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Determinants of stock returns in Pakistan: Evidence from linear and non-linear models

Abstract

Using the quarterly data for an extended period of 1991Q1 to 2015Q4 of Pakistan Stock

Exchange (PSX), we have applied linear and non-linear (threshold) estimation techniques

followed by Error-correction Model (ECM) and Vector Auto-Regression (VAR) estimation

techniques, to empirically investigate that what determines the stock returns in Pakistan. The

findings suggest that GDP is insignificant determinant of stock returns in Pakistan.

Furthermore, results show that depreciation in local currency impacts the stock returns

negatively and it could be an outcome of fix exchange rate policy that Pakistan is adopting

since long. The results of ECM model suggest that deviation in stock returns is corrected by

itself irrespective whether, the stock is in high or low volatile regime. VAR results indicate

that the stock returns tend to create a temporary bubble only for one year towards economic

growth. From the policy perspective, the study concludes that the use of appropriate

monetary policy tools to reduce makers volatility may lead the market to gain peoples’

confidence for long term investment.

Key Words: Stock Returns, VAR, ECM, Threshold Auto-Regression (TAR)

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1. Introduction

What drives stock return, has become more prominent question since the seminal work of

Campbell and Shiller (1987) and then Campbell (1991). There has been a growing interest in

connecting the variation in realized stock returns with shocks to future discount rates and

cash flows. Moreover, over recent decades, the global stock markets have surged and

emerged for economic boom. The development of stock market in developing countries

observed unprecedented growth and fundamental shift in the financial structures and in the

capital flows, whereas empirical research has shown that development of stock market

boosted economic growth. The theoretical underpinning around the relationship of stock

prices and macroeconomic variables has now become important in studying the stock markets

of developing countries. Particularly during 1990s, various measures were taken for

liberalization, privatization, boost foreign exchange and the opening of the stock markets to

international investors.

Several estimation techniques and procedures have been applied to the stock return

behaviour. Among them significant work is dedicated towards the causal links between stock

prices and macroeconomic variables followed by the VAR estimation. However, very little

contribution is found to test the error-correction specification, and to the best of our

knowledge none of the studies so far uses non-linear (threshold) determinants and ECM in

case of stock return.

Keeping these underpinnings in mind, the study investigates the determinants of stock returns

linearly and non-linearly in threshold fashion by follow the linear and non-linear Error

Correction Mechanism (ECM). The independent variables use in this study are GDP,

exchange rate, investment, interest rate, money supply and political index as a proxy of law

and order situation in the country. Finally, study estimates a 5-variable VAR estimation to

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test the impulse responses of different shocks. The study uses the quarterly data from 1991Q1

to 2015Q4.

The results show that GDP is insignificant determinant of stock returns and is not co

integrating with other variables in the system. The results also show that an appreciation in

dollar impacts the stock returns positively. The non-linear ECM results show that any

deviation of stock return is corrected by itself irrespective whether the stock is in high or low

volatile regime and such correction is supported by interest rate and exchange rate also. The

VAR response shows that the value of stock return seems a temporary bubble only for one

year to economic growth. Further, investment and interest rates responses show people invest

in PSX for short period (one year) and for long term they prefer alternative investment

avenues.

2. Literature Review

Over the past two decades, the global stock markets have growing significance in global

economic growth. Especially, the development of stock markets in developing countries has

observed unprecedented growth and fundamental shift in the financial structures, and in the

capital flows. Recent empirical research has shown that the development of stock market is

vital to countries’ economic growth. For example; Levine and Zervos (1998) find that the

stock market development are crucial to country’s overall potential to exploit the increasing

economic share in the globalizing world.

In studying the stock markets of the countries that are still in the developmental phase, the

theoretical underpinning around the relationship of the prices of the stock as well as the

macroeconomic variables has become substantial. Particularly during 1990s, various

measures were taken for liberalization, privatization, to boost foreign exchange and the

opening of the stock markets to international investors. In the stock markets of the countries

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in the developmental phase a significant improvement has been observed related to the size

besides the depth of stock markets.

A significant work is also dedicated to the causality between stock prices and macroeconomic

variables, which remained inconclusive. For instance, an observation by Nishat and Saghir

(1991) from stock prices to consumption in Pakistan shows unidirectional causality.

However, (Mookerjee, 1988) observed the opposite results in case of India. Conventional

Granger casualty test has been used in these papers, which can be used only if the variables

are not co-integrated. But, if the existence of cointegration among different variables is

followed by the error Correction Model only then Granger Casualty test is the appropriate

procedure.

Theoretically, stock markets accelerate economic growth by boosting domestic savings and

increasing investment. More specifically, it provides avenues for rising companies to raise

capital at lower cost and minimize their financing dependency from banks, which ultimately

reduces the credit risk and possible credit crunch.

Further, another expectation from the free stock markets is that they can guarantee the best

outcome if their assets are used through some other agent which can take-over the firm and

can bring out more profits and gains by replacing the management. Perotti and Van Oijen

(2001) conclude that political risk stability attracts more equity investments. Hence, the

improvement of quality institutions is essential to attract equity investment and lead to stock

market development. La porta et al. (1997) find, in countries with low quality of legal system

and law enforcement, that the capital markets are narrower and firms are characterized by

more concentrated ownership. Further Demirgüç‐Kunt and Maksimovic (1998) show that the

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countries with higher effective legal systems grow faster. The results conclude that political

risk and institutional quality are strongly associated that lead stock market capitalization.

The empirical evidence for the short and long run relationships of stock return with

macroeconomic variables is mixed because various studies have used different datasets and

employed different estimation procedures. Frennberg and Hansson (1993) and Chappel

(1997) and Fama (1991) suggest that stock returns are determined by interest rate

expectations and future economic activity. Moreover, stock returns affect the wealth of

investors which in turn affects the level of private consumption and investment. Further the

results of the study conducted in Athens also show the existence of short term and long term

relationship between trade, money supply, inflation and the stock returns. However, no

relationship has found between the exchange rate and stock return. The studies conclude that

the stock index movements may also be varied and may have different responses across the

markets which are still developing rather than developed for variables that are

macroeconomic in nature.

The importance of stock markets and macroeconomic variables is equal between the dynamic

linkages. Instability in politics, turbulence of strong currencies and high debts from foreign

characterize the emerging economies. This investigation of dynamic linkages of the stock

markets with the macroeconomic variable is done by (Füss, 2002) . The markets of the

developing economies are different from those of the developed world due to the level of

efficiency at the information dissemination and the infrastructure of the institutions. Kizys

and Pierdzioch (2009) focus on time varying parametric models to see the asymmetric shocks

impact of markets. They found that asymmetric macroeconomic shocks do not significantly

explain the international co-movement of stock returns.

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A positive relation between stock returns and real economic activity is suggested by Chen et

al. (1986). Further Nasseh and Strauss (2000) study the relationship among stock prices and

international and national economic activity and report significant long-run relationship in

France, Italy, Germany , Netherlands, U.K and Switzerland. Ibrahim and Aziz (2003)

employ co integration and VAR techniques to find the dynamic linkages among stock prices

and macroeconomic variables for Malaysia. They conclude a positive long-run relationship

between stock prices and industrial production. Maghyereh (2003) supports the results of

Ibrahim and Aziz (2003) and considered industrial production an essential variable in

predicting stock prices in capital market of Jordan. Also, Muhammad, Hussain, Ali, and Jalil

(2009) use foreign exchange rate and reserves, investment, whole sale price index, money

supply, and industrial production index (IIP) to find their relationship between shares prices

in Pakistan stock exchange. They report the insignificant impact of IIP on stock prices in their

empirical results.

Nominal interest rate has negative effects on stock returns while level of economic activity

has positive effects on future cash flows. So, level of economic activity can affect stock

prices in the same direction ((Fama, 1990). Uddin and Alam (2007) report linear but

negative relationship between interest rate and stock prices in India. The empirical findings of

Zoicas and Fat (2008) show a weak relationship between stock market index and interest rate.

Similarly, Alam and Uddin (2009) also examine the relationship between interest rate and

stock market index for fifteen developing and developed economies. Their results suggest

that random walk model is not followed by any of stock market and for six countries interest

rates have significant negative relationship with stock returns.

Money supply effects on stock prices works through inflation which has direct positive

relation with growth rates of money. Consequently, an increase in the discount rate is a result

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of increase in money supply which shows a negative impact of money growth rate on stock

return Mukherjee and Naka (1995). In Athens, (Patra & Poshakwale, 2006) find long-run

and short-run equilibrium relationship between stock return and money supply. The empirical

results of Azeez and Yonezawa (2006) suggest that money supply has a significant influence

on expected stock returns. (Muhammad et al., 2009) also explore significant negative relation

of money supply (M2) with stock prices.

Through local currency depreciation, exchange rate effects on stock returns can easily be

traced out. This results in cheaper exports which increase the value of exporting goods firms

who get benefit from depreciation. In contrast, domestic currency depreciation (appreciation)

leads to decrease (increase) value of importing firms. Influence of stock prices through the

trade effects and inflation has also been shown in the study of Geske and Roll (1983). Among

others, a Bayesian VAR model is used by Granger et al., (2000) to examine the relationship

between stock return and exchange rate. They conclude that in Indonesia and Japan no

relationship exist between the stock return and exchange rate. However, such relationship has

been seen in Korea, Taiwan, Thailand, Malaysia and Hong Kong. Further, no short-run and

long-run relationship has been found between the exchange rate and stock return Patra and

Poshakwale (2006). Ming-Shiun et al., (2007) examine the dynamic relationship among

exchange rates and stock return for East Asian countries, Hong Kong, Japan, Korea,

Malaysia, Singapore, Taiwan and Thailand. Their results show a significant causal

relationship for Japan, Hong Kong, Thailand and Malaysia. A more recent study conducted

by Yau and Nieh (2009) provide empirical evidence for existence of long term equilibrium

relationship among dollar/Yen and stock return for Japan and Taiwan. Other studies that

report causal relationship between stock return and exchange rate are Aydemir and

Demirhan (2009), and Muhammad et al. (2009).

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Whether or not, the overall economic activity (usual measure is GDP) has an impact on the

stock return, is still unclear in the existing literature. For instance Levchenko and Mauro

(2007) find insignificant effect of GDP on stock return, whereas Thapa and Poshakwale

(2010) conclude that GDP growth is significant but not for all countries. Moreover, the

relationship of stock return and aggregate economic activity sometimes provide conflicting

results (Diebold & Yilmaz, 2008). They find positive relation between stock and GDP,

whereas stock market volatility is negatively related with GDP. Sohail and Zakir (2011)

conducted study in Pakistan and examined the relationship among PSX-100 index and five

macroeconomic variables. The results show positive impact of GDP, inflation and exchange

rate while a negative impact of money supply and interest rate on stock returns. Singh et al.

(2011) conclude that GDP and exchange rate have a negative impact on portfolios, while

money supply has negative effect on stock returns in Taiwan. Hussain et al. (2012) find a

significant positive association among macroeconomic factors and stock prices, whereas rate

of exchange shows negative and insignificant results in case of Pakistan.

Keeping the above literature in mind, our study proposes to conduct a first order VAR

analysis of PSX index returns with other macro-economic variables, like GDP, interest rate,

foreign exchange, inflation, money supply, investment and political index as proxy for law

and order situation of the country to investigate shock responses of the macroeconomic

variables on the stock return. Secondly, our study also proposes, if there is any shock

deviation from the steady state then shock correction variables will be identified through

Error Correction Model following the (Engle & Granger, 1987). As there is number of ups

and downs in the stock market over the time, so liner ECM specification may be misleading,

so following the W. Enders and Siklos (2001) non-linear ECM through Threshold Auto

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Regression (TAR) ECM model will be applied. The study uses the quarterly data from

1991Q1 to 2015Q4.

3. Methodology

The study drives the determinants of stock market index (PSX-100 index). The independent

variables are GDP, interest rate, exchange rate, inflation, money supply, investment and

political index as a proxy of law and order situation in the country. The methodological

underpinning starts from simple linear relationship to non-linear Threshold Auto-Regressive

(TAR) regression to investigate whether stock index behaves differently over a threshold

value or not. Further, linear and non-liner Error Correction Model (ECM) is estimated to

determine the response of the different variables in case of deviation from the equilibrium

state. Finally, variance decompositions for stock returns based on short-run Vector Auto-

Regressions (VAR) is estimated, which enables to assess if the variation in realized stock

index returns is associated with different macro-economic variables. The study uses the

quarterly data from 1992-Q1 to 2015-Q4. Keeping the above propositions in mind, this

section explains the empirical testing procedure of determinants of stock index, its Co

integration, and shock response to other variables.

The following long run empirical relationship of stock index is estimated through OLS

estimation.

PSXt = α0 + α1PSXt-1 + α2GDPt + α3ERt + α4intrt + α4MSt + α5Investt + α6political indext + εt

(3.01)

Where PSX is Pakistan Stock Exchange index returns as dependent variable and its lagged

variable as independent along with Gross Domestic Product (GDP), Exchange Rate (ER), real

interest rate (intr), Money supply (MS), investment (invest) and political index1.

1Data on political index is available from 1997 and no variation is observed across the quarters. This may be the reason of its insignificant contribution in all the specification. Keeping this in mind it is dropped for further investigation here.

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After liner long run estimation for the determinants of PSX index, the objective of the study

is to estimate threshold model, for which first step is to estimate long run relationship as

given above in equation 3.01 and save the residuals. In the equation αi are the estimated

parameters and εt is disturbance terms that may be serially correlated.

In practice , the estimated residuals are sorted in ascending order and 15 percent highest and

lowest observations are excluded so a good number of observations should be above and

below the threshold value and within the band of 70 percent observations, each observation is

likely to be the threshold value. For the remaining observations, estimate as many equations

as observations by taking every observation as a potential threshold value by using the

following specification, and get the residual sum of squares.

PSXt = α0 + α1 It PSXt-1 + α2 (1-It )PSXt-1 + α3 ItGDPt + α4 (1-It )GDPt + α5 ItERt + α6

(1-It )ERt+ α7 Itintrt + α8 (1-It )intrt+ α9 ItMSt + α10 (1-It )MSt+ α11 ItInvestt + α4 (1-It )Investt +μt (3.02)

Where It is indicator function such that

I t={1 if εt−1≥ Threshold value0if εt−1<Threshold value (3.03)

The observational value, which has the smallest residual sum of squares, contains the

consistent estimate of the threshold Chan (1993). Based on the threshold value, two regimes

are formed, one likely is active regime in which stock markets have more volatility and other

is passive having less volatility. After identification of threshold value, the coefficients of the

response in two different regimes are estimated2.

Further to the long run relationship, the study employs to test the linear and non-linear

(Threshold) error correction mechanism in case system goes off-equilibrium. For such testing

2 For the estimation, E-VIEW 8.0 package is used.

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procedure the pre-requisite of ECM is the existence of co integration. The study uses Engle

and Granger (1987) co integration to test whether or not stock index holds long run

relationship with other macro-economic variables in case of Pakistan.

The first requirement of co integration is that all co integrating variables must be non-

stationary of same positive order of integration that is greater than one. The diagnostic test for

order of integration is Augmented Dickey Fuller (ADF) test. The lag length of dependent

variable is determined by Akaike Information Criterion (AIC). If all the variables are non-

stationary of same order of integration, which is likely that all variables are I(1) then OLS

estimation of following specification is carried out and get the residual series.

PSXt = α0 + α1PSXt-1 + α2GDPt + α3ERt + α4intrt + α4MSt + α5Investt + εt (3.04)

For the existence of long run relationship among above stated relationship, the order of

integration of residual series of integrating variables must be less than one, than the actual

variables. For instance if actual variables are I(1) than ε̂ t is stationary I(0). The standard ADF

critical values are used to detect unit root.

The existence of long run relationship based on co integration procedure, suggests that if

there is any deviation from long run equilibrium then system will be restored to the

equilibrium again. For such restoration at least one of the variables of the system must adjust.

This adjustment mechanism is called ECM and it can be estimated as a two-step procedure by

OLS or as a system by maximum likelihood technique.

The existence of long run linear relationship among PSX and other macro-economic variables

leads to error correction model. The linear specification of VECM is

C (L)∆ X t=−αβ ' X t+εt(3.05)

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Where Xt= [PSXt, PSXt-1, GDPt, ERt, intrt, MS t and Investt]. If the elements of X are non-

stationary and their linear combination is stationary then relationship implies that there exists

a long run relationship among elements of X and vector [−̂∝1−̂β ] is cointegrating vector of

X. The following specifications are used for linear ECM

∆ PSX t=α 01+α1 µt−1+β1 ∆ PSX t−1+β2∆ ERt−1+ β3 ∆ intr t−1+β4 ∆ MSt−1+β5 ∆ Invest t−1+ε1 t(3.06)

∆ ERt=α 01+α1 µt−1+β1∆ KSEt−1+β2 ∆ ERt−1+β3 ∆ intr t−1+β4 ∆ MSt−1+β5 ∆ Invest t−1+ε2 t(3.07)

∆ intr t=α 01+α1 µt−1+ β1 ∆ PSX t−1+β2 ∆ ER t−1+β3 ∆intr t−1+ β4 ∆ MSt−1+ β5 ∆ Invest t−1+ε3 t(3.08)

∆ MS t=α 01+α1 µt−1+ β1 ∆ PSX t−1+β2 ∆ ER t−1+ β3 ∆ intr t−1+β4 ∆ MS t−1+ β5 ∆ Invest t−1+ε4 t(3.09)

∆ Invest t=α01+α1 µt−1+β1∆ PSX t−1+β2 ∆ ERt−1+β3 ∆ intrt−1+ β4 ∆ MS t−1+β5 ∆ Invest t−1+ε5 t(3.10)

where β are policy variables and α are adjustment coefficients, and ut-1 is the error correction

term which is obtained as residual from least square estimation of long run relationship from

equation 3.04.

However if the responses are different across two regimes then above mention ECM is mis-

specified. In this case non-linear threshold error correction specification can be used which is

given as follows;

∆ PSX t=α 01+α11 I t μ t−1+α 12 (1−I t ) μt−1+β1 ∆ PSX t−1+β2∆ ERt−1+ β3 ∆ intr t−1+β4 ∆ MS t−1+β5 ∆ Invest t−1+ε1 t(3.11)

∆ ERt=α 01+α11 I t μt−1+α12 (1−It ) μ t−1+β1∆ PSX t−1+β2 ∆ ERt−1+β3 ∆ intrt−1+ β4 ∆ MSt−1+β5∆ Invest t−1+ε2 t(3.12)

∆ intr t=α 01+α11 I t μt−1+α 12 (1−I t ) μ t−1+ β1 ∆ PSX t−1+β2 ∆ ERt−1+β3 ∆intr t−1+ β4 ∆ MSt−1+ β5 ∆ Invest t−1+ε3 t(3.13)

∆ MSt=α 01+α11 I t μ t−1+α 12 (1−I t ) μt−1+ β1 ∆ PSX t−1+β2 ∆ ER t−1+β3 ∆ intr t−1+β4 ∆ MSt−1+ β5 ∆ Invest t−1+ε4 t(3.14)

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∆ Invest t=α01+α11 I t μt−1+α12 (1−It ) μ t−1+β1 ∆ PSX t−1+β2 ∆ ERt−1+β3 ∆ intr t−1+ β4 ∆ MS t−1+β5 ∆ Invest t−1+ε5 t(3.15)

Where α 1 i and α 2 i are the response coefficients to capture deviation from long run

equilibrium. It is the indicator function which assumes value 1 if deviation takes place in

regime one and zero otherwise. In both equations the adjustment coefficients are different for

two different regimes (above and below threshold). Generally threshold values are unknown

so they will be estimated along the parameters values using the method of (Hansen, 1993).

Finally, the study investigates the shock response of different variables through VAR. The

study uses following 6 variables VAR model specification

Xt = A(L)Xt-1+ Ut(3.16)

Where,X t=[ PSX t , GDPt , ERt , intr t ,MS t Invest t ]' is the vector of endogenous variables.

Usually impulse response functions are the interpretable results out of the VAR estimation. A

shock to i-th variable is the impulse response. It affects the i-th variable alongside it is

transmitted through the dynamic (lag) structure of the VAR to all of the other endogenous

variables. The effect of a one-time shock to an innovation on current and future values of

endogenous variables is also traced by an impulse response function. The interpretation of the

impulse response is very simple and to the point if the innovations are not related

contemporaneously. The i-th innovation is simply a shock to the i-th endogenous variable.

Innovations may have a common component which is unassociated with any specific variable

and therefore are correlated very often.

4. Results and Discussion

This section elaborates the results based on the methodology outlined in the previous section.

The table 01 shows the results linear estimation of the determinants of PSX with the

independent list of the variables that include GDP, exchange rate, interest rate, money supply,

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investment and political index. The results show that all variables are significantly

contributing to PSX index returns except political index and GDP. The political index is

insignificant may be due to data limitation, while GDP has no significant contribution in

determination of PSX index. Number of earlier studies have also found similar results.

Table 01: Results of OLS estimation

Variable Coefficient Probability

Constant 14770.54*** 0.0001

PSXt-1 0.482068*** 0.0000

GDP 0.000370 0.6364

Exchange rate -195.5156*** 0.0000

Interest Rate -27581.89*** 0.0000

Money Supply 0.002509*** 0.0005

Investment -0.001211*** 0.0168

Political Index -52.10090 0.1516

Adjusted R-squared 0.986958

F-statistic 779.3705

Prob (F-statistic) 0.000000

*** Indicates significance at 5% level** Indicates significance at 10% level

Table 02 shows the results without political index variable as it has limited data. All variables

are significant except GDP. Exchange rate, interest rate and investment have negative impact

on PSX index returns, while money supply increases the value of the index. The results can

be justified that an appreciation in dollar (depreciation of local currency) makes exports

cheaper and imports expensive. This impacts the PSX- index returns positively if volume of

exports is higher than imports otherwise vice-versa. In Pakistan, between two off-setting

effects, negative effect prevails because volumes of imports are higher than exports. The

negative impact of interest rate and investments are usual as saving or investment through

bank channel becomes more attractive than risky investment. The variable of investment is

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significant at 10% level of confidence and negative. Money supply has positive and

significant effect of the index showing an increase in money supply (loose monetary policy)

by either lowering the interest rate or through open market operation.

Table 02: Results of OLS estimation


Constant 3523.356*** 0.0004

PSXt-1 0.699530*** 0.0000

GDP -4.97E-05 0.9402

Exchange rate -59.60355*** 0.0022

Interest Rate -12845.04*** 0.0011

Money Supply 0.001360*** 0.0235

Investment -0.000864** 0.0695



Prob(F-statistic) 0.000000

*** Indicates significance at 5% level** Indicates significance at 10% level

Before going to discuss the results of threshold models, it is pertinent to mention here the

estimated threshold values at which the PSX-index returns behaviour changes. The estimation

of threshold models is simply performed by OLS if threshold values of the variables are

known. Otherwise, in practice 15 percent highest and lowest observations of the concerned

variables are excluded and within the band of 70 percent observations, each observation is

considered to be the threshold value. For each of these likely threshold values, specified

above is estimated. The threshold value, for which the residual sum of squares is the

minimum, is consistent estimate of the threshold Chan (1993).The regimes above and below

threshold value are high volatility and low volatility of the PSX index respectively. The

results are shown in table 03. GDP is insignificant for both low and high volatile regimes of

the index. Exchange rate and investment affect the PSX index returns negatively when it

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cross the threshold value (more volatile) meaning that people are risk averse and do not

prefer to risky investments (when it becomes riskier) rather prefer to invest on other stock or

in currency market. Further these variables are insignificant below the threshold value

meaning that public are indifferent. Interestingly money supply is insignificant above and

below threshold value which contrary to the linear estimated results.

Table 03: Results of Threshold estimation


Constant 2925.818*** 0.0001

It PSXt-1 0.700973*** 0.0000

(1-It)PSXt-1 0.711544*** 0.0000

It GDP -0.001909 0.2742

(1-It)GDP 0.000937 0.5694

It Exchange Rate -56.98069*** 0.0001

(1-It) Exchange Rate 0.983591 0.9833

It Interest rate -8319.393*** 0.0022

(1-It) Interest rate -34894.82*** 0.0126

It Money Supply 0.001978 0.2125

(1-It) Money Supply 7.23E-05 0.9623

It Investment -0.001388*** 0.0011

(1-It) Investment -0.000431 0.3573



Prob(F-statistic) 0.000000

*** Indicates significance at 5% level

** Indicates significance at 10% level

None of the study, at least in Pakistan, has tested such threshold estimation of PSX using

non-linear specification. However in the presence of non-linear cointegration among the

variables the linear ECM is mis-specified whereas, in general, this equation has not been

estimated as non-linear ECM. Enders and Siklos (2001) used threshold models to test

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cointegration and to specify non-linear ECM. Keeping these underpinnings in mind this study

has tested linear cointegration and non-linear ECM model.

The pre-requisite of cointegration is that all the variables should have greater than zero and

same order of integration, so the study first of all tested the presence of unit root in the series

through standard ADF test procedure and results are reported in table 04. The lag length has

been selected on the basis of AIC. The results show that all the series of different

macroeconomic variables are non-stationary at their level, but stationary at first difference,

excepot GDP. So we can conclude that the order of integration of all the series is one. Here

GDP is non-cointegarting varaible and insginificant in the linear estimation so it is dropped

for cointrgation followed ECM.

For cointegration, the order of integration for the linear combination of integrating variables

should be less than the actual variables’ order of integration. To test this, first step is to

estimate long run relationship between surplus as dependent variable and lag of debt as

independent variable and save the residual series. The second step is to check unit root

characteristics of the residuals. Rejecting the null hypothesis of non-stationarity implies

variables to be co-integrated. The results of ADF statistics are shown in table 04 that all the

variables are non-stationary at their level and stationary at first difference except GDP that is

non-stationary both at level and first difference. So in the ECM model we have to drop the

GDP as it does not contian cointegrating realtionship in the estimation.

Table 04 Results of Unit Root TestVariables Level 1st DifferencePSX -1.318170 -4.627909***

(0.8773) (0.0017)GDP -1.462358 -0.299844

(0.8348) (0.5749)Exchange Rate

-2.046079 -3.380419***

(0.5681) (0.0142)Interest Rate -0.583204 -7.979079***

(0.4622) (0.0000)

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Money Supply 3.232443 -3.745692***(0.9996) (0.0242)

Investment -2.573260 -8.699348*** (0.2934) (0.0000)

Probability values are reported in parentheses. The lag length is selected on the basis of Akaike Information Criterion (AIC). ***Indicates significance at 5% level** Indicates significance at 10% leve

The results of linear and non-linear ECM are shown in tables 05 and 06. The existence of co

integration suggests error correction mechanism that elaborates which variable adjusts to

restore the system into equilibrium against any discrepancy that leads the system to

disequilibrium. The liner results show that any shock in the PSX is corrected by itself,

interest rate and money supply. Further any shock in the exchange rate is corrected by itself.

The money supply shock is corrected PSX index. Moreover, investment shock is corrected by

itself and PSX index.

Table 05: Estimates of Linear Error Correction Mechanism Variables Constant α0 PSXt-1 ERt-1 Interest ratet-1 MSt-1 Investmentt-1

PSX 216.32*** 1.028*** -0.04 -17.73 -11390.92 0.00*** 0.00(0.02) (0.00) (0.51) (0.76) (0.09) (0.01) (0.51)

ER 0.60*** 0.00 0.00 0.39*** 7.10 0.00 0.00(0.00) (0.10) (0.32) (0.00) (0.56) (0.22) (0.72)

Interest rate

0.00 0.00 0.00 0.00 0.18 0.00 0.00

(0.90) (0.42) (0.85) (0.86) (0.10) (0.86) (0.49)MS 111872*** 41*** 42.55*** 11149 -226032 -0.19 -0.01

(0.00) (0.02) (0.01) (0.46) (0.90) (0.12) (0.81)Investment

39895 6.42 -52.02*** -28384 -1247011 0.03 -0.71***

(0.28) (0.81) (0.05) (0.23) (0.65) (0.85) (0.00)Probability values are reported in parentheses. ER and MS stands for Exchange rate and Money supply respectively *** Indicates significance at 5% level** Indicates significance at 10% level

The non-linear ECM results are shown in the table 06. The results show that any deviation of

stock return from equilibrium state is corrected by itself irrespective whether the stock is in

high or low volatile regime and such correction is supported by interest rate and exchange

rate also. Furthermore, exchange rate shocks are also corrected by itself in both regimes,

18

whereas interest rate shock is self-corrected only when stock return is in high volatile regime.

There is no evidence that money supply and investment return to their equilibrium by

themselves, rather other variables plays role to restore the system in equilibrium.

Table 06: Estimates of Threshold Error Correction Mechanism

Variables Constan

t

αi1 αi2 PSXt-1 ERt-1 Interest

ratet-1

MSt-

1

Investmen

tt-1

PSX 107.40 0.85**

*

0.33**

*

-0.11 -

170.49***

-16854** 0.00 0.00

(0.41) (0.00) (0.04) (0.23) (0.04) (0.08) (0.1

1)

(0.95)

ER 0.63*** 0.00** 0.00* 0.00 0.33*** 4.56 0.00 0.00

(0.00) (0.06) (0.00) (0.16) (0.00) (0.70) (0.2

1)

(0.82)

Interest

rate

0.00 0.00 0.00** 0.00 0.00 0.17 0.00 0.00

(0.94) (0.17) (0.09) (0.98) (0.62) (0.12) (0.8

9)

(0.43)

MS 90174*

**

53.98 43.48 37.60**

*

10591.5

8

-263801.40 -

0.18

-0.01

(0.00) (0.14) (0.12) (0.02) (0.46) (0.87) (0.1

2)

(0.79)

Investmen

t

34825.6

7

-14.04 29.44 -

54.12***

-

24903.5

9

-

1108131.00

0.04 -0.71***

(0.36) (0.82) (0.53) (0.05) (0.30) (0.68) (0.8

4)

(0.00)

Probability values are reported in parentheses. ER and MS stands for Exchange rate and Money supply respectively *** Indicates significance at 5% level** Indicates significance at 10% level

The results of VAR in terms of impulse response function against two standard deviation

shock to each variable are shown separately in the following penal of graphs. The responses

of different macroeconomic variables to the positive shock on the PSX index are shown in

the first panel. The response of PSX index to itself is positive and which lives more than 10

19

periods. It is usually called inertia. The response of GDP is positive to the positive PSX shock

for first four periods (that is one year) then it becomes negative. This shows that the growth

in the index is bubble type growth and leave positive impact only for one year. The response

of exchange rate is always negative and never dies out quickly. Response of interest rate is

negative for first four periods and then it converts to positive. This shows that people invest

in PSX for only one year and if they have to invest for more than one year then they prefer to

go for safe investment. Response of money supply to the PSX shock positive and remains

positive for more than 10 periods. This shows an increase in stock return puts pressure to

State Bank of Pakistan to follow loose monetary policy. The response of investment is

negative to the positive PSX shock. That shows people are looking for the alternative

investment options instead of stock returns.

The responses of PSX index returns to the shocks of different macroeconomic variables are

given from panel 2 to 6. There is positive response of the stock return to a positive GDP

shock, which is very usual that an increase in the overall economic activity put positive

impact on the stock return. The responses of stock return to positive shock to all other

economic variables (exchange rate, interest rate, money supply and investment) are negative.

20

-400

0

400

800

1,200

1,600

1 2 3 4 5 6 7 8 9 10

Response of KSE to KSE

-60,000

-40,000

-20,000

0

20,000

40,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to KSE

-1.2

-0.8

-0.4

0.0

0.4

0.8

1 2 3 4 5 6 7 8 9 10

Response of ER to KSE

-.006

-.004

-.002

.000

.002

.004

.006

1 2 3 4 5 6 7 8 9 10

Response of INTR to KSE

-40,000

-20,000

0

20,000

40,000

60,000

80,000

1 2 3 4 5 6 7 8 9 10

Response of MS to KSE

-120,000

-80,000

-40,000

0

1 2 3 4 5 6 7 8 9 10

Response of INVESTMENT to KSE

Response to Cholesky One S.D. Innovations ± 2 S.E.

-600

-400

-200

0

200

1 2 3 4 5 6 7 8 9 10

Response of KSE to ER

-40,000

-20,000

0

20,000

40,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to ER

0.0

0.4

0.8

1.2

1.6

2.0

1 2 3 4 5 6 7 8 9 10

Response of ER to ER

-.006

-.004

-.002

.000

.002

.004

1 2 3 4 5 6 7 8 9 10

Response of INTR to ER

-60,000

-40,000

-20,000

0

20,000

40,000

1 2 3 4 5 6 7 8 9 10

Response of MS to ER

-80,000

-40,000

0

40,000

1 2 3 4 5 6 7 8 9 10

Response of INVESTMENT to ER


21

-400

0

400

800

1 2 3 4 5 6 7 8 9 10

Response of KSE to GDP

-40,000

0

40,000

80,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to GDP

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1 2 3 4 5 6 7 8 9 10

Response of ER to GDP

-.006

-.004

-.002

.000

.002

.004

.006

1 2 3 4 5 6 7 8 9 10

Response of INTR to GDP

-40,000

-20,000

0

20,000

40,000

60,000

80,000

1 2 3 4 5 6 7 8 9 10

Response of MS to GDP

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

1 2 3 4 5 6 7 8 9 10

Response of INVESTMENT to GDP


-800

-600

-400

-200

0

200

1 2 3 4 5 6 7 8 9 10

Response of KSE to INTR

-40,000

-20,000

0

20,000

40,000

60,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to INTR

-0.4

0.0

0.4

0.8

1.2

1.6

1 2 3 4 5 6 7 8 9 10

Response of ER to INTR

.000

.004

.008

.012

1 2 3 4 5 6 7 8 9 10

Response of INTR to INTR

-60,000

-40,000

-20,000

0

20,000

40,000

1 2 3 4 5 6 7 8 9 10

Response of MS to INTR

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

1 2 3 4 5 6 7 8 9 10

Response of INVESTMENT to INTR


-600

-400

-200

0

200

400

1 2 3 4 5 6 7 8 9 10

Response of KSE to MS

0

40,000

80,000

120,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to MS

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Response of ER to MS

-.002

.000

.002

.004

.006

.008

1 2 3 4 5 6 7 8 9 10

Response of INTR to MS

0

40,000

80,000

120,000

1 2 3 4 5 6 7 8 9 10

Response of MS to MS

-40,000

0

40,000

80,000

120,000

160,000

1 2 3 4 5 6 7 8 9 10

Response of INVESTMENT to MS


5. Conclusion

The study investigates the determinants of PSX index returns linearly and non-linearly in

threshold fashion, using the linear and non-linear Error Correction Mechanism (ECM).

Furthermore, the study estimates a 5-variable VAR estimation to test the impulse responses

of different shocks. The study uses the quarterly data from 1991Q1 to 2015Q4.The results

show GDP is insignificant determinant of PSX and is not co integrating with other variables

in the system using linear and non-linear estimation techniques. Further results show

depreciation in local currency impacts the PSX negatively due to more exports than imports

in Pakistan. The non-linear ECM results show that any deviation of stock return is corrected

by itself irrespective whether the stock is in high or low volatile regime and such correction is

supported by interest rate and exchange rate also. The VAR response shows that the value of

stock return seems a temporary bubble only for one year to economic growth.

From the policy perspective study concludes that policy makers should take measures to

smooth the fluctuation of stock return so it can gain people confidence for long term

22

-600

-400

-200

0

200

400

1 2 3 4 5 6 7 8 9 10

Response of KSE to INVESTMENT

-20,000

0

20,000

40,000

60,000

80,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to INVESTMENT

-0.4

0.0

0.4

0.8

1.2

1.6

1 2 3 4 5 6 7 8 9 10

Response of ER to INVESTMENT

-.004

-.002

.000

.002

.004

.006

1 2 3 4 5 6 7 8 9 10

Response of INTR to INVESTMENT

-20,000

0

20,000

40,000

60,000

1 2 3 4 5 6 7 8 9 10

Response of MS to INVESTMENT

-100,000

0

100,000

200,000

1 2 3 4 5 6 7 8 9 10

Response of INVESTMENT to INVESTMENT


investment. Furthermore, appropriate monetary policy can reduce the price and interest rate

fluctuation that has the direct impact on the stock prices. Though GDP seems insignificant

determinant but shock responses advocate that GDP can promote the capital and equity

market of Pakistan, so such policies should be adopted that favours the encouragement of

stock prices through domestic productivity growth.

References

Alam, M. M., & Uddin, M. G. S. (2009). Relationship between interest rate and stock price:

empirical evidence from developed and developing countries. International journal of

business and management, 4(3), 43.

Aydemir, O., & Demirhan, E. (2009). The relationship between stock prices and exchange

rates evidence from Turkey. International Research Journal of Finance and

Economics, 23(2), 207-215.

Azeez, A., & Yonezawa, Y. (2006). Macroeconomic factors and the empirical content of the

Arbitrage Pricing Theory in the Japanese stock market. Japan and the World

Economy, 18(4), 568-591.

Campbell, J. Y. (1991). A variance decomposition for stock returns. Economic Journal,

101(no 405), 157-179.

Campbell, J. Y., & Shiller, R. J. (1987). Cointegration and tests of present value models.

Journal of Political Economy, 95(5), 1062-1088.

Chan, K.-S. (1993). Consistency and limiting distribution of the least squares estimator of a

threshold autoregressive model. The annals of statistics, 520-533.

Chappel, K. (1997). Cointegration, error correction and Granger causality: an application

with Latin American stock markets. Applied Economics Letters, 4, 469-471.

23

Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal

of business, 383-403.

Demirgüç‐Kunt, A., & Maksimovic, V. (1998). Law, finance, and firm growth. The Journal

of Finance, 53(6), 2107-2137.

Diebold, F. X., & Yilmaz, K. (2008). Macroeconomic volatility and stock market volatility,

worldwide: National Bureau of Economic Research.

Enders, C. K. (2010). Applied missing data analysis: Guilford Press.

Enders, W., & Siklos, P. L. (2001). Cointegration and threshold adjustment. Journal of

Business & Economic Statistics, 19(2), 166-176.

Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction:

Representation, Estimation, and Testing. Econometrica, 55(No. 2), 251-276.

Fama, E. F. (1990). Stock returns, expected returns, and real activity. The Journal of

Finance, 45(4), 1089-1108.

Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.

Frennberg, P., & Hansson, B. A. (1993). Stock Returns and Inflation in Sweden: Do Stocks

Hedge Against Inflation? : Department of Economics, School of Economics and

Management, University of Lund.

Füss, R. (2002). The financial characteristics between emerging and developed equity

markets. Paper presented at the Policy Modelling International Conference, EcoMod

Network, Brussels, July.

Geske, R., & Roll, R. (1983). The fiscal and monetary linkage between stock returns and

inflation. The Journal of Finance, 38(1), 1-33.

Granger, C. W. J., Huang, B. N., & Yang, C. W. (2000). A Bivariate Causality Between

Stock Prices and Exchange Rates: Evidence from Recent Asian Flu. The Quarterly

Review of Economics and Finance, 40, 337-354.

24

Hansen, L. P. (1993). Semiparametric efficiency bounds for linear time-series models. In P.

C. B. Phillips (Ed.). Models, Methods and Applications of Econometrics: Essays in

Honor of A. R. Bergstrom, Cambridge, MA: Blackwell, Chapter 17(253–271).

Hussain, M. M., Aamir, M., Rasool, N., Fayyaz, M., & Mumtaz, M. (2012). The impact of

macroeconomic variables on stock prices: an empirical analysis of Karachi stock

exchange. Mediterranean Journal of Social Sciences, 3(3), 295-312.

Ibrahim, M. H., & Aziz, H. (2003). Macroeconomic variables and the Malaysian equity

market: A view through rolling subsamples. Journal of economic studies, 30(1), 6-27.

Kizys, R., & Pierdzioch, C. (2009). Changes in the international comovement of stock returns

and asymmetric macroeconomic shocks. Journal of International Financial Markets,

Institutions and Money, 19(2), 289-305.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1997). Legal determinants

of external finance. Journal of finance, 1131-1150.

Levchenko, A. A., & Mauro, P. (2007). Do some forms of financial flows help protect against

“sudden stops”? The World Bank Economic Review, 21(3), 389-411.

Levine, R., & Zervos, S. (1998). Stock markets, banks, and economic growth. American

economic review, 537-558.

Maghyereh, A. (2003). Causal relations among stock prices and Macroeconomic Variables in

the small open economy of Jordan. King Abdul Aziz University: Econ. & Adm, 17(2),

3-12.

Mookerjee, R. (1988). The Stock Market and The Economy: The Indian Experience 1949-

1981. Indian Economic Journal, 36(2), 30.

Muhammad, S. D., Hussain, A., Ali, A., & Jalil, M. A. (2009). Impact of Macroeconomics

Variables on Stock Prices: Empirical Evidence in Case of KSE. Available at SSRN

1683357.

25

Mukherjee, T. K., & Naka, A. (1995). Dynamic relations between macroeconomic variables

and the Japanese stock market: an application of a vector error correction model.

Journal of Financial Research, 18(2), 223-237.

Nasseh, A., & Strauss, J. (2000). Stock prices and domestic and international macroeconomic

activity: a cointegration approach. The Quarterly Review of Economics and Finance,

40(2), 229-245.

Nishat, M., & Saghir, A. (1991). THE STOCK MARKET AND PAKISTAN ECONOMY-

1964-87. Savings and Development, 131-146.

Ooi, A.-Y., Wafa, S. A. W. S. K., Lajuni, N., & Ghazali, M. F. (2009). Causality between

exchange rates and stock prices: evidence from Malaysia and Thailand. International

Journal of Business and Management, 4(3), 86.

Pan, M.-S., Fok, R. C.-W., & Liu, Y. A. (2007). Dynamic linkages between exchange rates

and stock prices: Evidence from East Asian markets. International Review of

Economics & Finance, 16(4), 503-520.

Patra, T., & Poshakwale, S. (2006). Economic variables and stock market returns: evidence

from the Athens stock exchange. Applied Financial Economics, 16(13), 993-1005.

Perotti, E. C., & Van Oijen, P. (2001). Privatization, political risk and stock market

development in emerging economies. Journal of International Money and Finance,

20(1), 43-69.

Singh, T., Mehta, S., & Varsha, M. (2011). Macroeconomic factors and stock returns:

Evidence from Taiwan. Journal of economics and international finance, 3(4), 217.

Sohail, N., & Zakir, H. (2011). The Macroeconomic Variables And Stock Returns In

Pakistan: The Case Of KSE100 Index. Volume III Issue 1 (5), 76.

Thapa, C., & Poshakwale, S. S. (2010). International equity portfolio allocations and

transaction costs. Journal of Banking & Finance, 34(11), 2627-2638.

26

Uddin, M. G. S., & Alam, M. M. (2007). The impacts of interest rate on stock market:

Empirical evidence from Dhaka stock exchange. South Asian J. Manage. Sci, 1(2),

123-132.

Yau, H.-Y., & Nieh, C.-C. (2009). Testing for cointegration with threshold effect between

stock prices and exchange rates in Japan and Taiwan. Japan and the World Economy,

21(3), 292-300.

Zoicas, I. A., & Fat, M. (2008). The Analysis of the relation between the evolution of the bet

index and the main macroeconomic variables in Romania. Codruta Annals of Faculty

of Economics, 3(1), 632-637.

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