MODELING SPILLOVERS BETWEEN STOCK MARKET AND MONEY MARKET IN NIGERIA

Transcription

1 Working Paper Series: 16 Jan/2015 MODELING SPILLOVERS BETWEEN STOCK MARKET AND MONEY MARKET IN NIGERIA Afees A. Salisu and Kazeem O. Isah

2 MODELING SPILLOVERS BETWEEN STOCK MARKET AND MONEY MARKET IN NIGERIA Afees A. Salisu a,b,c and Kazeem O. Isah a,b Deparmen of Economics, Universiy of Ibadan, Nigeria. a Cener for Economeric and Allied Research (CEAR), Deparmen of Economics, Universiy of Ibadan, Nigeria. b Corresponding auhor: Deparmen of Economics and Cener for Economeric and Allied Research, Universiy of Ibadan, Oyo Sae, Nigeria. c aa.salisu@mail.ui.edu.ng; aa.salisu@cear.org.ng Mobile: (+234) ABSTRACT In his paper, wo innovaions are eviden: (i) we capure reurns, shocks and volailiy spillovers beween sock marke and money marke while drawing evidence from Nigeria; and (ii) we employ varians of recenly developed VARMA-AMGARCH 1 models by McAleer e al. (2009) which accoun for boh spillovers as well as asymmeric effecs. We find significan reurns spillovers from sock marke o money marke while shocks and volailiy spillovers are bidirecional. We also find ha ignoring he asymmeric effecs in he model will exaggerae he reurns and shocks spillovers and underesimae he volailiy spillover. In all, he spillover effecs beween he wo markes seem ransiory raher han permanen. Keywords: Shocks, Reurns, Volailiy, Asymmeric effec, VARMA-AMGARCH models JEL classificaion: C58, G10 1 VARMA-AMGARCH is he Vecor Auoregressive Moving Average -Asymmeric Mulivariae Generalized Auoregressive Condiional Heeroscedasiciy.

3 1. Inroducion Porfolio diversificaion has become a veriable invesmen sraegy for miigaing he unpredicabiliy of markes for invesors. I helps o reduce porfolio loss and volailiy paricularly during periods of increased uncerainy. The Modern Porfolio Theory appears o be he bedrock for diversifying porfolios. I assumes ha by combining asses ha are no perfecly correlaed, he risks embedded in a porfolio are lowered and higher risk-adjused reurns can be achieved. In essence, while one asse class is confroned wih high uncerainy over a paricular period, he oher may no and herefore, a combinaion of hese asse classes, for example, may reduce overall invesmen risk and preven damaging a porfolio's performance by he underperforming asse. Thus, an effecive combinaion of hese asse classes will necessarily require rigorous analysis of any possible spillovers beween he asses. In his paper, we focus on wo financial markes in Nigeria namely he money marke and sock marke. Consequenly, we provide wo innovaions: (i) we capure reurns, shocks and volailiy spillovers beween hese markes; and (ii) we employ varians of recenly developed VARMA- AMGARCH models of McAleer e al. (2009) which accoun for boh spillovers as well asymmeric effecs. In addiion o he less compuaional complicaions in obaining esimaes of he unknown parameers compared o oher mulivariae specificaions, he VARMA- AMGARCH models allow for he join esimaion of shocks, reurns and volailiy spillovers. Also, any possible asymmeric effecs can be deermined and ignoring hese effecs when hey are eviden may bias he resuls. To he bes of our knowledge here is no sudy in he lieraure ha has adoped his mehodology o capure spillovers beween money marke and sock marke. Noneheless, recen sudies in he lieraure dealing wih financial markes inerdependencies (wheher inernaional, regional or local) include, bu no limied o, Hammoudeh e al. (2009), Jaiswal-Dale and Jihendranahan (2009), Koulakiois e al. (2009), Beirne e al. (2010), Dean e al. (2010), Karmakar (2010), Corradia e al. (2012), Diebold and Yilmaz (2012), Raimony and El-Nader (2012), Abbas e al. (2013), Dua and Tueja (2013), Gafaoui (2013), Kanga e al. (2013), Louzis (2013), Wahyudi and Sani (2013), Weber (2013), and Nguyen and Nguyen (2014). Our conribuions however are non-exisen in he exising lieraure.

4 Following his inroducion secion, he remaining secions of he paper are divided ino four. Secion 2 describes he daa and also provides some preliminary analyses. Secion 3 presens he economeric mehodology implemened in he sudy. Secion 4 discusses he empirical resuls while Secion 5 concludes he sudy. 2. Daa and Preliminary Analyses Essenially, his sudy covers wo variables namely he sock marke and he money marke wih he former proxied by All Share Index (ASI) of he Nigerian Sock Exchange while he laer is denoed by he Nigerian money marke Ineres Rae (IR). The variables are sourced from he Cenral Bank of Nigeria s Saisical bullein over he period of January 2000 o December This secion provides some preliminary analyses involving he descripion of relevan saisical properies of he variables under consideraion. These analyses are carried ou in wo phases: he firs provides descripive saisics for he wo variables including heir reurns while he second involves performing ARCH LM ess and serial correlaion o jusify he consideraion of ime varying volailiy models. The reurns are compued as follows: RASI 100* log ASI (1) RIR 100* log IR where is a firs difference operaor. Table 1 below shows he descripive saisics for ASI and IR including heir reurns denoed by RASI and RIR respecively. There seems o be evidence of significan variaion in he rend of boh he sock marke and money marke as shown by he large differences beween heir respecive minimum and maximum values. Table 1: Descripive Saisics Sock Marke Money Marke Saisics ASI RASI (%) IR (%) RIR (%) Mean Maximum Minimum Sd. dev Skewness Kurosis Jarque-Bera (0.000) (0.000) (0.000) (0.000) Uncondiional Correlaion (ASI and IR) (2)

5 Uncondiional Correlaion (ASIR and IRR) Observaions Source: Compued by he Auhors. Noe: The probabiliy values are in parenhesis The noable difference in IR beween is minimum value of 8.93% and maximum value of 61.46% as well as when hese values are compared wih he mean value of 17.75% sugges some sor of volailiy in he series. Similar evidence can also be deduced from ASI saisics based on he minimum, maximum and mean values of he series. However, he magniude of flucuaions in ASI indicaes higher volailiy han IR as shown by he sandard deviaion of he series. Wih respec o he saisical properies of he reurns (i.e. RASI and RIR), he findings are in andem wih he findings from he descripive saisics for ASI and IR. Unlike ASI and IR, RIR appears o exhibi a higher volailiy han RASI judging by heir sandard deviaions as well. Regarding he saisical disribuions, he series (i.e. ASI and IR) are negaively skewed while heir reurns namely RASI and RIR are posiively skewed hereby implying ha he righ ail are paricularly exreme for he reurns while he lef ail is o he exreme for he series. Thus, as expeced of a volaile series, all he series as well as heir reurns are lepokuric in naure which is an indicaion of fa ails han he normal disribuion. Overall, his implies non-zero skewness and excess kurosis while he Jarque-Bera (JB) saisic ha akes ino consideraion informaion from skewness and kurosis o es for normaliy suggess non-normaliy of all he variables. Table 2: ARCH LM and Ljung Box Tess Sock Marke Money Marke P=5 P=10 P=5 P=10 ASI RASI ASI RASI IR RIR IR RIR 2(A): ARCH ess F-es * * * * * * nr * * * * * * 2(B): LB ess ASI RASI IR RIR LB(5) * ** * * LB(10) * *** * * LB 2 (5) * * * LB 2 (10) * * * Source: Compued by he Auhors Noe: The ARCH LM ess refer o he Engle (1982) es for Condiional Heeroscedasiciy while he LB and LB 2 imply he Ljung-Box ess for auocorrelaions applied o sandardized residuals in levels and squared sandardized

6 Sock Marke (Index) Money Marke (%) residual respecively. *,**,*** imply rejecion of he null hypohesis a 1%, 5% and 10% respecively wih p denoing he lag lengh for he es saisics. The null hypohesis for he ARCH LM es is ha he series has no ARCH effecs (ha is, i is no volaile) while LB es for null hypohesis is ha he series is no serially correlaed. As earlier menioned and shown in able 2 above, some pre-ess such as ARCH LM ess and serial correlaion ess are also conduced o furher jusify he need for he consideraion of imevarying volailiy models. The resuls of he ARCH LM ess indicae he presence of ARCH effec in all he series and heir reurns. These variables herefore exhibi condiional heeroscedaiciy ha has o be capured when modeling. Similarly, he resuls of he serial correlaion conduced using he Ljung Box ess for boh residuals in levels and squared sandardized residuals also reveal he exisence of serial correlaion in all he series. Furhermore, a graphical illusraion of he relaionship beween ASI and IR as well as heir respecive reurns is depiced in Figs. 1 and 2 below. As shown in hese figures, here seems o be a fairly raceable relaionship beween sock marke and money marke and is pariioned ino hree quadrans namely he period before, during and afer financial crisis. The ime series plo in fig.1 seems o be in conformiy wih he descripive properies of he variables wih ASI exhibiing a higher volailiy rend han IR while fig. 2 on he oher hand reveals higher volailiy for RIR. Fig.1: A combined graph for ASI and IR ( ) Pre-Financial Crisis Financial Crisis Period Pos Financial Crisis , , ,000 20, ASI IR

7 Sock Marke (%) Money Marke (%) Fig. 2: A combined graph for RASI and RIR ( ) Pre-Financial Crisis Financial Crisis Period Pos Financial Crisis RASI RIR In addiion o he negaive correlaion beween ASI and IR (see able 1), he period before financial crisis (i.e. pre-financial crisis) in fig.1 shows ha ASI and IR are moving in opposie direcions wih ASI rising seadily while IR on he oher hand declines slowly. In relaion o he second quadran (he financial crisis period), ASI is seen o be declining from is peak while IR sill mainains a seady decline alhough wih evidence of a noable spike in he mid year of 2007 ha coincided wih he period marking he beginning of he financial crisis. The pos-financial crisis period however, revealed ha he IR moves wihin a fixed bound while he upward rend of ASI is an indicaion ha he sock marke is gradually recovering from he financial crisis shock. Furhermore, he graphical represenaion of he markes reurns (see fig. 2) shows how volailiy has changed in he wo markes over ime. The wo markes reveal evidence of volailiy clusering in he wo markes in which periods of volailiy are followed by periods of ranquiliy.

8 3. Economeric Model In his sudy, our specificaion follows he VARMA-AMGARCH Model developed by McAleer e al. (2009). Consequenly, we esimae differen varians of his model namely: (i) VAR- MGARCH; (ii) VARMA-MGARCH; (iii) VAR-AMGARCH; and (iv) VARMA-AMGARCH. The VAR-MGARCH model capures boh reurns and volailiy spillovers while in addiion o hese feaures, VAR-AMGARCH model accouns for asymmeric effecs in he variance equaion. The VARMA-MGARCH model, in addiion o he underlying feaures of VAR- MGARCH, also deals wih shocks spillovers in he mean equaion. Finally, he VARMA- AMGARCH, in addiion o he underlying feaures of VARMA-MGARCH, also capures possible asymmeric effecs in he model. All he varians are evaluaed wih opions of CC, DCC and BEKK 2 in order o evaluae he robusness of esimaion resuls as well as ensure ha all he possible feaures inheren in he series are properly refleced in he esimaion process. Table 3 summarizes he saisical feaures of he models esimaed. Table 3: Summary of Model Feaures Model Reurns spillovers capured? Volailiy spillovers capured? Shocks spillovers capured? Asymmeric effecs capured? VAR-MGARCH Yes Yes No No VAR-AMGARCH Yes Yes No Yes VARMA-MGARCH Yes Yes Yes No VARMA-AMGARCH Yes Yes Yes Yes Source: Compiled by he auhors The bi-variae VARMA(, )-AMGAR CH1,1 pq model is specified below 3 : The Condiional Mean Equaion: R 1R 1 2R 2 pr p qq, ~ N(0, H ) (3) 2 CC denoes he Consan Correlaions, DCC is he Dynamic Condiional Correlaion and BEKK is he Baba, Engle, Kraf and Kroner s represenaion of he condiional variance equaion. 3 Noe ha we allow for more han one lag for he mean equaion (VARMA) while he variance equaion only conains one lag (i.e. MGARCH (1,1)). This is because if he mean model is wrong; his implies ha here is more dynamics in he model han included and his can be fixed by reasonably increasing he number of lags in he mean equaion. However, in he case of he variance equaion (MGARCH), he rejecion of MGARCH means ha he GARCH par of he model is somehow inadequae. I is no common o add lags o a GARCH in an aemp o fix his problem; insead, a differen version of he MGARCH such as CC/DCC/BECK-MGARCH model or he inclusion of asymmeric effecs may be considered o fix he problem.

9 Puing equaion (3) in a more compac form using Lag operaor, we have: P L R L; L I 1L pl and 1 q L I L ql (4) H v, v ~ N(0,1) (5) 12 Where, of consans for denoes he reurns series expressed; RASI RIR, R RASI RIR RASI and RIR mean equaions respecively; is a vecor i i 1,, p is a 2 2 marix of coefficiens on he lagged erms of he reurns series; j j 1,, q RASI RIR residuals and is a 2 2 marix of coefficiens on he lagged erms of he is a vecor of disurbance erms for RASI and equaions respecively; RASI RIR v v, v RIR mean is a vecor of whie noise errors; and H is a symmeric marix of condiional variances in which he diagonal elemens of erms, and he off-diagonal elemens of H diag h, h 12 RASI RIR RIR respecively. where The Condiional Variance Equaion: RASI h and The condiional variance for he VARMA( pq, )-AMGAR CH1,1 H H are he variance are he covariance erms. In essence, RIR h are he condiional variances for RASI and is given as: H A CI BH (6) where RASI RIR H h, h, RASI,, RIR,, and, A, and B are 2 2 marices. RASI RIR I diag I, I such ha I 1 0 if 0 and 1 1 oherwise. 4 The componens of he I 4 Noe ha he differen varians of he VARMA( pq, )-AMGAR CH1,1 were esimaed by imposing resricions on relevan erms in eiher he mean equaion or variance equaion or boh. For example, he VAR- GARCH Model can be obained by seing L 1 and 0 C while he VARMA-GARCH can be obained by seing jus C 0. Also, as earlier menioned, hree opions were considered in he esimaion of he differen VARMA( pq, )-AMGAR CH 1,1 namely CC, DCC and BEKK (see McAleer e al., 2009 for varians of he he compuaional procedure of hese opions). Also, Silvennoinen and Terasvira (2008) provide a review of he heoreical srucure for he hree opions when dealing wih MGARCH models.

10 asymmeric effecs capure he impacs of posiive and negaive shocks on volailiy. The srucural and saisical properies of VARMA-MGARCH were firs esablished in Ling and McAleer (2003) and furher exended by McAleer e al. (2009). These include he necessary and sufficien condiions for saionary and ergodiciy, sufficien condiions for he exisence of momens of, and sufficien condiions for consisency and asympoic normaliy of he Quasi- Maximum Likelihood Esimaor in he absence of normaliy of v. In addiion, he model wih he bes fi under each model caegory having considered he hree opions menioned, was deermined using sandard model selecion crieria namely, Schwarz Bayesian Crierion (SBC) and Akaike Informaion Crierion (AIC). 4. Empirical Analyses The resuls obained from he analyses of he differen varians of VARMA-AMGARCH are presened in ables 3(a) hrough 3(d) below. From hese ables, i is observed ha only BEKK MGARCH is able o achieve convergence afer cerain number of ieraions while CCC and DCC MGARCH have problem of achieving convergence. This may be due o he need o compue condiional correlaion coefficien by boh MGARCH opions which is no required by he BEKK MGARCH. The bes fi MGARCH is however, seleced from each model caegory and hus presened in able 4 below. Table 3: 3(a): VAR-MGARCH MODEL Model Convergence Saus Model Selecion Crieria Rank AIC SBC HQ BEKK: VAR(1)-MGARCH(1,1) Achieved BEKK: VAR(2)-MGARCH(1,1) Achieved BEKK: VAR(3)-MGARCH(1,1) No achieved CC: VAR(1)-MGARCH(1,1) No achieved CC: VAR(2)-MGARCH(1,1) No achieved CC: VAR(3)-MGARCH(1,1) No achieved DCC: VAR(1)-MGARCH(1,1) No achieved DCC: VAR(2)-MGARCH(1,1) No achieved DCC: VAR(3)-MGARCH(1,1) No achieved Source: Compiled by he Auhors

11 3(b): VARMA-MGARCH MODEL Model Convergence Model Selecion Crieria Rank Saus AIC SBC HQ BEKK: VARMA(1,1)-MGARCH(1,1) Achieved BEKK: VARMA(2,2)-MGARCH(1,1) Achieved BEKK: VARMA(3,3)-MGARCH(1,1) No achieved CC: VARMA(1,1)-MGARCH(1,1) Achieved CC: VARMA(2,2)-MGARCH(1,1) No achieved CC: VARMA(3,3)-MGARCH(1,1) No achieved DCC: VARMA(1,1)-MGARCH(1,1) No achieved DCC: VARMA(2,2)-MGARCH(1,1) No achieved DCC: VARMA(3,3)-MGARCH(1,1) No achieved Source: Compiled by he Auhors 3(c): VAR-MGARCH MODEL wih Asymmery Model Convergence Model Selecion Crieria Rank Saus AIC SBC HQ BEKK: VAR(1)-AMGARCH(1,1) No achieved BEKK: VAR(2)-AMGARCH(1,1) No achieved BEKK: VAR(3)-AMGARCH(1,1) No achieved CC: VAR(1)-AMGARCH(1,1) No achieved CC: VAR(2)-AMGARCH(1,1) No achieved CC: VAR(3)-AMGARCH(1,1) No achieved DCC: VAR(1)-AMGARCH(1,1) No achieved DCC: VAR(2)-AMGARCH(1,1) No achieved DCC: VAR(3)-AMGARCH(1,1) No achieved Source: Compiled by he Auhors 3(d): VARMA-MGARCH MODEL wih Asymmery Model Convergence Model Selecion Crieria Rank Saus AIC SBC HQ BEKK: VARMA(1,1)-AMGARCH(1,1) Achieved BEKK: VARMA(2,2)-AMGARCH(1,1) Achieved BEKK: VARMA(3,3)-AMGARCH(1,1) No achieved CC: VARMA(1,1)-AMGARCH(1,1) No achieved CC: VARMA(2,2)-GARCH(1,1) No achieved CC: VARMA(3,3)-AMGARCH(1,1) No achieved DCC: VARMA(1,1)-AMGARCH(1,1) No achieved DCC: VARMA(2,2)-AMGARCH(1,1) No achieved DCC: VARMA(3,3)-AMGARCH(1,1) No achieved Source: Compiled by he Auhors

12 Table 4: Seleced Bes Fi MGARCH Model in each Model Caegory Model Convergence Model Selecion Crieria Rank Saus AIC SBC HQ BEKK: VAR(1)-MGARCH(11) Achieved BEKK: VARMA(11)-MGARCH(11) Achieved BEKK: VARMA(22)-AMGARCH(11) Achieved Source: Compiled by he Auhors Noe: Only models ha achieved convergence are considered for selecion and same were consequenly ranked based on heir AIC, SBC and HQ values Discusion of Resuls 5 This secion discusses resuls obained from he parameer esimaes of seleced varians of VARMA-AMGARCH. The VAR-MGARCH model in able 6 5 below, accouns for he reurns and volailiy spillovers beween he wo variables via heir mean and variance equaions, while VARMA-MGARCH and VARMA-AMGARCH, in addiion, accoun for shocks ransmission and asymmeric effecs respecively Model 1: VAR-MGARCH Esimaion resuls of he reurn spillovers from he VAR(1)-MGARCH(1,1) model are repored in able 5(a) below. The reurns spillovers are capured in he mean equaion. The resuls reveal evidence of saisically significan reurns spillover effecs from sock marke o money marke Ψ 21 while he sock marke does no acively respond o reurns in he money marke. Thus, higher reurns in sock marke are capable of driving higher reurns in he money marke. The implicaion of his o policy makers is he fac ha sabiliy of he sock marke is crucial for money marke sabiliy. Also, he volailiy spillover effecs of he esimaion are capured in he variance equaion of he esimaed VAR(1)-MGARCH(1,1) model repored in able 5(b). The resuls reveal evidence of saisically significan shor-erm and long-erm cross-marke volailiy spillovers from money 5 Comprehensive resuls of all models are available on reques. Also, he RATS code for he esimaion of he differen varians of VARMA-AMGARCH Model can as well be provided on reques. 6 The subscrip 1 is for sock marke and 2 for money marke. In he variance equaion, Ω denoes he consan erm, A denoes he ARCH erm and B denoes he GARCH erm while Π accouns for he asymmeric effecs. In he mean equaion, ϕ 10 represens he effec of ASIR own inercep parameer on curren period while ϕ 20 explains same for RIR. The coefficien Ψ 12 and Ψ 21 for example denoe reurns spillover effecs from RIR o ASIR and ASIR o RIR while ϒ 12 and ϒ 21 represen shock ransmission from RIR o ASIR and ASIR o RIR respecively.

13 marke o sock marke (i.e. A 12 and B 12 ) as well as from sock marke o money marke (i.e. A 21 and B 21 ). In essence, here is bidirecional volailiy ransmission beween sock and money markes. Thus, volailiy in one marke may fuel volailiy in he oher marke Model 2: VARMA-MGARCH Taking a closer look a he mean and variance equaions of VARMA(1,1)-MGARCH(1,1) model, wih he excepion of he inroduced MA componens, we find evidence ha is similar o he VAR(1)-MGARCH(1,1) esimaes. Essenially, he MA componens accoun for shocks o reurns of he wo markes in heir respecive mean equaions and he resuls indicae ha boh sock and money markes respond significanly o own shocks and cross marke shocks and he sign is posiive. This indicaes ha exogenous own and cross marke shocks affec reurns Model 3: VARMA-AMGARCH This is he mos sophisicaed model applied as i allows for boh he ime-varying volailiy and he asymmeric effecs o be esed. By comparing he resuls wih he previous models, we find ha when he asymmeric effecs are no capured in he model, he reurns and shocks spillovers are exaggeraed while he volailiy spillover effec is underesimaed. We also find saisically significan own-marke asymmeric effecs alhough wih insignifican cross asymmeric effecs. Thus, bad (good) news in each marke may fuel higher (lower) volailiy in he affeced marke bu may no subsanially drive higher volailiy in he oher marke. Noneheless, our findings furher srenghen he fac ha he sock marke is more vulnerable o shocks han money marke. Therefore, invesors should ake cognizance of his when diversifying heir porfolio invesmens in Nigeria. Ineresingly however, he spillover effecs beween he wo markes seem ransiory raher han permanen Pos Esimaion The wo main diagnosic ess considered o validae he esimaed VARMA-AMGARCH models are he LB ess and he McLeod-Li es. Boh ess are applied o sandardized residuals, and a significan LB es implies ha he mean equaion is wrong while a significan McLeod-Li es is an indicaion ha he variance equaion of he model is somehow inadequae. However,

14 he resul of he LB es indicaes absence of serial correlaion and he McLeod-Li es also validaes he adequacy of he GARCH effecs in he variance equaion. Therefore, he heoreical srucure of he esimaed models in erms of heir mean and variance equaions is appropriae when dealing wih reurns, shocks and volailiy spillovers beween markes. Table 5: Parameer esimaes for seleced varians of VARMA-AGARCH Model 5(a): Reurns Spillover Effecs Mean Equaion VAR(1)-GARCH(1,1) VARMA(1,1)-GARCH(1,1) VARMA(2,2)- AGARCH(1,1) ϕ (0.3151) * (0.3108) * (0.3998) *** Ψ (0.0619) (0.0606) (0.3370) Ψ (0.0437) (0.0443) (0.0594) ϕ (0.1814) * (0.1743) * (0.3053) * Ψ (0.0361) * (0.0362) * (0.0412) * Ψ (0.0419) * (0.0433) * (0.0509) * ϒ (0.0000) * (0.0000) * ϒ (0.0000) * (0.0000) * ϒ (0.0000) * (0.0000) * ϒ (0.0000) * (0.0000) * 5(b): Volailiy Spillover Effecs Variance Equaion Ω (0.7475) * (0.7845) * (0.8959) * Ω (0.5038) (0.5272) (0.6904) Ω (1.2670) (1.2784) (0.6054) *** A (0.0923) ** (0.0958) ** (0.1011) A (0.0833) * (0.0886) * (0.0883) * A (0.1186) * (0.1213) * (0.1039) ** A (0.1363) * (0.1406) * (0.2398) * B (0.0951) * (0.0986) * (0.1228) * B (0.0432) * (0.0426) * (0.0647) B (0.0462) * (0.0480) * (0.0508) B (0.0237) ** (0.0241) ** (0.0275) C (0.1671) ** C (0.1267) C (0.1267) C (0.3989) * Log L AIC SBC Diagnosics

15 LB(10)RASI LB(10)RIR LB(5)RASI LB(5)RIR McLeod-Li(10) RASI McLeod-Li(10) RIR McLeod-Li(5) RASI McLeod-Li(5) RIR Noes: Sandard errors are repored in parenheses. *, ** and *** represen significance a 1%, 5% and 10% respecively. 5. Conclusion In his paper, we examine he spillover effecs beween money marke and sock marke in Nigeria. Essenially, we esimae reurns, shocks, and volailiy ransmission across he wo markes using he VARMA-AMGARCH model and is varians. The resuls of he mean equaions sugges a higher spillover effec of reurns from sock marke o he money marke. Noneheless, he ransmission of shocks o reurns across he wo markes is bidirecional and invesors are likely o be more risk averse owards he sock marke asses han money marke asses. The variance-equaions of he models prominenly indicae ha he wo markes are sensiive o own innovaions and previous period volailiies. Also, comparing he resuls of he esimaed models, we find ha he reurns and shocks spillovers are exaggeraed while he volailiy ransmission is under esimaed when he asymmeric effecs are no capured in he model. The evidence revealed suggess saisically significan own-marke asymmeric effecs alhough wih insignifican cross asymmeric effecs. Thus, bad (good) news in each marke may fuel higher(lower) volailiy in he affeced marke bu may no ransmi ino higher volailiy in he oher marke. In all, he spillover effecs beween he wo markes seem ransiory raher han permanen. References: Abbas, Q., Khan, S. and Ali Shah, S. (2013). Volailiy ransmission in regional Asian sock markes. Emerging Markes Review, 16, Abdallah, S. (2013). Modelling he Impac of Oil Price Flucuaions on he Sock Reurns in an

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