The Impact of Global Financial Crisis on the Economic Growth and Capital Market Returns: Evidence from Jordan Hussain Ali Bekhet a,*, Ali Matar b Graduate Business School, College of Graduate Studies Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia a,b *
[email protected];
[email protected] ;
[email protected] b Abstract The current paper attempts to examine the equilibrium relationship between economic growth and the capital market return (CMR) with consideration of financial crisis impact in Jordan’s capital market for the 1978-2011 period. It Utilized the Autoregressive Distributed lags (ARDL) approach to test both short-run and co-integration relationship. It, also, combined CUSUM and CUSUMQ tests to show the stability of that relationship. The empirical results identified that there is a co-integration between CMR and Jordan’s macroeconomic variables indicating long-term equilibrium relationship. In addition, the global financial crisis has a significant negative impact on the CMR. Keywords: ARDL; Capital Market Return, Global Financial Crisis, Equilibrium Relationship, Jordan.
1. Introduction The past decennium witnessed the most effective economical crashes since 1929 crisis that is the global financial crisis (GFC), which stated from USA and spread periodically to other countries. The emerging capital markets are becoming more integrated with each other and with developed markets (Collins & Biekpe, 2003). Numerous studies have examined the relationship between stock market and the state of the economy. Furthermore, there is extensive discussion in the finance literature that emerging and developed financial markets may be able to promote economic growth. The results have shown a positive correlation between the economic growth and the financial development. Therefore, if the economy is performing well, the stock market is likely to do the same returns (Kirman, 1992; De Gregorio & Guidotti, 1995). Past studies have been dedicated to test the impact of economic activities on the capital markets (see, Fama & Shwert, 1977; Chen & Ross, 1986). Several studies have confirmed long-term equilibrium relationship between Capital Market Returns (CMR) and relevant macroeconomic variables (Rushdi et al., 2012; Hussain, 2011; Hammoudeh & Sari, 2011; Abugri, 2008; Liu & Shrestha, 2008; Maysami, 2004; Wongbangpo & Sharma, 2003; and Bilson et al., 2001). Furthermore, numerous studies analyzed the impacts of GFC on the economic growth and equity markets (see Kenourgios et al., 2011; Moshirian, 2010; Cheung et al., 2010; Claessens, 2010; Markwat et al., 2009; and Saleem, 2008). In emerging market stock indices have been characterized as having higher volatility than indices in the more developed markets (Abugri, 2008). Nonetheless, do both the GFC and macroeconomic variables cause the volatility of emerging markets CMR? Due to the current decline of the Jordan’s economy with large budget deficit, this study aims to evaluate the macroeconomic variables and GFC impacts on the CMR in Amman Stock Exchange (ASE). The ASE a well-established, small, open market, providing a case for other emerging
1
markets in the world. The study adopts ARDL approach to allow for multiple co-integrations and for examining co-integration among variables for the purposes of evaluations. Since the macroeconomic variables are important in the economic growth, this study will highlight its impact on the Jordan’s capital market to fill this gap in the literature. The ASE becomes one of the most important markets in the Middle East due to the different developments, innovations and regulations done by sequential governments. Subsequently, we hypothesized a long-term equilibrium relationship between GDP, M2, DR, & CPI and CMR. Also, there is a significant relationship between GFC and CMR. This paper is constructed as follows: The next section reviews the Jordan’s economy, ASE and GFC. Section 3 presents literature review. Section 4 provides data sources. Section 5 illustrates methodology. Section 6 reports the empirical results while conclusions and policy implications are presented in the last section. 2. Jordan’s Economy and Financial Crisis The GFC has resulted in major financial and economic impacts on emerging markets and developing countries. Some of these countries benefited from their recovered essentials as they were better able to treat the negative impacts of the crisis on their economies. Whereas, the crisis has also highlighted some specific financial sector improve challenges for emerging markets and developing countries (Claessens et al., 2010). In 2007, the GFC started when the mortgage crisis begun in USA then quickly spread to several economies all over the world. It has been the most destructive global financial crisis, the world has experienced since the different sequential crisis like, great depression of 1930; the1987 crashes; the 1994 Mexican crisis; Asian financial crisis 1997; and Russian crisis 1998. However, the crisis plays the most complicated challenge to the globalization process. It was started as a local problem in USA called (subprime mortgage crisis), then spread across the whole world and ascended into a GFC. That crisis impacting both economic activities and financial systems in many countries resulted in a general recession worldwide. In 2008, with the breakdown of Lehman Brothers Bank and the disturbance on Wall Street, losses spread to other global financial system like a number of world banks failures, declines in many financial markets stock indices, and harshly drop in the market value of equities. Therefore, the Arab monetary fund composite index for Arab stock markets denominated in USD declined by 54.6%. The year 2008 was a disastrous year for most of the international stock markets with losses exceeding a 40%. World index calculated by Morgan Stanley to all stock exchanges worldwide (developed and developing) decreased by 43.5% in 2008 comparing with 2007. Whereas, Arab stock markets response were vulnerable to the GFC and it can summarized that most of them fell down. In general, the impact of the crisis on the Middle East and North Africa (MENA) may be less compared to the US and western countries. Early impact was visible in some countries with strong links to global financial markets like Arab gulf countries. Nevertheless, ASE was affected by the GFC in limited manner as compared to other Arab stock exchanges. Price indices in most of the Arab and international stock exchanges marked deeper declining rates compared with the ASE (http://www.ase.com.jo/). 2
The Central Bank of Jordan has taken preventive steps at the beginning of GFC to support the domestic money market, including full secure of all bank deposits. Thus, Jordan’s economy has limited effect from recent financial crisis avoiding major losses among banks or capital flight. The three most important of Jordan economy effects are strongly lower global and regional growth outlook, declining global goods prices, oil, and dropping private capital flows to developing countries. Consequently, Jordan’s growth rate skid to 3.0-4.0% during 2009.
Billions (JD)
During the (2000–2009) period, Jordan’s economy has slowed largely due to the global and regional downturn. The annual real GDP growth averaged about 6%, supported by the implementation of favorable external conditions and economic policies. It was consistent with the global economic slowdown. In 2009 output growth fell sharply, and economic activity rises up modestly (IMF, 2010). Pre-Crisis
25
Post-Crisis
20 15
GDP = 2E+09e0.0567x R² = 0.8538
10 5
1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
0
Figure 1: GDP Index for Jordanian Economy for the (1978-2011) period. Source: World Bank (2012), World indicators (GDP), available at http://data.worldbank.org/indicator/NY.GDP.MKTP.CD/countries/JO.
Figure 1 reveals the growth rate of Jordanian GDP which was 5.7 percent for the (1978-2011) period. Also, it shows a gradually upward trend over the targeted period. Despite of the GFC, and other several events during this study period, Jordanian GDP rate reached JD20.1 billion in 2011. The ASE was established in March 1999 as a nonprofit, private institution with administrative and financial autonomy. It is authorized to function as an exchange for the trading of securities. The exchange is governed by a seven-member board of directors. A chief executive officer oversees day-to-day responsibilities and reports to the board. Jordan’s capital market classified rank as frontier market according to S&P Country Classification in June 2011(see https://www.sp-indexdata.com). The ASE membership is consisting of Jordan’s 68 brokerage firms, 248 companies traded on ASE until February 2012. The performance of ASE in the 2009, 2010 and 2011 years was exceptional. The trading value of ASE that ended the trading transactions for these years was JD9.7, JD6.7, and JD2.9 billion respectively. Furthermore, Figure 2 shows a gradual development of the market capitalization (MC), value traded (VT) and the stock price index (SPI) before and after GFC crisis. VT started at JD5.62 million in 1978, rising gradually to reach the first peak in 2005 with a value of JD16.88 billion then dropped dramatically in 2006 and 2007 to JD14.21; JD 12.35 billion respectively. In 3
contrast it climbed back to reach the second peak in 2008 with a value of JD 20.32 billion, then dropped again affected by GFC to reach 181,1million in 2011. Besides, MC and SPI have taken the same trend with the VT. The growth rates for these indicators (MC, VT, and SPI) were 12.9%, 18.6% and 6.9% respectively (see Figure 2).
25
Post-Crisis
VT = 2E+07e0.1863x R² = 0.8358
8000 7000 6000
MC = 3E+08e0.1292x R² = 0.9397
20 15 10
9000
SPI = 501.82e0.0693x R² = 0.7924
5000 4000 3000 2000
5
1000
0
0
1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
MC & VT in Billions (JD)
30
Pre-Crisis MC VT SPI
Thousands (SPI Points)
35
Figure 2: Market Capitalization, Value Traded, and SPI of ASE for the (1978-2011) period. Source: ASE (2012), Major Financial Indicators for the ASE, available at http://www.ase.com.jo/en/major-financial- indicators-ase.
3. Literature Review The relationship between CMR and economic growth represented by GDP and other macroeconomic variables has been extensively researched in developed countries. Yet, there seems to be no assent regarding the relationship in developing countries. In the current paper, we classify the literature based on the countries. 3.1 Macroeconomic variables and capital markets In US stock market Karagianni et al. (2012) examined the causality relationship between US GDP and tax burden on production; imports and personal income. They found that when disaggregated at taxes on production and imports, taxes on corporate income, become significant determinants in the GDP growth empirical modeling. Gosnell & Nejadmalayeri (2010) determined that if macroeconomic announcements affect the Fama-French market, size, and momentum factor and book-to-market risk factors. The result suggested that Inflation, employment, consumption and business activities have a significant impact on risk factor volatilities. However, they found that industrial production and GDP influence only the level of the momentum factor and inflation. Enisan & Olufisayo (2009) tested the causality long-run relationship among stock market development and economic growth for seven countries in Africa by employing the ARDL approach. They suggested that the stock market development is co-integrated and has a
4
significant positive long run impact with economic growth in Egypt and South Africa. Also, there is a bidirectional relationship between stock market development and economic growth. In Australia, Rushdi et al. (2012) figured out the long-term relationship between the real stock return and inflation by using ARDL approach. They found that the expected inflation had no significant impact on real stock returns, while the observed inflation had a significant with negative effect. Maysami (2004) tested the co-integration and the long-term relationship between selected macroeconomics variables and the Singapore stock index. The results have shown a long-term relationship between stock market index and selected macroeconomic variables. The same results with the same model were suggested by Al-Sharkas (2004). He examined the impact of selected macroeconomic variables on ASE. In Thailand, Nidhiprabha (2010) investigated macroeconomic policy interaction to the GFC. He found fiscal policy is relatively less effective than monetary policy. 3.2 The impact of GFC on Capital markets Ha et al. (2001) explored the extent of fundamental factors affecting the financial crisis in Korea by employing VAR model. They found evidence of non-fundamental components of stock market prices in Korea causes the sudden decline in stock market prices during the 1997 financial crisis. Chakrabarti & Roll (2002) compared the Asian stock markets with European markets before and during the 1997 Asian crisis. Their result showed that correlations, volatilities and co-variances are increased from the pre-crisis compare with the crisis period in both regions, but the percentage increases were much larger in Asia. Asian financial crisis has been analyzed by using high-frequency data empirically on exchange rates and stock indices of the Philippines and Thailand (Nagayasu, 2001; Khalid & Kawai, 2003). The results suggested that the causality of the financial sector index in Thailand occurred in a unique direction. Furthermore, they did not find strong support for a contagion case and did not claim that there was absolutely no contagion effect working during the Asian Crises. By utilizing VAR model, Serwa & Bohl (2005) tested the contagion in European stock markets associated with seven big financial shocks focuses on a comparison between developed Western European markets and emerging stock markets in Eastern and Central Europe. They suggested a significant evidence of instabilities in cross-market linkages after the crises. Kenourgios et al. (2011) researched the financial contagion in a multivariate time-varying asymmetric framework, focusing on four emerging financial markets, namely Brazil, Russia, India, China (BRIC) and two developed markets (U.K. and U.S.A). They found that emerging BRIC markets are more prone to financial contagion, while the industry specific trouble has a larger impact than country-specific crises. Excessively, several studies examined the effect of GFC on stock markets (see, for example, Yang & Bessler, 2008; Cheung et al., 2010; Ji, 2010). Yang & Bessler (2008) found that the crash originated in the US market and an upward movement in the Japanese market after the crash helped the recovery in the US market. Cheung et al. (2010) found a significant effect from the US market to other global financial markets in Japan, UK, Hong Kong, Australia and China. 5
Eventually, Ji (2010) suggested that global money markets had failed to contain stress on the role of the Japanese yen as a liquidity source and US dollar funding appeared to be significant. 4. Model Specification, Data and Variables In this paper, the annual data of the CMR for the (1978 – 2011) period were gathered from ASE database. The stock returns are based on the closing prices of the last trading day in each year (stock price index). The macroeconomic variables were selected from the publication of ASE, IMF and CBJ. To explain the determinants of stock market return in Jordan, we limit this study to selected macroeconomic variables. The CMR, GDP, CPI and M2 variables have been transformed into natural logarithmic (L). Also, the unit of variables standardized by utilizing SPSS 20, Microfit 4.1, and E-views 7 packages. Equation (1) represents CMR as a function of all variables which are used in this study. It follows the standard procedure of time series data starting from unit root testing by employing the Augmented Dickey Fuller (ADF) and Phillip Perron (PP) tests, co-integration and causality tests (mentioned in the next section). LCMRt = μ0 + μ1 LGDPt + μ2 LM2t + μ3 LCPIt + μ4 DRt μ5 Dt +εt
(1)
Where: CMR is a measure of the performance of underlying stocks stems from the stock price index, changes in the index reflect changes in the value of the stocks over the time; μ0: constant term; μ1,…., μ5: are the coefficients of the model; εt: is error term. GDP: is gross domestic product reflects the Jordan economic growth during the study period. M2: (Money Supply) is the broad amount of money available in an economy usually including currency in circulation and demand deposits. We assume that M2 has positive impacts on CMR. CPI: (Consumer Price Index) is a measure of the inflation rate for the consumers in Jordan; it is a rise in the general prices level of goods and services in an economy over a period of time. There is a strong negative relationship between the inflation and the stock prices (Rangel, 2011). DR: is the interest rate variable of central bank charges depository institutions that borrow reserves from it used to represent the monetary policy. Finally, past studies confirmed that there is a negative relationship between DR and the CMR as a restrictive monetary policy (Jensen & Johnson, 1995; Chen, 2007). D: Dummy variable represents the GFC that will be analyzed as dummy variables by taking the value of (1) during the corresponding year of event date and (0) otherwise. The actual stock return on each stock is based on the stock price index in ASE calculated as follows:
Ri,t+1 =
Pi,t+1 - Pi,t Pi,t
(2)
6
Where Ri,t+1 is the return on stock i in the year t+1; Pi,t is the closing price of stock i of the year ; Pi,t+1 is the closing price of stock i in the yeart+1 (Bekhet & Matar , 2012). 5. Methodology Several studies were using Engle and Granger (1987) and Johansen Juselius (1991) techniques to find out the co-integration between macroeconomic variables and CMR. These techniques require that all variables (regressors) in the system must be stationary and with the same level of integration; uniform lag length; and large number of observations. Pesaran et al. (2001) have developed a model to introduce a surrogate co-integration technique known as ARDL bound testing approach. ARDL approach has many advantages over the previous co-integration techniques. First, it has more proper considerations than the J-J & Engle-Granger techniques for testing the cointegration among variables in small sample size (Ghatak & Siddiki, 2001). Comparatively, the Johansen co-integration techniques need large data sample for validity. Second, no need to examine the non-stationary property and order of integration, this means that we can apply ARDL whether underlying regressors are purely I(0) or purely I(1), while other co-integration techniques require all the regressors to be integrated of the same order (Pesaran et al., 2001). Third, the ARDL application allows the variables may have different optimal lags, while it is impossible with conventional co-integration procedures (Ozturk & Acaravci, 2011). Finally, the ARDL model has become increasingly popular in recent years (Jayaraman & Choong, 2009). Basing on these advantages of ARDL model this paper will employ bound test for testing cointegration among the variables in the current study. To examine the co-integration among variables in matrix form, the ECM representation of the ARDL approach is formulated for each variable using combinations of first differences and lagged levels for cointegrated variables and another combination of short and long-run relationship as the follows: n1
n2
n3
n4
n5
i=1
i=0
i=0
i=0
i=0
ΔLCMRt =α01 + δ11 ΔLCMRt-i + δ12 ΔLM2t-i + δ13 ΔLGDPt-i + δ14 ΔLCPIt-i + δ15 ΔDRt-i + λ11LCMRt-1 + λ12 LM2t-1 +λ13 LGDPt-1 + λ14 LCPIt-1 + λ15 DRt-1 +11Dt +εt1 n1
n2
n3
n4
n5
i=1
i=0
i=0
i=0
i=0
ΔLM2t = α02 + δ21 ΔLM2t-i + δ22 ΔLCMRt-i + δ23 ΔLGDPt-i + δ24 ΔLCPIt-i + δ25 ΔDRt-i + λ21LCMRt-1 + λ22 LM2t-1 +λ23 LGDPt-1 + λ24 LCPIt-1 +λ25 DRt-1 + 21Dt +εt2 n1
n2
n3
n4
n5
i=1
i=0
i=0
i=0
i=0
ΔLGDPt = α03 + δ31 ΔLGDPt-i + δ32 ΔLCMRt-i + δ33 ΔLM2t-i + δ34 ΔLCPIt-i + δ35 ΔDRt-i + λ31LCMRt-1 + λ32 LM2t-1 +λ33 LGDPt-1 + λ34 LCPIt-1 + λ35 DRt-1 + 31Dt +εt3 n1
n2
n3
n4
n5
i=1
i=0
i=0
i=0
i=0
ΔLCPIt = α04 + δ41 ΔLCPIt-i + δ42 ΔLCMRt-i + δ43 ΔLM2t-i + δ44 ΔLGDPt-i + δ45 ΔDRt-i + λ41LCMRt-1 + λ42 LM2t-1 +λ43 LGDPt-1 + λ44 LCPIt-1 + λ45 DRt-1 + 41Dt +εt4 n1
n2
n3
n4
n5
i=1
i=0
i=0
i=0
i=0
ΔDRt = α05 + δ51 ΔDRt-i + δ52 ΔLCMRt-i + δ53 ΔLM2t-i + δ54 ΔLGDPt-i + δ55 ΔLCPIt-i + λ51LCMRt-1 + λ52 LM2t-1 +λ53LGDPt-1 + λ54 LCPIt-1 + λ55 DRt-1 + 51Dt +εt5
(3) (4) (5) (6) (7)
Where: α01,…., α05: are the constant terms; δ11,…., δ55: are the short-term coefficients matrix for the variables; λ11,…., λ55: are the long-term coefficient for the variables; θ11,…., θ51 are the coefficients of the dummy variable (GFC); and εt1,….,εt5 are the standard errors for all models (Pesaran, 2009).
7
For testing the existence of short-term relationship among the above matrix we can formulate the H0 and H1 hypotheses as the following: H0 : No short – term relationship
H1 : Short – term relationship
11 12 13 14 15 16 0
11 12 13 14 15 16 0
21 22 23 24 25 26 0
21 22 23 24 25 26 0
31 32 33 34 35 36 0
31 32 33 34 35 36 0
41 42 43 44 45 46 0
41 42 43 44 45 46 0
51 52 53 54 55 56 0
51 52 53 54 55 56 0
61 62 63 64 65 66 0
61 62 63 64 65 66 0
However, for testing the existence of long-run relationship, the H0 and H1 hypotheses have been formulated as the following: H0 : No long–term relationship
H1 : A long–term relationship
11 12 13 14 15 16 0
11 12 13 14 15 16 0
21 22 23 24 25 26 0
21 22 23 24 25 26 0
31 32 33 34 35 36 0
31 32 33 34 35 36 0
41 42 43 44 45 46 0
41 42 43 44 45 46 0
51 52 53 54 55 56 0
51 52 53 54 55 56 0
61 62 63 64 65 66 0
61 62 63 64 65 66 0
The decision to reject or accept H0 (no co-integration among the variables) is based on the following procedures (Pesaran et al., 2001): If Fs > Upper bound then reject H0 and the variables are co-integrated. If Fs < Lower bound then accept H0 and the variables are not co-integrated. But if Fs Lower bound and Upper bound then the decision is inconclusive. Where: Fs is: F-statistic value. 6. Results Analysis 6.1 Unit Root Tests
As known, the problems associated with non-stationary time series, the practical question is what do to avoid the spurious regression problem that may arise from regressing a non-stationary time series. Gujarati & Porter (2009) confirmed that if we face a non-stationary time series, we have to transform it into stationary by using the first difference. It is very important to analyze the stationary requirement of the six variables as the stationarity characteristic is necessary in time series approaches. Tables 1 report the results of ADF and PP tests, both in levels and in firstdifferences.
8
Table 1: Unit Root Tests. Variables
ADF Level
ADF 1st Difference
PP Level
PP 1st Difference
Order of Integration
LCMR LGDP LM2 LCPI DR
-0.8971 -2.0719 -0.5165 1.6561 -2.5772
-4.7690*** -5.1711*** -6.1851*** -4.2670*** -3.8186***
-0.9545 -2.1993 -0.5165 1.5155 -1.8624
-4.7403*** -5.1592*** -6.1851*** -4.2939*** -3.5303***
I (1) I (1) I (1) I (1) I (1)
Notes: ***, ** denotes significant level of 1% and 5%, respectively. Source: output of E.Views Package, version 7.
The results reveal that we cannot reject the (H0) of unit roots for all variables in level forms which is stationary at I(0). However, the null hypothesis was rejected when the ADF and PP tests was applied to the first differences of each variable. The first differences of all variables are stationary of order one, I(1). Since all variables are stationary, now it is appropriate to check whether the variables are co-integrated or not without any problems like spurious. 6.2 Equilibrium testing of co-integration
To examine if there is an equilibrium relationship among the variables, bound testing approach (ARDL) has employed (the results presented in Table 2). Choosing the optimal lag length is based on the Schwartz Bayesian Criterion (SBC) which is minimizing lag and the most suitable for yearly data (Pesaran & Shin, 1999). Table 2: Bound Testing of Co-integration. F-statistic
Significan
Model
Critical bound
t- level
Lower
Decision
Upper
FLCMR (LCMR / LGDP, LM2,,LCPI,DR)
4.969**
10%
2.45
3.52
Co-integration
FLGDP (LGDP / LCMR, LM2,LCPI,DR)
2.554*
5%
2.86
4.01
Inconclusive
FLM2 (LM2 / LCMR, LGDP,LCPI,DR)
1.173
1%
3.74
5.06
No co-integration
FLCPI (LCPI, /LCMR, LGDP,LM2,DR)
3.621*
Co-integration
FDR (DR, /LCMR, LGDP,LM2,LCPI)
3.435*
Inconclusive
Notes: 1. the critical value bounds are from Table F in Pesaran et al. (2001) Table CI (iii) case (III) unrestricted intercept and no trend. 2. *,**,***, significant at 10%, 5%, 1% respectively. Source: Output of Microfit Package, version 4.1.
Table 2 reveals that there is a compelling long-term co-integration relationship among the variables when the regressions are normalized on LCMR and LCPI models. On the other hand, when the process was repeated for the other models, the computed F-statistics is less than the upper bound, for the LGDP model the result is inconclusive because the F-statistic located between upper and lower bound at the 10% level of significance, the same results obtained in DR model but at the 5% level of significance. However, there is no-co-integration among the variables when the LM2 is dependent variables. 9
Subsequently, Table 3 shows that the long-run coefficients are significant for DR only at the 10% level of significance which signals a negative impact on stock return. In contrast, the rest of variables have insignificant impact on the LCMR on the long-term. Furthermore, all coefficients have a correct sign as we discussed in Section 4 except for the LGDP and LCPI variables. Table 3: Long-Run Coefficient of the LCMR. LCMR is dependent variable
LCMRt 0.048 0.013LGDPt -1 +0.097LM2t -1 + 0.207 LCPIt -1 0.023DRt -1 0.133D t-value
-1.553
0.833
0.873
-1.886*
-1.652
p-value
[0.136]
[0.414]
[0.393]
[0.074]
[0.114]
Note: ***, **, * denotes 1%, 5% and 10%. level of significance, respectively. Source: Output of Microfit Package, version 4.1.
Moreover, Table 4 represents the results of the short-term dynamics equilibrium relationship between the LCMR and the regressors. The error correction terms (Ectt-1) indicates the speed adjustment back to equilibrium in the dynamic model. When Ectt-1 is significant with a negative sign in the short-run model confirms the existence of a long-term equilibrium relationship among the variables (Nayaran, 2005). The magnitudes of the coefficients of Ectt-1 denote the speed of adjustment in correcting any disequilibrium in the short-term and then the economy can return to its equilibrium (Pesaran & Pesaran, 2009). The Ectt-1 coefficient is found to be negative and significant [-0.345, 0.002] which is highly significant at 1% level with correct sign. This implies that 35% of disequilibrium from previous year can return to long-term equilibrium in the current year. In addition, the regressions for the underlying ARDL model passed the diagnostic tests of normality, serial correlation, functional form and hetroscedasitcity which reveal no evidence of any misspecification. Subsequently, the finding suggested that there is a negative impact of GFC on the LCMR in Jordan at the 10% level of significant, since the p-value of the dummy variable and has negative sign. This finding is consistent with the literature (see, Li et al., 2008; Chakrabarti & Roll, 2002; Kenourgious et al., 2011; and Dungey et al., 2006). On the other vein, it is inconsistent with some studies like Khalid & Kawi (2003) and Nagayasu (2001). Table 4: Error Correction Estimation, LCMR is Dependent Variable.
ΔLCMRt = - 0.016 +0.818ΔLGDPt +0.004ΔLGDP*t-1 0.033 ΔLM2t 0.046 ΔLM2t-1 *** +0.071ΔLCPI t 0.008ΔDRt * 0.046* D 0.345ECTt-1 *** Serial Correlation
χ2
(1) =.[0.262]
Functional Form
Heteroscedasticity
χ2
(1) =.[0.113]
D-W = 2.289
χ2
Note: ***, **, * denotes 1%, 5% and 10%. Level of significance, respectively. Source: Output of Microfit Package, version 4.1.
10
(1) =.[0.985]
Normality
χ 2 (2) = [0.897]
p-value of the D=[-0.08]
Finally, to check the estimated ARDL model stability of the long-run coefficients with the shortrun dynamics between LCMR and its determinants, we apply the cumulative sum of recursive residuals CUSUM and the cumulative sum of squares CUSUMQ (Brown et al., 1975; Pesaran & Pesaran, 1997; and Bahmani-Oskooee & Bohl, 2000). If the plot of CUSUM and CUSUMSQ statistic stays within 5% range of significance level (within the two straight lines) the null hypothesis that all coefficients in the error correction model are stable and cannot be rejected (Bahamni-Oskooee & Ng, 2002). If either of the lines is crossed, the null hypothesis of coefficient constancy can be rejected at the 5% level of significance. Figure 5 reveals that the plot of both CUSUM and CUSUMQ statistics stays within the critical boundaries showing stability of the long-run coefficient of the LCMR function. Thus, all coefficient are stable which reflects the stability of the LCMR and its determinants. Plot of Cumulative Sum of Recursive Residuals 20
10
0
-10
-20 1979
1987
1995
2003
2010
The straight lines represent critical bounds at 5% significance level
Plot of Cumulative Sum of Squares of Recursive Residuals 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 1979
1987
1995
2003
2010
The straight lines represent critical bounds at 5% significance level
Figure 5: Plots of CUSUM and CUSUMQ Underwriting LCMR. Source: Output of Microfit package.
7. Conclusion and Policy Implications This study has examined the relationship between stock return and its determinants with respect of the GFC impact. It has applied the ARDL time series approach on ASE annual data for the (1978-2011) period to test the co-integration among the variables. The empirical results provided strong evidence to reject the null hypotheses of unit roots in all series under investigation. The results imply that CMR in the short-term affected by M2 and DR only. These findings reflect the 11
disequilibrium of the Jordan’s economy in the short run. In addition, the ECM coefficient determines the speed of adjustment which has a highly significant negative sign. Thus, the results are consistent with the hypothesis, where the CMR is found to have a short and long-term significant relationship with the selective variables. The results of ARDL approach revealed the existence of long-term equilibrium relationship between the CMR and macroeconomic variables. Moreover, the findings suggest a negative significant impact of GFC on the CMR at 10% level of significance. The CUSUM and CUSUMQ stability tests also show that the coefficients of the error correction model are stable. Subsequently, these results are consistent with the earlier findings (see, Hammoudeh & Sari, 2011; Rushdi, 2012; Maysami, 2004; Birz & Loh, 2011; Hussain, 2011; Bilson et al. 2001; Wongbangpo & Sharma, 2003; Bekhet & Matar 2012; Bekhet & Mugableh, 2012; and Bekhet & Al-Smadi, 2012). In the current study, it adds to the existing literature by employing the most popular approach in examining co-integration among financial variables in ASE. In addition, studying the relationship between the macroeconomic indicators and the stock return can shed some light on the stock market’s response to macroeconomic factors for similar emerging markets. Therefore, it can be claimed that CMR variability is basically linked to economic variables, through the change in stock return that lags behind economic activities. Finally, for further studies, we suggest more factors that may cause the obvious structural breaks on CMR such as, FD, wars, terrorist attacks, and the revolutions especially the recently Arab spring). References Abugri, B.A. (2008). Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets. International Review of Financial Analysis, 17, 396–410. Al-sharkas, A. (2004). The Dynamic Relationship between Macroeconomic Factors and the Jordanian Stock Market. International Journal of Applied Econometrics and Quantitative Studies, 1, 97-114. Amman stock exchange, http://www.ase.com.jo Accessed on 15 October 2012. Bahmani-Oskooee, M., & Bohl, M. T. (2000). German monetary unification and the stability of the German M3 money demand function. Economics Letters, 66(2), 203-208. Bahmani-Oskooee, M. & Ng,W. (2002). Long-run demand for money in Hong Kong: an application of the ARDL model. International journal of business and economics, 1(2), 147-155. Bekhet, H.A. & Matar, A. (2012). Risk-Adjusted Performance: A two-model Approach Application in Amman Stock Exchang. International Journal of Business and Social Science, 3(7), 34-45. Bekhet, H.A. & Matar, A. (2012). Causality of Macroeconomic Variables Impacting the Stock Market Index: Time Series Approach in Amman Stock Exchange. Conference on Asian Forum on Business Education (AFBE), UNITEN, Selangor, Malaysia, July, 9-10/2012. Bekhet, H.A. & Matar, A. (2011). Analyzing Risk-Adjusted Performance: Markwoitz and Single-Index Approaches In Amman Stock Exchange, International Conference On Management (ICM) Proceeding, Penang, Malaysia, Jun, 13-14/2011 305-321.
12
Bekhet, H.A. & Mugableh, M. (2012). Investigating Equilibrium Relationship between Macroeconomic Variables and Malaysian Stock Market Index through Bounds Tests Approach. International Journal of Economics and Finance, 4(10), 69-81. Bekhet, H.A. & AL-Smadi, R. (2012). Exploring the Relationship among FDI Determinates: Evidence from Jordan, Conference on Asian Forum on Business Education (AFBE), UNITEN, Selangor, Malaysia, on July, 9-10/2012. Birz, G., & Lott, J. R. (2011). The effect of macroeconomic news on stock returns: New evidence from newspaper coverage. Journal of Banking & Finance, 35, 2791–2800. Bilson, C.M., Brailsford, T.J. & Hooper, V.J. (2001). Selecting macroeconomic variables as explanatory factors of emerging stock market returns. Pacific-Basin Finance Journal, 9(4), 401-426. Brown, R.; Durbin, J. & Evans, J. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 149-192. Chakrabarti, R., & Roll, R. (2002). East Asia and Europe during the 1997 Asian collapse: A clinical study of a financial crisis. Journal of Financial Markets, 5(1), 1-30. Chen, N.F; Roll, R. & Ross, S. (1986), Economic Forces and the Stock Market, Journal of Business, 59, 383-403. Chen, M.H. (2007). Macro and non-macro explanatory factors of Chinese hotel stock returns. International Journal of Hospitality Management, 26(4), 991-1004. Central Bank of Jordan, (http://statisticaldb.cbj.gov.jo). Accessed on 5 July 2012. Claessens, S. ; Ayhan Kose, M. & Terrones, M. E. (2010). The global financial crisis: How similar? How different? How costly? Journal of Asian Economics, 21(3), 247-264. Cheung, W; Fung, S. & Tsai, S. (2010). Global capital market interdependence and spillover effect of credit risk: evidence from the 2007–2009 global financial crisis. Applied Financial Economics, 20, 85–103. Collins, D., & Biekpe, N. (2003). Contagion: a fear for African equity markets?. Journal of Economics and Business, 55(3), 285-297. De Gregorio, J. & Guidotti, P.E. (1995). Financial development and economic growth. World Development, 23(3), 433-448. Dungey, M., Fry, R., González-Hermosillo, B., & Martin, V. (2006). Contagion in international bond markets during the Russian and the LTCM crises. Journal of Financial Stability, 2(1), 1-27. Engle, R.F. & Granger C.W.J.(1987). Cointegration and Error Correction: Representation, Estimation and Testing, Econometrica, 55, 251-76. Enisan, A. & Olufisayo, A.O. (2009). Stock market development and economic growth: Evidence from seven sub-Sahara African countries. Journal of Economics and Business, 61(2), 162-171. Fama, E. & Schwert, G. (1977). Asset returns and inflation. Journal of Financial Economics, 5, 115-146. Ghatak S. & Siddiki, J.(2001), The use of ARDL approach in estimating virtual exchange rates in india, Journal of applied statistics,11:573-583. Gosnell, T. & Nejadmalayeri, A. (2010). Macroeconomic news and risk factor innovations. Managerial Finance, 36(7), 566-582.
13
Hammoudeh, S., & Sari, R. (2011). Financial CDS, stock market and interest rates: Which drives which?. The North American Journal of Economics and Finance, 22(3), 257-276. Ha, I.B; Khil, J. & Lee,B.S. (2001). On the rationality of Korea’s stock market: was the recent Korean financial crisis due to fundamental factors?. Journal of International Financial Markets, Institutions and Money, 11, 423–441. Hussain, S.M. (2011). Simultaneous monetary policy announcements and international stock markets response: an intraday analysis. Journal of Banking & Finance, 35, 752–764. IMF. http://www.imf.org. Accessed on 5 October 2012. Jayaraman, T. & Choong, C.K. (2009). Growth and oil price: A study of causal relationships in small Pacific Island countries. Energy Policy, 37(6), 2182-2189. Jensen, G. & Johnson, R. (1995). Discount rate changes and security returns in the US, 1962– 1991. Journal of Banking & Finance, 19(1), 79-95. Ji, P. (2010). The impact of the global financial crisis on the cross-currency linkage of LIBOR– OIS spreads. International Financial Markets, Institutions and Money, 20, 575–589. Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegrating Vectors in Gaussian Vector Autoregressive Models, Econometrica, 59, 1551-80. Karagianni, S.; Pempetzoglou, M. & Saraidaris, A. (2012). Tax burden distribution and GDP growth: Non-linear causality considerations in the USA. International Review of Economics & Finance, 21(1), 186-194. Khalid, A. M., & Kawai, M. (2003). Was financial market contagion the source of economic crisis in Asia?: Evidence using a multivariate VAR model. Journal of Asian Economics, 14(1), 131-156. Kenourgios, D; Samitas, A. & Paltalidis, N. (2011). Financial crises and stock market contagion in a multivariate time-varying asymmetric framework. International Financial Markets, Institutions and Money, 21, 92–106. Kirman, A.P. (1992). Whom or what does the representative individual represent? The Journal of Economic Perspectives, 6(2), 117-136. Markwat, T., Kole, E., & Van Dijk, D. (2009). Contagion as a domino effect in global stock markets. Journal of Banking & Finance, 33(11), 1996-2012. Maysami, R; Howe, L. & Hamzah, M. (2004). Relationship between Macroeconomic Variables and Stock Market Indices: Cointegration Evidence from Stock Exchange of Singapore’s All-S Sector Indices. Jurnal Pengurusan, 24, 47-77. Moshirian, F. (2011). The global financial crisis and the evolution of markets, institutions. Journal of Banking & Finance, 35(3), 502-511. Nagayasu, J. (2002). Currency crisis and contagion: evidence from exchange rates and sectoral stock indices of the Philippines and Thailand. Journal of Asian Economics, 12(4), 529546. Narayan, P. K. & Smyth, R. (2005). The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration. Energy Policy, 33(4), 467474. Nidhiprabha, B. (2010). Effectiveness of Thailand's macroeconomic policy response to the global financial crisis. ASEAN Economic Bulletin, 27(1), 121-135. Liu, M.H., & Shrestha, K.M. (2008). Analysis of the long-term relationship between macroeconomic variables and the Chinese stock market using heteroscedastic cointegration. Managerial Finance, 34(11), 744-755. 14
Ozturk, I. & Acaravci, A. (2011). Electricity consumption and real GDP causality nexus: Evidence from ARDL bounds testing approach for 11 MENA countries. Applied Energy, 88, 2885-2892. Pesaran, M. & Pesaran, B. (2009). Time series Econometrics, 1st edition, Oxford University Press Inc., New York. Pesaran, M.; Shin, Y. & Smith, R. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634. Pesaran, M. & Pesaran, B. (1997). Microfit 4.0 (Windows Version). New York: Oxford University Press. Pesaran, M.; Shin, Y. & Smith, R. (2001) Bounds Testing Approaches to the Analysis of Level Relationships, Journal of Applied Econometrics, 16, 289-326. Rangel, J.G. (2011). Macroeconomic news, announcements, and stock market jump intensity dynamics. Journal of Banking & Finance, 35, 1263–1276. Rushdi, M., Kim, J. H., & Silvapulle, P. (2012). ARDL bounds tests and robust inference for the long run relationship between real stock returns and inflation in Australia. Economic Modelling, 29(3), 535-543. Saleem, K. (2009). International linkage of the Russian market and the Russian financial crisis: A multivariate GARCH analysis. Research in International Business and Finance, 23(3), 243-256. Serwa, D., & Bohl, M.T. (2005). Financial contagion vulnerability and resistance: A comparison of European stock markets. Economic Systems, 29(3), 344-362. S&P,https:www.spindexdata.com/idpfiles/shariah/prc/active/pressreleases/SP_Indices_Country_ Classification_Consultation_2011R.pdf. Accessed on 22 May 2012. Wongbangpo, P. & Sharma, S.C. (2002). Stock market and macroeconomic fundamental dynamic interactions: ASEAN-5 countries. Journal of Asian Economics, 13(1), 27-51. http://data.worldbank.org/indicator/NY.GDP.MKTP.CD/countries/JO. Accessed on 28 October 2012. Yang, J., & Bessler, A. (2008). Contagion around the October 1987 stock market crash. European Journal of Operational Research, 184(1), 291-310.
15
