CO-INTEGRATION TESTING, USING THE JOHANSEN METHOD, BETWEEN GENERAL INDICATORS OF SOME ARAB FINANCIAL MARKETS IN ASIA

Ateyah Alawneh

Tafila Technical University, College of Business, Jordan

Keywords:Financial markets, Indicators, Financial markets, The Johansson co- integration, Stocks indicators.

ARTICLE HISTORY: Received:31 October 2018 Revised:5 December 2018 Accepted:2 January 2019 Published:28 January 2019.

Contribution/ Originality:The study aims to examine Co-integration by using the Johansen method between general indicators of some Arab Financial Markets in Asia.

1. INTRODUCTION

Financial markets have largely developed through the flow of capitals among countries due to the important role of financial markets in dealing with investment decisions in countries, especially the Arab countries in Asia, which have relationships in finance and economics. The Co-integration of Arab countries, especially among the Gulf states in general and Jordan, comes to the convergence of geography and traditions, thus enhancing the flow of capital among the countries in question through the financial market, which is considered to be equipped for the investment decision in addition to the dependence of their economies on each other in addition to the movement of the capital, labor, goods exchanging and services. Arab countries seek to reduce transaction costs and legal restrictions to support financial and economic integration.  Arab countries depend on each other's economies. Jordan is a major source of investment, especially for the Gulf States; (Saudi Arabia, Bahrain and the United Arab Emirates). The Gulf States in particular rely on Jordan for the sake of labor, as well as importing agricultural and industrial goods. This is the major reason that the research project comes through the use of the Johansson co-integration method in order to examine whether there is a co-integration between the major indicators of financial markets in Asia (Saudi Arabia, Bahrain, Abu Dhabi, Dubai and Jordan), which have economic, social, political and regional links. The research project covers monthly data for the period from 1/1/2010 to 31/12/2017. These data concern Arab major financial markets in Asia (Saudi Arabia, Bahrain, Abu Dhabi, and Dubai, Jordan). Data are also based on the general indices of monthly stock indicator in the Arab countries. The monthly data on all indicators and accessibility are available in addition to the growing financial and economic relations between the sample countries during this period.

The study will analyze the descriptive statistics of the research sample data. Moreover, it will analyze the relationship between the indicators of the research sample and then it will find out the co-integration through the first step in finding the stationary data. The second step is applying the deceleration period of the data. The third step is finding co- integration based on the slowdown period. Finally, results will be illustrated and later the recommendations of the study will be inferred and stated.

2. THE PROBLEM OF THE STUDY

The problem of the research project comes from the use of the Johannes Co-integration test to examine whether or not there is a Co-integration of the major indicators of the Arab financial markets in Asia (Saudi Arabia Bahrain, Abu Dhabi, Dubai and Jordan) which have economic, social, political and regional linkage. Therefore, the problem of the study can be formulated through the following questions:

• Is there at least one co- integration relationship between the countries of the sample of the study by using the Johansson test?
• Is there more than one Co-integration relationship between the countries that represent the study sample by using the Johansson test?

3. OBJECTIVES OF THE STUDY

The study aims at achieving the following objectives:

• Identify the concept of financial indicators in the countries involved in the current study.
• Identify the development of financial indicators in these countries.
• Analyze the descriptive statistics of the countries of the study sample
• Analyze the correlation between the financial indicators of the countries in question.
• Analyze of Co-integration by using the Johansen test among the financial indicators of the countries involved.
• Obtain results and recommendations to assist investment decision makers of these countries.
• Find out where the investor puts his money to avoid the risks of international investment.

4. THE SIGNIFICANCE OF THE STUDY

The significance of this study is of a high value and benefit to the Arab and foreign investors in financial markets and investors in real investment. In addition, it is highly beneficial to researchers, students and decision makers who invest in the countries concerned. The results of the study are important to support the theoretical and practical aspects of the financial markets.

5. HYPOTHESES OF THE STUDY

The study hypotheses can be formulated depending on the problem as follows-
There is a common integration between the Arab financial markets in the research sample that paves the way for the existence of a unified Arab financial market among all the other Arab countries through the following hypotheses.

• There is at least one significant co-integration relationship with statistical significance (α <0.05) between the countries of the study sample.
• There is more than one co-integration relationship (α <0.05) between the countries involved in the current study.

6. LITERATURE REVIEW

There are several studies on the Co-integration of financial markets but very few of these studies tackled the precise idea of this research with its comprehensive study of the most Arab countries.

HandeErdincn (2009) conducted a study that aims at measuring the Co- integration of the stock markets at some of the main EU countries, France, Germany and the United Kingdom. The study is based on the market indicators of the European countries which (FTSE 100 Indicator, DAX Indicator, CAC4 Indicator, MSCI Indicator) ¹ the study used the Johansen test of co- integration where there was a co-integration between the financial markets of the countries concerned due to the similarity of the economic structure in these countries.

Canarella et al. (2008) examine the Co-integration relationship between NAFTA's financial markets, (Canada, México and the United States), through the use of the Johansen test of Co- integration where the study showed an integration relationship between the market of Mexico and the United States of America.

Komlavi (2010) analyzed the Co-integration during the global financial crisis for the period from 2008 to August 2009 by analyzing the co-integration of international financial market indicators for Group of States (OECD Group, Pacific Group, Asia Group). The analysis shows that the three groups haveat least one co-integration relationship. This made investors in Asian capital markets never avoid any impact from financial markets, even if some domestic markets are still not fully open to the international market.

Muhammad and Syed (2012;2018) analyzed the determinants of the stock market between Pakistan and Asian economies from 2001 to 2015 by using Philips-Perron (PP) test to verify the co-integration of their financial markets. The results indicate that there is a long-term Co-integration between the Pakistan stock market and Stock markets in China, India, Indonesia, Korea, Malaysia and Thailand.

Neda and Amir (2014) examine the links between equity markets in five Asian countries; Malaysia, Indonesia, Philippines, Japan and Turkeywith United States of America. The study used monthly data for the period 1995-2010 through the use of VAR analysis. AR results showed significant market interactions. Moreover, the results showed that the US stocks are interconnected with all Asian stock markets. Furthermore, Japan is strongly intertwined with other Asian markets.

Mohamad and Wael (2013) conducted a study whose aim was to investigate the Co-integration of Arab financial markets mainly through focusing on previously defined countries; Kuwait and Jordan. The study used Johansen test of Co-integration. The study results showed a Co-integration relationship between the Kuwaiti and Jordanian financial markets.

However, there are many studies and research relevant to financial markets integration of countries that are not economically similar. The study of Walid (2008) examined long-term and dynamic relationships in the short term between the Egyptian Stock Exchange and its counterparts in the Group of Seven (G7). Results showed that the Egyptian stock exchange does not share long-term Co-integration relationships With G7.

Mohanasundaram and Karthikeyan (2015) explore the nature of the relationship among the stock market indices in South Africa, India and the USA. Their study used monthly data from stock indices, JALSH (South Africa), NIFTY (India) and NASDAQ (United States of America). Accordingly, the study led to a strong relationship among the stock market indices in South Africa, India and the United States of America.

Eventually,  Mohamad and Wael (2013) examined the extent of Co-integration between EU countries and- Arab financial markets. Test results showed that the null hypothesis of non-integration cannot be accepted if the Arab market Indicator is a dependent variable. However, it can be accepted in the EU is a dependent Variable.

7. THE THEORETICAL FRAMEWORK OF THE STUDY

7.1. Historical Introduction

The financial market of Jordan is one of the oldest markets in the Arab world and was established in 1978. In 1989, the financial market in Bahrain was established and also started an active market in Saudi Arabia. In 2000, the Dubai Stock Exchange and the Abu Dhabi Stock Exchange were established in the United Arab Emirates (Ashraf, 2009).

7.2. Common Features of Arab Financial Markets

The recent Arab financial markets are characterized by some common features, most notably the narrowness of the market. The tightness of the market in the Arab financial markets is due to the limited supply and demand of financial investment instruments in these markets, as well as the concentration of trading in a limited number of shares, The active shares traded to the total volume of trading, reflecting the small number of attractive shares, due to the concentration of some major investors to keep the shares of promising companies, in addition to the low quality of the majority of listed stocks as such the  Arab financial markets are characterized by weak diversification of securities, poor liquidity and high volatility. This in turn leads to increased volatility in the profitability of the stock and consequently its market value, which raises fears among investors and presents them with high losses (Ashraf, 2009).

7.3. Benefits of Co- Integration

In this study will analyze the co-  integration of financial markets in some Arab countries Where there are many benefits of co- integration between the financial markets of the countries of the region, the most important  the management of local markets in an organized , efficient manner, the achievement of Found integration of financial institutions, markets and infrastructure together in the countries of financial integration also reduces the costs of financial intermediaries and increases competition because of the increasing number of financial institutions in the market, increasing financial services and increasing their contribution to growth also further financial development and efficiency, as integration leads to the elimination of financial imbalances and restrictions on the movement of capital In addition, the increase of foreign capital flows to the integration countries the larger and more liquid regional market may be more attractive to investors in international markets. Moreover, regional integration will allow financial institutions to diversify their resources better, reduce geographic and sectorial risks across companies and sectors, and improve the stability of financial systems in the average regional cost of capital and debt while at the same time providing opportunities for further risk reduction through diversification, and improvement inMarket liquidity and the availability of new investment instruments (Mark, 2007).

7.4. Indicators of Financial Markets for the Study Sample

The concept of the general market indicator measures the level of indicator in the market, based on a sample of enterprises shares traded in regulated or unregulated capital markets or both. The sample is often selected in such a way as to allow the indicator to reflect the situation in which the capital market is intended to be measured (http://www.arabapi.org) .

In addition, the indicators are the value that measures the actual changes in the indicator of securities traded in the financial markets, using a mathematical equation that matches the nature of each financial market so as to identify the movement of indicator, trends and market performance. The main objective is to identify the movement of stock indicator in the financial market indicators of financial markets, during the years of study (Dredid, 2012).

The general indicators of stock indicator in the financial markets were based on the market trend and the study of the co- integration of financial markets; because they indicate the levels of stock indicator and determine indicator general price Stock price during a certain period, compared with another period (Shukairy and Saleh, 2012).

7.4.1. The Saudi Financial Market Indicator (TASI)

(TASI) is a digital standard which reflects changes in the market value of shares of all companies traded on Saudi stock market, and then it serves as a thermometer that measures the activity and prosperity of stock market (Ibrahim, 2005).
Figure (1) represents the Saudi Financial Market Indicator. (TASI), which reflects the trend of Saudi’s Financial Market Indicator during the study period for the monthly data during  2010-2017. It can be noted from the form that the trend of indicator in the market is raising at the beginning of the study until the summit reached in mid-2014.then the market trend began to decline and continued until it reached the bottom in the first third of 2016, as a result the market went in a bullish direction.

Figure-1. Saudi Financial Market Indicator.

Source: E-views program dependent on appendix (5)

7.4.2. Dubai Financial Market Indicator (DFMGI)

Figure (2) Dubai Financial Market indicator (DFMGI) reflects the trend of Dubai’s Financial Market Indicator during the study period for the monthly data during 2010-2017. According to the form, it is notable that the trend of the indicator in the market is raising at the beginning of the study until it reaches the summit in mid of-2014, then the indicator began to decline and continued until it reaches the bottom in mid of-2015, followed by a gradual rise in indicator.

Figure-2. Dubai Financial Market Indicator (DFMGI)

Source: E- views program based on appendix (5)

7.4.3. Abu Dhabi Financial Market Indicator (ADI)

Figure (3) represents Abu Dhabi’s Financial Market indicator (ADI). Which reflects the trend Abu Dhabi’s Financial Market Indicator during the study period for the monthly data for the period 2010-2017. It is noted from that the indicator trend in the market is raising at the beginning of the study until it reaches the summit in mid-2014, then the indicator begins to decline and continues until it reaches the bottom in mid-2015 then the market started to rise.

Figure-3. Abu Dhabi’s Indicator (ADI).

Source: E- views program based on appendix (5).

According to the form  above, it is notable that the markets are moving according to Dow's technical analysis in the Saudi’s financial market, Dubai’s and Abu Dhabi’s Financial Market; indicating that the market is fluctuating then became stable based on Dow's theory in financial market (Dredid, 2012).

7.4.4. Bahrain’s Financial Market Indicator (BAX)

Figure (4) represents the general indicator of Bahrain’s Financial Market, which reflects the trend of the general Indicator of Bahrain Financial Market during the study period for the monthly data from 2010-2017. Based on the Figure, the indicator’s trend in the market begins to decline at the beginning of the study until it reaches the bottom at the end of 2012. Then the indicator inclines and continued until it reaches the summit at the third quarter of 2014, then the market declines until it reaches the bottom at the second quarter of 2016. In conclusion, the market started to rise one again.

Bahrain’s market is different from the other markets (Saudi Arabia, Dubai and Abu Dhabi) since the previous markets were having rising trends at the beginning of the study then they started to decline at the end of the study, while Bahrain market was at an opposite direction; it kept fluctuating between bearish trend and bullish trend at until the end of the study due to the fact that the financial markets are affected by economic events and investor movements among other financial markets.

Figure-4. Bahrain Indicator (BAX)

Source: E- views program based on appendix (5)

7.4.5. Amman’s Financial Market Indicator (AMGNRLX)

Figure (5) represents Amman’s Financial Market Indicator (AMGNRLX), which reflects the trend Amman Financial Market Indicator during the study period. As the form   shows price trend fluctuation in the market and began to decline at the beginning of the study until it reached the bottom in the third quarter of 2013. The indicator trend started to incline until it reaches the summit in the first quarter of 2014. The market kept fluctuating until the end of the study because financial markets are affected by political and economic events as well as by crises in neighboring countries and the whole region in general.

Jordanian market is different from the Saudi’s market, Dubai’s market and Abu Dhabi’s market. The previous markets were bullish at the beginning of the study, and then the markets declined at the end of the study, while Bahraini market was having the same position of the Jordanian market during the period of the study; whereas the markets were downward, then took the upward trend. The trend went downward at the end of the study because of the financial market is affected by political and economic events.

Figure-5. Amman’s Financial Market Indicator (AMGNRLX)

Source: E- views program based on appendix (5)

8. DESCRIPTIVE STATISTICS

It’s notable from Appendix (1) that the highest values ​​for the indicators were for the Saudi’s Indicator with (11112.12) points, while the lowest values ​​for indicators were for Bahrain with (1048) points. According to the average price, as can be noticed from Appendix (4) that the highest average price for the Saudi’s Indicator was (7357.543) points while the lowest values was for the Bahrain’s Indicator of (1282.894) points. In addition, the standard deviation (risk) Saudi Indicator had the highest value but least for Abu Dhabi Indicator as for Skewness, all indicators have torsion on the right except Abu Dhabi’s Indicator that has negative torsion and fluctuations to the left, meaning that the indicators do not follow the normal distribution, confirmed by the (Jargue – Bera) test at level (a <0.05).

9. THE CORRELATION BETWEEN THE INDICATORS OF ARAB FINANCIAL MARKETS IN COUNTRIES OF THE STUDY SAMPLE

Table (1), shows a strong correlation between (ADI); the Indicator and the (DFMGI) Indicator Correlation has reached 98%, while the correlation (61%) was between the (TASI) and (AD I). In addition, the (71%) correlation was between (AMGN) and (BAX). Furthermore the value of correlation was (71%) between (TASI) and (DFMGI), but the remaining indicators have lower relations among them. As for (TASI) and (AMGN), it was negative due to the rise in the general Indicator of the Saudi’s stock indicator, reflecting the activity in the Saudi market and pointing to a decrease in the Jordanian market activity. Consequently, the decline in the indicator of Jordanian market is due to the Saudi’s investors and their movements in between the two markets.

Table-1. Expresses correlation between indicators in countries of study sample.

	ADI	AMGNRLX	BAX	DFMGI	TASI
ADI	1		0.198382	0.980237	0.614469
AMGNRLX	0.014515	0.014515	0.766862	0.018808	-0.10423
BAX	0.198382	1	1	0.26829	0.365869
DFMGI	0.980237	0.766862	0.26829	1	0.715017
TASI	0.614469	0.018808	0.365869	0.715017	1

Source: E- views program based on appendix (5)

10. ANALYSIS OF JOHANSEN CO-INTEGRATION  

In order to find the Co-integration through the first step is to find out the stationary data, the second step is to apply a deceleration period of data whereas the third step is finding a Co integration as follows,

First: The Stationary Test

In order to calculate co- integration, all variables must be stable at the same level.

The rooted unit will be tested using the dickey fuller method in which the unit root is being applied. On other hand, all indicators of the study are found unstable, but they settle at the first difference. Appendix (2), shows that all variables are stabilized at the first difference. It’s necessary to achieve using Johansen co- integration.

The results of unit root test to be contained in the appendix (I). The Indicators; TASI, DFMGI, ADI , BAX , AMGNRLX  are not stationary  at any level ,but  stationary at 1 %, 5%, and 10% level, with the first difference (d(1)); indicated by ADF results at all levels less than the critical values in negative direction. The ADF value for (TASI) is (-8.372111) and the critical values are (-2.589795) (-1.944286) and (-1.614487) at 1, 5, and 10 percent, respectively. The ADF value for (DFMGI) is (-10.33292) and the critical values are (-2.589795) (-1.944286) and (-1.614487) at 1, 5, and 10 %, respectively. The ADF value for (ADI) is-11.00283) and the critical values are (-2.589795) (-1.944286) and (-1.614487) at 1, 5, and 10 %, respectively. The ADF value for (BAX) is (-6.918654) and the critical values are (-2.589795) (1.944286) and (-1.614487) at 1, 5, and 10 %, respectively. The ADF value for (AMGNRLX) is (-10.05620) and the critical values are (-2.589795) (- 1.944286) and (-1.614487) at 1, 5, and 10 %, respectively.

Second:  The Deceleration Test

It can be noticed from Appendix (3) the deceleration test (E-Views) values ​​by using (summarize all 5 sets of assumption ) . Additionally, it can be noted that the deceleration period shows the lowest value selected in (linear intercept no trend model) because it is a better Model of Johansen co- integration. In addition, Appendix (2) shows that the deceleration period (zero) gives the lowest value when testing (Schwarz ( because it gives the best results among tests.

Third: Johansen Co- Integration Test

It can be noticed from Appendix (4) which shows that there is one Co-integration between the general indicators of stock indicator in financial markets of the country in Asia (the study sample). As shown in Appendix (4) unrestricted Co-integration Rank Test ( (Trace). Furthermore, it also can be noticed from Appendix (4) that confirms the results of) Unrestricted Co-integration Rank Test (Maximum Eigen value) which means there is a single Co-integration between the general indicators of stock indicator in the financial markets of the country in Asia (The study sample). Consequently , it’s been accepted that there is one Co -integration between general indicators of stock indicator in financial markets of the countries in Asia (The study sample), but rejects the hypothesis that there is more than co - integration between general indicators of stock indicator in financial markets of the country in Asia (The study sample).

11. THE RESULTS

By reviewing the previous studies where there were many studies on the integration of some of the financial markets However, the results of this study were different in addition to the difference in the study sample as follows
The main financial markets in the Arab countries in Asia have positive correlation between some of them. There   is a positive relationship between Saudi Arabia and Dubai. There is also a positive correlation between Saudi Arabia and Abu Dhabi.  Also, there is a positive relationship between Dubai and Abu Dhabi, and a positive relationship between Jordan and Bahrain. The analysis of Johansen Co-integration test showed that there is at least one Co-integration between financial markets (the sample of the study) by testing Trace Unrestricted Co-integration Rank Test (and Co-integration Rank Test (Maximum Eigen value.

12. RECOMMENDATIONS

The following study has come up with the following recommendations:

• The Co-integration of financial markets of Arab countries should be supported by the flow of more funds between them to increase their co- integration.
• Support Co-integration among Arab countries towards enhancing economic and financial integration
• Achieve monetary integration among countries of the study sample to gradually spread to all Arab countries.
• Dealing with attractive financial instruments for Arab investors through the use of Islamic financial instruments to support joint integration between Arab countries. 
• Making more studies about co- integration of other Arab financial markets and other foreign countries.

Funding: This study received no specific financial support.

Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper.

REFERENCES

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APPENDICES

Appendix (1)

	ADI	AMGNRLX	BAX	DFMGI	TASI
Mean	3703.766	2120.804	1282.894	2780.149	7357.543
Median	4145.380	2119.450	1287.685	3074.385	7000.905
Maximum	5253.410	2575.470	1591.940	5087.470	11112.12
Minimum	2402.280	1850.590	1048.810	1353.390	5623.340
Std. Dev.	944.7470	146.6717	137.3364	1119.480	1228.129
Skewness	-0.14401	0.731302	0.103572	0.214465	1.150843
Kurtosis	1.326896	3.920888	1.811977	1.792564	3.544155
Jarque-Bera	11.52893	11.94899	5.817233	6.567531	22.37544
Probability	0.003137		0.002543	0.054551	0.037487
Sum	355561.6		203597.2	123157.8	266894.3	706324.1
Sum Sq. Dev.	84791949		2043697.	1791823.	1.19E+08	1.43E+08
Observations	96		96	96	96	96

Appendix (2)

Null Hypothesis: TASI has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-0.06425	0.6588
Test critical values:	1% level		-2.58953
	5% level		-1.94425
	10% level		-1.61451

*MacKinnon (1996) one-sided p-values

Null Hypothesis: D(TASI) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-8.37211	0.0000
Test critical values:	1% level		-2.5898
	5% level		-1.94429
	10% level		-1.61449

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: DFMGI has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			0.185705	0.7380
Test critical values:	1% level		-2.58953
	5% level		-1.94425
	10% level		-1.61451

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: D(DFMGI) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-10.3329	0.0000
Test critical values:	1% level		-2.5898
	5% level		-1.94429
	10% level		-1.61449

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: ADI has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			0.746064	0.8738
Test critical values:	1% level		-2.58953
	5% level		-1.94425
	10% level		-1.61451

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: D(ADI) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-11.0028	0.0000
Test critical values:	1% level		-2.5898
	5% level		-1.94429
	10% level		-1.61449

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: BAX has a unit root
Exogenous: None
Lag Length: 1 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-0.61904	0.4467
Test critical values:	1% level		-2.5898
	5% level		-1.94429
	10% level		-1.61449

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: D(BAX) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-6.91865	0.0000
Test critical values:	1% level		-2.5898
	5% level		-1.94429
	10% level		-1.61449

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: AMGNRLX has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-0.98323	0.2895
Test critical values:	1% level		-2.58953
	5% level		-1.94425
	10% level		-1.61451

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: D(AMGNRLX) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)

			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic			-10.0562	0.0000
Test critical values:	1% level		-2.5898
	5% level		-1.94429
	10% level		-1.61449

Appendix (3)

Date: 09/01/18 Time: 18:23
Sample: 2010M01 2017M12
Included observations: 93
Series: ADI AMGNRLX BAX DFMGI TASI
Lags interval: 1 to 2
Selected (0.05 level*) Number of Cointegrating Relations by Model

Data Trend:	None	None	Linear	Linear	Quadratic
Test Type	No Intercept	Intercept	Intercept	Intercept	Intercept
	No Trend	No Trend	No Trend	Trend	Trend
Trace	0	1	1	0	0
Max-Eig	0	1	1	0	0

*Critical values based on MacKinnon et al. (1999)

	Schwarz Criteria by Rank (rows) and Model (columns)
0	62.30937*	62.30937*	62.51119	62.51119	62.69288
1	62.56349	62.46057	62.62281	62.66780	62.80112
2	62.88529	62.76390	62.88655	62.97848	63.07975
3	63.27838	63.16190	63.23582	63.35725	63.43249
4	63.71964	63.61892	63.66671	63.80374	63.83084
5	64.20660	64.10893	64.10893	64.28414	64.28414

Appendix (4)

Date: 08/28/18 Time: 22:11
Sample (adjusted): 2010M02 2017M12
Included observations: 95 after adjustments
Trend assumption: Linear deterministic trend
Series: ADI AMGNRLX BAX DFMGI TASI
Lags interval (in first differences): B0 to
Unrestricted Cointegration Rank Test (Trace)

Hypothesized		Trace	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.308005	73.54019	69.81889	0.0245
At most 1	0.197686	38.56349	47.85613	0.2780
At most 2	0.101271	17.63927	29.79707	0.5928
At most 3	0.051763	7.495814	15.49471	0.5208
At most 4	0.025424	2.446521	3.841466	0.1178
Trace test indicates 1 co-integratingeqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon et al. (1999) p-values
Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
Hypothesized		Max-Eigen	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.308005	34.97670	33.87687	0.0368
At most 1	0.197686	20.92422	27.58434	0.2808
At most 2	0.101271	10.14345	21.13162	0.7311
At most 3	0.051763	5.049293	14.26460	0.7356
At most 4	0.025424	2.446521	3.841466	0.1178

Max-eigenvalue test indicates 1 co-integratingeqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon et al. (1999) p-values

Appendix (5)

obs	ADI	AMGNRLX	BAX	DFMGI	TASI
2010M01	2633.37	2525.1	1478.23	1589.97	6252.55
2010M02	2703.56	2470.88	1518.06	1592.91	6437.5
2010M03	2908.49	2517.72	1547.13	1843.47	6801.01
2010M04	2777.12	2575.47	1591.94	1739.88	6867.97
2010M05	2604.17	2401.57	1450.28	1579.54	6120.52
2010M06	2514.01	2348.56	1396.29	1461.8	6093.76
2010M07	2545.8	2334.77	1393.92	1512.4	6283.73
2010M08	2498.52	2249	1418.61	1483.67	6106.42
2010M09	2673.19	2306.46	1444.76	1683.69	6392.39
2010M10	2816.11	2335.61	1462.42	1764.54	6353.88
2010M11	2729.87	2354.6	1437.94	1668.82	6318.5
2010M12	2719.87	2373.58	1432.26	1630.52	6620.75
2011M01	2586.75	2373.78	1448.85	1534.4	6358.03
2011M02	2588.9	2251.73	1430.77	1410.7	5941.63
2011M03	2607.12	2175.59	1424.64	1556.04	6562.85
2011M04	2695.5	2198.01	1404.9	1634.13	6710.56
2011M05	2639.14	2159.83	1346.66	1559.92	6735.98
2011M06	2704.19	2093.52	1319.71	1516.93	6576
2011M07	2619.7	2082.8	1291.66	1517.58	6392.13
2011M08	2616.02	2036.43	1258.25	1492.44	5979.3
2011M09	2533.41	1991.6	1165.75	1431.71	6112.37
2011M10	2501.43	2018.15	1147.66	1408.06	6224.3
2011M11	2444.86	1964.22	1163.11	1378.94	6104.56
2011M12	2402.28	1995.13	1143.69	1353.39	6417.73
2012M01	2453.98	1946.61	1139.83	1435.72	6626.04
2012M02	2611.13	1959.75	1148.64	1730.43	7271.82
2012M03	2553	1990.4	1152.45	1648.87	7835.15
2012M04	2503.82	1981.2	1152.79	1630.95	7558.47
2012M05	2441.03	1874.5	1139.58	1471.49	6975.27
2012M06	2447.62	1882.07	1126.71	1451.87	6709.91
2012M07	2506.23	1852.48	1099.82	1542.64	6878.19
2012M08	2561.61	1923.95	1086.32	1547.82	7139.01
2012M09	2605.41	1902.68	1087.33	1578.79	6839.83
2012M10	2672.43	1917.87	1057.91	1619.61	6791.04
2012M11	2674.56	1929.28	1048.81	1607.9	6533.14
2012M12	2630.86	1957.6	1065.61	1622.53	6801.22
2013M01	2881.78	2045.73	1085.14	1887.59	7043.55
2013M02	3044.89	2042.42	1089.94	1927.1	6998.33
2013M03	3025.33	2101.36	1091.58	1829.24	7125.73
2013M04	3273.63	1998.13	1104.17	2135.4	7179.8
2013M05	3562.88	2017.47	1196.46	2366.79	7404.12
2013M06	3551.24	1980.53	1187.79	2222.57	7496.57
2013M07	3847.43	1956.52	1194.9	2588.53	7915.11
2013M08	3734.55	1874.96	1188.27	2523.13	7766.52
2013M09	3842.98	1850.59	1193.93	2762.5	7964.91
2013M10	3845.72	1969.34	1201.79	2922.18	8044.47
2013M11	3849.84	2022.63	1208.55	2945.91	8325.28
2013M12	4290.3	2065.83	1248.86	3369.81	8535.6
2014M01	4673.07	2206.96	1294.33	3770.38	8760.62
2014M02	4958.66	2178.17	1372.67	4220.45	9106.55
2014M03	4894.42	2148.93	1356.91	4451	9473.71
2014M04	5044.62	2124.15	1427.33	5058.95	9585.22
2014M05	5253.41	2130.92	1459.34	5087.47	9823.4
2014M06	4551.02	2113.03	1427.61	3942.82	9513.02
2014M07	4976.16	2136.57	1471.7	4833.24	10214.73
2014M08	5082.72	2131.91	1472.16	5062.96	11112.12
2014M09	5106.29	2114.98	1476.02	5042.92	10854.79
2014M10	4861.45	2106.13	1444.13	4545.39	10034.92
2014M11	4671.29	2132.49	1428.66	4281.43	8624.89
2014M12	4528.93	2165.46	1426.57	3774	8333.3
2015M01	4456.82	2169.61	1424.37	3674.4	8878.54
2015M02	4686.19	2195.46	1474.81	3864.67	9313.52
2015M03	4467.93	2135.43	1449.97	3514.4	8778.89
2015M04	4647.12	2115.53	1390.62	4229.04	9834.49
2015M05	4527.63	2183.57	1363.67	3923.24	9688.69
2015M06	4723.23	2115.64	1367.83	4086.83	9086.89
2015M07	4834.22	2125.72	1331.66	4143.21	9098.27
2015M08	4493.93	2097.59	1299.24	3662.56	7522.47
2015M09	4502.79	2045.23	1275.89	3593.28	7404.14
2015M10	4322.04	2034.42	1250.37	3503.75	7124.8
2015M11	4236.39	1993.72	1232.57	3204.28	7239.93
2015M12	4307.26	2136.32	1215.89	3151	6911.76
2016M01	4054.37	2147	1187.1	2997.77	5996.57
2016M02	4351.41	2116.25	1178.23	3239.7	6092.5
2016M03	4390.42	2151.89	1131.11	3355.53	6223.13
2016M04	4543.53	2094.72	1110.53	3491.91	6805.84
2016M05	4250.2	2118.44	1111.56	3313.72	6448.42
2016M06	4497.64	2091.35	1118.37	3311.1	6499.88
2016M07	4575.34	2102.13	1155.62	3484.32	6302.17
2016M08	4471.01	2076.83	1142.21	3504.4	6079.51
2016M09	4476.32	2120.46	1150	3474.38	5623.34
2016M10	4300.18	2107.58	1148.83	3332.41	6012.22
2016M11	4308.77	2170.98	1174.12	3360.91	7000.18
2016M12	4546.37	2170.29	1220.45	3530.88	7210.43
2017M01	4548.82	2161.47	1303.7	3642.85	7101.86
2017M02	4552.09	2212.76	1349.67	3630.34	6972.39
2017M03	4443.53	2250.18	1355.99	3480.43	7001.63
2017M04	4522.56	2185.26	1335.67	3414.93	7013.47
2017M05	4427.3	2175.18	1319.75	3339.37	6871.24
2017M06	4425.4	2167.4	1310.04	3392	7425.72
2017M07	4566.15	2139.82	1327.81	3633.18	7094.17
2017M08	4468.41	2157.26	1302.46	3637.55	7258.64
2017M09	4397.4	2121.52	1283.46	3563.99	7283.01
2017M10	4479.6	2093.19	1276.69	3635.87	6934.37
2017M11	4283.07	2122.47	1283.71	3420.17	7003.97
2017M12	4398.44	2126.78	1331.71	3370.07	7226.32

 

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