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Monetary Policy Transmission in Financial Markets

Empirical Evidences from India

Edwin Prabu A ( is with the Department of Economic and Policy Research, Reserve Bank of India, Chennai. Partha Ray ( is with the Economics Group, Indian Institute of Management Calcutta, Kolkata.

In the Indian context, a key question is addressed: What has been the influence of monetary policy on different segments of the financial markets? Constructing a structural vector autoregressive model with the monetary policy rate, the pattern of monetary transmission to financial markets is examined over three distinct periods of regime changes in the Indian monetary policy and liquidity management framework. The empirical evidence indicates that there is sufficient period-specific transmission of monetary policy across the different segments of the financial markets. While the transmission of monetary policy to the money and bond markets is found to be fast and efficient, the impact of the policy rates on the forex and stock markets is limited.

The authors would like to thank Deepak Mohanty, K U B Rao, B M Misra, O P Mall, Himanshu Joshi, S Arunachalaraman, Mridul Saggar, Somnath Chatterjee, Indranil Bhattacharyya, Saurabh Ghosh and Anand Shankar for their comments on an earlier draft of the paper. Special thanks to the anonymous referee for some valuable suggestions and comments on an earlier version of the paper. The paper reflects personal views of the authors and not necessarily of the institution(s) to which they belong.

It is well known that transmission of monetary policy, to begin with, takes place via the financial markets. This paper looks into the impact of monetary policy across various segments of the financial markets. Specifically, four markets are being probed, namely money, bond (both government and corporate), forex, and the stock markets. Often these markets are interlinked by the virtue of the commonality of market players as well as the general sentiment across the different segments of the financial markets. In this context, the current paper seeks to address the questions—what has been the influence of monetary policy on different segments of the financial markets?

There are three discerning features of the study. First, it uses the daily data over a period from April 2005 to December 2018, to decipher the extent of monetary policy transmission across the money, government securities (G-secs), corporate debt, forex, and the equity segments of the Indian financial market.1 Second, the study also seeks to probe the transition of monetary policy transmission in three different periods of regime changes. Third, given the short run nature of the data (notwithstanding the number of observations), it uses structural vector autoregressive (svar) models to discern econometrically robust conclusions.2

We are, however, aware of the limitations of the study. First, high frequency data are often noisier and hence signal extraction could be difficult. But, monetary policy also functions in such a noisy data environment, and hence, a priori, a high frequency model is expected to be more useful. Second, ours is a story of integration among the different segments of the financial markets and the impact of monetary policy on them. We are, thus, quite narrow in our focus. We do not consider variables such as output, prices, or even fiscal policy, all of which could impact financial markets and the monetary policy decisions, too. Put differently, we are confined to a partial equilibrium approach where the market players in a particular segment of the financial market are concerned only with what is happening in the other segments and the monetary policy. With such limitations our analyses would potentially capture the very short-run and represent the behaviour of a typical financial market player on a day-to-day basis.

Financial Market Development in India

As a prelude to investigating the question raised in the paper, this section gives some details on the financial markets’ microstructure in India, particularly with respect to the four key segments covered in this study.3

Money market: The money market, being an overnight market, is crucial for the Reserve Bank of India (RBI), as its monetary policy is first transmitted to it. In terms of constituents, the money market has been fairly diverse in India. First, in the call money market (being an uncollateralised market and accounting for less than one-third of aggregate money market transaction) the funds are borrowed and lent for a very short period—usually a day—which is the key to monetary transmission. Second, collateralised borrowing and lending obligations (CBLO) are, in some sense, unique to the Indian money market. This product was developed by the central counterparty (CCP), the Clearing Corporation of India Limited (CCIL) and was introduced on 20 January 2003. Since the CBLO is collateralised, the rates in the CBLO market are, in general, below the call money market rate. With the development of CBLO market, there has been a substantial migration from the uncollateralised market to it. The CBLO was converted into triparty repo on 5 November 2018 after the CCIL obtained permission from the RBI to act as a triparty repo agent undertaking the CCP clearing of triparty repo transactions under its securities segment. Since its launch in November 2018, its share, in the overall money market transactions was close to 61%. Third, the market repo is another facility for banks and non-banks to manage their short-term liquidity mismatches, which operate outside the RBI’s liquidity adjustment facility (LAF) and the transactions are backed by collaterals.

Bond market: From the standpoint of the issuer, there are two distinct constituents of the bond market, namely the government securities market and the corporate debt market. Moving away from a repressive financial regime, various reforms were initiated in the debt market so as to impart market-driven elements in the borrowing programmes of both the central and the state governments. These reforms have resulted in an increasingly broad-based market, characterised by an efficient auction process and an active secondary market. Notwithstanding this, the government securities market continued to be illiquid at the long end. Further, the major investors in government securities are institutional investors, who hold securities to maturity, on account of statutory prescriptions, which makes the market illiquid. With the permission to the foreign institutional investors (FIIs) to invest in dated government securities and T-bills within certain specified limits, the secondary market is expected to be more liquid. The corporate bond market in India continues to be nascent despite the various measures taken from time to time, over the past 15 years.

Forex and stock markets: The Indian foreign exchange market was virtually non-existent from the 1950s to the 1970s as India was using trade controls for fostering import substitution. While the origin of the forex market in India can be traced to 1978 when the banks were allowed to undertake intra-day trade in foreign exchange, the market was very limited due to the fixed exchange rate system (RBI 2007). In the 1990s, as in other segments of financial markets, various reforms were taken in the foreign exchange market, too. The introduction of market-based exchange rate regime in 1993, the adoption of current account convertibility in 1994, and substantial liberalisation of the capital account over the years, have paved the way for the emergence of the forex market.

On the other hand, before the initiation of economic reforms in the early 1990s the Indian stock market was governed by a plethora of complex regulations and extensive restrictions. Apart from the establishment of a regulator, namely the Securities and Exchange Board of India (SEBI), stock market reforms have focused on regulatory effectiveness, enhancing competitiveness, reducing information asymmetries, developing modern technological infrastructure, and mitigating the market transaction costs (Mohan 2004). As part of the reforms, however, the FIIs were allowed to participate in the market in 1992, while the Indian corporate sector was allowed to raise funds in the international market subject to certain conditions.

Developments of the Indian financial market would, therefore, imply a certain kind of integration between these different segments of the domestic market. This in turn would depend on the commonality of the key players in the market as well as their nature, that is, whether the firms are short term or long term (RBI 2007). Illustratively, one would expect that the money market and forex market to be integrated, not only because both markets are short term in nature, but also due to the key role played by commercial banks in both these markets. Conversely, even though money market is short term in contrast to the government securities markets, yet due to the dominant role played by commercial banks in the bond markets through the statutory liquidity ratio (SLR) mechanism, there could be certain integration between these two markets. Similarly, one would expect certain integration between the forex and the stock markets on account of the key role played by the FIIs. With the increased exposure of the FIIs in the stock market, they may hedge their exposure in foreign exchange through the forward market. We also expect close integration between the government securities yield and yield on corporate bonds, as the former are risk-free assets on which the corporate bonds are priced adjusted for risk premia.

Evolution of Monetary Policy in India

Before we proceed further, some details on the data used in the rest of this study are in order. For monetary policy transmission, we have used the weighted average call money rate for the money market segment; 10-year government securities yield for the G-secs market; 10-year AAA corporate bond yields for the corporate bond market; spot market exchange rate (rupees per dollar) for the forex market and the CNX-Nifty index4 for the stock market. These apart we have used the repo rate as the monetary policy instrument.5

It may be useful to go back to history at this juncture. Since the mid-1980s till 1997–98, the RBI had been following a monetary targeting framework based on the recommendations of an internal committee which viewed the existence of a stable relationship between money, output and prices (RBI 1985). In this framework, annual growth in broad money (M3) was used as an intermediate target of the monetary policy (Mohanty 2010). However, with the introduction of economic reforms in the financial sector, and increasing integration with the global markets, many of the postulates, particularly the assumption of stable money demand function, were found to be no longer valid. Accordingly, in April 1998 the RBI adopted a “multiple indicator approach,” under which a variety of indicators, including high frequency financial market rates, such as call rate, exchange rate, etc, and fortnightly/monthly indicators, such as deposit and credit growth, inflation, fiscal, trade and currency data are tracked along with the output data for monetary policy purposes.

The operating procedure of monetary policy, also, has undergone a sea-change following the financial sector reforms and the unprecedented capital flows from direct instruments to the indirect instruments, such as interest rates, in the 1990s. The RBI operationalised the LAF in June 2000 to manage liquidity, including that emanating from the large and persistent capital flows. Consequently, all liquidity management operations were based on the open market operations (OMO) in the form of outright purchases/sales of government securities and daily reverse repo and repo operations under the LAF. The LAF has enabled the RBI to modulate day-to-day liquidity under varied financial market conditions. In this new operating environment, changes in the repo and reverse repo rates have become primary instruments for interest rate signalling (Figure 1).

In May 2011, based on the report of the RBI (2011a) Working Group on the monetary policy operating procedures, the RBI had introduced a new operating procedure. The weighted average overnight call money rate was now the operating target of the monetary policy. The repo rate was the only independently varying policy rate. A new marginal standing facility (MSF) was instituted from which scheduled commercial banks (SCBs) can borrow overnight up to 1.0% of their respective net demand and time liabilities (NDTL) at 100 basis points above the repo rate. With the above changes, the revised LAF corridor had a fixed width of 200 basis points. The repo rate had been placed in the middle of the corridor, with the reverse repo rate at 100 basis points below it and the MSF rate at 100 basis points above it (RBI 2011a).

The liquidity framework was fine-tuned in September 2014 following the recommendation of an expert committee to revise and strengthen the monetary policy framework, chaired by the then governor, Urjit Patel. Under this new framework, predominant liquidity was provided through the variable term repo auctions, particularly, the 14-day term repo rate rather than unlimited accommodation of liquidity given at the repo rate earlier. It also introduced fine-tuning operations through the repo or reverse repo of varying maturities (up to 90 days) based on liquidity forecasting.

An important break in the operation of the monetary policy has been the adoption of the flexible inflation targeting in India since 2016. In fact, after considerable discussion a Monetary Policy Framework Agreement (MPFA) was signed between the Government of India and the RBI on 20 February 2015 (Mohan and Ray 2018). Its broad contours are as follows:

(i) Government has set a target for the RBI to bring down inflation below 6% by January 2016, to 4% for the 2016–17 financial year and for all subsequent years within a band of +/- 2%;
(ii) If the RBI fails to meet the target, it will report to the government with the reasons for the failure and propose remedial actions to be taken; (iii) The RBI will further estimate the time period within which the inflation target would be achieved following the implementation of the remedial measures.

Subsequently, there were statutory changes and the RBI Act was amended on 14 May 2016 to give the key provisions in the changed monetary policy framework a legal basis.Thus, monetary policy in the recent period has evolved with the changes in the financial sector and the increasing integration of the Indian economy with the global economy.

Empirical Results

The descriptive statistics of these variables reveal a number of interesting trends (Table 1). Return series for forex and stock market shows positive return. The skewness is positive for call rate and exchange rate, while other financial market variables are negative, indicating that there is an asymmetry of the probability distribution. All the variables except bond yields displays a kurtosis of more than three, thus indicating that the density function is characterised by fat tails.

In order to quantify the evidence on monetary policy transmission across different segments of the financial market, we used SVAR modelling. There are two reasons for this. First, given that co-integration is a long run concept, analytically, we felt that the span of 13 years (January 2005 to December 2018) is not sufficiently long to treat it for discerning a co-integrating relation.6 Furthermore, the continuous time specification ensures that the discrete time model satisfied by the observed data is independent of the sampling frequency, a feature that is not always true in temporal aggregation of discrete time models (Chambers 2011). After all, having high frequency data is no substitute for the length of the period. Second, it has been argued that co-integration among the markets essentially involves an error correction mechanism and implies that the co-integrated variables tend towards an equilibrium situation, by which the divergence between their values keep on vanishing in the short run. This adjustment by the market interest rates may lead to arbitrage opportunities and hence inefficiency in the market. Thus, co-integration and the other standard measures of degree of market integration could actually show the linkage among the markets and that closer linkages do not necessarily imply higher financial market integration (Ayuso and Blanco 1999). Besides, the strength of the SVAR framework is that it allows one to explore the dynamic linkages in an empirical model and its relationship with the underlying theoretical/prior foundation. These dynamic linkages are represented in the form of impulse response functions, which are easy to interpret.7 We, thus, proceed to estimate a SVAR of the form:8


where the Mi’s are (nxn) coefficient matrices and u1= (u1t,u2t,...,unt)is an unobservable independent and identically distributed (i.i.d.), zero mean error term, which are structural innovations, and et’s are the reduced form innovations.

In our choice of the variable vector, r = [r1, r2, r3, r4, r5, r6 ] = [Repo Rate, Call Rate, 10 Year G-secs rate, corporate bond rate, rupee dollar exchange return, Nifty return], k is the selected lag length and N denotes the transpose of a column
vector.9 These apart, we have included a constant term, a fortnightly dummy as an exogenous dummy to reflect the behaviour of the money market, so as to take care of the peculiarity of the commercial banks in India who usually frontload the maintenance of bank reserves with the RBI at the start of the reporting fortnight and drawdown in the second week.10

A SVAR model can be used to identify shocks and trace these out by employing impulse responses and/or forecast error variance decomposition through imposing restrictions on the matrices M and/or N. The restrictions on the M and N in the SVARs are as follows.

Met=Nut, or, ...(2)

For identifying restrictions, based on the stylised facts presented so far, we assume the following. While innovations in policy rate are self-determined, innovations in call rate are determined by policy rate; innovations in 10-year government securities rate is determined by policy rate and call rate; innovations in 10-year corporate bond yield is determined by policy rate and 10-year government securities rate; innovations in market exchange rate is determined by policy rate, call rate, G-secs rate and corporate bond yield and innovations in Nifty is determined by policy rate and market exchange rate. These restrictions are in general in tune with the block exogeneity Chi-squared Wald tests. As explained, we have run four such SVAR models corresponding to four distinct periods. And the identifying restrictions are the following:

(i) The diagonal terms of the M matrix are unity, that is, m11 = m22 = m33 = m44= m55 = m66= 1, by construction.

(ii) The policy rate is thought to be the most primitive among all the shocks, so that m12 = m13 = m14 = m15 = m16 = 0. This also means that the central bank does not respond contemporaneously to shocks in financial markets.

(iii) Call money market is the first stage of financial market where the monetary policy get transmitted. Thus, m23 = m24= m25 = m26 = 0; this implies that innovations in call money gets affected by innovations in policy rate only but does not get affected by the innovations in other financial markets, namely G-secs, corporate debt, forex and stock market.

(iv) As far as G-secs market is concerned, its innovations are dependent on policy rate and call money rate; thus, m31 0, and m32 0. But, m34 = m35 = m36 = 0, implies that innovations in G-secs market are invariant to innovations in corporate bond, forex or stock market.

(v) Shocks to the corporate bond yield are thought to be affected by shocks in policy rate and G-secs yield; thus, m41 0, and m43 0. But all other shocks such as those emanating from money, forex or stock market may not get transmitted, implying m42 = m45 = m46= 0.

(vi) Shocks to the forex market is affected by repo rate, G-secs corporate bond and stock market; thus, m51 0, m53 0, and m54 0 m56 0but m52 0.

(vii) Finally, the stock market shocks are assumed to be affected by policy rate (via implicit interest rate differential) and exchange rate, so that m61 0 and m65 0.

It should be noted that while these identifying restrictions are primarily based on institutional details, these are not necessarily atheoretical. After all, term structure theories would predict transmission of policy shocks from the short-end to the long-end of the market. Besides, the restrictions also reflect the market practices and the implied constraints on mobility of funds across different segments of the financial markets that we have described earlier. Furthermore, in terms of changing operating procedure of monetary policy, we divided the whole period under consideration, into three sub-periods, namely (i) April 2005–April 2011, (ii) May 2011–June 2016, and (iii) July 2016–December 2018.

Interpreting the Impulse Response Functions

The impulse responses of the SVAR for the full period show that a positive shock to the repo rate,11 in general, leads to increase in the call rate (Figure 2a).12 The peak effect occurred instantaneously. Thereafter, the effect was moderated and turned negative around the sixth working day, but was not significant. This result could be due to the call money rate being affected more by the liquidity in the system than the repo rate, particularly till April 2011 due to large swings in capital inflows and outflows. The impulse response function of 10-year G-secs yield shows that it increases after a positive shock, reaching a peak effect in seven days, and thereafter maintained the positive increase. In the case of corporate bond yield, the monetary policy shock had a positive effect and showed trends similar to the 10-year G-secs yield. In the case of exchange rate and the stock market, the monetary policy shock did not show any consistent effect and is not statistically significant.

In the first period (that is, April 2005 to April 2011), the positive shock to the repo rate did not have any impact on the call money rate. This result could be due to the fact that the operating policy rate was interchanged between the reverse repo and the repo rates, depending on the liquidity conditions (mainly) determined by the capital inflows or outflows. This resulted in the call money rate breaching the interest rate corridor on certain occasions, which could have hampered the instantaneous transmission of monetary policy (Figure 2b). The positive shock in the repo rate showed expected positive impact, on the 10-year G-secs yield after six days, and was significant. However, in the case of corporate bond yield, the monetary policy shock showed similar trends with the 10-year G-secs yield and had long run positive impacts. In the case of forex market, the impulse response functions did not show consistent impact and were not significant. In the case of stock market, it first decreased instantaneously, but thereafter showed volatile impact and the impulses were not significant. In the second period (that is, May 2011 to June 2016), where the liquidity framework was fine-tuned with clear operating target and introduction of the term repo, the monetary policy transmission was very high. The positive shock to the repo rate increased the call money rate instantaneously, with the peak effect reaching on the third day. Thereafter the impact declined while retaining the positive impact (Figure 2c). The positive shock to the repo rate also had persistent positive impact on the 10-year G-secs yields and the 10-year corporate bond yields. The impact on 10-year G-secs yield peaked on the second day while for the corporate bond yield the peak effect was seen at around the seventh day. In the case of exchange rate, the monetary policy shock instantaneously appreciated the rupee but only marginally. However, the impulses were not statistically significant. In the case of stock market, it showed no instantaneous impact, rather declined marginally with the impulses being insignificant, again.

In the third period (from July 2016 to December 2018), following the introduction of the flexible inflation targeting, the positive shock to the repo rate had a significant and persistent impact on the call money rate as in the case of the second period (Figure 2d). The positive shock to the repo also affected the G-secs yield and the corporate bond yield positively, and had persistent impact. However, the impulse responses were marginally insignificant. This could be due to the market concerns about the fiscal deficit in the recent period. As regards the impulse responses of the exchange rate and the stock market, the impact is volatile and insignificant.

Broadly, these evidences are suggestive of the fact that the transmission of monetary policy works well in the call money rate, as its impact is immediate and robust particularly since May 2011, after the refinement of the liquidity framework. These results also indicate the primacy of call rate in the money markets and are in consonance with the official RBI stance of treating the weighted average of overnight call money rate as the operating target of monetary policy (RBI 2011a). On the impact of the monetary policy shocks on the stock market prices, the impulse response shows volatile and insignificant impact. This could be due to high alignment of the domestic stock market with respect to other international stock markets, as well as the role played by the foreign institutional investors.

As regards the period-wise transmission of the monetary policy, the comparison of the impulse responses gives the following results. First, the transmission was swift and persistent in the third period compared to others, where the liquidity was fine-tuned with the term, variable and the reverse repo operations. Next, the transmissions to the bond market, in terms of both the G-secs and corporate bond yields, were positive and persistent across the periods, indicating a dominant role played by the monetary policy therein. Transmission was not on the expected lines for the first period for the call money market rate as liquidity conditions vacillated between surplus and deficit due to capital inflows or outflows, which could have limited the transmission of impulses from the repo rate changes to the call money market rate. Thus, there is asymmetry in monetary policy transmission to the financial markets in India, with the transmission being faster and persistent to the call and bond markets, and less impactful for the forex and stock markets.

Policy Implications

We can discern some policy implications of the empirical results presented above, even though at the risk of broad generalisation.

First, in broad terms the results underline the importance of the banks in the transmission mechanism in India. Specifically, for the smooth transmission of the monetary policy, a safe and sound banking sector could be seen as a necessary prerequisite, given a bank-based financial system like India. This view of the active role played by the banks in monetary transmission, has also been being reinforced during the recent financial crisis, which highlighted the importance of soundly functioning financial intermediaries (ECB 2011).

Second, these results continue to underline the importance of the interbank money market among the monetary transmission channels and confirm the usage of call money rate as an operating target, since the impact of policy change is fairly immediate on the money market for most of the period. The money market has indeed become the link between the various financial markets in India, especially CBLO, market repo, G-secs and corporate bond yields.

Third, in view of the diverse impact of the monetary policy across the different segments of the financial market, a related policy issue is the case for coordination among the different market regulators and government in India. In this regard, an important initiative had been the setting up of the Financial Stability and Developmental Council (FSDC) in India, chaired by the finance minister, in December 2010. The FSDC is assisted by a subcommittee to be chaired by the governor, Reserve Bank of India. The new FSDC council works towards fostering more integration among financial markets and ensuring financial stability in the economy, without eroding the autonomy of the regulators.13 Furthermore, the RBI has continued to release the Financial Stability Report on a biannual basis (by end June and end December of every year), reflecting the collective assessment of the subcommittee of the FSDC on risks to financial stability and the resilience of the financial system.

Finally, in the absence of perfect capital account mobility and considerable capital account restrictions, these results could be indicative of possible contagion effects (or its lack) from the international financial markets. While India has benefited greatly from the integration with the world economy and the global financial markets, given the high integration between the stock and forex markets, any contagion in the international financial markets could induce volatility in these markets, particularly when there is FII outflow on account of risk aversion or flight to safety or familiarity. This, in turn, may affect the other markets. In fact, during the 2008 financial crisis, notwithstanding India’s negligible exposure to the United States subprime market, the Indian financial markets were affected significantly by the global shocks through the finance channel.


Monetary transmission is often implicitly seen to be a two-stage process whereby in the first stage monetary policy affects the different segments of the financial markets, and in the second stage, the impact of the financial markets gets transmitted to the real sector of the economy. The present paper looks into the first stage of this process. Our results indicate that the impact not only varies across different segments of the financial markets, but it is also sensitive to the operating procedure of the monetary policy. Expectedly, our results find the primacy of the call rate in the money markets and are in consonance with the official RBI stance of treating the weighted average of overnight call money rate as the operating target of monetary policy. In particular, there is differentiation in the monetary policy transmission to the financial markets in India, being faster and persistent for the call and bond markets, to the least impactful for the forex and stock markets. As far as the periodicity the transmission is concerned, in the first period (April 2005 to April 2011), the positive shock to the repo rate did not have any impact on the call money, while in both the second (May 2011 to June 2016, when the liquidity framework was fine-tuned with a clear operating target and introduction of term repo) and the third (July 2016 to December 2018, following the introduction of flexible inflation targeting) periods it was found to be quite high.

As already indicated, these impacts are the first stage impacts of monetary policy on financial markets. As is well known, monetary policy works through the Wall Street but wants to influence the Main Street ultimately. How can we link this story of financial markets to the real sector? This question remains unanswered in this paper and constitutes the agenda for further research.


1 We choose to ignore the credit market for two reasons. First, there is a large literature on credit channel of monetary policy looking into transmission of monetary policy to credit market in India (for example, Pandit et al 2006). Second, credit market has a number of regulatory restrictions that may allow only partial integration with other financial markets.

2 While most of these studies using co-integration related technique tried to discern the market integration patterns over a fairly long period of time in which the monetary policy operating procedure might have undergone changes, our study departs and develops a structural VAR (SVAR) model using high frequency data.

3 See RBI (2007) and Mohan (2007) for details of the different aspects of financial market development in India.

4 Forex and the stock market are used as returns while others are used in levels.

5As far as the sources of the data are concerned, while all the money market rates are taken from the CCIL, other market rates are taken from Bloomberg. The data on monetary policy rates were taken from theHandbook of Statistics on the Indian Economy of the RBI.

6 Illustratively, using Monte Carlo methods, Zhou (2001) showed the potential benefits of using high frequency data series and that when the studies are restricted by relatively short time spans, increasing data frequency may yield considerable power gain and less size distortion.

7In addition, it is possible to quantify the role of the individual structural shocks for the variability of the variables in the model. For instance, Gali (1992) presents a historical decomposition of the output series, which links different business cycle episodes to specific shocks hitting the economy.

8 Unit root test shows that all variables except monetary policy rate (repo rate) is stationary. In estimating the reduced form of VAR models, we have taken all the variables except stock and forex in level form. After all, there is an influential view that specifying a VAR in differences (even if the variables are non-stationary) would amount to losing information on the co-movement among the variables which, in fact, is our primary interest (Brooks 2002). Furthermore, it has been established that while VARs with non-stationary variables incur some loss in the estimator’s efficiency, the consistency properties of the estimators remain intact (Sims et al 1990). Even in the case of loss in efficiency of estimates, differencing the variables has not been recommended since the goal of VAR analysis is to study interrelationships among variables and not to determine efficient estimates (Sims 1980).

9 In estimating the first step reduced form VAR models the lags are selected on the basis of the AIC criteria. In all the cases, for most of the lags, we are able to accept the null hypotheses of no residual correlation (Portmanteau test) and the null hypothesis of no serial correlation (LM test).

10 Further, CBLO liabilities are eligible for cash reserve ratio maintenance, hence banks generally do not prefer CBLO market on a reporting Friday. So, the rate and the volume in CBLO generally go down on the reporting Friday.

11 A priori, one would expect a faster transmission of policy shock to different segments of the financial markets than their real counterparts. In this spirit, we first concentrate on the first day shock.

12 Due to space constraint, we have provided only the impulse response function of policy rate on all markets; the full set of impulse responses is available with the authors.

13 For example, Subbarao (2011) noted, “The Government has held out a clear assurance that the setting up of the FSDC will not in any way erode the autonomy of the regulators.” 


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Updated On : 29th Mar, 2019


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