This document is an abstract for a study investigating the impact of central bank intervention on exchange rate volatility in India from 2000-2011. The study uses a GARCH model to analyze the relationship between the monthly Rupee-USD exchange rate, net FII inflows, RBI intervention amounts, and interest rate differentials. The results found that RBI intervention did not have a significant impact on exchange rate volatility, unlike some previous studies. This contrasts with observable successful RBI interventions to support the falling rupee. The study could be improved by accounting for structural breaks like the financial crisis.

1. Central bank intervention and exchange rate volatility: Evidence
from RBI
Parth Khare
IGIDR
April 24, 2012
Abstract
In this study we investigate the impact of central intervention on exchange rate volatility. Starting
with a brief background on the channels of central bank intervention, the analysis resumes with
using monthly data on Rupee-US Dollar exchange rate, net FII inflows, net dollar purchases of RBI,
treasury bill rates of India and the US over the post-reform period. Further, GARCH (1,1) model,
is employed to test for the effectiveness of the intervention of RBI is in reducing exchange rate
volatility.
1

2. Contents
1 Introduction 3
2 Literature review 4
3 Data and Methodology 5
4 Results and interpretation 7
5 Conclusion 8
6 References 9
7 Notes: theoretical background 10
8 Appendix 12
8.1 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.2 Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2

3. 1 Introduction
Growth and development in past decade has been complemented by unprecedented levels of dispropor-
tionate trade amongst the economies, which calls for regulation or central bank intervention. Interven-
tion(direct), can be defined as the purchase and sale of foreign exchange assets by monetary authorities.
A central bank buys (or sells) a currency in the foreign exchange market in order to increase (or
decrease) the value its nations currency possesses against the benchmark currency. This can be used
to appreciate or depreciate a currencies value, which in turn impacts the exports and productivity of
a nation. The degree or extent of intervention is determined by the type of the exchange rate regime
adopted by the respective country.
From within the spectrum of exchange rate regimes of respective countries four broad forms of
significant and frequent intervention have been classified. They are Intervention, Operational, Concerted
and Sterilized intervention. (source: http://www.trading-point.com/central-bank intervention)
Intervention (jawboning) is known to be less complex and cheaper than any other type of intervention
as foreign currency reserves are not disrupted during the process. Representatives of the central bank
and the Ministry of Finance negotiate over a currency by talking up or down about it. Through this, they
determine a currency to be over or under valued. Under Operational intervention, concrete buying or
selling of the currency takes place. Even though this form of intervention is considered a lot simpler than
others, it is not however the most efficient and effective. For instance, it is not suitable for nations whose
central banks intervene often; they are more likely to use verbal intervention so as to be more effective.
Concerted intervention can also be verbal so that many representatives from varying countries can unify
and discuss apprehensions over a currency that may be continuously fluctuating. Through Concerted
intervention, nations unify to escalate or lower specific currencies with the use of their individual foreign
currency reserves. The effectiveness and success of this type of intervention relies on the amount of
nations involved and the overall amount of intervention (known as the breadth and depth).
Sterilized Intervention involves a central bank using its monetary policy practices; doing so through
adjusting its interest rate goals and its open market operations to intervene in the forex market. Another
way of describing the event of sterilizing a currency is when a central bank sells market instruments to
try and claw back excess funds. There is a possibility of Forex interventions to go unsterilized or perhaps
slightly sterilized when performances in the currency market are aligned along with monetary policies
as well as foreign exchange policies. See Notes for the theoretical mechanism.
3

4. With the recent crisis, the global economies adopting a spectrum of exchange rate regimes rather
than just the two extremes. The volume of trade, presence of thin or deep market, nature of political
regime and their respective risk attitude etc are some of the factors determining the nature of the regime
being adopted.
Indian financial markets integrated with global financial market in 1992, permitting foreign institu-
tional investors (FIIs) to invest in Indian capital market. Augmented with full capital account convert-
ibility there is a surge in foreign exchange inflows, which has not only led the rupee to appreciate but
has also added uncertainties to the financial markets and increased speculation in the foreign exchange
market. Historically, the Indian forex market, which is basically the market for USD/INR, has been
within 1 to 4 percent monthly volatility(source: G Nagaraju). Including the recent intervention by the
RBI in December 2011, despite there being some high volatile times, the exchange rate in India has
shown a more or less stable character.
This paper intends to examine the empirical relationship between the reserve bank of India’s in-
tervention and the exchange rate volatility. Using GARCH (1, 1) model on the the monthly data on
India-US bilateral nominal exchange rate, net FII inflows, net interventions of the RBI, interest rate
differential and inflation rate differential between India and the US are used.
2 Literature review
IMF classifies Indian exchange rate regime as “managed floating” regime. Under such regime, the
exchange rate is closely watched and the movements are assessed against the fundamentals of the economy
and the developments in the rest of the world. When the authority simultaneously or with a very short
time lag take the necessary steps to offset the effects of the change in official foreign asset holdings on the
domestic monetary base, it is called sterilized intervention. Non-sterilized intervention affects exchange
rate by inducing changes in the stock of the monetary base.
When the central bank sells foreign currency assets for domestic currency assets, other things being
equal, this creates an excess supply of foreign currency assets, and an excess demand for domestic
currency assets. To re-establish the equilibrium economic agents need to be compensated by a higher
expected return on foreign currency assets. This may take the form of a widening interest-rate differential,
or an appreciation of the domestic currency. Thus, even sterilized intervention could have an effect on
exchange rates. There is however, not much empirical support for the working of intervention through
4

5. this channel Dominguez and Frankel (1993). Fatum and Hutchison (1999) investigated whether daily
intervention operations in the United States are related to changes in expectation over the stance of
future monetary policy using a GARCH model. They concluded that the interventions of Federal
Reserve appear to have significantly increased the conditional variance of Federal funds futures rate as the
conditional variance is positively and significantly related to the magnitude of intervention operations. In
the more recent literature, the effectiveness of central bank intervention is examined through its impact
on exchange rate volatility, where the volatility variable used is either conditional volatility or implied
volatility, calculated through GARCH models and from currency option pricing models respectively. In
several cases, it is found that intervention increases the volatility in the foreign exchange market.
Almekinders and Eijffinger (1994) using conditional volatility estimated by the GARCH model. con-
cluded central banks intervention in context of mark-dollar and yen-dollar exchange, as having largely a
non significant impact. Similar study was conducted by Dominguez (1998) explores the effects of foreign
exchange intervention dollar-mark and dollar-yen exchange rates and concluded that secret interventions
generally increase volatility.
In Indian context, Unnikrishnan and Ravi Mohan (2003), used ARCH procedure for period(1996:2002)
and concluded, that the RBI intervention affected the exchange rate level and volatility in the expected
direction. Behera,V. Narasimhan,K.N. Murty(2007) arrived at the same conclusion using data from 1995
to 2005, by using a dummy which coincides with the introduction of a package of policies (e.g. Resurgent
Indian Bond, RIB scheme) that were announced by RBI in August 1998 in order to contain the effect
of East Asian financial market crisis on Indian foreign exchange market. They introduced a dummy
variable to separate the sample period into two sub-periods, from June 1995 to July 1998 as pre-policy
period and from August 1998 onwards as post-policy period. Similar results were also concluded by
G nagaraju (2009) by creating hypothetical counterfactual scenarios. The current analysis in Indian
context paper draws from H.K. Behera,V. Narasimhan,K.N. Murty(2007) and G Nagaraju (2009) for
Reserve Bank of India.
3 Data and Methodology
The data period chosen from January 2000 to February 2011 with a monthly frequency. The variables
used in this study are:
• RBI intervention in Indian foreign exchange market
5

6. • Exchange rate return
• Interest rate differential and
• Net FII inflows (FII).
This data has been drawn primarily from RBI online database and Federal Reserve Bank of St.
Louis(for the US interest rate) generating a monthly series.
The (net) intervention variable (Inv) is defined as the net purchases of the US dollars by the RBI, i.e.
the difference between purchases and sales of foreign exchange by the RBI in core of rupees at contract
rate. RBI purchases and sales of foreign exchange include spot, swap and forward transactions in the
foreign exchange market. (Unit: million$)
The exchange rate return is computed as, Returnt = 100(ln(St) − ln(St−1)), using the bilateral
India-US nominal spot exchange rate, Re/$, (S).
Assessing the movement of the exchange rate, a gossamer of interrelated macroeconomic factors play
a significant role. Following from Behera,V. Narasimhan,K.N. Murty(2007), interest rate differential
between India and the US have been included the analysis. The interest rate differential (IntD) is the
difference between Indian 91-days Treasury bill rate and US 3-month Treasury bill rate, with monthly
data on both series.
The FII inflows have been considerably volatile, in the recent past (Figure 2), hinting of having an
impact on the exchange rate returns, therefore it has been included as a determinant of exchange rate
return and volatility. (Unit: million$)
The central bank intervention in the foreign exchange market affects exchange rate and its volatility
by changing the market fundamentals, expectations of future fundamentals or policies, and speculative
bandwagons. This is usually quantified through a regression model. In this study, we have related RBI
intervention to exchange rate level and its volatility using GARCH (1,1) model.
Returnt = a0 + a1Invt + a2FIIt + a3IntDt t (1)
t|Ωt−1 ∼ N(0, ht) (2)
ht = b0 + b1Invt + b2IFIIt + b3IntDtα 2
t − 1 + βht − 1 (3)
6

7. b0, α, β >0 and α + β <1
Equation (1) represents the mean equation in which the dependent variable is rate of return on
nominal exchange rate ( t S ) during a month. It is assumed that the average return depends on net
purchases of RBI ( Inv ), net FII inflows (FII). Further, it is also assumed that the random disturbance
term in the mean equation t has a conditional normal distribution with mean zero and variance ht.
Here, Ωt−1 indicates all the (lagged) information available to the participants in the foreign exchange
market at time t. According to theory, the intervention of a central bank is said to be effective if
the coefficient a1 is positive and significant. That means the increase in net purchases of RBI leads
to increase in exchange rate return. Thus, Indian rupee depreciates against the US $ as the net RBI
purchases increase. In equation (2), the conditional volatility depends on the same set of determinants
as that of the mean equation (1) along with two more determinants in the form of 2
t−1 and ht−1. RBI
intervention is said to be effective if an increase in net purchases of dollars lowers the volatility of the
monthly Re/$ returns. Hence, the expected sign for b1 is negative.
4 Results and interpretation
The variables were initially tested for the presence of unit root. The Dickey Fuller (DF),Augmented
Dickey Fuller(ADF) and Phillips Perron (PP) tests were conducted on the variables revealed that all
variables were stationary at 1% or 5% level of significance. Before employing GARCH, the distribution
of the exchange rate return series is analysed. The descriptive statistics reveal that the exchange rate
return series is positively skewed and leptokurtic, which indicates the presence of volatility in the return
series. Further, the J-B test has rejected the null hypothesis of normal distribution for this variable with
high level of confidence. This confirms the non-normality of the return series.
To assess the degree of volatility persistence, we have used the Wald test. The null hypothesis of this
test is α + β = 1, i.e. the error variance is integrated or non-stationary against the alternative α + β <1
or error variance stationary. In our case, the null hypothesis is rejected with high confidence level and
the sum of ARCH and GARCH coefficients, α + β = 0.56(Table 2), which is substantially lower than
unity. This confirms the stationarity of the variance and the presence of long memory. Further, the test
statistic being less than unity, it is still mean reverting. All these results confirm the adequacy of our
GARCH model.
The results obtained from the mean and variance equation are estimated together as a system. Mean
7

8. equation (Table-1), intervention variable has the expected positive sign, but it is highly insignificant.
The coefficient, a1 is positive and not statistically significant, which inhibits the causality or link between
central bank intervention and its impact on exchange rate. The estimated coefficients of the variance
(volatility) equation again does not confirm to the apriori understanding of impact of intervention on
volatility. This coefficient is expected to be negative to say that the RBI intervention is effective, i.e.
due to RBI intervention (net purchases) in the foreign exchange market, the volatility in the exchange
rate will decline. Similarily, the results failed to significantly establish a relation between volatility and
interest rate differential. The volatility seems to be affected significantly only by both the FII inflows
signifying that they increasing exchange rate volatility.
5 Conclusion
The results reveal that the central bank intervention does not have significant impact on volatility.
This result stands opposed to the similar researches conducted with the same variables for different
time period. Although the data drawn by the respective references studies were reported up until 2005,
implying that the current study has included almost 70 new observations. This should draw some weight
towards the results, but again this analysis has not taken into account for any structural break due to the
financial crisis by introducing a dummy for intervention, as undertaken by Behera,V. Narasimhan,K.N.
Murty(2007). Nevertheless, the result cannot be translated into a formal proposition in light of the
observable successful intervention by the central bank as recent as 6 months earlier where Reserve Bank
of India had intervened to effectively save the falling rupee.
8

9. 6 References
• “Relationship between exchange rate volatility and central bank intervention: An empirical analysis
for India”, H.K. Behera, V. Narasimhan, K.N. Murty (2007), IGIDR Working paper, 2007.
• “A Measure of Exchange rate Volatility”, G Nagaraju(2009), NIBM 2009.
• “Does Foreign and Exchange Intervention Work?” Dominguez and Frankel(1993) Journal of Money,
Credit and Banking, Vol 31, 1993, 54-69.
• “Is Intervention a Signal of Signal of Future Monetary Policy” Fatum and Hutchison (1999),
Quarterly Journal of Economics 110,(1995)
• “Foriegn Exchange Intervention: Theory and Evidence” Almekinders and Eijffinger (1994).
9

10. 7 Notes: theoretical background
Under the IMF classification of exchange rate regimes, Indian exchange rate management is classified as
managed floating regime. Under such regime, the exchange rate is closely watched and the movements
are assessed against the fundamentals of the economy and the developments in the rest of the world.
Based on its view, RBI resorts to moderate the exchange rate through intervention in the market. The
intervention takes the form of either buy or sell of USD in spot or swap markets. It is unknown what
level of exchange rate or volatility exactly trigger the intervention action by RBI and by how much of
intervention. However, the basic model is said to be as follows.
The exchange rate in India under the current regime, is by and large market determined. Even while
intervening, RBI takes care that through its operation, the basic market forces are not out of place.
The market players, while always keeping an eye on what could be the possible move by RBI, broadly
agree that the exchange rate moves based on the demand and supply forces. Therefore, the Indian forex
market could perhaps be studied by the regular supplydemand models of price determination.
The basic principle of price determination is that at any point of time, at the market clearing exchange
rate, the amount of forex purchased is exactly equal to the amount of forex sold.
TP = TS
TP represents the Total Purchase, ie., the amount of forex demanded in the market. It constitutes
the total of interbank and merchant demand. TS represent the Total Supply of forex in the market.
There are three sources of supply, viz., interbank, merchant and the autonomous supply by RBI by the
way of intervention. Separating RBIs intervention, the total supply can be written as sum of market
supply (MS which is from both interbank and merchant sections), and autonomous supply (AS which is
the autonomous supply from RBI) TP = MS AS
Autonomous Supply plays a crucial role in equating the total purchase with market supply. It is
important to understand the analytics of the AS very clearly. There are two kinds of intervention, 1)
Net Buy (+AS) and 2) Net Sell (-AS). Net Buy situation RBIs intervention as a net buyer in the market
usually comes when the perception is that exchange rate is falling below acceptable level. In the market,
there seem to be excess supply than what market is willing to absorb at the given price level. In order
to dry out the excess supply and to protect the exchange rate from falling further, RBI intervenes and
sucks the excess supply. In this situation, the market supply is more than what the market is purchasing,
and the difference between them is the autonomous supply of AS by RBI.
10

11. TP = MS AS
Therefore we have:
MS = TP + AS
Net Sell situation This situation is a corollary situation to the earlier one. The perceived price level
is higher than acceptable because of shortage of foreign exchange in the market. Therefore, to stabilise
the rate from further rising, RBI intervenes in the market by Net selling, augmenting the market supply
by +AS. The total purchase in the market is not only what is supplied by the market, but also supplied
by RBI. Therefore, market supply will be smaller than market purchase by AS
TP = MS + AS
Therefore we have:
MS = TP AS
In order to understand the effect of RBI in the exchange rate determination, the model should assess
the effect of absence of the autonomous supply AS in the price determination.
11

12. 8 Appendix
8.1 Figures
−20000020000400006000080000
Intervention by RBI(in Million $)
Intervention
2000 2002 2004 2006 2008 2010
Figure 1: Intervention by RBI over time
−6−4−20246
Exchange rate returns(%)
Returns
2000 2002 2004 2006 2008 2010
Figure 2: Exchange rate returns over time
12

13. 0246
Interest rate differential bw India and US(%)
Ratedifference(%)
2000 2002 2004 2006 2008 2010
Figure 3: India US interest rate differential
−20000−100000100002000030000
FII inflows(in Million $)
FIIinflows(inMillion$)
2000 2002 2004 2006 2008 2010
Figure 4: FII in India over time
13

14. 8.2 Estimation results
Dependent Variable: RETURN
Method: ML ‐ ARCH (Marquardt) ‐ Normal distribution
Date: 04/24/12 Time: 18:11
Sample: 2000M01 2011M03
Included observations: 135
Convergence achieved after 18 iterations
Presample variance: backcast (parameter = 0.7)
GARCH = C(5) + C(6)*RESID(‐1)^2 + C(7)*GARCH(‐1) + C(8)*RETURN +
C(9)*INTERVENTION_$M_ + C(10)*INT_DIFF_IND_US_ + C(11)
*FII__$M_
Variable Coefficient
Std.
Error z‐Statistic Prob.
INTERVENTION_$M_ 1.15E‐06 3.84E‐05 0.029954 0.9761
INT_DIFF_IND_US_ 0.256164 0.179543 1.426757 0.1536
FII__$M_ 9.06E‐05 9.52E‐05 0.951953 0.3411
C ‐1.02589 0.791366 ‐1.29635 0.1949
Variance Equation
C 2.810603 2.886177 0.973815 0.3301
RESID(‐1)^2 0.074544 0.183452 0.406338 0.6845
GARCH(‐1) 0.499762 0.489342 1.021295 0.3071
RETURN ‐0.00138 0.197695 ‐0.00696 0.9944
INTERVENTION_$M_ 3.56E‐05 8.72E‐05 0.40827 0.6831
INT_DIFF_IND_US_ ‐0.09506 0.274578 ‐0.34622 0.7292
FII__$M_ ‐0.00017 0.000112 ‐1.50441 0.1325
R‐squared 0.074342
Mean dependent
var 0.01254
Adjusted R‐squared 0.053144 S.D. dependent var 1.775645
S.E. of regression 1.727819
Akaike info
criterion 4.139757
Sum squared resid 391.0819 Schwarz criterion 4.376484
Log likelihood ‐268.434
Hannan‐Quinn
criter. 4.235956
Durbin‐Watson stat 1.494387
14