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The main motivation of this research is to analyze the linkages between stock market, money market and foreign exchange market under the different political regimes in Pakistan Since January to December , Pakistan is governed by military and democracy leadership both.

The military leadership took over the charge with October coup, and formally the chief of army staff Gen. Pervaiz Musharaf became the president of Pakistan in June and his tenure ended in August We have considered this regime as Military regime that starts from January to July The second regime of our sample time period consists on Democracy governance that starts from August , and it is still in progress.

We term this regime as democratic regime that covers the period from August to December This study is aimed to analyze the long term co-movement and causality linkage among money market, stock market and foreign exchange market in Pakistan. This study contributes in two different ways; first, it examines the long-run equilibrium relationship between stock market, money market and foreign exchange market under the different political regimes.

Second, we also estimate long run and short run causality relationship between the stock price and other monetary variables included in this study under the three different sub-samples. The study finds significant differences in the relationship between stock prices, money supply, interest rate and exchange rate across the political regimes in Pakistan. Furthermore, as a robust check, we also estimate a multivariate linear regression model that justifies the nature of relationship with partial differences. This study will make a significant contribution in the existing literature on Pakistan and the results will be useful for policy makers and financial analysts in the field of economics and finance.

Money market and foreign exchange market are noted as the fundamental factors of stock market movement. The investigation about the relationship between these markets has remained an area of prime interest for the researchers and policy makers.

2 Literature Review

A significant body of literatures has been put forward so for on the long-run equilibrium relationship and causal connection between stock market and macroeconomic variables. Humpe and Macmillan[ 29 ] explained that there can be two ways to establish the linkage between macroeconomic variables and stock prices. One way is Arbitrage Pricing Theory APT in which multiple risk factors are taken into account in order to explain asset returns. According to this approach, volatility in macroeconomic variables can be reflected in the underlying systematic risk factor that influences the future stock returns.

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Another approach used by Chen, et al. The main advantage of this approach in comparison to APT is that it focuses on the long-run relationship between stock prices and macroeconomic variables. But most of the empirical work[ 31 - 36 ] are based on APT have modeled a short run relationship between stock prices and macroeconomic variables. Mukherjee and Naka[ 10 ] also examined the long-run equilibrium relationship between stock market and macroeconomic variables money supply, industrial production, exchange rate, inflation, long term government bond rates, and call money rate for Japan.

By employing vector error correction model VECM on monthly data, they found a significant evidence of positive connection between money supply and stock market. Similarly, many other studies[ 20 , 38 , 39 ] also reported a positive connection between money supply and stock prices. While discussing a causal relationship between money supply and stock prices, Shostak[ 40 ] argued that an increase in stock prices also provide an incentive to liquidate the fixed income securities and use that money to buy stocks and other financial assets.

Thus, the demand deposits will increase that in turn increases the money supply. This trend can be reversed if stock prices fall. He further argues that causality cannot be achieved only by statistical figures without having a coherent definition of what money is and how it is related to stock prices and other financial assets. In case of Pakistan, Hasan and Javed[ 11 ] established a long-run equilibrium relationship between monetary variables money supply, treasury bill rate, consumer price index, and exchange rate and equity prices by employing multivariate cointegration approach on monthly data covering from M6 to M6.

They also employed granger causality test and VAR model impulse response functions to analyze the short term causal relationship between the selected variables. The results of multivariate Johansen and Juselius[ 1 ] cointegration test indicate a long-run equilibrium relationship between variables, while granger causality test indicates a unidirectional causality moving from monetary variables to equity prices. Moreover, the results of impulse response function are evident of positive relationship between money supply and equity prices while it is negative for interest rate and exchange rate.

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By using quarterly data, Abbas and McMillan[ 12 ] also established a long-run equilibrium between stock market index and monetary variables for Pakistan. Humpe and Macmillan[ 29 ] among others reported a negative relationship between stock prices and interest rate for Japan and US. Conversely, Ratanapakorn and Sharma[ 20 ] found a positive relationship between short-term interest rate and stock prices for USA.

Some of the studies[ 6 , 7 , 41 ] found no causal connection between stock prices and interest rate. In case of exchange rate, the studies of [ 10 - 12 , 20 ] among others found a long-run equilibrium relationship between exchange rate and stock prices. For feedback causal connection between exchange rate and stock prices in US, Bahmani-Oskooee and Sohrabian[ 42 ] found that there is a bidirectional causality between exchange rate and stock prices in short run, whereas, Choi, et al. Dominguez and Tesar[ 44 ] also mentioned that exchange rate fluctuations have a significant impact on the equity prices at firm level and sectoral level for developed economies.

Similarly, Chkili and Nguyen[ 45 ] examined the relationship between exchange rate and stock returns for BRICS countries by employing regime switching approach. They discovered that exchange rate movement has no effect on stock returns while stock returns have a significant impact of exchange rate changes except South Africa. Lee, et al. They found that the correlation between stock market and exchange rate becomes higher as stock market volatility increases.

For the same countries, Yang, et al.

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Similarly, Moore and Wang[ 22 ] also found a negative relationship between exchange rate and stock prices. Hasan and Javed[ 11 ] studied the causality relationship between these two variables and found a positive and unidirectional causality moving from exchange rate to stock prices in short run. Based on the literature review and the objectives of this study, we investigate the long-run equilibrium relationship between stock market, money market and stock market in case of all three sample periods. Moreover, we also investigate the short run granger causality relationship between all three markets in full sample period, military regime and democratic regime.

Monthly data covering from M1 to M12 has been used in this study to investigate the relationship among stock market, money market and foreign exchange market in case of Pakistan. KSE index has been used as a proxy measure for stock prices in the in Pakistan 90 days T-bill rate and M2 are used as a measure of short term interest rate and money supply.

By using monthly data, Wong, et al. The data for all these variables has been collected from the CEIC global database CEIC is a European institutional investor company founded in that provides most expansive and accurate economic and financial data about the emerging and developed markets. Time series plots of cumulative sum of recursive residuals for vector error correction models VECM.

The results of descriptive statistics are presented in Table 1. These statistics explain the basic features of the logged variables like mean, standard deviations, minimum and maximum, skewness and kurtosis and Jarque-Bera test-statistic. The results show that the value of standard deviation for KSE index is greater than the other three variables which indicate that Pakistan stock market is quite risky as compared to other macroeconomic variables.

The results also indicate that except KSE index, all the other variables are negatively skewed and kurtosis result shows that the distribution of all variables is fatter tailed Jarque-Bera test-statistic for all the variables is significant that indicates that all the variables are not normally distributed. In the time series data, presence of long-run equilibrium relationship between non-stationary variables is one of the core concerns of economists. Stock[ 53 ] investigated that if two series say Y t and X t are non-stationary and highly cointegrated, then they would produce highly efficient and consistent estimates of the parameters.

If in the time series data two variables have common trend, then they are more likely to have long-run relationship between them. So in order to check the nature of trend in variables, cointegration tests are important. A significant body of literature [ 48 , 54 - 56 ] discussed the details of this concept of cointegration. In this study, we use a multivariate approach developed by Johansen and Juselius[ 1 , 57 ] to capture the equilibrium relationship between stock market index and financial economic variables.

The first step in the empirical analysis is to check the stationarity of variables by using unit root test procedure developed by Dickey-Fuller[ 58 , 59 ]. Most of the non-stationary financial variables are integrated of order I 1 , but if they are stationary and integrated at zero difference then they are denoted by I 0. Suppose there are two variables say, Y t and X t are integrated at I 1 then the regression equation 1 is used to estimate the long-run equilibrium relationship between variables, and further stationarity tests are used to test the stationarity of estimated residuals.

If the variables are not cointegrated, then the estimated residuals will be integrated at order I 1 , otherwise the residuals will be stationary and integrated at order I 0. Moreover, the regression results of non-stationary series will be spurious and misleading and the residuals will not be I 0 in such conditions. As we have more than two variables in the model, so, there is a possibility of having more than one integrating vector. It means that the variables in the model might form several equilibrium relationships governing the joint evolution of all the variables. In general, for n number of variables we can have only up to n— 1 cointegrating vectors.

Therefore, an alternative approach is needed and this is multiple equations approach developed by Johansen and Juselius[ 1 ].

This approach can be presented by extending the single equation error correction model to a multivariate one. Equation 2 is comparable to the single equation dynamic model 1 of two variables of Y t and X t. Equation 4 can also be written as follows:. In Equation 5 the error correction part i.

The test consists of ordering the largest eigenvalue in descending order and considering whether they are significantly different from zero. To test how many of the numbers of characteristics roots are significantly different from zero this test uses the following statistics:.

The second method proposed by Johansen is based on likelihood ration test about the trace of the matrix and that is why, it is known as trace test. The trace test considers whether the trace is increased by adding more eigenvalue beyond the r th eigenvalue. This test-statistic value is calculated by. Critical values for both statistics are provided by Johansen and Juselius[ 1 ].

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Once it is decided that the long-run relationship between stock prices index and financial economics variables exists, then we adopt the bivariate VAR model to estimate granger causality relationship. For further investigation in this pairwise granger causality, two-way causation of variables, say X and Y can be tested.

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