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DYNAMIC INTER-LINKAGES AMONG EQUITY MARKETS IN SELECT EMERGING ECONOMIES-An Econometric Study

Dr. Shalini Talwar | Dr. Asha Prasuna

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Published by Dr. R. Satish Kumar on behalf o f Institute o f Finance and International Management, No-8 (P)

From the Editor's Desk

This is the April 2015 - September 2015 Vol: 11.1 issue of FOCUS: The International Journal of Management.

In this issue we have included papers and articles on wide variety of topics such as Equity Market, Micro Finance, Higher Education and Mobile Consumers. We have also included a case study and book review. In the theme paper the authors take us through the dynamic linkages among the equity markets of Mexico, Indonesia, Nigeria and Turkey (MINT) and the correlations and causation among these markets.

Our sincere thanks to the authors, reviewers, editorial board members, and subscribers for their continuous support. Without their support the publication of this Journal would not have been possible.

Dr. R. Satish Kumar Chief Editor - FOCUS

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Title and Name of the Author Page No.DYNAMIC INTER-LINKAGES AMONG EQUITY MARKETS IN SELECT EMERGING ECONOMIES

Dr. Shalini Talwar | Dr. Asha Prasuna06-19

IMPACT OF MOBILE PHONE USAGE AMONG YOUNG ADULT Dr. A Sasirekha 20-27

IMPACT OF MICROFINANCE PROGRAMMES ON RURAL WOMEN'S INCOME, SAVINGS AND CREDIT

Mihir Dash \ Anand Patil | Shivaprasad G

28-33

INDUSTRY INTERNSHIP PROGRAM IN A REPUTED BUSINESS SCHOOL IN INDIA

Rahul Gupta Choudhury | Rupesh Kumar Sinha34-42

HIGHER EDUCATION IN INDIADr. Radha.R 43-47

DESIGNING A FRAMEWORK OF PERFORMANCE MANAGEMENT SYSTEM TO AN EDUCATIONAL INSTITUTION Sheeja Krishnakumar

48-55

EFFICIENT MARKET HYPOTHESIS(EMH) - The Case of India's Foreign Exchange Market

Dr. Ullas Rao

56-61

AN OVERVIEW OF INDIAN RETAIL AND ORGANIZED FOOD &. GROCERY INDUSTRY

Mr. P. V Vijay Kumar Reddy | Dr Cherukuri Jayasankaraprasad62-70

A STUDY ON COMPARATIVE ANALYSIS OF INDIAN STOCK MARKET WITH INTERNATIONAL MARKETS

Zohra Bi | Abdullah Yousuf | Aatika Bi

71-87

CONSUMER INSIGHT- How to Use Data and Market Research to Get Closer to Your Customer (Book Review)

Dr R. Satish Kumar88

IFIM International Journal of Management (

Cover Story

DYNAMIC INTER-LINKAGES AMONG EQUITY MARKETS IN SELECT EMERGING ^ ECONOMIES-An Econometric Study

Dr. Shalini Talwar*

Dr. Asha Prasuna**

ABSTRACT:The paper studies dynamic linkages among the equity markets of Mexico, Indonesia, Nigeria and Turkey (MINT) with an objective to investigate the correlations and causation among these markets. The authors have tested the time series for stationarity by applying the Augmented Dickey Fuller test and Phillip Peron test. Thereafter, uni-directional and bi-directional causality was tested using Granger Causality test. Finally, VAR framework was applied to study the response of each market to shocks in other markets. O n the basis of various empirical tests conducted, it was found that Mexican and Indonesian markets show a bi-directional causality. VAR statistics showed that all markets display contemporaneous correlation with them and account for 99% of their variance. The study is useful from the perspective of both, investors and policy makers.

1.0 INTRODUCTION:Equity Markets have always attracted investors seeking high returns. W ith the gradual lowering of barriers to cross border investment, many opportunities have opened up for investing in markets providing potentially higher returns at lower risk. W ith the advent of opening up of economies with globalization and technological innovations, investors prefer to hold portfolio diversified not only across different asset classes but also across different countries. To hold assets issued by different countries, it is essential to understanding the inter-linkages and interrelationships between various assets in different countries. The current paper attempts to analyze the inter-linkages between the equity markets of four emerging countries namely Mexico, Indonesia, Nigeria and Turkey with a view to understand whether the equity markets of these countries have sufficiently

Key words: Augmented Dickey Fuller test; correlation matrix; Granger Carnality test; MINT; Phi!iif>-Peron test; unit root; Vector Autoregression. JELG15, C61, C87.*KJ Somaiya Institute os Management Studies and Research, Vidyanagar, Vidyavihar(E), Mumbai - 400 077, Maharashtra, India, Email: [email protected]**KJ Somaiya Institute os Management Studies and Research, Vidyanagar, Vidyavihar(E), Mumbai - 400 077, Maharashtra, India, Email: [email protected]


mailto:[email protected]:[email protected]

low correlations to enable international portfolio managers to include them in their portfolios. The current study also investigates whether the four markets have any correlation with the world markets, as represented by S&.P 500. Further, the study applies Granger causality test to investigate the bivariate causality between each pair market under the study. The authors have also tested the four markets using Vector Auto-regression framework (VAR) to study multivariate causality and the transmission of shock from one market to another.The four markets have been chosen on the basis of a grouping proposed by Terence James O'Neill who had coined the acronym BRIO for Brazil, Russia, India and China (it had later been changed to BRICS after South Africa joined in). The new grouping has been assigned the acronym 'M INT and it has been ideated by O'Neill to represent economic, trade and investment alliance of the constituent countries. MINT stands for Mexico, Indonesia, Nigeria and Turkey, all four representing emerging economies that O'Neill has clubbed together on the basis of their potential to grow into the next economic powerhouses in the coming years.Since the idea of MINT has been proposed by the same person who had brought BRICS into focus few years back, the authors feel that the new grouping should be given due thought and importance. The current study takes a look into these four econom ies to analyse the ir perform ance on some key macroeconomic variables during the past few years. This has been done primarily to understand the nature and potential of these four economies. The paper attempts to analyse the equity markets of the four countries as represented by their leading indices with an objective to investigate the correlations and causation among these markets.The study of dynamic linkages among the equity markets of these four countries is important from the perspective of both, investors and policy makers. Investors and portfolio managers looking for newer avenues for international diversification would be interested in knowing whether equities from these four markets can be held together in a portfolio. The policy makers and regulators would be interested in understanding contagion risk i.e. the risk of transmission of shock from one market to another. In addition to the investors and policymakers, the study is expected to be helpful in preparing automated trading that is getting popular known as algorithmic trading (Algo trading). As algos are prepared based on simple mathematics and statistics related to market and tested for a performance by using tools like pattern recognition, back testing, stress testing tools and simulations, the findings of the current study can provide useful inputs for their preparation and implementation to execute algo trading strategies.To study correlation and causation among the four MINT

markets, the authors have tested the closing price levels and lognormal returns of the key indices representing each of the above four markets for stationarity by applying the Augmented Dickey Fuller test and Phillip Peron test. The four indices, found to be stationary at first difference, were then used to generate correlation matrix to test the extent of co-movement between the variables under study. Thereafter, unidirectional and bidirectional causality was tested using the Granger Causality test to identify the influential markets. Finally, VAR framework was applied to the time series to understand the dynamic linkages among these markets from a multivariate perspective. On the basis of various empirical tests conducted, the authors have found that Mexican and Indonesian markets show a bidirectional causuality, as confirmed by both, bi-variate Granger causality test and the multi-variate Block Exogenity Wald test. VAR fram ework, used to confirm the dynam ic in terrelationship amongst the variables under the study, shows that no market (out of the four markets) can be called as an influential market. All markets display contemporaneous correlation with themselves. In all case, the response all each market to shocks in the changes in them fades out within two to three days. On the basis of these findings, it can be stated with relative confidence that these markets or their equities can be used to create international portfolios that satisfy the condition of low correlation and negligible co-movement between the markets.The paper is arranged as follows: section 2 deals with the review of existing literature, section 3 deals with data description and methodology, and the results of empirical tests are described in section 4 followed by summary and concluding remarks in section 5.

2.0 LITERATURE REVIEWT he focus o f lite ra tu re review is on fo rm ulating an understanding of the macroeconomic scenario of the MINT economies. Such backdrop is essential to form a view whether the underlying economic fundamentals of these four countries justify the attention of fund managers, investors and regulators. Further, the authors have also reviewed the existing research papers related to the study of inter-linkages between equity markets across the world to serve as a basis for the current study.

The country of Mexico represents the second largest economy in Latin America. The successive regimes in Mexico have pursued growth-oriented policies, placing it in a in a favorable position in terms of key macroeconomic indicators and financial stability. GDP Annual Growth Rate in Mexico as reported by the Instituto Nacional de Estadistica y Geografia (INEGI) was at an average of 2.57 percent from 1994 until 2014. The service sector

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http://www.forbes.com/sites/

Cover Story

accounts for 62 percent and the industry constitutes 18 percent of the total GDP. Mexico's GDP was 1260.91 billion USD in2013, representing 2.03 percent of the world GDP. For the year2014, the Mexican economy expanded by 2.1 percent 1.8% short of the 3.9 percent forecasted by its government at the start of2014. Interest Rate in Mexico as reported by Banco de Mexico averaged 5.65 percent for the period spanning 2005 through2014 while the inflation rate in the country averaged 26.62 percent from 1974 until 2015. Mexico has been successful in bringing the inflation rate down to 3 percent in February of2015 from an all time high of 179.73 percent reported in February of 1988. O n the Foreign Exchange Reserves front, Mexico reached an all time high of 2937902650 MXN THO in January 2015, having averaged 322880992.22 MXN THO from 1960 until 2015. Mexico has been successful in attracting Foreign Direct Investment in the recent past. The infloe of FDI in averaged 2216161.99 USD Thousand from 1960 until 2014 and reached an all time high of 20994535.70 USD Thousand in the second quarter of 2013. Though all key indicators discussed above illustrate an encouraging economic growth picture, Economic Survey of OECD has found the per capita income in the country lower than expected and has stated that more reforms were required to make the growth story more plausible. Exhibit 1 shows the key macroeconomic indicators for Mexico for a period from 2007 through 2012.

“EXHIBIT 1 ABOUT HERE”

Rapidly advancing infrastructure, increasing middle class, declining poverty rates and large domestic consumer market are seen as the strengths of Mexican economy.The second MINT country, Indonesia, is the largest economy in South East Asia, a region that has attracted the attention of the entire world during the past decade. The annual GDP growth rate of Indonesia as reported by the Statistics Indonesia, averaged 5.40 percent from 2000 to 2014, with an all time high of 7.16 percent in the fourth quarter of 2004. In Indonesia, industry accounts for 46.5 percent of total GDP and services constitute 38 percent of the total GDP. GDP in Indonesia was 868.35 billion USD in 2013 representing 1.40 percent of the world GDP. Though GDP in Indonesia expanded 5.01 percent in the fourth quarter of 2014 over the same quarter in 2013, yet, for the year 2014 as a whole, the GDP was recorded as 5.02 percent having expanded at its slowest pace in five years on account of a slowdown in privateand public spending and lower exports. Interest Rate in Indonesia as reported by the Bank Indonesia, averaged 7.71 percent for a period between 2005 to2015, recording an all time high of 12.75 percent in December 2005 while inflation rate in the country averaged 11.52 Percent

from 1997 to 2014.The inflation rate in Indonesia stood at 6.29 percent in February 2015, having touched a peak of 82.40 percent in September 1998. O n the Foreign Exchange Reserves front, Indonesia reached an all time high of 124637.75 USD Million in August of 2011, having averaged 62077.02 USD Million from 2000 to 2015. FDI in Indonesia has risen at a high rate since 2010. O n the whole, the inflow of FDI in Indonesia averaged 56085 Billion IDR from 2010 to 2014 and reached an all time high of 78700 Billion IDR in the fourth quarter of 2014- On the whole, though the forecast for GDP was revised in the downward direction from the April 2014 projection by Asian Development Outlook (ADO), yet the general outlook for the economy had improved on the back of reform agenda of the new government and the improved outlook for exports. Exhibit 2 displays the key macroeconomic indicators for Indonesia for a period from 2007 to 2012.


The remarkably steady growth of Indonesia in the past few years has been attributed mainly to high domestic consumption and acceleration in exports of m anufactured products and commodities.The next MINT country Nigeria represents the largest economy in Africa with a GDP of 522.64 billion US dollars in 2013. This all-time high GDP of Nigeria represented 0.84 percent of the world GDP. The annual GDP growth rate of Nigeria, as reported by the Central Bank of Nigeria, averaged 6.13 percent from 2005 to 2014, with an all time high of 8.60 percent in the fourth quarter of 2010. For the full year 2014, Nigerian GDP increased to 6.22 percent and the statistical official forecast for 2015 stood at 5.54 percent.

Interest Rate in Nigeria as reported by the Central Bank of Nigeria, averaged 9.70 percent from 2007 to 2014, recording an all time high of 13 percent in November 2014 while the inflation rate in the country averaged 12.27 percent from 1996 until 2015. The inflation rate in Nigeria stood at 8.20 percent in January 2015 having touched a peak o f47.56 percent in January 1996. O n the Foreign Exchange Reserves front, Nigeria reached an all time high of 4166778.95 NGN Million in December 2014, having averaged 735237.22 NGN Million from 2000 to 2015. FDI in Nigeria has been quite erratic and has shown a downtrend since January 2010.On the whole, the inflow of FDI in Nigeria averaged 212684.58 USD Thousand from 2007 until 2014 and reached an all time high 824311.38 USD Thousand in July 2007.Exhibit 3 displays the key macroeconomic indicators for Nigeria for a period from 2007 to 2012.

8 I IF1M International Journal of Management I FOCUS April 2015 - September 2015

“EXHIBIT 3 ABOUT HERE”Nigeria faces downside risks as the future prospects of its economy depend strongly on the continued global economic recovery due to their high dependence on exports.GDP in Turkey as reported by the World Bank Group, averaged 197.27 USD Billion from 1960 to 2013 and reached an all time high of 820.21 USD Billion in 2013. The economy of Turkey expanded by 2.1 percent year-on-year in the second quarter of 2014, having slowed down from a revised 4.7 percent expansion in the preceding period. Sharp reduction in investment and private consumption were the main causes behind a less than anticipated expansion. GDP in Turkey at 820.21 billion US dollars in 2013 represented 1.32 percent of the world GDP. Interest Rate in Turkey as reported by the Central Bank of the Republic of Turkey, averaged 60.09 percent from 1990 to 2015 , reaching an all time high of 500 percent in March of 1994 while the inflation rate in the country averaged 36.87 percent from 1965 until 2015. The inflation rate in Turkey stood at 7.55 percent in February 2015 having touched a peak of 138.71 percent in May of 1980.On the Foreign Exchange Reserves front, Turkey reached an all time high of 150378.70 USD Million in July 2014, having averaged 44882.67 USD Million from 1981 to 2014.FDI in Turkey in Turkey decreased to an amount of 12918 USD Million in 2013 from 13224 USD Million in 2012. It had averaged 12415.09 USD Million from 2003 until 2013 and an all time high of 22046 USD Million in 2007. The FDI has been relatively low as compared to 2007-08 levels. Exhibit 4 displays the key macroeconomic indicators for Turkey for a period from 2007 to 2012.

“EXHIBIT 4 ABOUT HERE”Turkey's growth story during 2000s had been scripted by business sector dynamism, strong public finances and a sturdy banking sector. However, according to an OECD survey, the Turkish economy was still facing a threat of being slowed down by key macroeconomic parameters like high inflation, exchange rate volatility and low productivity, even though the external demand is strengthening.On the basis of the preceding discussion, it can be said that the MINT economies are showing some promise of a brighter future and have the potential to yield positive returns for fundmanagers and investors.Study of inter-linkages among equity and other financial markets has attracted the attention of researchers since many decades. Financial literature has been enriched by many studies focusing on study of causality and cointegration among financial markets in both, advanced as well as emerging economies. Studies by Dungey, Fry and Martin, (2003); Wong,

Penm, Terrell and Lim (2004) and Cheng and Glascock (2006) have studied inter-linkages between equity markets in different regions of the world. Roca (1999) used Granger causality test to study the linkages between the equity markets of Australia, U.S., U.K, Japan, Hong Kong, Singapore, Taiwan and Korea to find that Australian market was significantly caused by both, the U.S. and the U.K. markets. Gupta-Bhattacharya, Talwar and Sachdeva (2014) studied the integration amongst the select equity markets in South-east Asia. The study revealed that there were some cointegrating relationships amongst the six markets under study. Jorion and Schwartz (1996) and Cheung &. Ho (1991) have revealed through their studies that inter-linkages between equity markets are usually strong in case of countries with strong economic ties.Correlations are also important part of the studies related to inter-linkages between equity markets as low correlation between markets are essential for global portfolio diversification as seen in studies by Grubel and Fadner (1971) and Lessard (1973).

3.0 DATA DESCRIPTION AND METHODOLOGY3.1 Data DescriptionThe dollar-denominated daily closing prices of stock Indices of the four MINT countries; Mexico, Indonesia, Nigeria and Turkey have taken from the Bloomberg terminal for a period from January 2000 through November 2014 and used in the analysis. Dollar-denominated values of indices are used so that the four indices representing four different currencies are expressed in same monetary unit. The closing price of S&JP 500 has been taken from Yahoo Finance for the corresponding period. These are also denominated in dollars. The details of indices with their brief description are exhibited in exhibit 5.

“EXHIBIT 5 ABOUT HERE”Firstly, to investigate the nature of the five indices under study, their descriptive statistics have been generated. The authors have reported skewness, kurtosis, Jarque-Bera statistic and probability value for all stock market indices. The descriptive statistics are useful in providing elementary evidence about behavior changes in the time series under study and their respective distribution. Descriptive statistics of the five indices are displayed in exhibit 6.

“EXHIBIT 6 ABOUT HERE”It can be seen in exhibit 6, that all stock market indices under study have negative skewness. Such stock markets offer investors frequent small gains but at the same time expose them to few extreme losses. Kurtosis values also reveal that all stock market indices follow L ep tokurtic d is tr ib u tio n , w here large

IFIM International Journal of Management FOCUS April 2015 - September 2015 I 9

fluctuations are more likely to occur within the fat tails. The Nigerian market, represented by All Share Index exhibits the highest kurtosis at 11.96, kurtosis indicating a higher probability of having extreme values in this market. The Mexican stock market, represented by Mexican Bolsa IPC Index has the lowest value of kurtosis in the group at 8.8.Jarque-Bera statistic is used to test the null hypothesis that data is normally distributed. By using probability values of Jarque- Bera statistics, null hypothesis is rejected for all indices at 1% level of significance. The indices are not normally distributed and this shows inefficiency in the all four markets under study.

3.2 MethodologyThe paper investigates the inter-linkages among the stock markets of the M INT countries by conducting various econometric tests on the dollar-denominated daily closing price level time series of their main indices described in exhibit 5. The stock prices represent financial time series and may suffer from the problem of non-stationarity or existence of unit root at levels. A stationary series tends to revert to its mean value and oscillates around it within a more or less constant range i.e. it has a finite variance.Any significant statistical inference or accurate forecasting is possible only if the time series under study is stationary. If a non- stationary time series is regressed to other non-stationary time series, it may result into a spurious regression wherein a highly significant fit might be obtained even though the actual relationship between the two non-stationary variables may be non-existent. Therefore, before testing the time series for correlation and causation, it should be tested for stationarity. Tests for stationarity begin with regressing the variable on time, with the residuals from such a regression forming a new variable that is stationary. The authors have used two formal tests of unit root namely, Augmented Dickey Duller (ADF) [Dickey, D. and Fuller, W. (1979, 1981)] test and Phillip Perron (PP) (1998) test. For both tests the null hypothesis of unit root against the alternative hypothesis of stationarity is tested. The decision rule used is to reject the null hypothesis if the value of probability is less than 0.05 for the test statistic thus computed.The models that have been used to check the stationarity properties are as follows.

Model (1): Y , = A(o) +A(1)Y,., + e,Test Statistics

Ho : A(o) = A (l) = 0 f ,

A (l) = 0 t m

Dover Story

Test Statistics

Ho A(o) = A (l) = A(2) = 0 f 2

A (l) = A(2) = 0 f 3

A (l) = 0 t,A(0) = 0 => No constant/drift

A (l) = 0 => Presence o f unit root (1- r ) = A (l)

A(2) = 0 => No trend

r = ln(P,/P,-l)*100Where, r = return, P, = Price of the day , P,-l = Price of the previous day The extent of integration among the MINT markets has been determined by examining if the changes in one market cause changes in another market i.e. can the value of one market be forecast using the past values of another market? The test used to assess such causality is called Granger Causality test. It is an econometric test proposed by Granger (1969, 1988) to infer cause and effect relationship between time series under the study. The test is based on a simple logic that the effect cannot lead the cause. Granger causality tests the null hypothesis of 'x does not granger cause y' and 'y does not granger cause x.' W hen the probability of the test statistic is below 0.05, one series is said to 'granger' cause another series.W hen a series granger causes the other, it implies that the variable granger causing the other variable can be used to make a more accurate prediction of the other variable.The authors have further studied the dynamic linkages among the MINT equity markets by conducting the Block Exogeneity Wald test and computing Impulse response function, that are the m ain sum m ary s ta tis tics rep o rted u n d e r V ector Autoregression (VAR) framework . Since VAR simulates the responses of a market to shocks in the other markets, time zone ordering becomes a very important consideration. Time zone ordering used for the VAR model in this paper is Indonesia, Turkey, Nigeria followed by Mexico.

In the simplest VAR model, known as standard VAR, all the variables are considered to be endogenous variables. In a compact manner, a VAR model for k variables may be represented as:

= a 0 + A ^ - i ......................+ 4 py t - p + e t

Where yt = is a vector of all the k variables included in the VAR system, aO is k*l vector of intercepts, A l, A2,...,Ap are (k*k) matrix coefficients, Et is the k-dimensional vector of error terms and p is the optimal number of lag length.

Model (2) Y, = A(o) + A (l) Y,., + A(2), + e,

10 IFIM International Journal of Management | FOCUS April 2015 - September 2015

4.0 Results of Empirical Tests and Discussion As explained above, all five time series were tested for stationarity using the Augmented Dickey Fuller test and Phillip- Perron test. The stock indices are found to follow an I (1) process i.e. non-stationary at levels but stationary at first difference. The time series plot of the closing levels and lognormal returns of the five markets are exhibited in exhibit 7 to 11 The plots of lognormal returns fluctuate around a constant mean, i.e. they are mean reverting. This shows that they follow a stationary process. A non-stationary series exhibits wild fluctuations as seen in the plots representing closing levels of the markets under consideration.

“EXHIBIT 7 ABOUT HERE”“EXHIBIT 8 ABOUT HERE”“EXHIBIT 9 ABOUT HERE”“EXHIBIT 10 ABOUT HERE”“EXHIBIT 11 ABOUT HERE”The results of the ADF test are tabulated in exhibit 11 and the results of the Phillip-Perron test are tabulated in exhibit 12 for financial time series of the MINT countries and S&P500. The levels were tested with both, intercept only and trend &. intercept. The test statistic was found to be statistically insignificant as the probability values were greater than 0.05 for all markets in both instances. Thus the null hypothesis that the series had unit root could not be rejected.The series at first difference were also tested with both, intercept only and trend &. intercept. The test statistic was found to be statistically significant as the probability values were less than 0.05 for all markets in both instances. Thus the null hypothesis that the series had unit root could be rejected. Hence, all the financial time series under study were found to be stationary at first difference.Non-stationarity of all series at levels and stationarity at first difference can also be visually confirmed with the graphs illustrated in exhibits 7 through 11.

“EXHIBIT 12 ABOUT HERE”“EXHIBIT 13 ABOUT HERE”Exhibit 14 exhibits the correlations between the markets under study. A positive correlation exists between S&JP500 and all four MINT indices but it is too low for the authors to conclude that the correlation between any of the pair of the MINT markets could be due to their correlation with S&.P500. The correlation between MEXBO &. JCI, MEXBO &. XU 100, NGSEI & XU 100 and JCI &. XU 100 is positive but it is low enough to offer some short-run advantages of diversification. The negative correlation that NGSEI has with JCI &. MEXBO offers more lucrative investment opportunities.

“EXHIBIT 14 ABOUT HERE”The results of Granger Causality test are illustrated in exhibit 15. The test statistic and the related probability values of less than 0.05 indicate a bidirectional causality relationship between MEXBO & JCI stock indices. The low value of probability implies that the null hypothesis of 'MEXBO does not Granger Cause JCI1 and 'JCI does not Granger Cause MEXBO' can be rejected.For all other pairs of markets, the value of probability is high for the test statistic computed for the purpose. The high value of probability implies that the null hypothesis of each market not Granger causing another market cannot be rejected. Thus, there exists no causality from JCI to NGSEI or vice versa, from XU 100 to JCI or vice versa, from MEXBO to NGSEI or vice versa, from XU 100 to NGSEI and vice versa &. from MEXBO to XU 100 and vice versa. As explained in the preceding section, the causality indicates that the index that Granger causes another index can be used to make better prediction of the said index.

“EXHIBIT 15 ABOUTHERE”Since no market could be identified as an influential market using the G ranger Causality test, Vector Autogression framework was used by the authors to study the impact and transmission of shock from one market to another. As mentioned in the preceding section, time zone ordering for VAR may be listed as Indonesia, Turkey, Nigeria and Mexico. Exhibit 16 illustrates the results of various lag length criteria tested to determine the lag length to be used for the VAR model. It can be seen that four criteria have yielded the same result. “EXHIBIT 16 A BO U TH ERE”Since FPE, AIC, SC and HQ indicate a lag length of 1, the same has been used to compute the VAR model.Thus, the model computed is VAR (1) and it is specified as:

RETJCI = 0.113*RETJC1(-1) + 0.025*RETXU100(-1) + 0.001*RETNGSE(-1) + 0.040*RETMEXBO(-1) + 0.0003

RETXU100 = 0.014*RETJCI(-1) + 0.049*RETXU100(-1) + 0.018*RETNGSE(-1) + 0.042*RETMEXBO(-1)

-6.793e.-05

RETNGSE = - 0.009*RETJCI(-1) - 0.012*RETXU100(-1) + 0.003*RETNGSE(-1) + 0.018*RETMEXBO(-1) +

0.0003

RETMEXBO = 0.045*RETJCI(-1) - 0.002*RETXU100(-1) - 0.011*RETNGSE(-1) + 0.119*RETMEXBO(-1) +

0.0003

IFIM International Journal of Management ! FOCUS April 2015 - September 2015 11

Cover Story

Due to the problem of in built multicollinearity in the VAR model, instead of the model, certain summary statistics of VAR, namely block exogenity and impulse response function or variance decomposition are used for interpretation of VAR results. Before interpreting the summary statistics, the usefulness of VAR model needs to be evaluated. The VAR stability condition check reveals that no root lies outside the unit circle as shown in the exhibit 17. Thus, the VAR model computed in the current study satisfies the stability condition.

“EXHIBIT 17 ABOUT HERE”The usefulness of estimated VAR model also depends on the possible presence of serial correlation in the residuals. Exhibit18 displays VAR Residual Serial Correlation LM Test results. The computed LM statistic, which follows a Chi-square distribution, is statistically insignificant as p is more than 0.05. Thus, Null Hypothesis of no serial correlation at lag order h cannot be rejected. Thus the VAR model is stable and there is no serial correlation between the residual.

“EXHIBIT 18 ABOUT HERE”The result of Block Exogeneity Wald Test illustrated in exhibit19 shows that causality runs from XU100 and MEXBO to JCI. This can be seen from the value of probability below 0.05, because of which, the null hypothesis of XU 100 and MEXBO not causing JCI can be rejected. XU 100, NGSE and MEXBO also jointly cause JCI as deduced from the p value of 0.01. XU 100 is not caused byJCI, NGSE and MEXBO individually or jointly as the test-statistic is statistically insignificant (p>0.05). NGSE is also not caused by JCI, XU 100 and MEXBO individually or jointly as the test-statistic is statistically insignificant (p>0.05). MEXBO is caused by JCI as the p value is 0.0040.Thus, there exists bidirectional causality between RETJCI and RETMEXBO. There is no causality between any other pair of markets. Same results had been obtained by using the bivariate Granger Causality test illustrated in exhibit 15.

“EXHIBIT 19 ABOUT HERE”The impulse response function has been generated by following a time zone based VAR ordering as mentioned in the preceding section. Thus the Cholesky Ordering is: RETJCI RETXU100 RETNGSE RETMEXBO. Exhibit 20 displays the individual graphs of impulse response of one market on the other. The first row displays the changes in RETJCI in response to shocks in the changes in it and in RETXU100, RETNGSE and RETMEXBO. Column one of the exhibit displays changes in RETJCI, RETXU 100, RETNGSE and RETMEXBO in response to shocks in the changes in RETJCI.

In graph one of the first row, it can be seen that JCI has contemporaneous correlation with itself, which makes sense. It implies that RETJCI changes immediately in response to shocks in the changes in itself. Further, the impact of the shock dies down by the third day, as seen by the flat line beyond 3. The response of RETJCI to the shocks in other three begins at zero, showing no contemporaneous correlation. This is logical because, in the Cholesky Ordering RETJCI precedes the other three. There is a small change in RETJCI in response to shock in the change in RETXU 100. The response seems to last up to the third day. The response of RETJCI to shocks in the changes in RETMEXBO is also almost same as its response to RETXU 100. The flat line in graph 3 in first row indicates that RETJCI does not respond at all to shocks in the changes in RETNGSE. Similarly, row 2 displays the changes in RETXU 100 in response to the shocks in the changes in the other three markets. As in the case of RETJCI, RETXU 100 also shows contemporaneous correlation with itself only. Further, the impact of the shock dies down by the third day, as seen by the flat line beyond 3.It shows no response to RETJCI and RETNGSE. Further, its response to shocks in RETMEXBO is negligible.Row 3 displays the changes in RETNGSE in response to the shocks in the changes in the other three markets. As in the case o f RETJCI and RETXU 100, RETN G SE also shows contemporaneous correlation with itself only. Further, the impact of the shock dies down by the second day, as seen by the flat line beyond 2. Its response to RETJCI, RETXU 100 and RETMEXBO is of a very low magnitude. In fact its response to RETXU 100 begins below zero.Row 4 displays the changes in RETMEXBO in response to the shocks in the changes in the other three markets. As in the case of RETJCI, RETXU 100 and RETNGSE, RETMEXBO also shows contemporaneous correlation with itself only. Further, the impact of the shock dies down by the third day, as seen by the flat line beyond 3. Its response to RETJCI is of a very low magnitude and it is negligible in the case of RETXU 100 and RETNGSE.The responses depicted in the impulse response graphs confirm that there is no clear leading market among the four markets under study. Impulse graph shows that Indonesian market transmits shock to only Mexican market, that too of low magnitude, which disappears in a two to three days.Turkish market transmits shock to Indonesian market, but again of low magnitude, which fades off in a two to three days. It causes the Nigerian market to respond in reverse direction, confirming the negative correlation and has no impact on the Mexican market.N igerian market does not transmit shock to any market.Shocks in Mexican market impact the changes in Indonesian

12 1FIM International Journal of Management | FOCUS April 2015 - September 2015

market at a low magnitude for 2 to 3 days. It has an impact of lower magnitude on the other two markets, which fades off in 2- 3 days.


5.0. Summary and Concluding Remarks In this paper, the authors have analyzed the correlations, causality and dynamic linkages among the stock markets of Mexico, Indonesia, Nigeria and Turkey using the dollar denominated daily closing price data during the period from January 2000 to November 2014. Bidirectional causality was found between Indonesian and Mexican markets, indicating precedence, information content and usefulness for making better predictions. This implies that the two markets are interlinked to some extent. This inter-linkage may be attributed to the greater integration in the economies of the two countries. This can be confirmed historically by tracing the transmission of shock of ASIAN currency crisis directly from South-east Asia to the Latin American countries.Positive, but low correlations amongst the pairs of indices indicate some opportunities for diversification in the short run. Further, low correlation of each index with S&.P500 and beta values, obtained by regressing each index to S&P 500, shows that any correlation between the markets under the study cannot be attributed to their individual linkage with world market, as represented by S&.P500.Impulse response function, reported as a summary statistics for interpretation of VAR showed that none of the market acted as a lead for influential market for the other three markets. Each market showed a contemporaneous correlation with shocks in the changes in itself, and for all markets the impacted of shock appeared to die down within two to three days. Response of the Indonesian market to Mexican market of some magnitude and vice versa, confirms the existence of bi-directional causality between the two. The response of each market to the shocks in the changes in other markets has already been discussed in detail in the preceding section.Further, the markets under study are not completely isolated from each other, as exhibited by the results of the decomposition of variance forecast, which showed tha t no variance is completely accounted by its own innovation. However, at the 10- day horizon, the percentage of forecast error for all markets under study accounted for their own innovation to the extent of 99%. This confirms that the movement in each market under study is impacted to a very low extent and a short duration by all other markets under consideration. The influence of the markets on each other was found to be confined to below 1%.It needs to be clarified here that change in ordering of variables

in VAR system can substantially alter the outcome of the tests. It has been discussed in the preceding sections that the outcome of VAR framework is dependent on the sequence of the indices. The authors have chosen the sequence for the current study on the basis of time zones as it is considered to be the best way of sequencing of time series related to financial markets.The findings of the study can prove to be extremely useful in policy making for protecting the markets against the risk of transmission of shock, country asset allocation, making effective hedging &. portfolio diversification decisions and generating the code for algotrading related to the markets under the study.

6.0. ReferencesCheng, H and Glascock, JL, 2006 'Stock Market Linkages Before and After the Asian Financial Crisis: Evidence from Three Greater China Economic Area Stock Markets and the US’, Review o f Pacific Basin Financial Markets and Policies, Vol. 9, No. 2, pp 297-315.Cheung, YL &. Ho, YK, 1991 The Intertemporal Stability of the Relationships Between the Asian Emerging Equity Markets and the Developed Equity Markets', Journal of Business, Finance, and Accounting, Vol. 18, pp 235-254.Dickey, DA and Fuller, WA, 1979 'Distribution of the Estimators for Autoregressive Time Series with a Unit Root', Journal o f the American Statistical Association, Vol. 74, pp 427-431.Dickey, D and Fuller, W 1981, 'Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root1, Econometrica, pp 49.Dungey, M, Fry, R and Martin, VL, 2003, 'Equity Transmission Mechanisms from Asia to Australia: Interdependence or Contagion.7', Australian Journal of Management, Vol.28, N o.2, pp 157-182.Granger, CWJ, 1969, 'Investigating Causal Relations by Econometric Models andCross-Spectral Methods', Econometrica, Vol. 37, N o.3, pp 424-38.Granger, C. W. J, 1988 'Some Recent Developments in a Concept o f Causality', Journal o f Econometrics, Vol.39, No. 1/2, pp 36-67.Grubel, H. G and Fadner, K, 1971 T he Interdependence of International Equity Markets', Journal of Finance, Vol. 26, N o .l, pp 89-94.Gupta-Bhattacharya, N, Talwar,S and SachdevaJ, 2014 'Cointegration among Equity Markets: A study of Select South Asian Markets', Journal o f Global Economy, Vol. 10, N o.4 , accepted and communicated for publication.Jorion, P and Schwartz, E, 1986, 'Integration vs. Segmentation in the Canadian Stock Market', Journal of Finance, Vol. 41, pp 603-614.Lessard, D.R, 1973, 'International Portfolio Diversification: A multivariate Analysis for a Group o f Latin American Countries', Journal o f Finance, Vol.28, No.3, p. 619-33.Lutkepohl, H, 1993 ’Introduction to Multiple Time Series Analysis', Springer, New York.Phillips, P.C.B and Perron, P, 1988 Testing for a Unit Root in Time Series Regression', Biometrika, Vol. 55, pp 277-301.Schwarz, G, 1978 'Estimating the Dimension o f a Model', The Annals of Statistics, Vol. 6, pp 461- 464.Wong, WK, Penm, J, Terrell, R.D. and Lim, K.Y.C, 2004, T he Relationship between Stock Markets o f Major Developed Countries and Asian Emerging Markets', Journal o f Applied Mathematics and Decision Sciences, Vol. 8, No.4,pp 201-218.

Variance decomposition was com puted but not reported as there is a norm to report either impulse response function or variance decomposition as summary statistics to interpret the results of VAR.

IF1M International Journal of Management ! FOCUS April 2015 - September 2015 I 13

Cover StoryEX1HIBITS

Exhibit 1: Key Macroeconomic Indicators for Mexico (2007-2012)2007 2008 2009 2010 2011 2012

GDP growth (annual_%)___ 3.15 1.40 -4.70 5.11 4.04 3.98

Current account balance (BoP, current US$) -14267765239 -19561518936 -7850633825 -3284766643 -12261773418 -14642175872Foreign direct investment, net (BoP, current US$) -23371263010 -26711019270 -7133676990 -7582462230 -11168375700 7782338910Total reserves (% of total external debt) 43.78 46.10 49.99 49.49 52.10 47.08Inflation, consumer prices (annual %) 3.97 5.13 5.30 4.16 3.41 4.11Official exchange rate (LCU per US$, period average) 10.93 11.13 13.51 12.64 12.42 13.17Source: Compiled by the authors (Data source: databank.worldbank.org)

Exhibit 2: Key Macroeconomic Indicators for Indonesia (2007-2012)2007 2008 2009 2010 2011 2012

GDP growth (annual %) 6.35 6.01 4.63 6.22 6.49 6.26Current account balance (BoP, current US$) 10492590000 125583310.5 10628491686 5144284990 1685068003 -24073887520Foreign direct investment, net (BoP, current US$) -2253330000 -3418723399 -2628247483 -11106333135 -11528394762 -14309235970Total reserves (% of total external debt) 38.51 32.70 36.86 48.04 48.98 44.25Inflation, consumer prices (annual %) 6.41 9.78 4.81 5.13 5.36 4.28Official exchange rate (LCU per US$, period average) 9141.00 9698.96 10389.94 9090.43 8770.43 9386.63

Source: Compiled by the authors (Data source: databank.worldbank.org)

Exhibit 3: Key Macroeconomic Indicators for Nigeria (2007-2012)2007 2008 2009 2010 2011 2012

GDP growth (annual %) 6.83 6.27 6.93 7.84 4.89 4.28Current account balance (BoP, current US$) 27643445782 29154225208 13867630391 14459202642 12554056121 20352840955Foreign direct investment, net (BoP, current US$) -5167441548 -7145016212 -7029701168 -5133465521 -8025110602 -5564172195Total reserves (% of total external debt) 1343.76 1293.45 664.59 493.53 402.54 471.87Inflation, consumer prices (annual %) 5.38 11.58 11.54 13.72 10.84 12.22Official exchange rate (LCU per US$, period average) 125.81 118.55 148.90 150.30 154.74 157.50


Exhibit 4: Key Macroeconomic Indicators for Turkey (2007-2012)2007 1 2008 2009 2010 2011 2012

GDP growth (annual %) 4.67 0.66 -4.83 9.16 8.77 2.13Current account balance (BoP, current US$) -37781000000 -40438000000 -12168000000 -45447000000 -75092000000 -48507000000Foreign direct investment, net (BoP, current US$) -19941000000 -17211000000 -7110000000 -7572000000 -13698000000 -8944000000Total reserves (% of total external debt) 29.60 25.49 27.03 28.73 28.79 35.31Inflation, consumer prices (annual %) 8.76 10.44 6.25 8.57 6.47 8.89Official exchange rate (LCU per US$, period average) 1.30 1.30 1.55 1.50 1.67 1.80


5Variance decomposition was com pwted but not reported as there is a norm to report either impulse response /unction or variance decomposition as summary statistics to interpret the results of VAR.


Exhibit 5: Description of Stock Market IndicesName of C ountry

Name o f Index Description o f IndexMexico Mexican Stock

Exchange Mexican Bolsa IPCIndex(MEXBO)

The Mexican IPC index (Indice de Precios y Cotizaciones) is a capitalization weighted index o f the leading stocks traded on the Mexican Stock Exchange. The index was developed with a base level o f .78 as o f October 30, 1978.*

Indonesia Jakarta Stock Exchange Composite Index(JCI)

The Jakarta Stock Price Index is a modified capitalization-weighted index o f all stocks listed on the regular board o f the Indonesia Stock Exchange. The index was developed with a base index value o f 100 as o f August 10, 1982. Please look at CDR JA for exchange trading days.*

Nigeria Nigerian Stock Exchange All Share Index (NGSEI)

The Nigerian Stock Exchange All Share Index was formulated in January 1984 with a base value o f 100. Only ordinary shares are included in the computation o f the index. The index is value-relative and is computed daily.*

Turkey Borsa Istanbul 100Index(XUlOO)

The Borsa Istanbul 100 Index is a capitalization-weighted index composed o f National Market companies except investment trusts. The constituents o f the BIST National 100 Index are selected on the basis o f pre-determined criteria directed for the companies to be included in the indices. The base date is January 1986 and base value is 1 for the TL based price*

USA S&P 500 Standard and Poor's 500 Index is a capitalization-weighted index o f 500 stocks. The index is designed to measure performance o f the broad domestic economy through changes in the aggregate market value o f 500 stocks representing all major industries. The index was developed with a base level o f 10 for th e 1941 -43 base period *

‘http://www.bloomberg.com/Source: Based on authors’ data collection

Exhibit 6: Descriptive Statistics of Stock Market IndicesRETUSINDEX RETMEXBO RETJCI RETXU100 RETNGSE

Mean 6.14E-05 0.000390 0.000433 -7.49E-06 0.000354Median 0.000550 0.001388 0.001454 0.000856 0.000229Maximum 0.109572 0.151214 0.128918 0.200216 0.119186Minimum -0.094695 -0.115317 -0.163976 -0.263406 -0.110946Std. Dev. 0.013131 0.017256 0.018393 0.029300 0.015566Skewness -0.175506 -0.110389 -0.657291 -0.284605 -0.151364Kurtosis 10.70376 8.803537 10.55839 9.982604 11.96437Jarque-Bera 8776.955 4977.955 8686.372 7243.503 11873.31Probability 0.000000 0.000000 0.000000 0.000000 0.000000

Source: Based on authors’ calculations

Exhibit 7: The time series plot of the closing levels and lognormal returns of MEXBO

IFIM International Journal of Management FOCUS April 2015 - September 2015 15

http://www.bloomberg.com/

Cover Story

Exhibit 8: The time series plot of the closing levels and lognormal returns of JCI

Exhibit 9: The time series plot of the closing levels and lognormal returns of NGSE

Exhibit 10: The time series plot of the closing levels and lognormal returns of XU100

Exhibit 11: The time series plot of the closing levels and lognormal returns of S&P500 (USIndex)


Exhibit 12: Results of Augmented Dickey-Fuller Unit Root Test for MINT Stock M arket Indices and S&P500 (January, 2000- November 2014)

A u gm en ted D ick ey F u ller T est S tatistic

At Level with

InterceptProbability

*At Level

withIntercept & Trend

Probability* At First Difference

withIntercept


with Intercept & Trend

Probability*

M E X BO 1.017521

0.74912 .763047

0.2113 -54 .57774 0.0001 -54 .57774 0.0001

JC I0.370838

0 .91172.857165

0 .1769 -55.38123 0.0001 -55 .38244 0 .0000

N G SE I1.481944 0 .5429 1.187383 0 .9120 -31 .92464 0 .0000 -31.94133 0 .0000

X U 1001.582585

0 .49142.714243

0 .2307 -58.20621 0.0001 -58 .20075 0 .0000

S& P 5000.934991 0 .7776 1.596949 0 .7944 -46 .20030 0.0001 -46 .23919 0 .0000

‘ M acK innon (1 9 9 6 ) one-sided p-values ‘ Exogenous: Constant ‘ Lag Length: 1 (based on SIC, m ax lag -29) Source: B ased on authors’ calculations

Exhibit 13: Results of Phillips Perron Unit Root Test for MINT Stock M arket Indices and S&P500 (January, 2000- November 2014)

‘ MacKinnon (1996) one-sided p-valuesP hillips-Perron T est

At Level with

InterceptProbability* At Level

withIntercept &

Trend

Probability* At First Difference with

Intercept


withIntercept X

Trend

Probability*

M EX BO0.935327

0.7774 -2.561896 0.2981 -54.38473 0.0001 -54.37659 0.0000

JC I0.310108

0.9211 -2.774519 0.2069 -55.26082 0.0001 -55.26098 0.0000

NG SEI1.460929 0.5535 -1.142455 0.9203 -44.58654 0.0001 -44.58822 0.0000

X U 1001.627980

0.4681-2.775526 0.2065 -58.22375 0.0001 -58.21771 0.0000

S& P5000.961305 0.7688 -1.666159 0.7661 -65.10904 0.0001 -65.23754 0.0000

‘ Exogenous: Constant‘ Bandwidth: 13 (N ew ey-W est automatic) using Bartlett kernel Source: Based on authors’ calculations

Exhibit 14: Correlation Matrix for Daily Stock Returns for MINT Stock Market Indices and S&P500 (January, 2000- November 2014) __

RETUSINDEX

RETMEXBO

RETJCI

RETXU100

RETNGSE

RETUSINDEX 1RETMEXBO 0.0148 1RETJCI 0.0472 0.0240 1RETXU100 0.0021 0.0131 0.0133 1RETNGSE 0.0335 -0.0097 0.0209 -0.0348 1

Source: Based on authors’ calculations

IFIM International Journal of Management | FOCUS April 2015 - September 2015 17

Cover Story

Exhibit 15: Results o f Pair-wise Granger Causality between M INT Stock M arket Indices (January, 2000- November 2014)

Null Hypothesis F-Statistic Probability

InterpretationMEXBO does not Granger Cause JCI

4.06566 0.0172MEXBO Granger causes JCI at 1% significance level. This causality is bidirectional.

JCI does not Granger Cause MEXBO4.37137 0.0127

JCI Granger causes MEXBO at 1% significance level. This causality is bidirectional.

NGSEI does not Granger Cause JCI 0.04394 0.9570JCI does not Granger Cause NGSEI 2.60077 0.0744XU 100 does not Granger Cause JCI 2.69844 0.0674JCI does not Granger Cause XU 100 0.51726 0.5962

NGSEI does not Granger Cause MEXBO 0.20393 0.8155MEXBO does not Granger Cause NGSEI 0.95628 0.3844XU 100 does not Granger Cause NGSEI 1.10514 0.3313NGSEI does not Granger Cause XU 100 0.15272 0.8584XU 100 does not Granger Cause MEXBO 0.61441 0.5410

MEXBO does not Granger Cause XU 100 1.38785 0.2497Source: Based on authors’ calculations

E xhibit 16: L ag length criteriaLag LogL LR FPE AIC SC HQ0 35675.24 NA 2.05e-14 -20.16464 -20.15766 -20.162151 35742.24 133.7977 2.00e-14* -20.19346* -20.15858* -20.18102*2 35754.27 23.99877 2.00e-14 -20.19122 -20.12842 -20.168823 35760.41 12.23949 2.01e-14 -20.18565 -20.09494 -20.153294 35768.44 15.98469 2.02e-14 -20.18114 -20.06253 -20.138835 35784.02 30.97355 2.02e-14 -20.1809 -20.03438 -20.128646 35799.76 31.25575 2.02e-14 -20.18076 -20.00633 -20.118547 35810.96 22.22521 2.03e-14 -20.17805 -19.97571 -20.105878 35837.87 53.31456* 2.01e-14 -20.18421 -19.95396 -20.10208

* indicates lag order selected by the criterionLR: sequential modified LR test statistic (each test at 5% level)FPE: Final prediction errorAIC: Akaike information criterionSC: Schwarz information criterion

HQ: Hannan-Quinn information criterion Included observations: 3538Endogenous variables: RETJCI RETXU100 RETNGSE RETMEXBOExogenous variables: CSource: Based on authors’ calculations

18 IFIM International Journal of Management 1 FOCUS April 2015 - September 2015

Exhibit 19: Result of Block Exogeneity Wald TestExhibit 17: VAR Stability Condition Check

Inverse Roots of AR Characteristic Polynomi, 1.51.0

0.5

0.0-0.5

-1.0

-1.5-1.5 -1.0 -0.5 0.0 0.5 1.0 5

Source: Based on authors’ calculations1.5 -1.0 -0.5 0.0 0.5 1.0

Exhibit 18: Autocorrelation LM testLags LM-Stat Prob

1 27.80693 0.13332 22.76297 0.12023 13.86098 0.60914 15.67938 0.47565 30.96118 0.01366 31.93157 0.0102

*Probs from chi-square with 16 df.Source: Based on authors’ calculations

D e p e n d e n t v a ria t>le: R E TJC I

E xc luded C h i-sq d f P rob.

R E T X U 100 5 .8 86365 1 0 .0153

R E T N G S E 0 .0 0 27 3 5 1 0 .9583

R E T M E X B O 5 .2 98595 1 0 .0213

A ll 11.32791 3 0.0101

D e p e n d e n t va ria b le : R E T X U 10 0

E xc luded C h i-sq d f P rob.

R E TJC I 0 .271079 1 0 .6026

R E T N G S E 0 .3 08826 1 0 .5784

R E T M E X B O 2 .2 3 41 3 9 1 0 .1350

A ll 2 .850471 3 0 .4153

D e p e n d e n t va ria b le : R E T N G S E

E xc luded C hi-sq d f Prob.

R E TJC I 0 .380860 1 0.5371

R E T X U 100 1 .812114 1 0 .1783

R E T M E X B O 1.402290 1 0 .2363

A ll 3 .544282 3 0.3151

D e p e n d e n t va ria b le : R E T M E X B O

E xc luded C hi-sq d f Prob.

R E TJC I 8 .286562 1 0 .0040

R E T X U 100 0 .061390 1 0 .8043

R E T N G S E 0 .3 2 60 9 6 1 0 .5680

A ll 8 .582063 3 0 .0354Source: Based on authors’ calculations

EXHIBIT 20: Impulse Response Function of MINT Market at Time Zone VAR Ordering *

Response of RETJCI 10 RETJCI

Response to Cholesky One S.D. InnovationsResponse of RETJCI to RETXU100 Response of RETJCI to RETNGSE Response of RETJCI to RETMEXBO

o,» \ 013 •is 013-

cio- \ 010 010- OIO-

005 -V

005- 005. 003-

Response of RETXU100 to RETJCI Response Of RETXU100 to RE7XU100

t ' z ' j ' e ' s ' s T

Response of RE7XU100 to RETNGSE Response of REIXU100 to RETMEXBO03

02- 02\

« 0,

\

01 01

1 2 5 4 5 5 7 1 2 5 4 5 5 7 1 2 ' 3 ' 4 ' 5 ' 5 ' 7 1 2 3 4 3 5 7

Response of RETNGSE to RETJCI Response Of RETNGSE to RE7XU100 Response of RETNGSE to RETNGSE Response of RETNGSE to RETMEXBO015 P15 015

012 012 012-\ 0.2

000- 005- 005-\

005-

004- 004- 004-\

004-

POO \_1 2 3 4 5 • T

Response of RETMEXBO to RETJCI

1 2 3 4 3 5 7

Response of RETMEXBO to RETXU100 Response of RETMEXBO to RETNGSE

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IFIM International Journal of Management | FOCUS April 2015 - September 2015 ! 19

FOCUS Research Papers

IMPACT OF MOBILE PHONE USAGE AMONG YOUNG ADULT

AN EMPIRICAL STUDY

Dr.A Sasirekha

ABSTRACT:In the current digital era, the introduction of m odern technological gadgets has captured the attention of global population. The dependency of people on these technological gadgets is very high. The degree of dependency is leading to addiction of the tech-devices and services. Youth is the most vulnerable group among the population to be habituated towards one of such technological devices, mobile phone. The study was designed to study the usage of mobile phone by young adults i.e. the time spent, the purposes,Scproblems faced, if any and its impact on themBoth primary and secondary data were collected by the researcher. Using structured questionnaire and observation by the researcher, primary data was collected and secondary data collected from various published sources. Appropriate statistical tools were used foranalyzing the datato draw meaningful conclusion. The study revealed that though the respondents use mobile phones optimistically, no doubt, there too exists adverse impact on them.

INTRODUCTION:The word mobile has its origin from latin word mobilis means 'to move'. In Latin 'mobile' vulgus meant 'the common people, the fickle crowd'. It started to be used at the beginning of the 20th century as people's ability to move between social levels.The mobile phone came into existence in earlier 20thcentury.An article published in The Hindu (2013) stated that India, which is the world's second largest mobile phone market, next to China, in terms of subscribers, had a total of 86.16 crore mobile connections as on February 2013, data from telecom regulator Telecom Regulatory Authority of India (TRAI) showed (The Hindu, May5 2013). It is also noted by TRAI, the number of telephone subscribers in India increased from 962.63 million at the end of October, 2014 to 964.20 million at the end of November, 2014, thereby showing a monthly growth rate of 0.16 per cent. The mobile subscriber rate increased to 93.7 crore in November from 93.53 crore in October while fixed line customer base declined by half per cent

Key words: Mobile phones- usage- impacts •young adult.

‘Assistant professor, Department of Fashion Management Studies, National Institute of Fashion Technology, Chennai-113


'ti.

to 2.71 crore from 2.72 crore. It is also found that Tamilnadu stands second among the states subscribing mobiles next to Uttra Pradesh. The statistical handbook of Tamilnadu (2013) says that the number of mobileular phone in use as on 31st march, 2013 counts to 94,81,726. It is prominentnowadays that students may walk inside classroom without notebook or pen but not without mobilephones.Nevertheless,the mobile phones are used constructively.Though it is used by everyone, young adult is the most vulnerable group affected by mobile phones.lt has become one of their basic needs. Integrated online market research company JUXT had studied on mobile phone users in India and stated that 19-24 years age group users show highest 'penetration' and highest 'propensity'.Data published by the Statistical portal shows the projected growth in the number of mobile phone users' from 2011 to 2017. It rises from 581.1 million in 2014 to 730.7 million in 2017.A report published in quality writings online had discussed the negative effects of mobile phone usage. They had listed the following as major and most destructive effects: Wastage of time and money, negative effects on students Sueenagers, as a communication tool for crim inal purpose, negative effects on our health and environment.As thereare two sides for a coin, mobile phone too has its own virtue and shortcoming.Hence it was decided to study and understand both positive and negative impacts of mobile phones among young adult.

Framework of the studyThe following figure represents the framework of the research work. The frequently used mobile functions, frequency of using mobile and its impact were studied. Also, tested the existence of gender difference in the above mentioned variables.

Fig. 1. Framework o f the study

Review LiteratureCox (2003) studied on the health hazards arise from

usage of mobile phones. He had discussed on the possible health hazards arise from using mobile. He had differentiated the effects on the basis of short term and long term usage of mobile. He had mentioned the distraction of attention as short

term effects. The findings of the study says that there is evidence of biological effects due to usage of mobiles for long time but not that they are harmful to health.Poysti (2005) studied the factors influencing use of mobile phone during driving. They found that the old age people, female and person who travels small distance are not using mobile phone while driving, however, those who use mobile phones habitually are exposed to higher risks.Srivastava(2005) had studied on how social behaviour had been influenced by mobile phone usages. He had had discussed mobile phones are used as a culture &. political identity, as fashion, commercial identity in pocket, concern for privacy, safegaurding personal information, camera phones and for entertainments. He had studied on youths' behaviour also and results revealed that young people prefer SMS over voice call. They are more likely to personalise their mobile phones through ringing tones and wall papers.Hanley and Becker(2008) studied on phone usage and advertisement acceptance among college students. They conducted the survey for a period of four years. The results showed that incentives remain key motivating factor for mobile phone advertising accepatnce. They have also stated that the text messaging rem ains the m ost pervasive mobile content application. The results also state that theusage of mobile phone camera has increased significantly.Totten and Lipscomb(2008) studied mobile phone usage among college students. The study revealed that the college students use mobile frequently for text- messaging, time keeping, wake-up/ reminder functions, phone book, wireless- internet accessing, receiving and transmitting photos. They have also stated that there exixts a significant difference between men and women in the usage of mobile phones.Jha (2008) had studied 14 different functions to understand the mobile usage pattern among college goers in hyderabad, India. He had tested whether there exists any significant difference in the mobile usage pattern with respect to gender, monthly voucher amount and years of owning mobile. The study revealed that there exists a significant difference between the variables mentioned above. Also, results of the study can be used by the marketers to develop effective production and promotion mix for different segments.Priyanka (2010) studied on mobile phone usage amongyouth in India, with special reference to Kanpur and Mumbai. The study revelaed thatyoung people in both the cities used mobile phones for a variety of communication, news and entertainment needs, m aintain privacy and have private conversations. The quantitative analysis of the data revealed that young people in the two cities used mobile phones differently due to the differences in their lifestyles and socio-cultural factors.

1FIM International Journal of Management FOCUS April 2015 - September 2015 21

Additionally, the study found there were only a few gender differences in the use of mobile phones by young people, mainly in the use of mobile phones for entertainment purposes,Ahmed et al.(2011) studied the extent of addictive behaviour towards mobile phone usage among youngsters in pakisthan. They found that majority of them use under reasonable limits and only very few exhibit extreme addictive behaviour. The study revealed that the respondents are able to have definite priroty between their responsibilities &. commitment and the mobile phone usage.

Surajbasha et al.(2011) studied the buying behavior of customers in India. They did survey on the factors that influence a customer to purchase mobile and found that price is the most influencing factor. 57 percent of the respondents themselves choose their mobile phone. Their decisions are not influenced by others.Akanferi et al (2014) studied on about Mobile Phone usage among young adults in Ghana. They found that male adults spend more money on mobile phones per month than female adults.The adults between age group 20 and 25 years spend more time with their mobile.The results also revealed that spend on an average 4 hours ,51 minutes for instant communication through whatsapp, text message and so on. They spend 2 hours and 9 minutes per day, to listen music in their mobile. The study overall conclude that the adults are using mobile phone habitually for entertainment activities than for productive. Talukder, et al. (2014) had studied the impact of few variables such as perceived usefulness, ease of use, perceived credibility, trust, system quality and social influence on intension to use mobile phone in financial servises, among residents of Austalian capital territory.The result shows that the predicted variables have positive and significant im pact on the respondents behaviour intention and usage of mobile phones. The authors have also discussed the implications of those variables for providers, users, regulators, marketing and the economy.

Based on the above reviews, it is interesting to find that irrespective of the age group, mobile phones are used as a symbol of esteem. However, the usage pattern differs with the geographical region. Hence, the study was conducted to understand the mobile phone usage pattern of young adult in Tamilnadu,

ObjectivesTo study the common functions used in mobile phones, by the respondents.To understand the existence of significant difference between male and female, in using mobile phones.To examine the impact of mobile phone usage on the behavior of respondents.


MethodologyAn empirical study was conductedto understand the mobile function frequently used by the respondents. The respondents constitute young adults, age group between 18-25 years, in India.According to Sigelman and Rider (2009), young adult refers to age group between 18 and 25 years. The study used both primary and secondary data. Primary data was collected from respondents through a questionnaire and secondary data was collected from research journals, national survey reports, articles published in newspapers and so on. The questionnaire was so designed to understand the behavior of the respondents in using mobile phones. Primary data was collected through questionnaire from college students. The questionnaire was sent to around 100 respondents through email and 100 collected in person. From which, 183 valid questionnaires received. Convenient sampling was adopted. The data collected was analyzed through SPSS Version 22. Percentage analysis was used to present the demographic data of the respondents. Chi- square test is used to test the association between variables. Correlation coefficient was used to determine the relationship between variables. Friedman's test was used to fid the mean rank difference among the mobile functions used. Student's t test was used to test the existence of significant difference between male and female with respect to the mobile functions used.

Data analysisDemographic data of the respondentsThe following table presents dem ographic data of the respondents.

Table 1: Demographic dataParticular* Respondents inNumber PercentageGender

Male 102 56Female 81 44Total 183 too

Qualification PG 84 46Total 183 100No of mobiles owned

One 153 84Two 30 16Total 183 100

Frequency of changing mobile

Once in a year 15 8Once in two years 54 30Once in three years 12 7When required 36 20No response 66 36Total 183 100

Network usedAirtel 99 54Vodafone 48 26Others 36 20Total 183 100

Reason for using

Brand name 6 6Good connectivity 39 39Internet 3 3Offers 24 24Plans 15 15Reliable 3 3Service 3 3Updated technology 6 6Total 99 100

Mode of paymentPost paid 15 8Prepaid 168 92Total 183 100

Brand used

APPLE 9 05G10NEE 6 03HTC 6 031 PHONE 6 03INTEX 6 03KARBON 3 02MICROMAX 12 07MOTOG 6 03MOTOROLA 18 10NEXUS | 3 02NOKIA 45 25ONEPLUSO 3 02SAMSUNG 36 20SONY 15 08Total 183 100

S o urc e Prim ary clan


From table 1, it maybe inferred that 56% of the respondents are male and 46 % female. The table reveals that 56% of the respondents are studying UG and 46 % are studying PG.

Around 84 % of the respondents own only one mobile whereas only 16 % of them own two mobiles. It is also evident that 46% of them change their mobile phone once in two years and around 31% prefer to change their mobile when it is necessary. The reasons mentioned by the respondents include when their mobiles are broken, lost and if there is any functional issues. About 13 % changes their mobile once in a year and 10% of them change it once in three years.

A majority of the respondents (around 54%) use Airtel network and 26% use Vodafone. 20% use other network which includes Aircel, BSNL, Docomo, and Idea. The respondents prefer a particular network for its uninterrupted connectivity (39%), offers(24%), Plans(15%). Other reasons considered while selecting a network being internet, reliability, services and updated technology. Around 92 % of the respondents use prepaid network.

Nearly 23% of the respondents use NOKIA mobile phone and 20% use SAMSUNG. Other brands being used are Apple, Gionee, HTC and so on.

Average

The following table 2, presents average values of the Value of the mobile owned, Bill amount, Number of hours spent.

Table 2. Average valuesS. No Particulars Average

1 Value o f the mobile owned Rs. 13210/-2 Bill amount Rs.547 per m onth3 Num ber o f hours spent 8 Hours per day

Source: Primary data

From table 2, it is clearly understood that the average value of a mobile of the respondentsamounts to Rs. 13,210. The respondents spent Rs.547 per month, on an average.They are using their mobile 8 hours per day.

Significant difference between the mean ranks of mobile functions frequently used bv the respondents.

The following hypothesis was tested using Freidman'srank test.

Null hypothesis: There is no significant difference between the mean ranks of mobile functions frequently used by the respondents.

Table 3: M ean ranks o f m obile fun ctions

Functions M eanR ankChi-square

value P value

Browsing 9.28W hatsapp 9.10Making and receiving 7.83email 7.51Taking picture 7.47Texting 7.35Listening music 7.31 406 .186

more frequently than the female respondents. Similarly, there exists a significant difference in watching video and texting in mobile among female and male at 5 % level of significance. Female respondents watch more videos than male respondents. O n the other hand, excluding the above mentioned functions, there is no significant difference between female and male in using other mobilefunctions. There is also no difference among them in number of hours spent with mobile and monthly charges paid.

Impacts of using mobile phones

The following table indicates the physical problems confronted by the respondents.


Table 5 Physical problems

G ender Physical problems TotalYes NoFemale 12(15)

66(85) 78

Male 18(18)84

(82) 102Total 30 150 180

The following table indicates the psychological problems confronted by the respondents.

Table 6 Psychological problems

G enderPsychological

problems TotalYes No

Female 15(9)63

(91) 78

Male 9(19)93

(81) 102Total 24 159 180

The above table 5&t6 explainthat the male have more physical and psychological effect than the female respondents.

A question was included to understand whether the respondents can be without mobile. The responses for the question were presented below in table 7.

From the table 7 it is found that 42 % of the female respondents are not willing to be without mobile where as 38% of the male respondents have said that they are not willing to be without mobile. The respondents have listed the following reasons for not willing to be without a mobile: to call their parents as they are away from their residence (staying in hostel), source information, tool for communicating with their family members, friends.

Existence of association between gender and usage of mobile in class room

To study whether there exist an association between gender and usage of mobile in class room, the following null hypothesis was set.

Null Hypothesis: There is no association between gender and usage of mobile in class room.

To test this hypothesis, Chi-square test was administered and the results of the test are given in Table 8:

Table 8 Association between gender and usage of mobile in class room

GenderIntention to use in

class room TotalChi-

Squarevalue

Pvalue

Yes NoFemale 27 54 81

2.200 0.138Male 45 57 102Total 72 111 183


Since P value is greater than 0.05, the null hypothesis is accepted at 5 percent level of significance. Hence, there is no association gender and intention to use mobile in class room.

Mobile while driving

To test the following null hypothesis, Chi- Square test was administered.

Table 7: Willingness to be without mobile

GenderW illing to be without

mobile TotalYes No

Female 45(58)33

(42) 78

Male 63(62)39

(38) 102Total 108 72 180Source: Primary data

Null Hypothesis: There is no association between gender and usage of mobile while driving.

The following table 9 describe the practice of using mobile while driving vehicle.

Table 9: Association between gender and usage of mobile while driving

G enderUsing mobile while

driving TotalChi-

Square PvalueYes No valueFemale 9 69 78

Male 9 93 102 0.362 0.547Total 18 162 180


24 1IFIM International Journal of Management FOCUS April 2015 - September 2015

Since P value is greater than 0.05, the null hypothesis is accepted at 5 percent level of significance. Hence, there is no association gender and intention to use mobile while driving are independent.

f~nrrelation coefficient between price of mobile owned by the respondents and Monthly income of their parents.

To understand the degree of relationships between price of mobile owned by the respondents and Monthly income of their parents, the correlation coefficient was determined and the results are presented below.

V a r ia b l e C o r r e l a t i o n c o e f f i c i e n t

P r ic e o f t i r e m o b i le o w n e d b y

r e s p o n d e n t s X M o n t b ly i n c o m e o f t h e i r p a r e n ts .

0 .3 2 0 * *

** Correlation is significant at l%level.

The correlation coefficient between prices of the mobile owned by the respondents X Monthly income of their parents is 0.320 which indicates 32 percentage positive relationships and is significant at 1% level.

Findings and discussionFrom the study it is very clear that only 16% of the respondents own two mobile phones whereas rest own only one.They use a particular network for its connectivity, offers and discount plans. As the study group constitutes college goers, it is found that they limit their monthly expenses.Maximum used mobile brand is Samsung (20%) next to Nokia(23%). Average value of the mobile phone owned by the respondents isRs. 13,210. They spent Rs. 547. On an average they spent 8 hours per day using mobile phones.

It is very interesting to note that the young adults use mobile for browsing information relevant to their assignments. Next to that they use whatsapp frequently, which is the easiest way to communicate and share information with their friends and relatives.The least used functionis Bluetooth.As through whatsapp, they can share video, the usage had been reduced. Male uses Bluetooth more frequently than female. There is also significant difference between male and female in texting and

watching videos in mobile. Other than the above mentioned functions, there is no significant difference between male and female in using other functions of mobile. Other applications are used for booking taxi, movie tickets and so on.

There is also no significant difference among them in number of hours spent with mobile and monthly charges paid.

It is also found that there is no association between gender and intention to use mobile in the class room and also using mobile while driving. Ahmed(2011), in his study stated that only 31% of the respondents are disturbed by mobile phones while there are studying. From the study it is clear that, above 60% of them had said that they are not influenced to use mobile in the class room.The study reveals that 90 % of the respondents are not willing to use mobile phones while driving vehicle. 60 % of the respondent had said that they can be without mobile. However rest said, it may not be possible as they have to communicate to their family members, friends. Some of them said they are used to it.

Around 17 percent of the respondents said that they have encountered some physiological problems due to continuous usage of mobile. The problems mentioned are neck ache, eye irritation, shoulder pain. Only 12% of the respondents have stated that they are being affected psychologically. The reasons quoted include disturbance while talking to someone, irritation, anxious. It is also found that there is no associationbetween gender and the problems encountered by them.

ConclusionThe study revealed that mobile phones are being used fruitfully by the young adults. They use it for study purpose and connecting to their family and friends. However, it is evident that they are distracted form their work, studies and also come across some physical and psychological problems. The study also had shown light on addicted behavior of few respondents, who tend to use for more than 18 hours per day. It may be concluded that due care should be taken by the parents to protect their wards from such kind of destructive elements of mobile phone.

References1 * Ahmed, I., Tehmina, F.,

2. Akanferi, A. A., Aziale, L. K., & Asampana, I. (2014). An Empirical Study on Mobile Phone usage among Young Adults in Ghana: From the Viewpoint o f University Students. International Journal o f Computer Applications, 98(5), 15-21.

3. Cox, D. (2003). Com m unication o f risk: health hazards from mobile phones. Journal o f royal statistical society, 166(2), 241-246.

4. Hanley, M., &. Becker, M. (2008). Mobile Phone usage and advertising acceptance among college students; a four year analysis. International Journal o f Mobile marketing, 3(1), 67-80.

5. Jha, S. (2008). Understanding Mobile Phone Usage Pattern among College-Goers. The 1CFAI Journal o f Services Marketing, 6(1), 51-61.

6. Priyanka, M. (2010). Mobile phone usage amongyouth in India: A Case Study. University o f Maryland (College Park, Md.)

7. Poysti, L., Rajalin, S., &. Summala, H. (2005). Factors influencing the use o f mobileular (mobile) phone during driving and hazards while using it. Accident Analysis & Prevention, 37(1), 47-51.

8. Sigelman, C. K., & Rider, E. A . (2009). Life-Span Human Development. Boston: Cengage Learning.

9. Srivastava, L. (2005). M obile phones and the evolution o f social behaviour. Behaviour &. Information Technology, 24(2), 111-129.

10. Surajbasha, S., Lakshmanna, B.,&.Fayaz, K. (2011). Empirical study on buying ehaviour o f mobile phone in Indai. Asia Pacific Journal o f Research in Business Management, 2(6), 298-316.

11. Talukder, M., Quazi, A., & . Sathye, M. (2014). Mobile Phone Banking Usage Behaviour: An Australian Perspective. Financial Planning St Financial Instruments, 8(4), 83-104.

12. Totten, J. W., St Lipscomb, T. J. (2008). College Students' Usage o f Mobile Phones. International Journal o f Mobile marketing, 3(1), 67-80.

13. Krithika, M StVasantha, S.(2013). The M obile Phone Usage Am ong Teens andYoung Adults Impact O f Invading Technology. International Journal o f Innovative Research in Science, Engineering and Technology. Vol.2 (12). Pp. 7259-7265.

14. h ttp ://d a ze in fo .eo m /2 0 1 4 /0 7 /l l/m obile-internet'india-2014'349-m illion-unique-m obile- phone-users-70-traffic-mobile-india-shining- infographic/

14. http://ww w.statista.com /statistics/274658/ forecast-of-mobile-phone-users-in-india/

15. http://ww w.sm a

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