Stock Returns and Volatility in Emerging Stock Markets

Transcription

1 Inernaional Journal of Business and Economics, 005, Vol. 4, No., 3-43 Sock Reurns and Volailiy in Emerging Sock Markes Jaeun Shin * KDI School of Public Policy and Managemen, Korea Absrac Boh parameric and semiparameric GARCH in mean esimaions find a posiive bu insignifican relaionship beween expeced sock reurns and volailiy in emerging sock markes. The global emerging marke crisis seems o induce changes in GARCH parameers. Key words: emerging markes; sock reurns; volailiy; semiparameric GARCH JEL classificaion: G; G5; C4. Inroducion Undersanding he risk-reurn rade-off is fundamenal o equilibrium asse pricing and has been exensively explored in he finance lieraure. I is perhaps surprising o noe, herefore, ha here is sill much conroversy around his imporan issue. Many radiional asse-pricing models (e.g., Sharpe, 964; Meron, 980) posulae a posiive relaionship beween a sock porfolio s expeced reurn and he condiional variance as a proxy for risk. However, as demonsraed in Campbell (993), such a posiive relaionship is coningen on srong assumpions of earlier models. Under more general assumpions, a posiive relaionship beween a sock porfolio s expeced reurn and he condiional variance may no necessarily apply. More recen heoreical works (Whielaw, 000; Bekaer and Wu, 000; Wu, 00) consisenly asser ha sock marke volailiy should be negaively correlaed wih sock reurns. Obviously, here is no heoreical agreemen over he issue. Empirical sudies on he relaionship beween expeced reurns and condiional volailiy also yield mixed findings. Mos of hese sudies focus on developed markes, paricularly he U.S. sock marke, and ypically employ he parameric (G)ARCH in mean (GARCH-M) model of Engle e al. (987) o allow for imevarying behavior of volailiy. Alhough some earlier sudies (e.g., French e al., 987) find a posiive and significan relaionship, more sudies (e.g., Baillie and DeGennaro, 990; Theodossiou and Lee, 995) repor a posiive bu insignifican Received December 7, 004, revised March 5, 005, acceped March 30, 005. * Correspondence o: KDI School of Public Policy and Managemen, Cheongnyangri-dong, Dongdaemoon-gu, Seoul , Korea. jshin@kdischool.ac.kr. The auhor hanks Qi Li and Jian Yang for superb research suppor.

2 3 Inernaional Journal of Business and Economics relaionship. Furhermore, consisen wih he asymmeric volailiy argumen, many researchers (Nelson, 99; Glosen e al., 993; Bekaer and Wu, 000; Wu, 00; Brand and Kang, 003; Li e al., 003) recenly repor a negaive and ofen significan relaionship. The issue of a proper funcional form in modeling condiional volailiy has been conroversial in his line of research. In paricular, many previous sudies (e.g., Baillie and DeGennaro, 990; Theodossiou and Lee, 995; Choudhry, 996) are concerned abou he appropriaeness of modeling condiional variance as a parameric GARCH process and aribue he finding of he weak relaionship o he lack of a proper measure of condiional variance. As poined ou by Pagan and Schwer (990, p ), he nonparameric esimaes of condiional variance adap more quickly han he parameric (G)ARCH esimaes o he fas increase in volailiy and o is decrease when a financial panic subsides. The parameric specificaions of condiional variance show slow adjusmen o large volailiy shocks and hus persisen effecs of hese shocks because hese models are only able o capure he persisen, smooher aspecs of he daa by consrucion (p. 88). Such a possibiliy of misspecificaion in parameric GARCH modeling becomes nooriously serious in he conex of GARCH-M esimaion because consisen esimaion of he GARCH-M model criically depends on correc specificaion of he full model (Bollerslev e al., 99, p. 4). By conras, consisen parameer esimaes of he condiional variance equaion can sill be obained even in he presence of misspecificaion in condiional variance in a parameric GARCH model (Bollerslev e al., 99). Recenly, researchers have empirically demonsraed (e.g., Harvey, 00; Li e al., 003) ha he relaionship beween reurn and volailiy depends on he specificaion of condiional volailiy. In paricular, using a parameric GARCH-M model, Li e al. (003) find ha a posiive bu saisically insignifican relaionship exiss for all he major developed markes. By conras, using a flexible semiparameric GARCH-M model, hey documen ha a negaive relaionship prevails in mos cases and is significan in 6 ou of he markes. This sudy comprehensively examines he relaionship beween expeced reurn and risk in a number of emerging sock markes. Compared o a large empirical lieraure on developed markes, only a few sudies have been conduced on emerging markes, including Choudhry (996) on 6 emerging markes, De Sanis and Imrohoroglu (997) on 4 emerging markes, and Lee e al. (00) on China s sock markes. All hese sudies are based on a parameric GARCH-M model, and all repor posiive bu no saisically significan relaionships beween sock marke reurns and condiional variance in mos of he emerging sock markes under invesigaion. The main conribuion of his sudy is o presen more reliable evidence on he relaionship beween sock reurns and volailiy in emerging sock markes by exploiing a recen advance in nonparameric modeling of condiional variance. This sudy employs boh a parameric and a semiparameric GARCH model for he purpose of esimaion and inference. The use of a flexible semiparameric

3 Jaeun Shin 33 specificaion of condiional variance is paricularly appealing here, as esimaion of a parameric GARCH-M model is very sensiive o model misspecificaion. Very lile was previously known abou he infinie sample properies of such nonparameric echniques (Bollerslev e al., 99, p. 3), which migh explain why nonparameric modeling of he condiional variance was no widely used. Recenly, Baagi and Li (00) demonsrae he asympoic normaliy of a nonparameric esimaor of condiional variance as proposed by Pagan and Ullah (988), which is applicable o he semiparameric esimaor used in his paper (see Li e al. (003) for more deails). The problem ha inferences drawn on he basis of GARCH-M models may be highly suscepible o model misspecificaion is also well known o applied researchers. For example, Jones e al. (998) do no esimae a GARCH-M model o measure a possible change in he risk premium simply due o he concern ha a poenial misspecificaion problem may conaminae he esimaion of condiional variance parameers. This sudy also exends he lieraure (e.g., Choudhry, 996; De Sanis and Imrohoroglu, 997) by invesigaing how he recen global emerging marke crisis migh affec he relaionship beween sock marke reurns and he condiional variance as well as marke volailiy. The res of his paper is organized as follows. Secion briefly discusses he empirical mehodology, Secion 3 presens empirical resuls, and Secion 4 concludes.. Empirical Mehodology This secion presens a brief review of he empirical mehodology used in his sudy. To examine he relaionship beween sock marke reurns and volailiy, boh parameric and semiparameric GARCH-M specificaions are used.. A parameric GARCH-M specificaion The ime varying paern of sock marke volailiy has been widely recognized and modeled as condiional variance in a parameric GARCH framework. To be comparable wih previous sudies on emerging sock markes (e.g., Choudhry, 996; De Sanis and Imrohoroglu, 997), he parameric mehod in his sudy is based on an AR()-GARCH (,)-M model specified as follows: y = µ + by + δσ + ε () ε Ω ~ N(0, σ ) () σ ω ε β σ, (3) = + + where y is he sock marke reurn, or he firs difference of log sock marke index prices, and ε sands for a Gaussian innovaion wih zero mean and a ime-varying condiional variance σ. Among he parameers o be esimaed, he mos relevan one for his sudy is he parameer δ, because boh he sign and significance of his parameer direcly shed ligh on he naure of he relaionship beween sock marke

4 34 Inernaional Journal of Business and Economics reurns and volailiy. More precisely, a significan posiive esimae of δ implies ha invesors who rade socks are compensaed wih higher reurns for bearing higher levels of risk. A significan negaive esimae indicaes ha invesors are penalized for bearing higher levels of risk. To guard agains he possibiliy ha a parameric GARCH-M model may be misspecified, I use a recenly developed model specificaion es o check he correcness of a GARCH specificaion. Specifically, Hsiao and Li (00) propose a consisen es for a parameric condiional heeroskedasiciy funcional form, which can be applied o a ime-series regression wih weakly dependen daa. In ligh of consisency, his new es ouperforms many of he exising ess because, under cerain forms of condiional heeroskedasiciy, exising ess have only rivial power asympoically. Hsiao and Li (00) show ha under he null hypohesis of he correcly specified condiional heeroskedasiciy, he es saisic hey developed follows an asympoically normal disribuion. See Hsiao and Li (00) for more echnical deails.. A semiparameric GARCH-M specificaion This sudy also considers a semiparameric GARCH-M model defined by: y = 0 + y + δσ + u x + u, (4) where y is he sock marke reurn, x = (, y, σ ), = ( 0,, δ )', and σ = var( y Ω ) is he variance of y condiional on Ω (wih Ω he informaion se available a ime ). Consider he following simple semiparameric GARCH model (wih σ = var( y Ω ) = var( u Ω ) ): σ, (5) = m( u ) + γσ where he funcional form of m() is unspecified. If m( u ) = + βu, hen (5) reduces o a sandard GARCH(,) model. Thus, he semiparameric model (5) conains he parameric GARCH(,) model as a special case. The objecive is o assess wheher he condiional variance affecs he condiional mean of he sock reurns. Thus, i is necessary o es he null hypohesis H 0 : δ = 0. Under H 0 and from (4), one obains u = y 0 y. A more general condiional variance specificaion is: σ, (6) = var( y Ω ) = g( y, y ) + γσ where he funcional form of g() is no specified. When g ( y, y ) = m( y 0 y ) = m( u ), (6) reduces o (5). Thus, (6) is more general han (5) and allows he condiional variance o have general ineracions beween y s and y s ( s =,,...). Nex σ is esimaed based on a recursive version of (6) using he nonparameric series esimaion mehod. See Li e al. (003) for a deailed

5 Jaeun Shin 35 illusraion of he esimaion procedure. Once σ, he resuling nonparameric series esimaor of σ, is obained, replacing σ by ˆ σ in Equaion (4) yields y = + y + δσˆ 0 + ε, (7) where ε ( ˆ = u + δ σ σ ). The coefficien vecor = ( 0,, δ )' is esimaed by leas squares, regressing y on he vecor (, y, ˆ σ ). Equaion (7) conains a nonparamerically generaed regressor, ˆ σ, and he asympoic disribuional properies of model (7) are worked ou by Balagi and Li (00). ˆ 3. Empirical Resuls The daa for his sudy cover 4 relaively well-esablished emerging markes which have sock price index series available from he Inernaional Finance Corporaion (IFC) Emerging Markes daabase. These emerging markes are he same as hose sudied in De Sanis and Imrohoroglu (997). Regionally speaking, here are 6 Lain American emerging markes (Argenina, Brazil, Chile, Colombia, Mexico, and Venezuela), 6 Asian emerging markes (India, Korea, Malaysia, Philippines, Taiwan, and Thailand), and wo European emerging markes (Turkey and Greece). The sample period is from January 989 o May 003, afer he 987 inernaional sock marke crash. Similar o De Sanis and Imrohoroglu (997), daily daa are convered ino weekly observaions o address he poenial auocorrelaion problem, yielding a oal of 750 weekly observaions. All daa are in local currency erms. The daa selecion of his sudy has wo main advanages. The applicaion of he weekly daa insead of he monhly daa makes findings of his sudy direcly comparable o hose of De Sanis and Imrohoroglu (997). Thus, employing a semiparameric model o his daabase is expeced o provide more reliable evidence for he previous parameric resuls on he relaionship beween sock reurns and volailiy wih lile concern abou he misspecificaion problem and possible influence of daa frequency on resuls. By including in he sample period observaions as recen as May 003, his sudy examines he impac of he global emerging sock marke crisis on GARCH parameers in line wih Choudhry (996), who sudies he impac of he 987 sock marke crash using monhly daa beween January 976 and Augus 994. Esimaion wih monhly daa ends o reflec long-erm movemens in volailiy by providing he advanage of covering a longer period (Baillie and DeGennaro, 994, p. ). As a consequence, he size and significance of parameers in he condiional variance process are expeced o depend on daa frequency: he GARCH parameer, denoed in Equaion (3), akes values larger in magniude when monhly daa is used if he impac of shocks on he condiional variance ges amplified over ime. I reflecs higher volailiy clusering in he long-run han in he shor-run. In his case, i is noed ha esimaion wih monhly daa may capure he long-erm effec of he sock marke shock on volailiy. Noe ha (he

6 36 Inernaional Journal of Business and Economics auocorrelaion parameer) β, he coefficien in he condiional variance equaion, migh be larger when using weekly as opposed o monhly daa. The auocorrelaion in he volailiy is sronger in he weekly series (shor-run wih less noise) han in he monhly series (long-run wih more noise), which comes as no surprise. Higher auocorrelaion means beer predicabiliy of fuure volailiy using pas observaions. Finally, he persisence of he condiional variance process measured by + β depends on he relaive difference in he sizes of he wo parameers beween he weekly series and he monhly series. These conjecures are well verified by findings of his sudy in comparison wih Choudhry (996, pp ). Even hough here are some differences in he parameer esimaes of he condiional variance process resuling from daa frequency choice and even hough he monhly daa se seems o capure he long-erm effec of shocks and auocorrelaion of he volailiy, I find no paricular reason for he major conclusions of his sudy o be subjec o daa frequency in any significan way. In fac, empirical resuls of his sudy are found o be consisen wih previous research ha uses monhly daa. The sample period covers he firs ruly global emerging marke crisis (Kamin, 999, p. 506), i.e., he period 997 o 998. Boh he Asian and he Russian financial crises were major evens reflecing he prolonged global emerging marke crisis. The lieraure (e.g., Forbes and Rigobon, 00) ypically defines he window of he Asian financial crisis from Ocober 997 o November 997 and he Russian financial crisis from Augus 998 o Ocober 998. Alhough i is generally hard o precisely define he onse of hese crises, he modes variaion in he definiion of crisis periods does no appear o affec he main findings of his sudy. There were also oher, smaller, and more counry-specific crises in he Lain American region during he sample period (e.g., he Brazilian crisis in January 999). One migh rea hese evens as par of normal life in emerging sock markes. Hence, o comprehensively invesigae he impac of he global emerging marke crisis on sock volailiy and he relaionship beween sock reurns and volailiy in hese sock markes, I conduc analyses for sub-periods conservaively defined as follows: pre-crisis period (January 989 June 997) and pos-crisis period (February 999 May 003 for Lain America and January 998 May 003 for Asia and Europe). The sock reurn used in his sudy is defined as he firs difference of he logarihm of sock index prices ( y = ln( p p ) ). Table provides some basic summary saisics. Because risk-free ineres raes were no available o all he emerging markes under consideraion during he enire sample period, I use sock reurns insead of excess sock reurns o conduc analysis. Many researchers (Baillie and DeGennaro, 990; Nelson, 99; Choudhry, 996) show ha using sock reurns insead of excess reurns produces lile difference in esimaion and inference in his line of research.

7 Jaeun Shin 37 Table. Summary Saisics for Weekly Sock Reurns Counry Mean Sandard Deviaion Skewness Kurosis India Korea Malaysia Philippines Taiwan Thailand 5.69E Argenina Brazil Chile Colombia Mexico Venezuela Turkey Greece Noes: The indices of skewness and kurosis for he normal disribuion are equal o zero and hree, respecively. Panels A, B, and C in Table presen he resuls for he mos relevan parameers (, β, and δ ) obained from he parameric GARCH (,)-M ess for he enire period, he pre-crisis period, and he pos-crisis period, respecively. Resuls from he whole period (Panel A) show ha he GARCH effec is presen for all 4 markes. This is evidence of significan volailiy clusering in he sock reurns of hese markes during The sum of he GARCH parameers ( + β ) is ofen close o uniy, implying ha volailiy shocks are highly persisen in emerging markes. Based on he whole sample period, he influence of volailiy on sock reurns (δ ) is found o be posiive for 0 ou of 4 markes. A posiive ime-varying risk premium is significan for 3 markes (Taiwan, Argenina, and Venezuela). Furher resuls based on sub-period analyses sugges a similar paern. In paricular, his influence of volailiy on reurns is posiive for 0 ou of 4 markes and a significan posiive relaionship only exiss for Argenina in he pre-crisis period. In he pos-crisis period, I find sronger evidence for he significance of he risk premium effec on reurns, as he relaionship is significan for 4 markes. This implies ha he global emerging crisis generaes he effec of a risk premium on he sock reurns by making invesors more aler o marke risk. Among hese 4 markes, he risk premium is significan and posiively correlaed wih reurns for Taiwan, Thailand, and Greece. In conras, he Brazilian daa shows a negaive relaionship beween sock reurns and marke risk.

8 38 Inernaional Journal of Business and Economics Table. Parameric GARCH(,)-M Esimaion Resuls Panel A: Whole Sample Period Coefficien India Korea Malaysia Philippines Taiwan Thailand Argenina β *** (3.449) 0.859*** (.48) 0.06*** (3.037) *** (44.3) *** (3.065) 0.893*** (8.366) 0.059** (.587) 0.967*** (4.95) 0.776*** (5.654) 0.759*** (0.394) 0.96*** (.7534) *** (9.8) ** (.77) 0.963*** (4.683) + β * *** δ (.69) (0.963) (0.3494) (.465) (.7499) ( 0.46) (3.58) Coefficien Brazil Chile Colombia Mexico Venezuela Turkey Greece β 0.00*** ( 866.6) 0.59 (.0466) 0.070*** (.9004) 0.848*** (5.9) 0.699*** (4.099) *** (8.604) 0.085*** (.9539) *** (4.34) *** (4.030) 0.449*** (4.977) 0.760*** (0.05) 0.564*** (0.04) 0.054*** (3.509) *** (50.804) + β * δ ( 0.907) (0.86) ( 0.336) (0.773) (.6553) ( ) (0.94) Panel B: Pre-Crisis Period Coefficien India Korea Malaysia Philippines Taiwan Thailand Argenina β 0.378*** (3.507) 0.8*** (5.075) (.80) 0.03 ( 0.409) 0.004** (.9666) *** (3.6) (.5959) *** (3.53) 0.345*** (3.53) 0.785*** (9.886) 0.383* (.798) 0.793*** (9.656) 0.068* (.86) 0.998*** (6.84) + β *** δ (0.667) (.4066) ( ) (0.0475) (0.6033) (.88) (3.9) Coefficien Brazil Chile Colombia Mexico Venezuela Turkey Greece β 0.004*** ( 03.0) (.48) 0.444*** (.4959) *** (8.8444) *** (3.095) *** (6.98) 0.083* (.806) *** (0.99) *** (0.0) (0.6864) 0.97*** (0.0664) 0.608*** (0.0967) 0.73*** (0.0543) 0.76*** (0.048) + β δ ( ) (0.96) (0.367) (0.0756) (0.7673) (.959) (0.494)

9 Jaeun Shin 39 Panel C: Pos-Crisis Period Coefficien India Korea Malaysia Philippines Taiwan Thailand Argenina β * (.975) *** (33.035) 0.00 (0.768) *** (4.0975) (.453) 0.975*** (59.30) 0.638*** (.6744) *** (3.65) 0.069* (.944) 0.80*** (9.0805) 0.776** (.40) *** (5.045) (.4986) 0.836*** (7.64) + β δ 6.60 (.36) (0.7939).80 (0.905) 3.39 (.460).786* (.748) * (.676) (.476) Coefficien Brazil Chile Colombia Mexico Venezuela Turkey Greece β 0.066*** ( ) 0.540*** (7.4463) (0.84) *** (3.6) *** (3.0776) (.47) * (.667) 0.948*** (33.577) 0.89** (.39) 0.775*** (8.967) 0.7 (.0777) 0.56 (0.435) (0.038).00*** (8.543) + β *** *** δ (.9336) ( 0.367) (0.445) (0.934) (0.948) (0.754) (.953) Noes: The symbols ***, **, and * denoe saisical significance a he %, 5%, and 0% levels, respecively; -raios are in parenheses. As a robusness check, I apply he Hsiao and Li (00) consisen model specificaion es o es he correcness of he parameric GARCH(,)-M specificaion. The esing resuls are repored in Table 3, where i can be observed ha a he 5% level, he null hypohesis of a parameric GARCH(,)-M condiional heeroskedasiciy is no rejeced for 0 ou of 4 markes wih he only excepions of Philippines (afer crisis), Mexico (whole sample and before crisis), Turkey (before crisis), and Greece (whole sample and before crisis). Alhough Hsiao and Li s (00) es may sill suffer from some finie sample size disorions due o is slow convergence o is asympoic disribuion, he es resuls are quie reassuring and sugges ha he parameric GARCH(,)-M is fairly well behaved. To he bes of my knowledge, his is he firs ime evidence based on a saisical es ha a GARCH(,)-M model adequaely capures he GARCH effec of sock reurns has been presened. Previous sudies ypically examine some gross saisics such as skewness and kurosis, which may capure he effecs of higher momens oher han GARCH effecs. The resuls also hin ha here migh be only minor differences beween he parameric and he more flexible semiparameric esimaion discussed nex. The semiparameric esimaion resuls, as repored in Table 4, o a grea exen confirm he findings based on parameric esimaion resuls. Specifically, from Table 3, i can be observed ha he esimaes of δ are negaive in ou of 4 markes, which apparenly suppors he hypohesis of a negaive relaionship beween sock marke reurn and volailiy in hese sock markes. The only significan relaionship

10 40 Inernaional Journal of Business and Economics is found for Argenina and is posiive a he % significance level. However, as shown below, he esimaes based on he whole sample period appear o be srongly biased due o ignoring a poenial srucural change due o he crisis. Table 3. Specificaion Tes Resuls for Parameric GARCH (,)-M Model Esimaion India Korea Malaysia Philippines Taiwan Thailand Argenina Whole sample Before crisis Afer crisis Brazil Chile Colombia Mexico Venezuela Turkey Greece Whole sample Before crisis Afer crisis Table 4. Semiparameric GARCH(,)-M Esimaion Resuls Whole Sample Pre-Crisis Period Pos-Crisis Period δ -raio δ -raio δ -raio India Korea Malaysia ***.346 Philippines *.8984 Taiwan Thailand Argenina *** * **.0 Brazil Chile Colombia Mexico Venezuela *.8056 Turkey Greece Noes: The symbols ***, **, and * denoe saisical significance a he %, 5%, and 0% levels, respecively. The resuls for he pre-crisis period show ha he parameer esimaes are negaive only for 7 of he 4 markes. Again, he only significan relaionship is found for Argenina and is posiive a he 0% significance level. The resuls for he pos-crash period furher show ha he coefficien esimaes of δ are posiive for 0 markes and negaive only for 4 of he 4 markes, which more clearly conradics he finding of Li e al. (003) and he asymmeric volailiy argumen of Bekaer and

11 Jaeun Shin 4 Wu (000) and Wu (00). Furhermore, among he 4 markes where a significan coefficien esimae is repored a he 0% significance level or lower, a significan posiive relaionship is repored for 3 markes (Philippines, Argenina, and Venezuela) and a significan negaive relaionship only for marke (Malaysia). Finally, he esimaion resuls of his sudy also clarify he impac of he crisis on emerging sock marke volailiy. To be consisen wih he lieraure and for he ease and richness in inerpreaion of parameers, discussion is based on he parameric GARCH(,)-M model esimaion resuls (Table ). I also separaely conduced a parameric GARCH(,) model esimaion. The resuls based on he whole period were qualiaively he same as hose for he parameric GARCH-M esimaion. There were some differences for a few counries during boh sub-periods, paricularly hose counries where a GARCH-M is found o be significan. I migh be expeced ha he global emerging marke crisis may have a subsanial impac on emerging sock marke behavior. Choudhry (996) provides evidence of changes in he ARCH parameers, he risk premia, and volailiy persisence before and afer he 987 crash in several emerging markes. Comparing he resuls for he pre-crisis period and pos-crisis period (Panels B and C in Table ), he GARCH parameers usually exhibi nonrivial changes in erms of heir significance and magniude, alhough no consisen paern can be summarized across all markes. For example, here was no significan GARCH effec in Philippines in he pre-crisis period, while such an effec emerged in he pos-crisis period. By conras, here was a significan GARCH effec in Turkey in he pre-crisis period, while his effec disappeared in he pos-crisis period. Furhermore, he persisence of sock marke volailiy as measured by + β increased for 7 markes and decreased for he oher 7 markes in he pos-crisis period. Hence, consisen wih Choudhry (996), changes in sock marke volailiy are no uniform and depend on he individual marke, which suggess ha facors oher han he crisis may also be responsible for changes in sock marke behavior. 4. Conclusions This sudy examines he relaionship beween expeced sock reurns and condiional volailiy in 4 emerging inernaional sock markes. Using boh a parameric and a flexible semiparameric GARCH in mean model, I find ha a posiive relaionship prevails for he majoriy of he emerging markes, while such a relaionship is insignifican in mos cases. The basic finding of his sudy is largely consisen wih he lieraure using a parameric GARCH-M model (e.g., Baillie and DeGennaro, 990; Choudhry, 996; De Sanis and Imrohoroglu, 997; Lee e al., 00), where he exisence of a weak relaionship beween risk and reurn is documened. However, he resuls lend lile suppor o he recen asymmeric volailiy argumen ha sock reurn volailiy should be negaively correlaed wih sock reurns, ye his argumen has received much suppor from research on developed

12 4 Inernaional Journal of Business and Economics markes (Nelson, 99; Glosen e al., 993; Bekaer and Wu, 000; Wu, 00; Brand and Kang, 003; Li e al., 003). Also noeworhy, he resuls of he sudy sand in sharp conras o Li e al. (003), who apply similar mehods o major developed markes and find ha a negaive and ofen significan relaionship prevails for he majoriy of developed markes based on he more robus semiparameric esimaion. The findings of his sudy also sugges fundamenal differences beween emerging markes and developed markes. An imporan facor is he degree of inegraion beween emerging markes and he world marke. Arguably, he local marke volailiy in emerging markes can be considered quie relevan in hese markes as hey are largely segmened from he world marke. By conras, he local marke volailiy in emerging markes can be considered irrelevan in major developed markes as hese markes are largely inegraed wih he world marke. Hence, i is possible ha invesors in emerging markes are ofen compensaed for bearing relevan local marke risk, while invesors in developed markes are ofen penalized by bearing irrelevan local marke risk. Anoher facor could be relaed o he mos commonly known characerisics of emerging sock markes ha heir sock marke volailiy is nooriously high compared o developed markes (De Sanis and Imrohoroglu, 997, p ). In his conex, differen findings on risk-reurn radeoff paerns beween developed and emerging markes could be aribuable o he differen hreshold levels of volailiy. Obviously, furher research is needed o examine wheher here are indeed differen paerns beween developed and emerging markes and, if any, wha facors migh accoun for such differences. References Baillie, R. T. and R. P. DeGennaro, (990), Sock Reurns and Volailiy, Journal of Financial and Quaniaive Analysis, 5, Balagi, B. H. and Q. Li, (00), Esimaion of Economeric Models wih Nonparamerically Specified Risk Terms: Wih Applicaions o Foreign Exchange Markes, Economeric Reviews, 0, Bekaer, G. and G. Wu, (000), Asymmeric Volailiy and Risk in Equiy Markes, Review of Financial Sudies, 3, -4. Bollerslev, T., R. Y. Chou, and K. F. Kroner, (99), ARCH Modeling in Finance, Journal of Economerics, 5, Brand, M. and Q. Kang, (004), On he Relaionship beween he Condiional Mean and Volailiy of Sock Reurns: A Laen VAR Approach, Journal of Financial Economics, 7(), Campbell, J. Y., (993), Ineremporal Asse Pricing wihou Consumpion Daa, American Economic Review, 83, Choudhry, T., (996), Sock Marke Volailiy and he Crash of 987: Evidence from Six Emerging Markes, Journal of Inernaional Money and Finance, 5,

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