Vidyasagar University Journal of Commerce Vol. 20, 2015/ISSN 0973-5917 IMPACT OF MACRO ECONOMIC VARIABLES ON CALL MONEY MARKET RATE IN INDIA Anjala Kalsie* Abstract The objective of the paper is to study the impact of Wholesale Price Index (WPI), Index of Industrial Production (IIP), Oil price, Gold price, Balance of Trade (BOT), Foreign investments in India (FII in Equity), Purchasing Manager Index (PMI), Money Supply and USDINR currency prices on Call Money Market. Another objective is to build a predictive model using regression techniques based on these macroeconomic indicators. The analysis has been carried out based on the monthly time series data for the period April 2005 to December 2013. The results of granger causality shows that Money Supply causes movements in Call Money Market rate. Based on regression model it was determined that USDINR exchange rate, Balance of Trade, Brent Crude Oil Prices and Gold Price impacts monthly call rate. The paper concludes that more than 90% of the movements in call money rate can be explained by modelling the key economic factors through VAR. Key words: Call Money Market, Macro Economic Variables, Forecasting, VAR, Causality Test. 1. Introduction The call/notice/term money market is a market for trading very short term liquid financial assets that are readily convertible into cash at low cost. The money market primarily facilitates lending and borrowing of funds between banks and entities like Primary Dealers. An institution which has surplus funds may lend them on an uncollateralized basis to an institution which is short of funds. The period of lending may be for a period of 1 day which is known as call money and between 2 days and 14 days which is known as notice money. Term money refers to borrowing/lending of funds for a period exceeding 14 days. This market is governed by the Reserve Bank of India which issues guidelines for the various participants in the call/notice money market. The entities permitted to participate both as lender and borrower in the call/notice money market are Scheduled Commercial Banks (excluding RRBs), Co-operative Banks other than Land Development Banks and Primary Dealers. The average daily turnover in the call money market is around Rs. 12,000-16,000 Cr every day and trading occurs between 9 am to 5 pm on Monday to Friday and 9 am to 2 pm on *Assistant Professor, Dept. of Management Studies, University of Delhi, E-mail : kalsieanjala@gmail.com Kalsie Saturday. The trades are conducted both on telephone as well as on the NDS Call system, which is an electronic screen based system set up by the RBI for negotiating money market deals between entities permitted to operate in the money market. The settlement of money market deals is by electronic funds transfer on the Real Time Gross Settlement (RTGS) system operated by the RBI. The repayment of the borrowed money also takes place through the RTGS system on the due date of repayment. In India, money and credit situation is subject to seasonal fluctuation every year. The volume of call money transactions and the amount as well as call rate levels characterize seasonal fluctuation/volatility. A decrease in the call/notice money requirements is greater in the slack season (mid-April to mid-October) than in the buy season (mid-October to mid-April). Conventionally call money market rate depends upon certain factors such as Liquidity conditions, Reserve requirement A cut in CRR reduces call rates while an increase in CRR increases call rates. Structural factors such as government legislation, conditions of the stock markets and so on which affect the volatility of the call money rate and Liquidity changes and gaps in the foreign exchange market. The present paper is attempt to explain that call money rate besides the traditional factors also governed by macroeconomic factors such as Wholesale Price Index (WPI), Index of Industrial Production (IIP), Oil price, Gold price, Balance of Trade (BOT), Foreign investments in India (FII in Equity), Purchasing Manager Index (PMI), Money Supply and USDINR currency prices. The paper is divided into following sections section 2 is about the Literature Review, section 3 is about Objective and Methodology, section 4 talks about Analysis and Interpretation and section 5 finally concludes. 2. Literature review Regardless of the argument that federal funds rate and T-bill rate move together because they are linked as the expectations hypothesis holds there is a strong evidence of a co-integrating relationship between the federal funds rate and the T-bill rate (Sarno and Thornton, 2002). The higher spreads between the two-week market rate and the official repo rate result in lower money market volatility and rate dynamics at the short end of the money market curve, and hence, the effects at the longer end are much weaker (Wetherilt, 2002). In the Italian interbank market, largest increase in volatility and the most notable variations of its intraday pattern occur at the last working day of reserve maintenance period and at the end of each quarter. Furthermore, the overnight interest rate volatility is not influenced by trading volume. This finding by Palombini (2003) indicates the difference between a financial market, where interest rate level is determined by information arrival and the market for overnight liquidity, where the volume of trading is more influenced by institutional factors like the functioning of the payment system. While the call money rate is tracked reasonably accurately during surplus Vidyasagar University Journal of Commerce [ 125 ] Impact of Macro Economic Variables on ... liquidity conditions, the predictive power suffers a loss when liquidity shortage suddenly emerges. In addition, it argues that introducing a wider range of eligible collateral in the repo market could help in improving the efficiency of interest rate targeting (Palombini, 2003). (Parmar,2002)The inter-linkages, which have been studied under a co-integration framework, suggest the existence of one co-integrating vector among them. Furthermore, this long-term relation implies that the volatility in one of the markets possibly gets transferred to the other markets. So, players in these markets keep shifting their funds in the expectation of earning higher returns and to reduce their exposure to risk. Bhatt and Virmani (2005) talk about cross border market integration that is between Indian Money Market and USA money market.The motivation of the paper arises out of capital mobility precipitated by globalization. Here it needs to be pointed out that the money market deals in short term instruments and is essentially about liquidity rather than capital funds. Our approach is to observe the Indian Money Market with respect to domestic market integration especially because short term funds are not likely to be sought from international markets . There may be an exception to this which is about the foreign exchange market since banks may need liquidity in foreign exchange. However, the foreign exchange market is not similar to the short term credit market, in as much as foreign exchange as an asset is not interest bearing. While in the money market, all other instruments are fixed interest bearing instruments. The short-term (up to 3 month) money markets in India are getting progressively integrated with those in the USA even though the degree of integration is far from perfect. Covered interest parity was found to hold for while uncovered interest parity fails to hold. (Jain and Bhanumurthy, 2005). There are various issues related to integration of financial markets in India. Given the growing movement of capital flows, particularly short-term capital, into the domestic financial markets, it is necessary to examine this issue so as to reap the positive benefits with having stable markets. There study found that there is a strong integration of the domestic call money market with the LIBOR. Though there is a long-term co-movement between domestic foreign exchange market and LIBOR this may be due to frequent intervention by the Central Bank in the foreign exchange market. As the Government securities market in India is still in the developing stage, it was not found to be integrated with the international market. Policy measures (or reforms) are necessary to increase integration of financial markets. This would help in reducing the arbitrage advantage in some specific segment of the financial markets. Similarly Porter and Xu (2012) examine interest rates in China which are composed of a mix of both market-determined interest rates (interbank rates and bond yields), and regulated interest rates (retail lending and deposit rates), reflecting China’s gradual process of interest rate liberalization. The movements in administered interest rates are important determinants of market-determined interbank rates, in both levels and volatility. The announcement effects of reserve requirement changes also influence interbank rates, as well as liquidity injections from open market operations in recent years. Also the regulation of key retail interest rates influences the behaviour of market [ 126 ] Vidyasagar University Journal of Commerce Kalsie determined interbank rates, which may have limited their independence as price signals. Further deposit rate liberalization should allow short-term interbank rates to play a more effective role as the primary indirect monetary policy tool. The important link between the federal funds and repo markets, before, during, and emerging from the financial crisis that began in August 2007 has been evaluated byBech, Klee and Stebunovs (2011). In particular the initial transmission of monetary policy is closely related money markets, pricing of risk, and liquidity effects, and then how they interact if the Federal Reserve removes the substantial amount of liquidity currently in the federal funds market. The results suggest that pass-through from the federal funds rate to the repo deteriorated somewhat during the zero lower bound period, likely due to limits to arbitrage and idiosyncratic market factors. In addition, during the early part of the crisis, the pricing of federal funds, which are unsecured loans, indicated a marked jump in perceived credit risk. Moreover, the liquidity effect for the federal funds rate, or the change in the federal funds rate associated with an exogenous change in reserve balances, weakened greatly with the increase in supply of these balances over the crisis, implying a non-linear demand for federal funds. They also show simulations of the dynamic effects and balance sheet mechanics of liquidity draining on the federal funds and repo rates – a tool that might be used in an exit strategy to tighten monetary policy. 3. Objective and Methodology 3.1 Objective and Hypothesis: The objective of the papers is to understand the impact of key macroeconomic variables namely Wholesale Price Index (WPI), Index of Industrial Production (IIP), Oil price, Gold price, Balance of Trade (BOT), Foreign investments in India (FII in Equity), Purchasing Manager Index (PMI), Money Supply and USDINR currency prices on Call Money Market. The analysis has been carried out based on the monthly time series data gathered from the website of RBI, SEBI and Bloomberg database for the period January 2009 to December 2013. Reporting- Lag and Adjustment Many of the variables included in the study are reported with a time lag. For example, the monthly WPI data is reported with a time lag of approximately two weeks from the reference month. This means that the WPI data of the previous month will affect the stock market of the current month. So while considering the monthly impact of WPI on Nifty, we need to adjust this by considering a one month lag in the WPI data. Similarly, IIP is reported with a lag of six weeks, Balance of trade is reported with a time lag of two weeks, and the PMI of the month is usually released at the start of the next month. Hence the lag of these variables is suitably adjusted using E-VIEWS. Vidyasagar University Journal of Commerce [ 127 ] Impact of Macro Economic Variables on ... Table 1 : Lag Adjustement Variable Lag Adjustment (in months) WPI 1 Exchange Rate Nil IIP 2 Oil Price Nil Gold Price Nil Call Money Rate Nil Balance of Trade 1 FII in Equity Nil PMI 1 Source : Author’s calculation 3.2 Methodology 3.2.1 Correlation Matrix Multicollinearity is the undesirable situation where the correlations among the independent variables are strong. So before we proceed, it is utmost important to figure out that the variables are not mutually correlated amongst themselves. Logic behind assumption of no multicollinearity is simple that if two or more independent variables are linearly dependent on each other, one of them should be included instead of both, otherwise it will increase standard error thereby making our results biased. In order to check multicollinearity among independent variables, a Pearson’s correlation analysis has been performed. A suggested rule of thumb is that if the pair wise correlation between two regressors is very high, in excess of 0.8, a strong multicollinearity exists and may pose serious problem. 3.2.2 Time series properties of the Variables : ADF Unit Root test By already incorporating the percentage change (first difference) of the original time series data set, endeavour has been made to eliminate the trend line exhibited by the variables arising due to non- stationary time series data. If a further non-stationarity of data is observed, the same has to be eliminated by using a suitable statistical method, before conducting the regression. One of the common methods to find whether a time series is stationary or not, is the Unit Root test. There are numerous unit root tests. One of the most popular among them is the Augmented Dickey-Fuller (ADF) test. Augmented Dickey -Fuller (ADF) is an extension of Dickey [ 128 ] Vidyasagar University Journal of Commerce Kalsie Fuller test. Therefore before we proceed to the multiple linear regression, the ADF test is computed using E-VIEWS to ensure the stationarity of the data set. For ADF Test Null Hypothesis H0: The data set has a Unit-Root, data set is non-stationary Alternate Hypothesis H1: The data set doesn’t have a Unit-Root, data set is stationary 3.2.3 Granger Causality: Granger Causality test was applied to understand the impact of key macroeconomic variables such as Wholesale Price Index, Industrial Production Index, Oil Price, Gold Prices, Call Money Rate, Balance of Trade, FII investment in Equity and Nifty on USDINR currency prices. According to Granger (1969), this test will answer the question of whether X causes Y. Y is said to be Granger-caused by X if X helps in the prediction of Y, or equivalently if the coefficient on the lagged X are statistically significant. To show that X Granger cause Y, first step is to consider an autoregression for Y. Next, lagged values of X are added as the extra independent variables. Granger Causality test results are very sensitive to the number of lags used in the analysis. There are four different criteria for specifying the lag length. This study will adopt Akaike information criterion (AIC) suggested by Akaike (1974). The equation for the pairwise Granger causality tests are as follow: Yt   0  1Yt 1  ....   iYt 1  1 X t 1  ....   i X t 1  t (4) Where, Xt and Yt = Monthly time series data of Wholesale Price Index, Industrial Production Index, Oil Price, Gold Prices, Call Money Rate, Balance of Trade, FII investment in Equity Nifty and USDINR currency prices. The null hypothesis is rejected if the computed F-value exceeds the critical F value at the chosen level of significance (0.05). This implies that X does Granger cause Y. The test is performed in pair-form. µt = error term at time t The F test is used to test the hypotheses of the Granger Causality as follow: H0: β1 = β2= 0 (X does not Granger cause Y) H1: At least one of the β1 # 0 3.2.4 Regression Analysis: Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + β7 X7 + β8 X8 + β9 X9 + εi Where: Y Monthly change in the weighted average Call money rate (CMR) X1 Monthly percentage change in the average Gold Price (G): Rs per 10 gram X2 Monthly percentage change in the average Oil Price (OP): Brent Crude in USD per Barrel Vidyasagar University Journal of Commerce [ 129 ] Impact of Macro Economic Variables on ... X3 Monthly FII investments in Equity Market (FII_Equity): USD billion X4 Monthly percentage change in the Index of Industrial Production (IIP) X5 Monthly Money Supply: USD billion X6 Monthly percentage change in the Balance of Trade (BoT): USD million X7 Monthly percentage change in the Purchasing Manger Index(PMI) X8 Monthly percentage change in the Wholesale Price Index (WPI) X9 Monthly percentage change in the USDINR εi Error Term βi Co-efficient of the corresponding Regression variable β0 Constant term 3.2.5 Vector Autoregressive Regression Vector Autoregressive Model (VAR model) Vector autoregression (VAR) is an econometric model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate auto regression (AR) models by allowing for more than one evolving variable. All variables in a VAR are treated symmetrically in a structural sense (although the estimated quantitative response coefficients will not in general be the same); each variable has an equation explaining its evolution based on its own lags and the lags of the other model variables. yi,t = A(L)yi,t + B(L)xi, t + εi,t where : y and x are two variables of interest; L is lag operator; A and B are vectors of coefficients; εi,ti, t is regression residuals. The subscript t denote time. A VAR model describes the evolution of a set of k variables (called endogenous variables) over the same sample period (t = 1, ..., T) as a linear function of only their past values. The variables are collected in a k × 1 vector yt, which has as the i th element, yi,t, the time t observation of the i th variable. For example, if the i th variable is USDINR, then yi,t is the value of USDINR at time t. In the paper lag values of upto 4 was considered for VAR analysis An estimated VAR model can be used for forecasting, and the quality of the forecasts can be judged, in ways that are completely analogous to the methods used in univariate autoregressive modelling. 4. Results and Analysis 4.1 Correlation Analysis Before proceeding to analysis following corrections were applied to data points to make sure that errors do not creep in the regression analysis. The correlation results values among the independent variables were shown in Table 1 below. [ 130 ] Vidyasagar University Journal of Commerce Kalsie Table 2: Correlation values among variables CMR USD INRExchange rate PMI BoT USD MS IIP FII_Equity Brent_crude Gold price WPI 0.58 -0.29 -0.52 0.87 0.68 -0.04 0.87 0.92 0.71 Source : Author’s calculation Above table 2 indicates that there is a negative correlation between CMR and purchasing manager index (PMI), balance of trade BOT, and FII Equity. None of these negative correlation were high. Positive and high correlation exists between CMR and money supply (MS), gold price, WPI and Brent crude oil. 4.2 Stationarity Analysis The Augmented Dickey-Fuller (ADF) test shows for all the ten variables the level data was non-stationary except for FII Equity ; however stationarity was reached after the first difference. Since data at Level is not stationary we take 1st difference. The paper finds that 1st differenced data is stationary as absolute value of the ADF test statistics is larger than test critical values 5% level of significance. The results of Unit root test was shown in Table 3 below: Table 3 : Results of Stationarity at levels and first differences Intercept LEVEL Trend+Intercept None Intercept -2.026943 -3.585422** -1.808609 -2.447715 -5.214117* -5.205255* -2.377305 -1.35683 -84.76179* -1.49051 1.380326 0.199348 -0.146217 10.25342 0.626797 -3.759249* 0.652097 2.004529 0.559304 0.742683 -5.730352* -6.720221* -9.899097* -8.381416* -5.590395* -5.597674* -5.656542* -10.1713* -8.504337* -8.820004* -9.949796* -2.058284** -13.42606* -34.99847* -6.972322* -120.895* -5.948911* -35.01355* -6.944941* -117.9683* -5.899839* -34.97641* -6.545907* -84.0144* -5.791742* USD Exchng rate_avg mnth 0.349607 PMI -2.638414*** Monthly BoT USD -2.465798 mny supply IIP 0.847549 FII_Equity -3.786811* Brent_crude_daily -5.25378* Avg gold prc -2.269813 WPI Wt Avg Monthly CMR -1.192566 -8.324109 -0.989202 Source : Author’s Calculation *1% level of significance **5% level of significance ***10% level of significance Vidyasagar University Journal of Commerce [ 131 ] FIRST DIFFERENCE Trend+Intercept None Impact of Macro Economic Variables on ... 4.3 Results of Granger Causality The results of granger causality was shown in Table 4 below. The results of granger causality was shows that Money Supply causes movements in Call Money Market rate as it is evident from p value. Table 4 : Results of Granger Causality Equations WT_AVG_MONTHLY_CMR does not Granger Cause FII_EQUITY FII_EQUITY does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause IIP IIP does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause MNY_SUPLY MNY_SUPLY does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause MONTHLY_BOT_USD MONTHLY_BOT_USD does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause PMI PMI does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause USD_EXCHNG_RATE_AVG_MNTH USD_EXCHNG_RATE_AVG_MNTH does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause WPI WPI does not Granger Cause WT_AVG_MONTHLY_CMR AVG_GOLD_PRC does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause AVG_GOLD_PRC BRENT_CRUDE_DAILY does not Granger Cause WT_AVG_MONTHLY_CMR WT_AVG_MONTHLY_CMR does not Granger Cause BRENT_CRUDE_DAILY Source: Author’s calculation No. of variables 56 56 56 56 56 56 56 56 56 [ 132 ] t Stat 0.20245 P-value 0.9358 Results Accepted 1.68542 0.1692 Accepted 1.62181 0.1845 Accepted 1.14071 0.349 Accepted 0.51691 0.7236 Accepted 2.09563 0.0963 Rejected 1.29124 0.287 Accepted 1.42282 0.2411 Accepted 0.77415 0.5476 Accepted 0.66439 0.6199 Accepted 1.07982 0.3772 Accepted 1.3591 0.2624 Accepted 1.3862 0.2532 Accepted 0.51559 0.7246 Accepted 0.30535 0.8729 Accepted 0.87811 0.4843 Accepted 0.57509 0.6821 Accepted 2.08426 0.0978 Rejected Vidyasagar University Journal of Commerce Kalsie 4.4 Results of Regression Analysis The results of the regression analysis were summarised in the Table 5 below. Based on regression model USDINR exchange rate, balance of trade, Brent Crude Oil Prices and Gold Price significantly impacts monthly call rate as it is evident from t stat. the adjusted R square is 0.89 which indicate good fit of the model. Call rates increase during volatile forex market conditions. This increase is a result of monetary measures for tightening liquidity conditions and short position taken by market agents in domestic currency against long positions in US dollars in anticipation of higher profits through depreciation of the rupee. Banks fund foreign currency positions by withdrawing from the inter bank call money market which leads to a hike in the call money rates. Table 5 : Results of Regression Analysis Variables INR USD Exchange rate PMI Monthly BOT Money supply IIP FII_Equity Brent_Crude_Oil Price Gold price WPI Null Hypothesis INR USD Exchange rate has no significant relationship with Avg. Monthly Call Rate PMI has no significant relationship with Avg. Monthly Call Rate Monthly BOT has no significant relationship with Avg. Monthly Call Rate Money supply has no significant relationship with Avg. Monthly Call Rate IIP has no significant relationship with Avg. Monthly Call Rate FII_Equity has no significant relationship with Avg. Monthly Call Rate Brent_Crude_Oil Price has no significant relationship with Avg. Monthly Call Rate Gold price has no significant relationship with Avg. Monthly Call Rate WPI has no significant relationship with Avg. Monthly Call Rate Source: Author’s calculation Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.953425 0.909019 0.892643 0.707897 60 Source: Author’s calculation Vidyasagar University Journal of Commerce [ 133 ] t Stat P-value Results -1.78063 0.081049 Reject Accept -1.12707 0.265093 3.328228 0.001646 Reject 0.796034 0.429776 Accept -0.03704 0.970597 Accept -0.34304 0.733003 Accept 2.129474 0.038162 Reject 5.444085 1.58E-06 Reject -1.36713 0.177701 Accept Impact of Macro Economic Variables on ... 4.5 Results of VAR The paper concludes that more than 90% of the movements in call money rate can be explained by modelling the key economic factors through VAR. the value of call money rate is affected by the lag values of Oil price (up to four lags), call money market rate (up to three lags), Foreign investments in India (up to a certain limit) and lag values of money supply. The monthly call money market rate is also effected to a certain extent by the lag values of Balance of Trade, Purchasing Manager Index (PMI) and Wholesale Price Index (WPI). The results of VAR model is reported in Table 6 below. Table 6: VAR model results Equation: WT_AVG_MONTHLY_CMR = C(361)*USD_EXCHNG_RATE_AVG _MNTH(-1) + C(362)*USD_EXCHNG_RATE_AVG_MNTH(-2) + C(363) *USD_EXCHNG_RATE_AVG_MNTH(-3) + C(364)*USD_EXCHNG_RAT E_AVG_MNTH(-4) + C(365)*AVG_GOLD_PRC(-1) + C(366) *AVG_GOLD_PRC(-2) + C(367)*AVG_GOLD_PRC(-3) + C(368) *AVG_GOLD_PRC(-4) + C(369)*BRENT_CRUDE_DAILY(-1) + C(370) *BRENT_CRUDE_DAILY(-2) + C(371)*BRENT_CRUDE_DAILY(-3) + C(372)*BRENT_CRUDE_DAILY(-4) + C(373)*FII_EQUITY(-1) + C(374) *FII_EQUITY(-2) + C(375)*FII_EQUITY(-3) + C(376)*FII_EQUITY(-4) + C(377)*IIP(-1) + C(378)*IIP(-2) + C(379)*IIP(-3) + C(380)*IIP(-4) + C(381)*MNY_SUPLY(-1) + C(382)*MNY_SUPLY(-2) + C(383) *MNY_SUPLY(-3) + C(384)*MNY_SUPLY(-4) + C(385) *MONTHLY_BOT_USD(-1) + C(386)*MONTHLY_BOT_USD(-2) + C(387)*MONTHLY_BOT_USD(-3) + C(388)*MONTHLY_BOT_USD(-4) + C(389)*PMI(-1) + C(390)*PMI(-2) + C(391)*PMI(-3) + C(392)*PMI(-4) + C(393)*WPI(-1) + C(394)*WPI(-2) + C(395)*WPI(-3) + C(396)*WPI(-4) + C(397)*WT_AVG_MONTHLY_CMR(-1) + C(398)*WT_AVG_MONTHLY_ CMR(-2) + C(399)*WT_AVG_MONTHLY_CMR(-3) + C(400) *WT_AVG_MONTHLY_CMR(-4) Observations: 56 Mean dependent var R-squared 0.9943 6.681313 S.D. dependent var Adjusted R-squared 0.980406 2.118758 S.E. of regression 0.296579 Sum squared resid Durbin-Watson stat 2.22396 1.407347 Source: Author’s calculation 5. Conclusion Correlation exercise shows that there is a negative correlation between CMR and purchasing manager index (PMI), balance of trade BOT, and FII Equity. None of these negative correlation [ 134 ] Vidyasagar University Journal of Commerce Kalsie were high. Positive and high correlation exists between CMR and money supply (MS), gold price, WPI and Brent crude oil.ADF test shows for all the ten variables the level data was non-stationary except for FII Equity ; however stationarity was reached after the first difference.The results of granger causality was shows that Money Supply causes movements in Call Money Market rate. The result of regression analysis shows that USDINR exchange rate, balance of trade, Brent Crude Oil Prices and Gold Price significantly impacts monthly call rate. The paper concludes that more than 90% of the movements in call money rate can be explained by modelling the key economic factors through VAR. the value of call money rate is affected by the lag values of Oil price (up to four lags), call money market rate (up to three lags), Foreign investments in India (up to a certain limit) and lag values of money supply. The monthly call money market rate is also effected to a certain extent by the lag values of Balance of Trade, Purchasing Manager Index (PMI) and Wholesale Price Index (WPI). References Bhatt, V. and Virmani, A. (2005). Global integration of India’s money market: Interest rate parity in India, ICRIER Working Paper Series; Working paper no.164, July. Bhole, L. M. (2004). Financial institutions & markets. Tata McGraw Hill Publishing Company Limited; Fourth Edition. Jain Surbhi and Bhanumurthy, N. (2005). Financial markets integration in India, Asia-Pacific Development Journal, 12(2). Joshi, Himanshu (2004). The Interbank Money Market in India: Evidence on Volatility, Efficacy of Regulatory Initiatives and Implications for Interest Rate Targeting. Reserve Bank of India Occasional Papers, 25 (1, 2 & 3). Morten Bech, Elizabeth Klee, and Viktors Stebunovs (2011). Arbitrage, liquidity and exit: The repo and federal funds markets before, during, and emerging from the financial crisis. Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Murthy and Goel (2009). Financial liberalization and market efficiency: Testing for market integration of the money market in India. First international conference on Time series and applied econometrics held at IBS, Hyderabad. Nathan Porter, TengTeng Xu (2013). Money Market Rates and Retail Interest Regulation in China: The Disconnect between Interbank and Retail Credit Conditions. Bank of Canada Working Paper 20. Palombini, Edgardo (2003). Volatility and Liquidity in the Italian Money Market. Working Paper No. 6, Fondo Interbancario di Tutela dei Depositi. Vidyasagar University Journal of Commerce [ 135 ] Impact of Macro Economic Variables on ... Parmar, R. (2002). Empirical characterization of call money market in India, available at ssrn: http://ssrn.com/abstract=290559. Sarno, Lucio and Thornton, Daniel L. (2002). The Dynamic Relationship between the Federal Funds rate and the Treasury Bill rate: An Empirical Investigation. Working Paper 2000032C, Federal Reserve Bank of St. Louis. Wetherilt, Anne Vila (2002). Money Market Operation and Volatility of UK Money Market Rates, Working Paper No.174, Bank of England. [ 136 ] Vidyasagar University Journal of Commerce