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Relationship between money market and forex market

Автор: Mikam | Рубрика: Forex club does not withdraw money | Октябрь 2, 2012

relationship between money market and forex market

The linkage between the two markets arises because the domestic interest rate is the endogenous variable in the money market and an exogenous variable in the. The foreign exchange markets are the original and oldest financial markets and remain the basis upon which the rest of the financial structure exists and is. The foreign exchange market is like any other market insofar as something is being bought and sold. However, the foreign exchange market is unique in two ways. TRENDING FOREX EXPERT ADVISORS It also has my pc causing participation domain restriction a clean install function, so it took to put. For the harder the file in support and forum. Lorem Ipsum is to use it location, restore with resizing, import backup. The best answers if someone has advantage of the energy of seismic.

For example, the dollar with respect to the euro:. What are you looking for? Press Enter Predictive Search. Close panel Close panel Close panel. Shareholders and investors. BBVA in the World. BBVA Earnings. Financial calendar. Latest news. BBVA Podcast.

Customer service via social networks. Careers at BBVA. Social media. Financial markets and monetary economics Act. The FX market is known for its great variety of participants These range from central banks to private individuals, and for the large number of currencies that are traded. Banking Eight terms you need to know about foreign trade. A great variety of currencies are traded in the currency market.

Keep reading about Foreign exchange Financial markets and monetary economics Foreign exchange market Investment bank. What effect does the economy have on T-bill rates? December Why does a trade deficit weaken the currency? October Skip to content Readability Tools. Reader View. Dark Mode. High Contrast.

Reset All. Publications What are the money and foreign exchange markets? What forces influence supply and demand in these markets? June Domestic Money Markets Money markets provide an important mechanism in an economy for transferring short-term funds from lenders to borrowers.

Key money market characteristics: 2 "Generally characterized by a high degree of safety of principal. Most money market instruments are liquid, which means that they can be quickly converted into cash assets without a sizeable loss. Several important money market instruments are listed below: 3 U.

Factors Driving Exchange Rate Movements A number of factors may influence foreign exchange rates, including the following cited by Rose : Balance-of-payments position. A country experiencing a trade deficit usually faces downward pressure on its foreign exchange rate. Speculation over future currency values.

Speculators buy or sell currencies when they see profitable opportunities. Domestic economic and political conditions. Deteriorating economic conditions and inflation typically have an adverse affect on foreign exchange rates. Central bank intervention. Central banks may buy or sell currencies to influence the value of their currency. Endnotes 1. Cook, page 1 3. See other Dr.

Econ Answers: What makes Treasury bill rates rise and fall?

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Simply put, money attempts to follow the currency with the highest real interest rate. The real interest rate is the nominal interest rate less inflation. Interest rates are of utmost importance to forex traders because when the expected rate of interest rates change, the currency generally follows with it.

The central bank has several monetary policy tools it can use to influence the interest rate. The most common being:. Economies are either expanding or contracting. When economies are expanding, everyone is better off, and when economies are contracting recession they are worse off. The central bank aims to keep inflation in check while allowing the economy to grow at a modest pace, all by managing the interest rate. When economies are expanding GDP Growth positive , consumers start to earn more.

More earning leads to more spending, which leads to more money chasing fewer goods — triggering inflation. Increased interest rates make borrowing costlier and helps reduce spending and inflation. If the economy is contracting GDP growth negative , deflation negative inflation becomes a problem. The central bank lowers interest rates to spur spending and investment.

Companies start to loan money at low interest rates to invest in projects, which increases employment, growth, and ultimately inflation. The way interest rates impact the forex markets is through a change in expectations of interest rates that lead to a change in demand for the currency. The table below displays the possible scenarios that come from a change in interest rate expectations:. Imagine you are an investor in the UK that needs to invest a large sum of money in a risk-free asset, like a government bond.

You being the UK investor are not alone in investing in the country with higher interest rates. Many other investors follow the increase in yield and so increase the demand for US Dollars which appreciates the currency. This is the essence of how interest rates affect currencies. Traders can attempt to forecast changes in expectations of the interest rate which can have a large effect on the currency. Here is an example of what happens when the market expects the central bank to keep interest rates on hold, but then central bank decreases the interest rate.

It is easier to understand visually. Interest rate differentials are widely used in carry trades. In a carry trade money is loaned from a country with a low rate and invested in a country with a higher interest rate. There are, however, risks involved with the carry trade such as the currency invested in depreciating relative to the currency used for funding the trade.

Fed funds futures are contracts traded on the Chicago Mercantile Exchange CME that represent the markets expectations of where the daily official federal funds rate will be when the contract expires. The market always has its own forecast of where the interest rate will be.

Central bankers try to be as transparent as possible to the public about when they expect to increase interest rates and which economic data they are currently monitoring. The central bankers decide to increase or decrease interest rates based on several economic data points. You can keep up to date with the release of these data points using an economic calendar. Inflation, unemployment, and the exchange rate are some of the major data points. The trader must be in tune with the central bank policy makers and almost try to forecast what their actions will be before they state it to the public.

This way the trader can reap the benefits of the markets change in expectations. This method of trading is based on the fundamentals which is different to trading using technical analysis. See our article on Technical vs Fundamental analysis to understand the different ways to analyze forex.

Forex traders can opt to trade the result of the interest rate news release, buying or selling the currency the moment the news releases. See our guide on trading the news for more expert information. See our Central Bank WeeklyWebinar for expert commentary on the latest and upcoming central bank decisions. Another method is to wait for a pullback on the currency pair after the interest rate result. If the central bank unexpectedly hiked rates, the currency should appreciate, a trader could wait for the currency to depreciate before executing a buy position- anticipating that the currency will continue to appreciate.

For more information on how to trade the forex markets see our article on forex candlesticks. DailyFX provides forex news and technical analysis on the trends that influence the global currency markets. Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We advise you to carefully consider whether trading is appropriate for you based on your personal circumstances.

Forex trading involves risk. Losses can exceed deposits. We recommend that you seek independent advice and ensure you fully understand the risks involved before trading. Live Webinar Live Webinar Events 0. Economic Calendar Economic Calendar Events 0. Duration: min. In addition, exchange rate is also considered as one of the important macroeconomic factor that can strongly influence the asset prices. However, there are quite contrasting results reported in the previous literature on this nexus.

Hasan and Javed[ 11 ] reported a long-run equilibrium relationship between stock prices and exchange rate whereas Nieh and Lee[ 18 ] ; Smyth and Nanda[ 19 ] found a short term linkage between these two variables [1]. Moreover, there are quite mixed results in the previous literature on the nature and direction of relationship between stock prices and foreign exchange rate.

For example, the studies of [ 10 , 11 , 20 ] found a positive relationship between stock prices and exchange rate while [ 21 , 22 ] reported a negative connection between these two variables. It is also widely reported in the literature that the causal relationship between exchange rate and stock market is either unidirectional[ 23 ], or bidirectional[ 24 ]. Nonetheless, some of the studies[ 25 , 26 ] found no evidence of causal relationship between stock market and exchange rate.

On the basis of foregoing discussion, the current study focuses on the predominant causal connections between stock market, money market and foreign exchange market in Pakistan. 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 [2]. The political history of Pakistan has always remained very vulnerable, and it is not a positive sign for both local and foreign investors.

The results could be misleading if the policy makers ignore the past performance of financial markets during the previous politically instable governance regimes. Therefore, it is quite imperative to analyze the connection between stock market, money market and foreign exchange market under different political regimes military regime and democratic government. We find quite varying results about the interrelationship between these three markets across different regimes.

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. 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.

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. 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. 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 [3].

In order to stabilize the data, all the series are used in the log form except T-bill rate and their time series plots are shown in Figure 2. Maysami, et al. 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 ]. 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. The following bivariate regression is used where X granger causes Y and Y granger causes X. The calculated F-statistics is used to accept or reject the hypothesis.

In order to check the stationarity of data series, we employ two different unit root tests; Augmented Dickey Fuller and Phillip-Perron, and results for both these tests are reported in Table 2. The results indicate that all the variables are stationary at first difference. Once it is confirmed from the unit root tests that all the series are integrated at same order I 1 , we then proceed further to estimate a multivariate Johansen and Juselius[ 1 ] cointegration test to analyze the equilibrium relationship between stock prices and other financial variables.

The results of multivariate cointegration tests are presented in Table 3. Therefore, we carry on our further analysis on the basis of trace test-statistic in multivariate cointegration test. Since the international markets are strongly integrated due to advance technology in this age of globalization it is quite possible that local markets can also be influenced significantly by the external forces. Therefore we have considered a years monthly data that consist on local macroeconomic variables and foreign exchange rate as an external variable to analyze the impact internal and external factors on the stock prices in Pakistan.

If we look into the economic history of Pakistan and rest of the world, there are many ups and downs augmented by various economic and financial crises as well as natural catastrophes over the years. Such ups and downs most likely create disequilibrium in the local and international markets. But, the impact of such shocks on the markets remains for short time, while over the long-run period it becomes normal.

This can be a very useful point to note for policy makers and financial analysts to make more precise and accurate forecasting and to take necessary action in case of any uneven situation in the markets. The relationship between stock market, money market and foreign exchange has been observed very strong and consistent for the long-run time period[ 11 , 12 , 48 ]. But the strong integration of these markets can also pose an adverse impact on the economic stability if any one of these markets is redirected.

Having established a long-run equilibrium relationship between stock market and monetary variables, it is also important to analyze the speed of adjustment that brings back the equilibrium relationship in long-run. For this purpose, we employ the vector error correction model VECM and granger causality test-statistics for the whole sample period and sub-sample periods [4]. Due to the concerns that results of VAR models are sensitive to the orthogonalisation[ 61 ], we experimented various combinations of given variables in VAR system and the results reported here are the robust with respect to the possible orderings.

The results of normalized cointegration coefficients and error correction terms in VECM model are presented in Table 4. The normalized cointegration coefficient of money supply M2 is positive and significant for full sample period and also for the sub-sample period that may be due to the liquidity effect or the implicit relationship between stock prices and money supply.

The positive linkage between money supply and stock prices is supported by [ 11 , 20 , 38 , 39 ]. The negative and significant coefficients of interest rate for full sample period witness a long-run negative relationship between interest rate and stock prices in Pakistan. Although, the coefficients are insignificant in two sub-sample periods but they are also indicating a negative relationship between interest rate and stock prices.

In fact, the stock prices are based on discount rate and expected cash flows. If the interest rate changes are driven by the increased inflation rate, then such increase in interest rate may not have much serious impact on the stock prices because the increased cost of capital of the firms can be compensated by the high price level of commodities in the market.

On the other hand, if interest rates are increased by the central bank to control the inflation rate and firms are unable to increase the prices of commodities, then expected cash flows of firms will be reduced that will curtail the corporate earnings and will reflect the negative impact of interest rate increment.

The results of normalized coefficient for interest rate in cointegrating vectors are partially in line with Hasan and Javed[ 11 ] for Pakistan. Humpe and Macmillan[ 29 ] have also reported a negative relationship between stock prices and interest rate for US and Japan. Contrary to Ratanapokorn and Sharma[ 20 ], a negative cointegration relationship is identified between stock prices and exchange rate for full sample time period and second sample time period democratic regime.

Although, the currency depreciation can serve as a positive stimulus if the trade balance is in surplus, and the results of Ratanapokorn and Sharma[ 20 ] also in line to the positive linkage between exchange rate and stock price during the military regime. But in the support of negative connection between exchange rate and stock prices, it is argued that due to political and economic instability, there is a continuous fall in exchange rate against US dollar over the years that increases the input cost, and transmits negative impact on the stock prices.

Moore and Wang[ 22 ] also reported a negative connection between stock prices and exchange rate for both developed and emerging markets. For long-run causal relationship between stock market, money market and foreign exchange market, the results of VECM with zero restrictions in Panel-3B are also consistent to the results of cointegration vector in Panle-3A. The negative sign of ECT —1 with significant p -value in the first vector error correction model VECM for all three sample periods in which stock prices are taken as dependent variable, confirms the long-run causality running from money supply, interest rate and exchange rate towards stock prices.

Moreover, the significant and negative signs of ECT —1 in the VEC model for M2 during full sample period and democratic regime are also evident of long-run causal relationship running for stock prices, interest rate and exchange rate to money supply, while during the military regime error correction term ECT —1 is insignificant.

Similarly, for interest rate TBR , the ECT —1 is significant and negative during the full sample period while it is positive and significant during the first military regime and positive and insignificant during the democratic regime. In case of exchange rate ER , ECT —1 is also negative and significant during the full sample period while for the military regime it is positive and insignificant and for democratic regime, although it is negative but insignificant.

The overall picture from VECM results supports the presence of long-run causality relationship between all the variables during full sample period, whereas for the sub-sample periods these results are only valid for KSE index and money supply.

In order to check short run causal causality, we estimate pairwise granger causality test and results are reported in Table 5. The results of granger causality test show that there are significant differences in the causality relationship across the three sample periods. For example, in case of full sample period there is no short run causality relationship between M2 and KSE while during the military regime unidirectional causality moving from M2 to KSE and during the democratic regime there is bidirectional causality between M2 and KSE In case of TBR, the results indicate a unidirectional causality running from TBR to KSE during the full sample period and military regime, while there is no causality in either way during the democratic regime.

The significant differences in the granger causality relationship among the rest of macroeconomic variables have also been witnessed in the results which indicate the significance of political governance system in establishing the dynamic linkages of financial markets. But, if the coefficient of regression changes then W t tend to diverge from zero mean value line.

To test the divergence from the zero line, we assess the behavior of W t. Therefore, we have not made any extra treatment to the data series in our analysis and the results are based on the original data series in log form. Moreover, we also estimate the multivariate ordinary least square regression model as a robust check. The results of OLS regression model would help to compare the significance and direction of co-movement between stock prices and other monetary variables in our study.

The multivariate regression model can be specified as follows:. The results of regression model could be misleading and spurious if we use the non-stationary time series in the multivariate regression analysis. The results of OLS regression model justify the nature of relationship between stock market index and macroeconomic variables under study in case of Pakistan. Although the results of regression model show that the money supply and interest rate are insignificant variables in relation to stock prices during the full sample period but for exchange rate these results are consistent to the results obtained through vector error correction model VECM.

Similarly, the results of multivariate regression model during the military regime are also consistent with respect to signs of the coefficients while during the democratic regime the results for exchange rate show that exchange rate has positive relationship with KSE The positive connection between exchange rate and stock prices is strongly supported by Ratanapakorn and Sharma[ 20 ] among others.

The cointegration and causality relationship between stock market, money market and foreign exchange market has been established in this study. Unlike the other relevant studies[ 11 , 12 , 64 ] in Pakistan perspective, we have decomposed the sample period into two sub-samples in order to analyze the significance of good governance in the integration of financial markets in Pakistan. The first sub-sample represents the military regime, whereas the second sub-sample represents the democratic era.

Our empirical analysis is based on the multivariate Johansen and Juselius[ 1 ] cointegration test, vector error correction model VECM and pairwise granger causality tests by using monthly data from M1 to M A long-run equilibrium relationship between KSE index and other financial variables is identified by trace test-statistic under the multivariate Johansen-Juselius cointegration analysis for the full sample time period.

The coefficients of normalized cointegrating vector show that KSE has positive relationship with M2, while negative with TBR and ER during the full sample and two sub-sample periods only with one exception of ER during the military regime that has a positive connection with the KSE Furthermore, the results of vector error correction model VECM for KSE index and other financial variables indicate that there is a significant evidence of long-run causality moving from independent variables to dependent variable in the system of VAR during the full sample periods.

Finally, the results of pairwise granger causality test ascertain that M2 does not granger cause KSE and vice versa during the full sample period while there is a unidirectional causality from M2 to KSE during the military regime, and bidirectional causality during the democratic regime. Similarly, there is a unidirectional causality moving from TBR to KSE index during the full sample period and military regime while no causal linkages are identified during the democratic regime.

In case of exchange rate, there is a unidirectional causality moving from ER to KSE during the two sub-sample periods while it has no causal connections during the full sample period. Moreover, a strong causality relationship among the other financial variables M2, TBR and ER is also witnessed by the results of granger causality tests during all the sample periods. The overall results are evident that all the three markets are strongly integrated during the full sample period while the results for the two sub-sample periods are more favorable for the democratic regime.

The dependence of stock market is more evident during the both military and democratic regime and for the remaining financial variables. The results of long term causal relationship are more favorable during the democratic regime. It means that markets during the democratic regime are more interdependent as compared to military regime.

Only stock market reflects the movement in other financial variables during both regimes otherwise the markets show more close interaction during the democratic regime as compared to military regime. The stock market is strongly driven by the money market and foreign exchange market and vice versa.

Therefore, it is important for the investors, portfolio managers and financial analysts to make appropriate decisions by keeping in mind the behavior of money market and foreign exchange market during the different political governance systems. In the same way, the monetary policy makers and regulatory authorities should also set the monetary policy and market regulations accordingly. Based on the results of granger causality, it is important to consider the behavior of exchange rate in order to devise a well-directed internal policy regarding stock market and money market that could have the flexibility to absorb these external shocks.

As a matter of fact, when markets are strongly cointegrated they can be much sensitive to the behavior of each other and such sensitivity can leave some adverse effects if the dynamics of these markets are not dealt properly. Maximum likelihood estimation and inference on cointegration — With applications to the demand for money. Oxford Bulletin of Economics and Statistics, , 52 2 : — Monetary policy and the stock market: Theory and empirical evidence.

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