relationship between stock prices, exchange rate and demand for money in pakistan

Middle Eastern Finance and Economics ISSN: 1450-2889 Issue 3 (2009) © EuroJournals Publishing, Inc. 2009 http://www.eurojournals.com/MEFE.htm

Relationship between Stock Prices, Exchange Rate and Demand

for Money in Pakistan

Qazi Muhammad Adnan Hye M.Phil Student; Applied Economics Research Centre

University of Karachi, Karachi E-mail: [email protected]

Syed Khurram Arslan Wasti

M.Phil Student; Applied Economics Research Centre University of Karachi, Karachi

E-mail: [email protected]

Narjis Khatoon M.Phil Student; Applied Economics Research Centre

University of Karachi, Karachi E-mail: [email protected]

Kashif Imran

M.Phil Student; Applied Economics Research Centre University of Karachi, Karachi

E-mail: [email protected]

Abstract

In this paper, we use the robust time series tools in order to estimate Pakistan’s money demand function for the period 1971:1-2006:4.We find that there are four co-integrating vectors in money demand, interest rate, economic activity, inflation, stock prices and exchange rate. Important findings of this paper i.e. stock price have positively and statistically significant wealth effect and exchange rate insignificantly effect on money demand in the long run. But in the short run the inflation has negative and significant effect on money demand. Keywords: Money Demand, Stock prices, FMOLS, Pakistan. JEL Classification Codes: E3, G12, C1

1. Introduction According to Friedman (1988) increase in the stock prices have two effects on demand for money, positive wealth effect and negative substitution effect. Positive wealth effect due to the three factors (i) the increase in nominal wealth (ii) an increase in expected return in the risky assets relative to the safe assets which induces the economic agents to hold larger amounts of safe assets, such as money (iii) an induced rise in the volume of financial transactions which will require higher money balances to facilitate them. On the other hand negative substitution effect of real stock prices on money demand

Middle Eastern Finance and Economics - Issue 3 (2009) 90

implies that, as stock prices rise, equities become more attractive when compared to other components in a portfolio; thus there may be a shift from money to stocks. The information about the factors causing the demand for money is important for conducting a fruitful monetary policy. A stable and well-specified money demand function is essential for statistical interpretation, forecasting, and for the policy analysis. So, if dominates the positive wealth effect of an increase in stock prices, then higher stock prices imply that the monetary authorities can allow faster monetary growth to achieve a given nominal income or inflation target to avoid the target being undershot .On the contrary, if the substitution effect dominates, higher stock prices imply the need to tighten monetary policy. Friedman (1988) and Mc-Cornac (1991) estimated the direct relationship between money demand and stock prices in U.S.A and Japan, found positive and substitution effect. Gerdesmerier (1996) estimated the demand for money function in Germany, included equity holdings indirectly as part of household wealth and found positive wealth effect on the demand for money. Choudhry (1996) estimate the money demand function, by using the co-integration and error correction modeling approach in the U.S.A and Canada, his findings shows that stock prices play a significant role in real M1 and M2 money balance. John Thornton (1998) estimate the long run money demand for Germany by using JJ Co-integration technique found that real stock price have a significant and positive wealth effect on the long run demand for real M1 balance. Baharumshah (2004) study the demand for money function for Malaysia, using the multivariate co-integration and error correction model and found Stock prices have a significant negative substitution effect on long-run as well as short-run broad-money demand (M2). Yu Hsing (2007) using the Box-Cox model and the Newey-West method, estimate the money demand function for Poland. He concludes that the stock prices cannot determine the demand for money function in Poland. Yu Hsing(2007) estimate the money demand function for Slovakia’s , his findings shows that the stock prices insignificantly effect the money balance (M2).

Like other countries, in case of developing economy like Pakistan researchers have ample effort made in estimating money demand functions . Akhtar (1974), Abe, et al. (1975), Burney and Akmal (1990), Khan (1980, 1982a) and Raza (1989) , Mangla (1979) and Nisar and Aslam (1983).Bahmani-Oskooee and Malixi (1991) estimate the demand for money (M1) in Pakistan on the inflation rate, real output and the exchange rate. .Rehman (2005) found that the demand for money (M2) in Pakistan has a positive relationship with real output and the exchange rate, and a negative relationship with the inflation rate and the demand for money would increase as the rupee depreciates or that the wealth effect dominates the substation effect. Qayyum (2005), estimate demand for money function (M2), employing the co-integration and error correction approach and concludes that the money demand function (with M2 money balance) stable for Pakistan. . Zakir Hussain et.al (2006) find that no co-integration and no-unit root in the demand for money function, regression analysis was performing by using the OLSQ method. Yu Hsing (2007) estimates the money demand function by using the linear, log linear and Box-Cox transformation and concludes that log linear transformation best. Demand for money positive related with GDP, currency appreciation and negatively related by domestic interest rate and foreign interest rate.

The objective of this research is to evaluate the impact of stock prices and exchange rate on the demand for money function in developing economy like Pakistan by employing the JJ co-integration and FMOLS methods. The rest of the paper planned as follows: Section-2 represent empirical data, model and methodology use in the study.Section-3 represent the empirical result and final section represent the conclusion and policy implication. 2. Data, Model and Econometrics Methodology The data is used in this study, consist of quarterly observation on broad money supply (M2), economic activity (proxy by GDP), stock prices(stock price index) , call money rate, exchange rate (Rupees per US Dollar) and inflation ( CPI ).The time series quarterly data has collected from the International

91 Middle Eastern Finance and Economics - Issue 3 (2009)

Financial Statistics ( IFS ), but the quarterly GDP data not available in IFS. So, quarterly data is taken from Farooq Arby publication 1. Model

This study implements the following money demand function for Pakistan.

543210 ++++++= ttttttt SPRERYM ναααπααα (1)

Where tM = Money demand

tY = Economic activity

tπ = Inflation

tER = Exchange rate

tR = Interest rate

tSP = Stock prices

tν = Error term The demand for money is expected positive relationship with economic activity and a

negative relationship with the opportunity cost variable (interest rate). Stock prices may reduce or increase the demand for money due to substitution effect or the wealth effect2.Appreciation of exchange rate may increase or reduce the demand for money due to the substitution effect or the wealth effect3.

All variables are used in natural logarithms form in the context of small developing economy like Pakistan. Ehrlich (1977) and Layson (1983) were argued on theoretical and empirical grounds that the log-linear function superior to the linear function. Both Cameron (1994) and Ehrlich (1996) were suggested that a log-linear function more likely to find evidence of a restraint effect than a linear function. So the natural logarithms transform model i.e.

)()()()()()( 543210 −++++++= ttttttt SPLnRLnERLnLnYLnMLn ναααπααα (2)

Econometric Methodology

ADF Unit Root Test The Augmented Dickey and Fuller (ADF) (1979, 1981) test is based on the following regression model:

11 −+Δ+++=Δ −

=− ∑ tjt

jjtt XTXCX εγλβ

ρ (3)

Eq (3) tests for a unit root in tX , where X consists of each of the six variables in our model, t = 1,.....,T is an index of time, jtX −Δ is the lagged first differences to accommodate serial correlation in the errors, tε .Eq.3 tests the null of a unit root against a trend stationary alternative. The null and the alternate hypotheses for a unit root in tX are: 0:0 =βH and 0:0 <βH . To select the lag length

)(ρ we use the Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC).

1 All index is used in this study has based on the year 2000 =100 2 See: Friedman (1988);Fase and Winder (1998). 3 See: Arango and Nadiri (1981); Mckinnon (1982); Bahmani-oskooee and Techaratanachai (2001) ; Bahmani-oskooee and Ng (2002)


Fully Modified Ordinary Least Square (FMOLS)

When order of integration is decides than for the long run elasiticities, utilize the FMOLS method. FMOLS was originally designed first time by [Philips and Hansen, (1990); Pedroni, (1995, 2000); and, Philips and Moon, (1999)] to provide optimal estimates of Co-integration regressions (Bum and Jeon, 2005). This technique employs kernal estimators of the Nuisance parameters that affect the asymptotic distribution of the OLS estimator. In order to achieve asymptotic efficiency, this technique modifies least squares to account for serial correlation effects and test for the endogeneity in the regressors that result from the existence of a Co-integrating Relationships 4. Although this non-parametric approach is an elegant way to deal with nuisance parameters, it may be problematic especially in fairly very small samples. To apply the FMOLS for estimating long-run parameters, the condition that there exists a Co-integration relation between a set of I(1) variables is satisfied. There fore we have to confirm the presence of the unit root and test the Co-integrating relation. Standard tests of the presence of the unit root based on the work of Augmented Dicky Fuller (1979, 1981) used to investigate the degree of integration of concerned variables.

Engle and Granger (1987) discussed that, a set of economic series is not stationary, there may have to exist some linear combination of the variables that is stationary. Now, when all the variables are non-stationary at their level but stationary in their 1st difference, this allows proceeding further for the implementation of Johansen co-integration technique. Economically speaking, two variables will be co-integrated if they have a long-term relationship between them. Thus, co-integration of two series suggest that there is a long integration tests and of course, the system approach developed by Johansen (1991,1995) can also applied to a set of variables containing possibly a mixture of I(0) and I(1) [Pesaran and Pesaran, (1997) and Pesaran et al., (2001, p.315)]. The general form of the vector error correction model is as follows:

t

p

itt ZZ ηαψ ++= ∑

−

=− o

1

11

This can also be written in standard form as:

∑−

=−− ++∂−ΔΠ=Δ

1

11

p

itktktit ZZZ εα (4)

Where;

ti I ∂++∂+∂+−=∏ ......21

1,...3,2,1 −= ki and kI ∂−∂−∂−=∂ ....21

Where p represents total number of variables considered in the model. The matrix ∏ captures the long run relationship between the p-variables. Now for the Johansson Test; we employed the Trace test, which is based on the evaluation of )1( −rH o against the null hypothesis of )(rH o , where r indicates number of co-integrating vectors. The co-integration test provides an analytical statistical framework for investigating the long run relationship between economic variables in the model. Johansen and Juselius (1990) provide critical values for the two statistics. The statistical distribution depends on the number of non-stationary components and model telling of constant and trend term. To determine the non-stationary components, it is necessary to choose the lag length for VAR portion of the model. To overcome this problem, this work determines the optimal lag length using Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) 5. The lowest values of AIC and SBC to select the lags give the most desirable results.

4 See Philip and Hansen (1990), Hansen (1995) for details. 5 The distribution of test statistic is sensitive to the order of lag used. If the lag order is used less than true lag, then the

regression estimates will be biased and residual term will be serially correlated. If the order of lag used exceeds the true order, the power of the test is to be reduced.


3. Empirical Result In this section we analyze time series properties of the data during the period 1971:1-2006:4.The ADF tests result (Table-1) shows that the existence of unit root all the six variables that are included in the model. However, the first differences of these variables are stationary under the test. Hence, we conclude that these six variables are integrated of order 1 or I(1). Table 1: ADF Unit root Test Results

Variables Level First Difference Ln (SP) -2.377 -5.727* Ln (Y) -0.926 -5.421* Ln (π ) -1.730 -4.985* Ln(ER) -2.964 -5.610* Ln ( R ) -2.968 -8.681* Ln (M) -2.207 -7.348*

Test critical values *:1% level -4.025

**:5% level -3.442

***:10% level -3.146

On the basis of the above unit root tests, we apply the producer of Johansen (1988, 1991) and Johansen and Juselius (JJ) (1990, 1992, and 1994) is to determine whether any combinations of the variables are co-integrated. Before undertaking the co-integration tests, we first specify the relevant order of lags )(ρ of the VAR model. Table 2: Johansen Maximum Likelihood Test for Co-integration

Null Hypothesis Alternative hypothesis Trace Statistic 0.05 Critical Value r =0 r ≥ 1 181.93 103.84 r≤ 1 r ≥ 2 111.69 76.97 r ≤ 2 r ≥ 3 69.56 54.07 r≤ 3 r ≥ 4 39.04 35.19 r ≤ 4 r ≥ 5 19.84 20.26 r ≤ 5 r ≥ 6 8.01 9.16

The results obtained from the JJ tests are presented in the Table-2: starting with the null

hypothesis of no co integration (r =0) among the variables the trace statistic is (181.93) which above the critical value of (103.84) .Hence it rejects the null hypothesis r =0 at 5% level of significance in the favor of specific alternative that there is co-integrating vector r ≥ 1 .As is evident in Table-2 the null hypothesis of r ≤ 1, r ≤ 2 and r ≤ 3 can also be rejected at a 5% level of significance and the r ≤ 4 and r ≤ 5 cannot be rejected at 5% level of significance. Thus, we conclude that there are four co-integrating relationship among the six variables of money demand, interest rate, economy activity, inflation, stock prices and Exchange rate. Long-run elasticities

Having found the long-run relationship exists between the money demand function and its determinants; in this section our goal is to estimate long-run elasticities. We achieve this through by using, Phillips and Hansen (1990) fully modified ordinary least squares (FMOLS) and JJ normalized co-integration regression. We report the result in table-3, results shows that the economic activity and inflation is positively and opportunity cost variable is negatively (statistically significant) associated to demand for money.


Table 3: Long Run Elasticities

Dependent Variable: Ln (M) Normalized co-integrating coefficients FMOLS Variable Coefficient T- Statistic Coefficient T- Statistic Constant 4.48 6.60 4.39 6.85 Ln (SP) 0.24 6.25 0.18 4.83 Ln (π ) 0.28 3.62 0.26 3.86 Ln (Y) 1.23 13.66 1.18 14.77 Ln(ER) - 0.09 -0.99 0.07 0.77 Ln (R) - 0.21 -5.5 -0.20 -5.76

Exchange rate is statistically insignificant. The important result of this study is that stock prices

elasticity positive and significantly indicates that stock prices have a positive wealth effect on the money demand in Pakistan. Error Correction Estimation

Co-integral relationship establish between the money demand, economic activity, interest rate, inflation, stock returns and exchange rate, it is possible to specify and estimate an error correction model. Table 4: Error correction estimation result

Variable Coefficient t-Statistic Prob. Δ (M(-1)) -0.22 -2.67 0.01 Δ (M(-2)) 0.15 1.81 0.07 Δ (Y) 0.07 2.98 0.01 Δ (ER) -0.04 -0.78 0.43 Δ (π ) -0.29 -2.39 0.01 Δ (R) 0.007 0.68 0.49 Δ (SP) 0.02 0.88 0.37 ECM(-1) -0.092 -3.64 0.01 C 0.04 7.19 0.00 R-squared 0.423060 Adjusted R-squared 0.388094 Durbin-Watson stat 1.849394

Table-4 shows the result of error correction model, the error correction term has the negative

sign and statistically significant (at 1% level). The coefficient value of error correction term is 0.092, which suggest that 9.2 per cent of discrepancy between; long run is eliminated within quarter of year. The coefficient of inflation is negative and statistically significant. This means that the in the short run increase in prices will leads to decrease the demand for money in the developing economy like Pakistan. Economy activity positively and significantly effects on the demand for money in the short run. On the other hand the exchange rate, stock prices and rate of interest insignificantly effect on the demand for money in the short run. 4. Conclusion The goal of this paper is to estimate the association between exchange rate, stock Prices and demand for money function in Pakistan for the period 1971:1 – 2006:4.We estimate the money demand function by using the robust co-integration tools. The JJ Co-integration result suggest that there are four co-integrating vectors in money demand, interest rate , economic activity, inflation, stock prices and exchange rate. Important findings of this paper are that stock prices positively and statistically


significant and exchange rate insignificantly associated to money demand in the long run. But in the short run the inflation negatively and significantly effect on the money demand. The stock prices positively wealth on money demand in the long run. Hence an increase in stock prices is expected to dictate an easier monetary policy to prevent a given nominal income or inflation target being undershot. References [1] Ahmad, M and Ashfaque H.Khan (1990). “A Re-examination of the stability of the Demand

for money in Pakistan”. Journal of Macroeconomics, Vol.12, No.2, spring. [2] Abe, S.et al. (1975) “The Demand for Money in Pakistan: Some Alternative Estimates”.

Pakistan Development Review, 14(2), 249-57. [3] Akhtar Hossain (1994). “The Search for a Stable Money Demand Function for Pakistan: An

Application of the Method of Co-integration”. Pakistan Development Review, April 2-5, 1994. [4] Akhtar, M.A. (1974) “The Demand for Money in Pakistan”. Pakistan Development Review,

13(1), 40-54. [5] Baharumshah, A.Z. (2004) “Stock Prices and Long Run Demand For Money Evidence from

Malaysia”. International Economics Journal, Vol 18, Issue.3 September 2004, 389-407. [6] Bahmani-Oskooee, et.al (1991). “Exchange Rate Sensitivity of Demand for Money in

Developing Countries”. Applied Economics: 23, 1377-84. [7] Cameron, S. 1994. “A review of the econometric evidence on the effects of capital

punishment”. J. Socio-Econo. 23:197-214. [8] Choudhry, T. (1996) “Real Stock Prices and the Long Run Money Demand Function: Evidence

From Canada and the U.S.A”. Journal of International Money and Finace 15, 1-17. [9] Engle, R.F and C.W.J Granger (1987). “Co-integration and Error Correction: Representation,

Estimation and Testing”.Econometrica.Vol.55, No.2. [10] Ehrlich, I. 1977. “Capital punishment and deterrence: some further thoughts and additional

evidence”. J. Political Econ. 85(4):741-788. [11] Ehrlich, I. 1996. “Crime, punishment, and the market for offenses”. J. Econ. Perspectives.

10(1):43-67. [12] Friedman, M. (1988) “Money and the Stock Market”. Journal of Political Economy, 96, 221-

245. [13] Hsing, Y (2008) “Impact of Financial Stock Prices and Exchange Rates on the Demand for

Money in Poland”. The South East European Journal of Economics and Business, Vol [14] Hsing, Y (2007) “Currency Substitution, Capital Mobility and Functional Forms of Money

Demand in Pakistan”. The Lahore Journal of Economics.Vol,12 .35-48 [15] Hsing, Y (2008) “Role of Stock Prices and Exchange Rate in Slovakia’s Money Demand

Function and Policy Implications”. Transition Studies Review, 14(2):274-282. [16] Khan, Ashfaque H. (1982a).” Permanent Income, Inflation Expectation and the Money Demand

Function in developing countries”. Pakistan Development Review, Vol.XXI, No.4, winter. [17] Khan, Ashfaque H. (1982b). “The Demand for Money and the Variability of the Rate of

Inflation: An Empirical Note”. Economics Letters, Vol.10. [18] Khan, Ashfaque H. (1982c). “Adjustment Mechanism and the Money Demand Function in

Pakistan”. Pakistan Economic and Social Review, Vol, XIX, No.1, summer. [19] Khan, Ashfaque H. (1980). “The Money Demand in Pakistan: Some Further Result”. Pakistan

Economic and Social Review, Vol, XIX, No.1, spring. [20] Khan, Ashfaque H. (1994). “Financial Liberalization and The Demand For Money in Pkaistan”.

Pakistan Development Review, April 2-5, 1994. [21] Khan, Ashfaque H. and Bilquess Raza (1989). “The Money Demand in Pakistan: Quarterly

Result”. Pakistan Economic and Social Review, Vol, XXVII, No.1, summer.


[22] Layson, S. 1983. “Homicide and Deterrence: Another view of the Canadian time series evidence Cand”. J. Econ. 16:52-73.

[23] Mangla, I.U. (1979). “An Annual Money Demand Function for Pakistan: Some Further Results”. Pakistan Development Review, Vol.XVII, No.1, spring.

[24] Mc-Cornac, D. (1991) “Money and The Level of Stock Market Prices: Evidence From Japan, Quarterly Journal of Business and Economics, 30,42-51.

[25] Nisar, S.and N.Aslam (1983). “The Demand For Money and the Term Structure of Interest Rates in Pakistan”. Pakistan Development Review, Vol.XXII,NO.2, Summer.

[26] Phillips and Hansen (1990). “Statistical Inference in Instrumental Variables Regression With I(1) Process” Review of Economic Studies (1990) 57, 99-125.

[27] Zakir, H et.al (2006) “Demand for Money in Pakistan” International Research Journal of Finance and Economics. Issue, 5.

relationship between stock prices, exchange rate and demand for money in pakistan

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