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The Financial Integration An Empirical Analysis Of Pakistan Finance Essay

This paper investigates the problem of integration in financial markets in Pakistan. In rising economy variation of capital flow is mainly because of short-term funds, in domestic financial market, as stable market can help to obtain encouraging results. For this purpose we examine financial co-integration in the time period (2000 to 2008) by using monthly data of Call money Rate (CMR) and t-bills (TBR) of Pakistan and United Kingdom (UK),London Inter Bank Offered Rate (LIBOR) and the Pakistani Rupee/ Us dollar exchange rate. Results were obtained by utilizing multiple co-integration analysis and Granger causality analysis. Co-integration analysis results revealed that there is strong integration in domestic market of Pakistan and presence of long-run relation is also determined in domestic CMR with LIBOR. The study determined that co-movement between TBR of domestic and foreign market is long-term but not robust. Granger causality is found between majority of domestic and foreign market variables.

Introduction

Integration of financial market has significantly increased during the period of 1980s and 1990s. Wave of recent reforms and internationalization in emerging markets has enhanced linkage within various sectors of national and international markets. Some of the key factors behind this change are increased globalization of investment in order to get higher rates of return and diversification of risk internationally. Several researches are conducted on financial integration

Investigation by (Obstfeld 1994) explains that now the admittance towards capital markets has raised the prospects of Portfolio Diversification for the investors and also provides them with more potential opportunities to obtain a higher risk-adjusted rate of return. International Risk Sharing also leads economies towards plain consumption pattern during the periods of adverse shocks, better growth and welfare benefits. Strong integration is present in domestic call money market with the LIBOR and robust co-movement between domestic foreign exchange market and LIBOR (Jain & Bhanumurthy 2005). (Feldstein and Horoika 1980) used annual data of OECD countries for the period 1960-74, to test the financial market integration. High saving investment correlation indicate low capital mobility was determined by (Feldstein 1983, Tobin 1983, Penati and Dooley 1984, Dooley et.al. 1984, Sinn 1992 and Bayoumi 1990). Many direct and indirect methodologies were used which also concluded that capital is not perfectly mobile (Monadjem 1990) , further study by (Haque and Montiel 1994) gauges the level of monetary autonomy in struggling economies which resulted that the capital mobility level is quite greater.

Pakistan also implemented policies similar to many other emerging economies of financial sector reform and liberalization as early as the 1990s. Transformation and reform factors implemented a positive effect on the economy, which improved credit rating by appreciation of the currency .As a result of these reforms and deregulation of many sectors of the economy, the movements of important financial market indicators such as exchange rates, stock prices and interest rates became reflective of market forces. Dynamic linkage among exchange rate, stock and money markets was determined by (Khalid & Rajaguru 2006)

An open and well-integrated financial market helps to maximize the benefits of an increasing globalized economy. A well integrated financial market with rest of the nations in world can help country to level its consumption pattern and to enhance their productivity by attracting large number of investors. Developing and emerging nations can attain high level economies of scale, growth and better improved living standard for people.

The primary objective of the study is to investigate whether the financial liberalization undertaken in Pakistan since 2000 has created integration among domestic and foreign market. Further to analyze it there is any relation or co-movement among the rate of returns in Pakistan and UK. This study is structured as follows: section 2 discusses literature review related to the financial factors and cash dividends. Third section consists of theoretical framework followed by fourth section about data, model and methodology description. Data interpretation and results will be discussed in the fifth section. Sixth section concludes of the paper followed by the references.

2: Literature Review

Financial Markets integration is a process of mingling markets which makes them enough potent to allow union or risk adjustment on assets possessing related maturity. The financial growth is evident and admitted around the world which is resultant from deregulation, globalization and enhancement in information and technology sectors. Now the central banks of different countries across the globe are struggling to expand financial markets especially because of several crises faced during 1990s. Expectation was developed financial market to be better integrated in developed markets. Highly integrated financial markets also help investors to diversify and their individual’s portfolio risk by investing in different countries worldwide. (Sundarajan et al 2003) clarifies that the hierarchal structure of financial markets portray that domestic financial markets are at top and then followed by global and regional markets. Moreover, the advantages of domestic financial markets are difficult to match as compared to international financial markets.

The development of local financial markets also reduces the risks associated with excessive reliance on foreign capital, including currency and maturity mismatches (Prasad et al., 2003). Domestic integration provides an effective channel for the transmission of policy impulses (Pétursson, 2001; Bhoi and Dhal, 1998). Analytical arguments supporting financial openness rotate around main considerations such as the reimbursement of international risk sharing for smoothing consumption. Impact of capital flows on domestic investment and growth, enhanced macroeconomic discipline and increased efficiency as well as greater stability of the domestic financial system associated with financial openness (Agenor, 2001). The study by (Levine 2001) proved that international financial integration has a positive impact on overall productivity. The financial productivity also leads to the financial liberty which broadens the depth and breadth of financial markets. It leads to the increased effectiveness level of financial intermediation processes. It is obtained because of reduced costs and increased profits which are related with monopolistic and centralized markets. This overall approaches to lower cost of investment and enhanced resource utilization. (Levine, 1996; Caprio and Honhan, 1999). Worldwide integration also permits for the quick transfer of ideas and technology, which are highly critical ingredients of today’s knowledge-based economies. Hence, regional integration would provide significant advantages in the form of, lower prices for financial services, a more efficient, liquid and broader securities market and innovative financial products and services. Other advantages like industrial transformation of markets, cheaper corporate financing, more efficient allocation of capital and enhanced risk return frontiers.

Benefits of Financial market integration also create some risks and entails costs. Fear associated with integration were heightened by a series of several financial crises – including peso crisis of December 1994 in Mexico, collapse of the Thai Baht prompted the Asian crisis in July 1997, August 1998 Russian crisis, and finally the collapse of the Brazilian Real in 1999. Although uneven fundamentals played some role in all of the above crises, they have called attention to the inherent instability of financial markets and the risks that cross-border financial transactions can pose for countries with relatively fragile financial systems and not so strong regulatory and supervision structures. Study conducted by (Ayuso & Blanco 1999) suggested that during the nineties there has been an increase of the level of market integration between stock markets of different nations. Investigation conducted by (Bhoi and Dhal 1998) studied this issue by using monthly data up to 1997. This study also explained that domestic financial markets are integrated with each other but it is not the case when we check their integration with international markets. Severe increased in last two decades was noted due to International financial market integration, leading to financial interconnectedness not only of regions but also of geographically distant country

In most of the countries around the world the liberalization of capital account has been slowed down because of the fact that international financial integration which inflates capital inflows encourages the appreciation of real exchange rates. (Dornbusch and Park, 1994) The analysis of financial markets also brings out another policy aspect which shows increasing significance of foreign interest rates in the formation of domestic rates. The level of integration not only influences the behavior of domestic rates but also has serious impacts on the decisions of monetary authorities towards independent monetary policies formation.  (Agenor, 2001).

Dynamic relationship between South Asian Market (India, Sri Lanka and Pakistan) and with major developed markets (US, UK and Japan) was investigated by (Lamba 2003). Results indicated that the large developed equity markets influence market of Indian market and this relationship has build up in recent time. India does not influence the stock markets of Sri Lanka and Pakistan; rather Pakistan and Sri Lanka stock markets are relatively isolated. Comparison among emerging countries and developed countries financial integration was done by (Kumar Tambi 2008).Countries selected were USA, CANADA, UK, Singapore, Malaysia and India sample was selected for the purpose of the investigation. Several tests were used for co-integration; results disagree with existing literatures. Study also specified that world equity market is divided; where developed nations and emerging markets are in separate grouping. India was found positively correlated with all the markets, but this relationship is not highly positive. (Makin 1996) stated that there is a consensus that UIP remained unsuccessful to provide any information regarding the financial integration level. Casual relationship between monetary variables and equity return was determined by (Hasan & Javed 2009).Variable used were treasury bills, foreign exchange rate and the consumer price index. Results reveal that there is negative relation of exchange rate on equity returns. Similarly interest rate also has negative impact on equity returns. The eras of developments in financial market of Pakistan can broadly be segregated into following segments, 1947-1960, 1961-1970, 1971-1990 and 1991 to date periods. The Private Sector development was dominant in the period of 1947-1960. This development was overtaken by Public Sector during the years 1961-1970. The era of 1971-1990 shows further enhancement of public sector and the shrinking of private sector as all the banks were publicized.

Liberalization stance in Pakistan was witnessed 1990 onward, Government based market Securities came in to existence along with introduction of long term paper in 1992; long term yield curve started giving opportunity. Private Sector also brought many instruments which became reality in 1995 i.e. the issuance of first TFC. The actual growth became rapid in from year 200 with the introduction of long term instruments which were Pakistan Investment Bonds (PIBs). Under the umbrella of President Pervez Musharraf government, in 1999 onward shift occurred in from of state ownership of several industries and heavy regulation of the shift of private economy to privatization of a few state industries under heavy regulations. But still, slack in fiscal and monetary policies, infrastructural shortage, a poorly developed human resource support, and persistent market twist that benefit small privileged landowners, industrialists, and others undercut economic potential. Several factors affected the economic growth during this era firstly sensitive issue of Pakistan and India relations during 2000-2002 ,military tensions across the border with India where a million troops on the border was on vigilant, giving predictions of approaching (potentially nuclear) war. Secondly incident of history, 9/11 military action in neighborhood Afghanistan, brought a massive arrival of refugees from that country. Thirdly natural tragedy in 2005 affected the building economy 2005 earthquake across the northern areas of Pakistan. Despite these unfavorable events, Pakistan's economy showed growing trend, and economic growth picked up the pace towards the end of this period. This flexibility has escorted to a change in view of international institutions such as the World Bank, IMF, and the ADB for praising Pakistan's performance and economy in the face of adversity.

The reduction in government borrowings from domestic money markets has lead to the decrease in fiscal deficit.  This fiscal deficit reduction is also because of lowering of interest rates and growth in private sector lending to the businesses and consumers. Foreign exchange reserves continued to grow in 2003, supported by robust export growth and steady worker remittances. Credit card market continued its strong growth with sales crossing the 1 million mark in mid-2005 Foreign Direct Investment has raised sharply to US$ 949.4 million as compared to $376 million in 1999. Trade barriers were seen, no government permission is required to invest in most sectors to the economy; foreign investors can own their businesses 100% in the industrial and services sectors, and can take their entire capital, profits and dividends out of the country. External debt has declined from US$ 37.6 billion to $35.3 billion in 2004 - a decline of $2.3 billion in 5 years. Pakistan’s official currency, the rupee (Rs), has devalued against the U.S. dollar for over a decade. The official exchange rate was Rs4.76 and Rs9.85 to US$1 in 1970 and 1980, Rs21.61 and Rs53.65 to US$1 in 1990 and 2000, and approximately RS.86 to US$1 in Dec 2010.

Pakistan faced severe economic difficulties in the last few years particularly 2008 and has been able to achieve some stability in the last few months with the assistance of the IMF. The trouble in Pakistan started because of governance problems, postponed policy decisions for the sake of short term political expediency, the assassination of the most popular leader and the resulting political instability. Although different observers may have varied interpretations and readings of the Asian crisis in 1997-98 and the ongoing crisis I would attribute the following factors as contributing to the damage control in case of Pakistan.

Theoretical Framework

Literature view suggested one of the most extensively used popular test for co-integration developed by (Johansen and Juselius 1990) that tests for the presence of multiple long-run relationships. In this study we use this co-integration approach to examine the integration of returns in both domestic and foreign markets. One of the pre-requisites for undertaking the co-integration framework is that the variables that are expected to have long-run relationship should be non-stationary at their levels and should be stationary at the same order (or difference).The long-run relationship that we are examining here can be expressed as below:

Where ‘i’ and ‘i*’ are the return (interest rates) in domestic and foreign markets respectively and the constant term is a wedge parameter between interest rates possibly caused by a risk premium or other asset differences. The co-relation matrix is used to check the negative or positive relationship among the variables.

In order to find co-integration among financial markets Pakistan and United Kingdom we selected following TBR & CMR of bother countries, Exchange Rate (Rs/$) and LIBOR from year 2000-2009. Similar variables are investigated by (Adnan et. al., 2009; Hasan & Javed 2009; Rehman et. al., 2009). A short-term debt obligation backed by the government with a maturity of less than one year. Treasury Bills (T-bills) are the most money-making market security and short-term securities that are grown-up in one year or less from their issue date. Such securities are issued with three-month, six-month and one-year maturities. T-bills are among the one of essential way government raise money from the public. The only negative aspect to T-bills is that returns are not great because Treasuries are unusually safe. Call money Rate (CMR) is a short-term money market that lend at interbank rates to large financial institutions, such as mutual funds, banks and corporations to borrow and lend money at interbank rates. The loans in the CMR are very short, usually lasting no longer than a week and mostly used to help banks gather reserve requirements. LIBOR or the T-bill United Kingdom (TBRUK) yield plus basis points are used as reference rate by most of the swaps and floating rate contracts on the global dollar. The spread between LIBOR and T-bill yields over the life of a contract affects long-term financing costs for a growing number of financial instruments. LIBOR is higher. Similar variables are used by several studies conducted by (Bhoi & Dhal 1998; Jain & Bhanumurty 2005) .

In order to investigate the co-integration of Pakistan and United Kingdom financial market following hypothesis has been developed.

H1: No co-integration between CMR of both countries

H01: There is co-integration between CMR of both countries

H2: No co-integration between TBR of both countries

H02: There is co-integration between TBR of both countries

H3: No co-integration between TBRPAK and LIBOR

H03: There is co-integration between TBRPAK and LIBOR

H4: No co-integration between CMRPAK and LIBOR

H04: There is co-integration between CMRPAK and LIBOR

H5: No co-integration between ER of Pakistan and LIBOR

H05: There is co-integration between ER of Pakistan and LIBOR

H6: There is co-integration between CMR and TBR of Pakistan

H06: There no is co-integration between CMR and TBR of Pakistan

Data and Methodology

Our data consist of monthly rates of the entire domestic (Pakistan) and foreign (United Kingdom) variables. Variables selected for analysis are T-Bill Rate (TBR), Call Money Rate (CMR), Exchange Rate (Rs/$) and London Inter Bank Offered Rate (LIBOR). The sample period is January 1st 2000, to December 31st 2008, which includes 96 monthly observations for each variable used. All the variables are expressed in Natural Logarithm. The sources for data collection are State Bank of Pakistan (SBP), Economic Survey of Pakistan, and IMF CD-2009. All the estimations for tests are done in E-Views (6)

Unit Root Testing for Stationarity

Notion of a spurious regression was introduced by (Granger-Newbold 1974). According to researchers macroeconomic variables are in general non-stationary and involving variables in regressions at different levels of variables, the average significance tests were frequently misleading. In order to investigate to examine data in time-series the first step is to resolve the Stationarity of data and that shocks are only temporary and will revert to their long run mean. Time series with non-Stationarity has a trend and do not return to their mean, so it is always advised to convert these series into stationary. Similarly dependent and independent variable in a classical regression model should be free on non-Stationarity and errors to have zero mean and finite variance. Data having time series property is often examined through widely used tests Augmented Dickey Fuller (1980), Phillip-Perron and KPSS. Unit root test are conducted on the logarithm of the time series data.

Co-integration Analysis

As it’s discussed earlier that macroeconomic variables are normally non-stationary, so if two time series variables are non-stationary, but co-integrated, then at any point in time the two variables may drift apart. But yet there will be a tendency for them to retain a reasonable proximity to each other. In our case, the estimated model is the relationship that tends to tie together the six non-stationary variables in the long run. There may be more than one co-integrating relationship among co-integrated variables. Johansen test provides estimates of all such co-integrating equations and provides a test statistic for the number of co-integrating equations.

Granger Casualty Test

Testing for long-run relationship was first tackled by Engle and Granger in 1987.A bivariate test of co-integration is usually conducted through Engle and Granger test of Co-integration. In this test, first prerequisite is that all the series should be integrated of same order. So this test formally begins with the identification of integration order of the series. Then in the first stage of the test, one should estimate the normal OLS regression and obtain the residuals. In our model, we run the following equation in the first stage:

lni = a + b lni*t-1 + et

lni* = a + b lni-1 + et

Granger causality can be explained as if variable i does not help to predict the future values of variable i*, we say that i does not Granger-cause i* according to (Granger, 1969; Sims, 1972) Results of causality are determined by using t-statistics (single coefficient), or F-test, Log-likelihood ratio-test, Wald-test (multiple coefficients).

Empirical Results

Before discussing the results evaluated through different techniques, procedures and models if we just view the following Table 1(a), Table 1(b) and Table 1(c) we can note some interesting results. In table 1(a) all the variables are plotted to check any long run movement, it shows some relationship across the years.

Table: 1(a)

By analyzing table 1(b) we determined very close linkage in the domestic interest rate in case of both TBR and CMR. They are following almost same trend across the year which shows there is strong relationship among interest rate. Exchange Rate doesn’t show any visible relationship but yet so some similar fluctuation last years.

Table: 1(b)

Table: 1(c)

Table (c) show’s the similar result as shown in case of domestic market trend. There is strong interlinkage between TBR and CMR of foregin market . And collectively both variables’s are having close co-movement in trend with LIBOR.

Descriptive Statistics

Brief description of all variables (in logarithms form) used is given in Table 1. Table suggests low variability in the all rates during the period of our study. Similarly all the variables are right skewed. The analysis regarding descriptive statistics of t-bills of both countries, cmr of countries, exchange rate and libor is shown in Table 2.1. The mean value of TBR of both countries is 1.718 & 1.753 respectively and variation of TBR of both nations from mean value is 0.665 & 0.164. Over all the data is not of a high variation.

Table 2(a): Summary statistics of Variables

Rates

Pakistan

United Kingdom

Others

CMR

TBR

TBR

CMR

ER

LIBOR

Mean

1.718

1.753

1.523

1.537

4.077

1.152

Median

1.940

2.070

1.530

1.550

4.090

1.340

Maximum

2.560

2.560

1.780

1.850

4.160

1.950

Minimum

-0.300

0.190

1.200

1.140

3.950

0.110

Std. Dev.

0.665

0.659

0.164

0.188

0.046

0.602

Skewness

-1.268

-1.123

-0.115

-0.350

-1.455

-0.386

Kurtosis

3.779

2.922

1.937

2.120

5.445

1.668

Observations

96

96

96

108

96

96

Several ways are used to test co-integration among different variables, highly co-integrated markets should result highly correlated interest rates. In Table 2(b) co-efficient of correlation between all possible pairs of variables are shown. Results indicated that CMR of Pakistan is highly positively correlated with all other variables except exchange rate where degree of correlation is low. Exchange rate is negatively correlated with all variables except TBR of Pakistan where degree of correlation is low but positive. Remaining all variables TBR of both countries, CMR of UK and LIBOR are highly positive correlated.

Table 2(b): Coefficient of correlation

Rates

Pakistan

United Kingdom

Other

CMR

TBR

CMR

TBR

ER

LIBOR

Pakistan

CMR

1.000

 

 

 

 

 

TBR

0.909

1.000

 

 

 

 

United Kingdom

CMR

0.609

0.665

1.000

 

 

 

TBR

0.624

0.647

0.901

1.000

 

 

Other

ER

0.228

0.290

-0.110

-0.202

1.000

 

LIBOR

0.786

0.816

0.834

0.881

-0.080

1.000

This correlation matrix show high correlation but for caution it’s not necessary neither sufficient condition to prove high level co integration between the market of both countries. Variable used are interdependent thus correlation among them is high in certain cases but we will ignore multicollinearity, also we will not be using OLS and T-Statistics. As revealed from result that the correlation coefficients for majority variables were low (all < 0.7500) multicollinearity does not seem to pose a serious problem in our study.

After correlation matrix we perform further econometric tests to determine if the two markets are interlinked over the sample period. So first we perform a unit root test on all variables to determine the integration order of the series. Results in Table 3(a) indicate that data is non-stationary at levels using Augmented Dickey Fuller Test (ADF) with and without intercept, similarly with and without trend as well. By changing trend and intercept we can also get stationary series. Further test was conducted at first difference to make data stable. Result is Table 3 (b) revealed that all variable in data series are stationary at first difference using ADF test with both trend and intercept.

Table 3(a): Sequential unit root (ADF) tests at levels

Model

Llibor

LCMRPAK

lTBRPAK

lER

lCMRUK

lTBUK

With Trent & Intercept

-1.513

-2.18

-1.644

-0.947

-1.9

-1.95

With Intercept and no Trend

-2.7067

-1.98

-1.516

0.0198

-1.77

-1.83

With no Intercept & Trend

-1.33

-0.51

-0.151

2.053

-0.1959

-0.661

Result

Unit Root Exist

Unit Root Exist

Unit Root Exist

Unit Root Exist

Unit Root Exist

Unit Root Exist

Table 3(b): Sequential ADF unit root test on first differences

Model

ΔlLIBOR

ΔlCMRPAK

ΔlTBRPAK

ΔlER

ΔlCMRUK

ΔlTBUK

With Trend & Intercept

-7.077

-14.88

-7.101

-7.53

-9.48

-5.71

Result

NO Unit Root

NO Unit Root

NO Unit Root

NO Unit Root

NO Unit Root

NO Unit Root

Next step is to determine if the variables in data series have any long-run association linking all variables in data series. The co-integration test results are described in Table 4(a) to Table 4(f) and give a mix results. There is long run relationship among few variables at 95% confidence interval; whereas few of them are in long run relationship at 90% confidence which is considered comparatively weaker.

Table 4 (a). Co-integration between CMR of Both Countries

With no intercept or Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Trace Statistic

95%

Critical Value

90%

Critical Value

Max-Eigen Statistic

95%

Critical Value

90%

Critical Value

r=0

r>=1

5.94

12.32

10.48

5.85

11.20

9.47

r<=1

r=2

0.08

4.13

2.98

0.08

4.12

2.97

With an intercept and no Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Trace Statistic

95%

Critical Value

90%

Critical Value

Max-Eigen Statistic

95%

Critical Value

90%

Critical Value

r=0

r>=1

13.255

15.49

13.43

11.55

14.26

12.29

r<=1

r=2

1.702

3.841

2.7

1.70

3.84

2.70

Table 4 (b). Co-integration between TBR of Both Countries

With no intercept or Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Max-Eigen Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

4.97

12.32

10.47

4.31

11.22

9.47

r<=1

r=2

0.65

4.12

2.97

66

4.12

2.97

With an intercept and no Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Max-Eigen Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

13.87

15.49

13.42

9.63

14.26

12.29

r<=1

r=2

4.24

3.84

2.705

4.24

3.84

2.7

Table 4 (c). Co-integration between TBR of Pakistan and LIBOR

With no intercept or Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Trace Statistic

95 % Critical Value

90 % Critical Value

Max-Eigen Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

8.85

12.32

10.47

6.50

11.22

9.47

r<=1

r=2

2.35

4.12

2.97

2.35

4.12

2.96

With an intercept and no Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Trace Statistic

95 % Critical Value

90 % Critical Value

Max-Eigen Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

13.066

15.49

13.42

7.80

14.26

12.29

r<=1

r=2

5.261

3.84

2.7

5.26

3.84

2.70

Table 4 (d). Co-integration between CMR of Pakistan and LIBOR

With no intercept or Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Test Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

11.8

12.3

10.47

9.64

11.22

9.47

r<=1

r=2

2.15

4.12

2.97

2.15

4.12

2.97

With an intercept and no Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Test Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

18.22

15.49

13.42

14.64

14.26

12.29

r<=1

r=2

3.57

3.84

2.7

3.57

3.84

2.7

Table 4 (e). Co-integration between ER of Pakistan and LIBOR

With no intercept or Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Test Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

7.85

12.32

10.47

7.53

11.22

9.47 

r<=1

r=2

0.317

4.12

2.97

0.31

4.12

 2.97

With an intercept and no Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Test Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

9.93

15.49

13.42

8.69

14.26

12.29

r<=1

r=2

1.24

3.84

2.7

1.24

3.84

2.7

Table 4 (f). Co-integration between CMR & TBR of Pakistan

With no intercept or Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Test Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

33.47

12.32

10.47

33.47

11.22

9.47

r<=1

r=2

0.00177

4.12

2.97

0.0017

4.12

2.97

With an intercept and no Trend

Null Hypothesis

Alternative Hypothesis

Trace Test

Maximum Eigenvalue

Test Statistic

95 % Critical Value

90 % Critical Value

Test Statistic

95 % Critical Value

90 % Critical Value

r=0

r>=1

36.65

15.49

13.42

35.15

14.26

12.29

r<=1

r=2

1.5

3.841

2.7

1.5

3.84

2.75

These all tests are sensitive to the lag-length chosen. Using Akaike AIC and Schwarz SC criteria we have chosen the number of lags as two. After performing the johansen’s co-integration it may be noted that there is presence of long-run relationship between CMR and TBR of Pakistan. We found that the relationship between TBR of both countries seems to be weak (at 10 per cent level of significance). Whereas it’s determined that there is strong long-run relationship between CMR Pakistan and LIBOR as well. There is no long-run relationship between CMR of both countries and Exchange and LIBOR. The results indicate that while the domestic short-term money market is more integrated in comparison with the international financial market. There is no so robust integration between the domestic foreign exchange market and the foreign market. This may be due to the financial market reforms that are initiated in the money market.

Granger Causality Test

Since the entire variable in the series are not co-integrated we can test any possible linkage between all the variables during the sample period by using Granger causality method. Results suggested for the variables are reported in Table 5.

Table 5: Granger Causality between Domestic and Foreign Rates

Null Hypothesis:

Obs

F-Statistic

Probability

 

 

 

 

CMRUK does not Granger Cause CMRPAK

106

4.4537

0.01431

CMRPAK does not Granger Cause CMRUK

 

0.49268

0.61262

TBRPAK does not Granger Cause TBRUK

106

1.52949

0.2221

TBRUK does not Granger Cause TBRPAK

 

7.85873

0.00071

LIBOR does not Granger Cause LER

106

0.89694

0.41104

LER does not Granger Cause LIBOR

 

2.0554

0.13336

LIBOR does not Granger Cause TBRPAK

106

8.988

0.00026

TBRPAK does not Granger Cause LIBOR

 

3.19648

0.04508

LIBOR does not Granger Cause CMRPAK

106

6.36572

0.00249

CMRPAK does not Granger Cause LIBOR

 

2.49909

0.08723

TBRPAK does not Granger Cause CMRPAK

106

14.7819

2.30E-06

CMRPAK does not Granger Cause TBRPAK

 

5.63846

0.00477

Granger causality test reveals that CMRUK Granger causes fluctuation in CMRPAK with probability of 0.0141, which means that CMRUK helps to predict CMRPAK, whereas CMRPAK doesn’t Granger cause CMRPAK. Similar results are obtained for the TBR of both countries as well. LIBOR doesn’t granger cause ER but it causes and helps to predict TBR and CMR of Pakistan at 0.0002 & 0.0024 probability. Finally relationship concluded from co-integration test of long-run relationship is concluded from this test as well CMR & TBR of Pakistan Granger cause each other. From Table 5 we can conclude that foreign interest market Granger causes the domestic interest market. Finally the domestic market is highly co-integrated with each other.

Conclusion

Call money rate is found to be co-integrated with the LIBOR, which verifies that there exists a general stochastic movement among the domestic and foreign market returns. Result obtained from exchange rate not integrating with short-term market was determined by the previous study of (Khalid & Rajaguru 2006).This study investigated whether any co-integration existed amongst the sum of the foreign and domestic variables. We used moderate frequency data (monthly observations for the exchange rate and interest rate) and three different empirical testing procedures to determine if both the markets are co-integrated. Based on co-integration tests, the empirical results find support for a long-run relationship among four variables (TBRPAK & CMRPAK and CMRPAK & LIBOR). The Granger causality tests, however, provided some empirical evidence suggesting several causal relations among different variables. These results thus recommend that there is a link amongst domestic and foreign market. It is interesting to note that our results are consistent with the theoretical hypothesis mentioned in Section 1 of this paper. There summary is given in Table 6:

Table 6: Summary of Results

Items

Hypothesis

Results

H1

No co-integration exist between CMR of both countries

 Accepted

H2

No co-integration exist between TBR of both countries

 Accepted

H3

No co-integration exist between TBRPAK and LIBOR

 Accepted

H4

No co-integration exist between CMRPAK and LIBOR

 Rejected

H5

No co-integration exist between ER of Pakistan and LIBOR

 Accepted

H6

Co-integration exist between CMR and TBR of Pakistan

 Accepted

Given that both markets are linked, any internal or external shock would affect all three markets in a direct or indirect way. This is an important finding and could have important policy implications. For example, policy makers, while making a decision on internal policy should be mindful of the implications of their decision. On the flip side, policy makers could take a priori measure in one of the markets (e.g. interest rate) if an external shock is forthcoming and expected to hit a market (e.g. foreign exchange).

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