shwkt M. Hmmoudeh * Lebow College of Business Drexel University Phildelphi, PA Yun Yun Lebow College of Business Drexel University Phildelphi, PA Michel McAleer School of Economics nd Commerce University of Western Austrli And Econometric Institute Ersmus University Rotterdm EI November 2008 Corresponding uthor: hmmousm@drexel.edu. Fx: (215) yy23@drexel.edu. Fx: (215) michel.mcleer@gmil.com. +31 (0) Acknowledgement: The uthors wish to thnk Frooq Mlik, Mrk Thompson nd Aksel Kibr for helpful comments. The third uthor is most grteful for the finncil support of the Austrlin Reserch Council. 1

2 Abstrct Upon exmining own voltility dependency for the three mjor sectors, nmely Service, Industril nd Bnking, in four GCC economies (Kuwit, Qtr, Sudi Arbi nd UAE), the empiricl findings suggest tht Bnking seems to be the lest sensitive mong the sectors to pst own voltility, while Industril is the most voltile to the onset of pst shocks or news. Sector voltility spillovers show tht Sudi Arbi hs the lest intersector spillovers, while tiny Qtr hs the most. Sudi Arbi seems to be the most sensitive to geopolitics, while Kuwit is the lest ffected. The constnt conditionl correltions between the three sectors for ll four GCC mrkets echo different economic dvntges nd vrying roles in the economy. We lso provide two exmples using the estimtes of the GCC equity sector mrkets for portfolio designs nd hedging strtegies. 2

3 1. Introduction In developed countries, equity investing hs been populr for mny yers. Investors invest in defensive stocks, such s those of the non-cyclicl consumer goods sector, when the economy is teetering into recession. They invest in high tech sector s stocks when the economy is booming. In interntionl investing, portfolio mngers who follow the top down pproch usully pick countries nd then sectors. Even informed investors choose sectors without pying much ttention to interctions nd voltility trnsmission mong sectors. In emerging mrkets such s the mrkets of the rich oil-producing countries, sector investing hs not yet reched similr populrity nd their mrkets lck orgnized sector indices. While there hve been studies tht exmine the trnsmission of returns mong individul sectors within system, informtion is still needed on how voltility spillovers occur mong sectors in multivrite settings. This knowledge is prticulrly useful becuse of the increse in globliztion nd contgion mong world finncil mrkets. The current trnsmission of high voltility mong sectors of individul countries nd mong countries is vivid nd topicl exmple. More recent literture on MENA mrket voltility uses univrite GARCH models nd exmines voltility behvior t the mrket index level. Hmmoudeh nd Li (2008) exmine sudden chnges in voltility for five GCC stock mrkets t the mrket index using the iterted cumultive sums of squres (ICSS) lgorithm, nd nlyze their impcts on the estimted persistence of voltility. They find tht most of these stock mrkets re more sensitive to mjor globl events thn to locl nd regionl fctors. 3

4 Zrour nd Siriopoulos (2008) use the CGARCH model developed by Engle nd Lee (1993) to investigte the existence of voltility decomposition into short run nd long run components. They pply this model to dily index returns dt for nine emerging mrkets in the Middle Est region, including three of our GCC countries. Hmmoudeh nd Choi (2007) employ the univrite GARCH pproch with Mrkov-switching to study the voltility behvior for the trnsitory nd permnent components of the individul GCC mrket indices, llowing for two voltility regimes to exist. While Mlik nd Hmmoudeh (2007) use trivrite GARCH models, their systems include one individul GCC mrket index, the WTI oil price nd S&P 500 index to nlyze return voltility trnsmission for three GCC mrkets. The voltility trnsmission does not involve more thn one GCC mrket within one system. This pper uses more recent multivrite technique tht exmines shock nd voltility trnsmission mong three sectors, nmely bnking, industril nd service, for Kuwit, Qtr nd Sudi Arbi, nd bnking, insurnce nd service, for UAE which does not hve dt for the industril sector. The technique is the vector utoregressive moving verge GARCH (VARMA-GARCH) model developed by Ling nd McAleer (2003) (see Chn et l. (2005) for n erly ppliction of the model). This method enbles us to exmine the conditionl voltility nd conditionl correltion cross effects with meningful estimted prmeters nd less computtionl complictions, s compred with other methods such s the BEKK model of Engle nd Kroner (1995). BEKK is multivrite GARCH (1,1) model with dynmic covrinces nd dynmic correltions, 4

5 but typiclly is not ttched to VARMA model. The VARMA version of BEKK hs not yet been nlyzed theoreticlly (see McAleer et l. (2008) for further theoreticl detils). For more thn four or five ssets or commodities, BEKK typiclly does not converge becuse it hs fr too mny prmeters. In short, there is little rgument in fvour of BEKK, other thn tht it leds to positive definite dynmic covrince mtrix (see McAleer (2005) for further elbortion). The brod objective of this pper is to exmine conditionl voltility nd conditionl correltion cross effects for the three mjors sectors in the four GCC stock mrkets using the VARMA-GARCH model. This method enbles n exmintion of the conditionl voltility nd conditionl correltion cross effects with meningful estimted prmeters nd less computtionl compliction compred with severl other methods. A complementry objective is to use the estimted results to compute the weights of the sectors in n optiml portfolio of ech GCC country, nd the optiml hedge rtios tht minimize overll risk for holding the sectors in portfolios without ffecting the expected returns in the individul country. The empiricl results indicte tht optiml portfolio weights of investors own much more bnking stocks thn service or industril stocks in Sudi Arbi nd Qtr nd more thn service or insurnce stocks in UAE in order to minimize risk without lowering expected returns. Investors in Kuwit hold more industril stocks. The vlues for the hedge rtios for the GCC sectors re smller thn those for US equity sectors, reflecting the possibility of greter hedging effectiveness in GCC mrkets thn in the USA, thereby leding to 5

6 more sophisticted hedging techniques nd strtegies. These empiricl results re importnt for the GCC countries which hve recently embrked on estblishing equity funds for both individul nd institutionl investors. The reminder of the pper is orgnized s follows. Section 2 provides description of the dt nd summry sttistics. Section 3 presents the empiricl VARMA-GARCH model. Section 4 discusses the empiricl results, nd Section 5 provides the economic implictions for designing optiml portfolios nd formulting optiml hedging strtegies. Section 6 gives some concluding comments. 2. Dt Description The dt cover the three mjor sector dily indices for four of the six GCC countries, nmely Sudi Arbi, Kuwit, Qtr nd UAE. The sectors re the Service, Industril nd Bnking sectors for the first three countries, nd Service, Insurnce nd Bnking for UAE, which does not hve n index for the Industril sector. Bhrin ws excluded becuse this kingdom chnged its index series in 2003, so tht there re currently no dequte series for its sectors. Furthermore, resonble sectorl dt do not exist for Omn. 1 The smple covers the dily period from December 31, 2001 until December 31, 2007 for Sudi Arbi, Kuwit nd Qtr. The smple period for UAE strts with the sme dte 1 Aksel Kibr of Abu Dhbi Investment Compny indicted in privte communiction tht: It is fct tht these two mrkets [Bhrin nd Omn] re very illiquid. Sometimes stocks do not trde for weeks. 6

7 but ends on December 10, It should be noted tht the GCC countries do not shre the sme week-end, nd their week-ends re different from week-ends in western mrkets. Therefore, we cnnot pool vribles cross countries on dily bsis. The dt set lso includes dummy vrible for the 2003 Irq Wr, nd is intended to cpture the impct of geopolitics on sector return nd voltility. Tble 1 provides the descriptive sttistics for the dily indices of the three sectors in ech of the four GCC countries over the smple periods. The verge dily index return vries mong the sectors within the sme country, nd for the sme sector cross the four countries. In Sudi Arbi, the Industril sector gives the gretest verge return reltive to the other two sectors. This is consistent with the empiricl findings reported in Hmmoudeh nd Al-Qudhe (2006). It is not surprising tht the Industril sector in Sudi Arbi yields the highest verge return becuse the country hs the lrgest economy, defined in terms of GDP, in the Middle Est nd North Afric (MENA) region. Its economy cn thus support reltively lrge industril bse. In Kuwit nd tiny Qtr, the Service sector nd Bnking sector, respectively, yield the gretest returns. Qtr is competing with Dubi nd Bhrin in hving the best finncil center in the region, but it does not hve solid industril bse. In UAE, both the Service nd Bnking sectors give the sme highest return which is much higher thn the return in the Insurnce sector. Stocks of the Insurnce sector re not s liquid s those of the other sectors. Overll, mong the three sectors in the four countries, the Service sector in Kuwit returns the highest verge yield, while the Insurnce sector in UAE gives the 7

8 lowest. In terms of sector risk, s defined by the stndrd devition, most of the risk is in the Service sector for Sudi Arbi, Kuwit nd UAE, but in the Industril sector for Qtr, whose highly concentrted industries re bsed on the voltile oil nd nturl gs. Thus, in Kuwit nd UAE, sector risk is commensurte with return. Most of the returns re skewed to the left, implying tht there is greter chnce tht the sectors go down thn up in given period of time. This result suggests tht investors invest in these sectors for the long hul to override the intermittent declines. The kurtosis is mixed, with some indices hving kurtosis tht is higher thn the norml distribution, while for others it is lower. 3. Empiricl Model As indicted bove, the univrite GARCH pproch hs been used in modeling voltility in the generl indices of the GCC stock mrkets. Our objective is to pply recent techniques in modeling voltility to upgrde the use of the univrite GARCH pproch to multivrite system. This pproch will enble us to exmine the conditionl voltility nd correltion dependency, nd interdependency of equity sectors of the GCC mrkets. With this pproch, we will be ble to focus more on the estimtion of meningful, interpretble prmeters with miniml computtionl difficulties thn for severl other models. We use the VARMA-GARCH model developed by Ling nd McAleer (2003) to focus on the interdependence of conditionl vrinces nd conditionl correltions mong these sectors. 8

9 The equity sectors for ech GCC stock mrket in the VARMA-GARCH system re indexed by i, nd n is the totl number of sectors. Bsed on informtion criteri, the men eqution for the i th sector in this system is AR(1), nd is given by: R = + br + ε, (1) it, i i it, 1 it, ε = h η, 1/2 it, it, it, where R i,t is the return of the i th sector of the nx1 vector R t, defined s the log differences. The innovtion η it, is n i.i.d. rndom shock, nd h it, is the conditionl vrince of the ith sector t time t. Ling nd McAleer (2003) specified the interdependent conditionl vrince s: n n 2 it, = i+ αε ij jt, 1 + βij jt, 1 j= 1 j= 1, (2) h c h which is generliztion of the Bollerslev (19900) univrite GARCH process, where h i,t is the conditionl vrince t time t, h j,t-1 refers to own pst vrince for i=j, nd pst conditionl vrinces of the sectors in the mrket or system for i j, Σα ij ε 2 j,t-1 is the short run persistence (or the ARCH effects of pst shocks), nd Σβ ij h j,t-1 is the contribution to the long run persistence (or the GARCH effects of pst voltilities). 9

10 From (2), the conditionl vrince for the i th sector is impcted by pst shocks nd pst conditionl vrinces of ll the sectors in the mrket, thereby cpturing interdependencies or spillovers. Therefore, this specifiction llows for the cross-sectionl dependency of conditionl voltilities mong ll the sectors. The pst shock nd voltility of one sector re llowed to impct the future voltilities not only of itself but lso of ll the other sectors in the system. The prmeters of the VARMA-GARCH system defined bove re obtined by using the mximum likelihood estimtion (MLE) when the distribution of η it, is stndrd norml, nd by qusi-mximum likelihood estimtion (QMLE) when the distribution is not stndrd norml. Ling nd McAleer (2003) estblished the structurl nd sttisticl properties of VARMA-GARCH, using the second nd fourth moments. The i.i.d. property of η it, implies tht conditionl correltion mtrix of εt = [ ε1, t, ε 2, t, L, ε n, t]' my be modeled s constnt over time (see Engle (2002) nd McAleer et l. (2008) for dynmic extensions of the constnt conditionl correltion model). The constnt correltion mtrix is given by Γ= E( ηtηt '), whereηt = [ η1, t, η2, t, L, ηn, t]'. 4. Empiricl Results We will discuss the empiricl results in terms of own sector voltility nd shock dependence, inter-sector voltility, shock spillover nd politicl risk for the three sectors in ech of the four GCC countries. As is the cse with the BEKK version of the 10

11 multivrite GARCH model, we re lso constrined by the number of sectors tht cn be included in the system to chieve computtionl convergence. Voltility nd shocks dependence Most of the three sector indices in the individul GCC countries show significnt nd positive sensitivity to pst own voltility in the long run, but to considerbly vrying degrees. This implies tht pst own voltility cn be used in predicting future voltility for those sectors. The Bnking sector seems to be the lest voltile mong the sectors for most countries. This should not be surprising s Bnking is dominnt sector in most GCC economies, flooded with petrodollrs nd flushed with liquidity. Given the current globl finncil crisis, this reltive bnking stbility is crucil strength of the GCC economies. For Sudi Arbi, own voltilities (β s) re, to some extent, similr cross sectors, with the Service sector exhibiting the gretest reltive β voltility dependency (0.686), s displyed in Tble 2. The sitution is different in Kuwit. In contrst to Sudi Arbi, the voltility discrepncy cross sectors in Kuwit is reltively high (Tble 3). While the Kuwiti Service sector is the most voltile (0.868), higher even thn in Sudi Arbi, Bnking shows the highest stbility (0.400), which is more stble thn in Sudi Arbi (0.664). This implies tht the Sudi Monetry Agency (SAMA) should py closer ttention to its bnks. The bnks in Kuwit re strongly supervised by its centrl bnk. In Qtr, only the Industril sector shows significnt positive voltility, which is close to its 11

12 counterprts in Sudi Arbi nd Kuwit (Tble 4). In the UAE, which does not hve n index for the Industril sector, Insurnce hs the highest voltility, while Bnking hs the lowest own voltility (Tble 5). The sensitivity to pst own shocks or news is lso positive for ll sector indices in the short run. But this α sensitivity is much smller for ll sectors cross countries thn the β own voltility, suggesting tht pst own voltilities re more importnt in predicting future voltility thn pst shocks or news. Among the individul GCC countries, Qtr hs the highest shock sensitivity in the Service nd Bnking sectors. In Sudi Arbi, ll three sectors hve similr shock sensitivities, but with bout one third of the sensitivity to pst own voltility. Among the GCC sectors, the Industril sector shows the lest news sensitivity for most GCC countries. This suggests tht this sector is more sensitive to pst voltilities relted to chnges in the fundmentls such s the supply nd demnd for oil nd nturl gs, oil nd nturl gs products, petrochemicls, energy-intensive goods nd other commodities thn to news or noise. In contrst to pst own voltility sensitivity, the Bnking sector seems to be the most sensitive to pst news. This is not surprising becuse of this sector s interconnectedness with the globl finncil sector. Long run voltility nd shock interdependency The inter-sector voltility results re significnt, s expected for the most sectors nd countries. Still, the results show moderte voltility spillovers between the sectors within the individul countries, with the exception of the UAE, for which we hve substituted the Insurnce sector for the Industril sector due to non-existence of dt on the Industril 12

13 sector. Interestingly, tiny Qtr, which exports both oil nd nturl gs products, hs the most voltility spillover from the Industril sector to the other two sectors. Moreover, Kuwit hs more sector voltility trnsmission thn Sudi Arbi, which reltively hs the lest inter-sector voltility spillovers. Among the sectors, the voltility results re generlly significnt nd s expected. Cross voltility, or spillover, is more widespred from the Industril sector to the Service sector thn the reverse. When GCC economy grows or contrcts, it first ffects the demnd for goods rising from the Industril sector. In turn, this requires services for huling nd distribution of the goods, thereby leding to fluctutions in the Service sector. On the other hnd, the demnd rising from the Service sector for goods produced or generted in the Industril sector is much less voluminous, giving rise to significntly less voltility spillover towrds this sector. Surprisingly, the Bnking sector shows cross-voltility independence. This my be due to the supervision of the GCC centrl bnks nd this sector s inter-connection with the globl finncil mrkets. In terms of inter-sector shock spillovers, the shock contgion is wek, nd even weker thn own sector shock sensitivity. As in the own shock cse, Sudi Arbi hs the wekest inter-sector shock spillover links, nd Qtr hs the strongest, excluding UAE, which does not hve the sme three sectors. Geopolitics 13

14 With regrd to sensitivity of the mjor sectors indices to geopoliticl events, s represented by the 2003 Irq Wr, geopolitics prtly elevted the men return of the industril sector in Sudi Arbi nd the Insurnce sector in UAE. The wr incresed prices of oil, refined products, petrochemicls, energy-intensive goods nd other commodities, which Sudi Arbi produces the most. This country lso received more thn its shre of domestic politicl violence during the smple period. Shipping premiums on freights going through the Gulf lso incresed substntilly becuse of the wr. Moreover, incresed crowdedness nd congestion incresed insurnce rtes in Dubi nd Abu Dhbi, the two mjor emirtes in the seven-emirte UAE. Constnt conditionl correltions (CCC) As expected, ll the CCCs between the three sectors for ll GCC mrkets re positive, reflecting simultneous growth in the overll economy. They re ll below 0.64, echoing different dvntges nd vrying-roles plyed by those sectors in the economy. The estimtes demonstrte tht the highest CCC for ll the countries is between the Service nd Industril sectors, suggesting more mutul responses economic fctors between those two sectors thn other sectors. The Industril nd Service sectors re highly complementry to ech other. While services crete demnd for other services, the industries initite originl services. The CCC between Industril nd Bnking, nd between Services nd Bnking, re very close, reflecting bnks mutul ties to ll sectors in the economy. 5. Implictions for Portfolio Designs nd Hedging Strtegies 14

15 We now provide two exmples using the estimtes of the GCC equity sector mrkets for portfolio design nd hedging strtegies Portfolio weights The first exmple follows Kroner nd Ng (1998) by considering portfolio tht minimizes risk without lowering expected returns. In this cse, the portfolio weight of holdings of two equity sector indices in the sme mrket is given by: w 12, t h22, t h12, t = h 2h + h 11, t 12, t 22, t nd 0, if w12, t < 0 w12, t = w12, t, if 0 w12, t 1 1, if w12, t > 1 where w 12,t is the weight of, sy, the first sector index in one dollr portfolio of the two sector indices t time t, h 12,t is the conditionl covrince between sector indices 1 nd 2, nd h 22,t is the conditionl vrince of the second sector index. Obviously, the weight of the second sector index in the one dollr portfolio is 1-w 12,t. The verge vlues of w 12,t for the sectors in ech GCC country re reported in Tble 6. For instnce, the verge vlue of w 12,t of portfolio comprising the Service nd 15

16 Industril sector indices in Sudi Arbi is [Hssn nd Mlik (2007) used the BEKK model nd estimted the verge weight between the finncil nd technology sectors t 0.66, while the verge risk-minimizing hedge rtio between these sectors is 0.64.] This suggests tht the optiml holding of the Service index in one dollr of Service/Industril index portfolio for Sudi Arbi is 48 cents, compred with 52 cents for the Industril index. These optiml portfolio weights suggest tht investors in Sudi Arbi should own more industril stocks thn service stocks in their portfolios. This finding confirms the result in Hmmoudeh nd Al-Gudhe (2006). The result is more pronounced in Kuwit, where the holdings tilt more hevily towrd industril stocks. The cse is opposite for Qtr, where the Service sector overwhelmingly domintes the Industril sector, possibly becuse Qtr hs the highest own voltility nd voltility nd shock spillovers in the Industril sector. Additionlly, investors in Sudi Arbi, Qtr nd UAE should lso possess much more bnking stocks thn other sectors stocks to minimize risk without lowering the expected returns. However, the current finncil crisis is n exceptionl period tht hppens once in lifetime. The optiml portfolios in Kuwit fvor industril stocks over bnking stocks Hedge rtios As second exmple, we follow the exmple given in Kroner nd Sultn (1993) regrding risk-minimizing hedge rtios nd pply it to the GCC mrkets. In order to 16

17 minimize risk, long position of one dollr tken in one sector index in given GCC stock mrket should be hedged by short position of $β t in nother sector index in the sme mrket t time t. The β t is given by: h 12, t β t =, h22, t where β t is the risk-minimizing hedge rtio for two sector indices, h 12,t is the conditionl covrince between sectors 1 nd 2, nd h 22,t is the conditionl vrince of the second sector. The second column of Tble 6 reports the verge vlues of β t for the GCC mrkets. The vlues of the hedge rtios for the GCC sectors re smller thn those for the US equity sectors (Hssn nd Mlik, 2007), reflecting the possibility of greter hedging effectiveness in GCC mrkets thn in the USA. By following this hedging strtegy, one dollr long in the Service index, for exmple, in the Sudi mrket should be shorted by 66 cents in the industril sector in tht mrket. The most expensive hedge in the Sudi mrket nd the other GCC mrkets is by hedging the Service index with short positions in the Bnking sector. However, the most (hedging) effective to hedge long positions is between the Insurnce nd Bnking in UAE, where one dollr long position is the former cn be hedged by 23 cent short position in the ltter. 6. Conclusion 17

18 The results suggest tht pst own voltility is the stronger driver in determining future voltility. This implies tht sector s fundmentls hve more influence on voltility thn shocks or news. In countries like the oil-rich GCC countries, chnges in the fundmentls for oil nd nturl gs, s well s for their products nd energy-intensive goods, mtter more when it comes to sector voltility. This is not surprising, given these countries hevy dependence on oil nd nturl gs exports. It is importnt for the GCC countries to ccumulte foreign ssets in boom times nd invest them prudently in the region to stve off the negtive impcts of fluctutions in bust periods nd the migrtion of foreign cpitl. The GCC mrkets differ in terms of optiml portfolio holdings tht minimize risk without lowering expected returns, thereby llowing investors to hold more stocks in certin sectors thn others nd effecting some diversifiction between sectors nd countries. For exmple, investors in Sudi Arbi, Qtr nd UAE should possess much more bnking stocks thn service, industril or insurnce stocks, while in Kuwit they should fvor industril over bnking stocks. These results should be relevnt for the GCC countries, which hve recently embrked on estblishing equity funds for both individul nd institutionl investors. Since the vlues for rtios of hedging long positions with short positions in the GCC sectors re smller thn those for the US equity sectors, which reflect the possibility of greter hedging effectiveness in the GCC mrkets thn in the USA, the GCC countries 18

19 should develop hedging techniques nd strtegies, such s futures, options nd swps, tht reduce voltility. The high voltility of the GCC mrkets in 2008 mkes such conclusion impertive. 19

20 References Abu Zrour, B. nd Siriopoulos, C.P Trnsitory nd permnent voltility components: The cse of the Middle Est stock mrkets. Review of Middle Est Economics nd Finnce 4, Bollerslev, T Modelling the coherence in short-run nominl exchnge rtes: A multivrite generlized ARCH pproch. Review of Economics nd Sttistics 72, Chn, F., Lim, C. nd McAleer, M Modelling multivrite interntionl tourism demnd nd voltility. Tourism Mngement 26, Engle, R.F Dynmic conditionl correltion: A simple clss of multivrite generlized utoregressive conditionl heteroskedsticity models. Journl of Business nd Economic Sttistics 20, Engle, R.F. nd Kroner, K.F Multivrite simultneous generlized ARCH. Econometric Theory 11, Engle, R.F. nd Lee, G.G.J A permnent nd trnsitory component model of stock return voltility, Deprtment of Economics, UCSD, Discussion Pper No: 92-44R Hmmoudeh, S. nd Al-Gudhe, S Return, risk nd globl fctors in Sudi equity sectors. Journl of Emerging Mrkets 10 (Fll/Winter), Hmmoudeh, S. nd Choi, K Chrcteristics of permnent nd trnsitory returns in oil-sensitive emerging stock mrkets: the cse of the GCC countries. Interntionl Review of Economics nd Finnce 17, Hmmoudeh, S. nd Li, H Sudden chnges in voltility in emerging mrkets: The cse of Gulf Arb stock mrkets. Interntionl Review of Finncil Anlysis 17,

21 Hssn, H. nd Mlik, F Multivrite GARCH model of sector voltility trnsmission. Qurterly Review of Economics nd Finnce 47, Kroner, K.F. nd Ng, V.K Modeling symmetric movements of sset prices. Review of Finncil Studies 11, Kroner, K.F. nd Sultn, J Time dynmic vrying distributions nd dynmic hedging with foreign currency futures. Journl of Finncil nd Quntittive Anlysis 28, Ling, S. nd McAleer, M Asymptotic theory for vector ARMA-GARCH model. Econometric Theory 19, Mlik, S. nd Hmmoudeh, S Shock nd voltility trnsmission in the oil, US nd Gulf equity mrkets. Interntionl Review of Economics nd Finnce 17, McAleer, M Automted inference nd lerning in modeling finncil voltility. Econometric Theory 21, McAleer. M., Chn, F., Hoti, S. nd Liebermn, O Generlized utoregressive conditionl correltion. To pper in Econometric Theory. 21

22 Tble 1. Descriptive Sttistics for GCC Sector Returns Sector Service Industril Bnking Sudi Arbi Men S.D Skewness Kurtosis Kuwit Men S.D Skewness Kurtosis Qtr Men S.D Skewness Kurtosis Sector Service Insurnce Bnking UAE Men S.D Skewness Kurtosis Notes: (1) The numbers re log differences, or returns. (2) UAE does not hve n index for the Industril sector. 22

23 Tble 2. Estimtes of VARMA-GARCH for Sudi Arbi Vribles Service Industril Bnking Men Eqution C b b AR(1) D Vrince Eqution C 3.39E-06 b 8.97E E-06 ε 2 service(t-1) ε 2 industril(t-1) ε 2 c bnking(t-1) h service (t-1) h industril (t-1) h bnking (t-1) D E E E-06 Service 1.00 Industry 0.62 Bnking 0.53 Log Likelihood AIC #Obs Constnt Conditionl Correltions Notes: ε 2 j(t-1) represents the pst unconditionl shock of the jth sector in the short run, or news, j = Service, Industril, Bnking h jj (t-1) denotes the pst conditionl voltility dependency or interdependency. D03 is the dummy for the 2003 Irq Wr. The September 11, 2001 dummy vrible, D01, gives similr results when it replces D03. 23

24 Tble 3. Estimtes of VARMA-GARCH for Kuwit Vribles Service Industry Bnking Men Eqution C AR(1) D E E E-05 Vrince Eqution C 4.63E-06 b 3.93E-06 c 3.15E-06 ε 2 service(t-1) ε 2 industril(t-1) ε 2 bnking(t-1) b h service (t-1) h industril (t-1) b h bnking (t-1) b b D E E E-06 Constnt Conditionl Correltions Service 1.00 Industry Bnking Log Likelihood AIC #Obs Notes: ε 2 j(t-1) represents the pst unconditionl shock of the jth sector in the short run, or news, j = Service, Industril, Bnking h jj (t-1) denotes the pst conditionl voltility dependency or interdependency. D03 is the dummy for the 2003 Irq Wr. The September 11, 2001 dummy vrible, D01, gives similr results when it replces D03. c 24

25 Tble 4. Estimtes of VARMA-GARCH for Qtr Vribles Service Industry Bnking Men Eqution b b C AR(1) D E E E-05 Vrince Eqution C 8.92E-06 c -6.52E-06 b 3.36E-05 ε 2 sevice(t-1) ε 2 industry(t-1) b b ε 2 bnking(t-1) c h service (t-1) h industry (t-1) h bnking (t-1) b D E E-06 c -7.96E-06 Constnt Conditionl Correltions Service 1.00 Industry Bnking Log Likelihood AIC #Obs Notes: ε 2 j(t-1) represents the pst unconditionl shock of the jth sector in the short run, or news, j = Service, Industril, Bnking h jj (t-1) denotes the pst conditionl voltility dependency or interdependency. D03 is the dummy for the 2003 Irq Wr. The September 11, 2001 dummy vrible, D01, gives similr results when it replces D03. 25

26 Tble 5. Estimtes of VARMA-GARCH for UAE Vribles Service Insurnce Bnking Men Eqution C AR(1) D E E E-05 Vrince Eqution C 4.10E E-07 b 1.43E-06 ε 2 sevice(t-1) ε 2 insurnce(t-1) ε 2 bnking(t-1) h service (t-1) h insurnce (t-1) h bnking (t-1) D E E E-06 Constnt Conditionl Correltions Service 1.00 Insurnce Bnking Log Likelihood AIC #Obs Notes: ε 2 j(t-1) represents the pst unconditionl shock of the jth sector in the short run, or news, j = Service, Industril, Bnking h jj (t-1) denotes the pst conditionl voltility dependency or interdependency. D03 is the dummy for the 2003 Irq Wr. The September 11, 2001 dummy vrible, D01, gives similr results when it replces D03. The tble uses the dt for the NBAD indices, which re superior to the NBAD Emirtes indices. 26

27 Tble 6. Optiml Portfolio Weights nd Hedge Rtios Sudi Arbi Portfolio Weight of First Sector in 1$ Portfolio (Kroner nd Ng,1998)) Short/Long Bet (Kroner nd Sultn, 1993) Service/Industril Service/Bnking Industril/Bnking Kuwit Portfolio Weight of First Sector in 1$ Portfolio (Kroner nd Ng, 1998) Short/Long Bet (Kroner nd Sultn,1993) Service/Industril Service/Bnking Industril/Bnking Qtr Portfolio Weight of First Sector in 1$ Portfolio (Kroner nd Ng, 1998) Short/Long Bet (Kroner nd Sultn, 1993) Service/Industril Service/Bnking Industril/Bnking UAE Portfolio Weight of First Sector in 1$ Portfolio (Kroner nd NG, 1998) Short/Long Bet (Kroner nd Sultn, 1993) Service/Insurnce Service/Bnking Insurnce/Bnking Notes: w 12,t is the portfolio weight of sector index 1 reltive to sector index 2 in two-sset holding t time t, while verge β t is the risk-minimizing hedge rtio for the two sector indices. 27