Transcript An Empirical Investigation on the Relationship between

AN EMPIRICAL INVESTIGATION ON THE RELATIONSHIP
BETWEEN ONSHORE AND OFFSHORE RUPEE MARKET
GAUTAM JAIN, PALANISAMY SARAVANAN, SHARAD NATH BHATTACHARYA
1
OBJECTIVES
 To analyse the movement of exchange rate in all the three markets that is spot, forward and Non-delivery
forward market (Singapore)
 To study the causal relationship between the three currency markets i.e. spot market, forward market and NDF
market
 To study the dynamics of the relationship by breaking the period into sub-periods and analysing the causal
relationship among the three markets
 To understand the cause of break point by looking at various macroeconomic events and policy decisions taken
by central bank
2
INTRODUCTION

In two years Indian currency has depreciated its value by approximately around 15 rupees and hence became one
of the most volatile currencies in the recent period

The nominal value of rupee has depreciated nearly 27.68% in the period 2004-05 to 2013-14
Rupee/USD
Trend in NEER Index (Trade Weighted): Base 200405
65
60.5
60
110
55
54.4
50
45
40
44.9
44.3
45.9
45.3
47.4
45.6
47.9
40.2
35
100
90
102.24
104.75
97.63
93.34
90.94
93.54
80
87.38
78.32
72.32
70
60
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14
30
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14
NEER(36 currency weights)
3
FOREIGN EXCHANGE MARKETS AND THEIR DEPTHS
Foreign
exchange
Offshore
trading
trading
currency
of emerging
wisemarket
scenario
currencies,
in
billions2013
ofbillions
US dollars
Currency
distribution
of
Global
global
foreign
foreign
exchange
exchange
market
market
turnover
turnover
in
of US dollars
2004
2007
2010
2013
Offshore Foreign
Share of
regional
Total Net-net
Spot
Currency
Offshore
basis,
daily
averagesRank
in April, inShare
billions of US
dollars Share FX Rank
Share
Rank
Share
Rank
Euro
share in
financial centers
UK
US
turnover
transactions Outright
exchange
swaps
options
zone
global1
(Hongkong,
USD
88.0
1
85.6
84.9
1
87.0
Instrument
1998 2001
2004
2007
2010
20131
(daily) forwards
swaps
Singapore)
EUR
37.4
2
37.0 turnover
2
39.1
2
33.4
2
Foreign exchange instruments
1,527 1,239 1,934
3,324
3,971
5,345
Total
5,34520.8
2,046
6803
2,228In percent
54 23.0 337
bn 17.2
JPY
3USD
19.0
3
3
Spot
transactions 678.7
568
631 1,488 16.4 2,0464.6
Emerging
market
currencies
67.4
USD
4,652
5884 386 12.9
2,030 1,0054 29.9
50 11.8 293
GBP
16.5
4 1,691 14.9
4
Outright
forwards
128
130
209
362
475
680
Emerging
Asian
currencies
277.2
59.2
25.3
18.8
8.4
2.6
AUD
6.0
6 754 6.6
7.6 766
5
5
EUR
1,786
1786
18 8.6
70
exchange swaps
734
95443.7 1,714
ChineseForeign
renminbi
86.1
72 656
181,759 5.8 2,2281.5
CHF
6.0
5
6.8
5
6.3
6
5.2
6
JPY Kong1,231
612
123 7
332 31
11
153
Hong
dollar
52.6
22.9
Currency
swaps 40.7
10
21 8.1
43 8.9 54 5.1
CAD
4.2
7 48.8
4.3
7
5.3 11.4
7 27.8 5 4.6
73.7
GBP
631
227
69
301
29
Singapore
dollar
65.4
15.5
Options and other products
87
60
119
212
207
337
BRL
0.3
21
0.4
21
0.7
21
1.1
191.5
Korean won
27.4
42.7
21.1
11.30 7.1
INR
53
15
24
10
3
Currency
INR
Indian rupee 0.3
20
28
0.7
53 19
1.0 26.9
15
15.1
1.0
8.5
201.1
Source : BIS
4
LITERATURE REVIEW

The focus of various studies i.e. Ludwig et al (2004), Pedroni et al (2004), Case et al (2001) was mainly on spillovers within
equity, fixed income securities and foreign exchange markets

Park (2001) examined the impact of financial deregulation on relationship between onshore and offshore market of Korean
won and concluded that the interrelationship is dynamic and varies with the extent of deregulation in the foreign exchange
market and liberalisation of capital flows. He argued that in the Korean economy with a managed float exchange rate and
restriction on capital flows, movements in the domestic spot market influences the NDF market. This was reversed as exchange
rate policy was shifted to free float and capital flow restrictions were reduced. The domestic market was mainly driven by
offshore NDF market where price innovations originated

Wang et al (2007) shows that the NDF market seems to be the driver for the domestic spot market of Korean won, while for
Taiwanese dollar, it is the spot market which contains more information and influences the NDF market

Ma et al (2004) provide evidence that volatility in NDF currency rates has been higher than that in local spot markets for six
Asian currencies namely Chinese renminbi, Indian rupee, Indonesian rupiah, Korean won, Philippine peso and New Taiwan dollar

Colavecchio et al. (2008) finds that offshore markets are important in price discovery process, particularly in Asian countries. It
is concluded that NDF markets do have significant impact on onshore markets. He emphasized that until full capital
convertibility is achieved, NDF market rates and activity are important information signal and thus need to be monitored by
investors and regulators
5
LITERATURE REVIEW

In India context there are only few studies, Sangita Misra and Harendra Behera (2006), they found out that NDF market is
generally influenced by spot and forward markets and the volatility spillover effect exists from spot and forward markets to
NDF market. Research was also done for volatility spillover in the opposite direction, i.e., from NDF to spot market, though it
was found that the extent of spill over is marginal

Behera et al. (2008) empirically explores the relationship between Central Bank intervention and exchange rate behaviour in
the Indian foreign exchange market and found that the intervention of the RBI is effective in reducing volatility in the Indian
foreign exchange market. In this study also while studying the relationship among spot exchange rate, domestic forward
exchange rate, off-shore exchange rate is conditional upon intervention done by RBI to curb the volatility and on various
macroeconomic shocks such global financial crisis

Guru (2009) also finds somewhat similar evidence on interdependencies between the NDF and onshore segments (spot and
forward) of rupee market. It is argued that dynamics of relationship between onshore and offshore markets has undergone a
change with the introduction of the currency future market in 2008 and returns in NDF market seem to be influencing the
domestic spot as well as forward market
6
LITERATURE REVIEW

Sharma (2011) focuses on the relationship between volatility in the exchange rate in the spot market and trading activity in the
currency futures. The results show that there is a two-way causality between the volatility in the spot exchange rate and the
trading activity in the currency futures market. The period for analysis was taken from 2007 to 2010 without any structural
breaks whereas proxy for the futures trading activity, the values of futures daily trading volume and futures open interest were
used

Goyal, et al. (2013) concluded that there exists a bidirectional relationship between onshore and NDF markets and relationship
is bidirectional but that bidirectional relationship turns unidirectional from NDF to onshore during the period when rupee
comes under downward pressure
7
METHODOLOGY
 The time period for the study is from January 2002 to February 2014. All the data series is monthly in nature
making total 146 observations. Data pertaining to Indian market is extracted from RBI publications. Data of
Singapore market is collected from Reuter’s database
 The study uses Toda &Yamamoto (1995) test which is a modified Wald test for restriction on the parameters of
the VAR (p) with p being the lag length of the VAR system. In their approach the correct order of the system (p) is
augmented by the maximal order of integration (m). It is in terms of avoiding integration and complexity that this
study adopts the Toda and Yamamoto (1995) procedure to improve the power of the Granger-Causality test
 . Toda and Yamamoto procedure is a methodology of statistical inference, which makes parameter estimation valid
even when the VAR system is not co-integrated
 One advantage of Toda and Yamamoto procedure is that it makes Granger-Causality test easier. Researchers do
not have to test cointegration or transform VAR into ECM
8
METHODOLOGY
 Suppose one want to see if Y affects X or vice-versa then he has to test the null hypothesis with the model as
shown below
 For the above, Null hypothesis: α1 (12) = α2 (12) = α3 (12) = …..αk (12) = 0 where α (12) are the coefficients of
X. If the null hypothesis is rejected, then the one-way effect can be confirmed
 Augmented Dickey Fuller (ADF) test is done to check the Stationarity of the series. If the series are not
stationary at levels, the order of Integration for each series is obtained and the maximum order is considered as d
9
STATIONARITY CHECK

All the three series are Non-stationary in nature as shown below, the Augmented Dickey-Fuller

The result above shows that all the three series are of I(1) in nature since the 1st difference of all the three series is stationary
(P-value < 0.05). However this will not affect the T-Y procedure to examine the granger causality
The ADF test without intercept and without Trend
The ADF test with intercept and without Trend
The ADF test with intercept and with Trend
ADF test
Series
ADF test statistic
P-value
-0.8301
0.3470
Series
LnSpot
Level
LnSpot
ADF test
statistic
P-value
Series
Level
-2.8128
0.0589
LnSpot
First Difference
-8.481
0.000
Level
-2.7140
0.0740
First Difference
-8.644
0.000
Level
-2.7440
0.0691
First Difference
-9.869
0.000
First
LnNDF
Difference
-8.460
0.000
Level
-0.8052
0.3656
LnNDF
LnNDF
statistic
P-value
Level
-2.2995
0.4312
First Difference
-8.675
0.000
Level
-2.1490
0.5135
First Difference
-8.848
0.000
Level
-2.1351
0.5215
First Difference
-10.083
0.000
First
LnFW
Difference
-8.620
0.000
Level
-0.8386
0.3510
-9.853
0.000
LnFW
LnFW
First
Difference
10
RELATIONSHIP BETWEEN SPOT MARKET AND NDF MARKET

VAR model was setup in the levels of data to determine the appropriate lag length. With NDF exchange rate and spot exchange rate
as endogenous variable and constant as exogenous variable two out of three information criteria says that lag of 2 is appropriate

However to remove the serial correlation present in the residuals lag of three was selected. The results of lag autocorrelation LM
test shows that there is no serial correlation at lag length of three
Lag length Criteria (Spot market and NDF market)
Autocorrelation LM test (Spot market and NDF market)
Lag
LogL
LR
FPE
AIC
SC
HQ
0
722.3893
NA
1.00E-07
-10.4404
-10.398
-10.4232
1
1223.175
979.7977
7.48E-11
-17.6402
-17.5129
-17.5885
2
1256.864
64.93805
4.87E-11
-18.0705
-17.85838*
-17.98430*
4.77e3
1262.259
10.24226*
11*
-18.09071*
-17.7938
-17.97
4
1264.072
3.389283
4.92E-11
-18.059
-17.6772
-17.9039
5
1267.109
5.59061
4.99E-11
-18.0451
-17.5784
-17.8554
6
1269.353
4.06457
5.13E-11
-18.0196
-17.4681
-17.7955
7
1272.119
4.930044
5.22E-11
-18.0017
-17.3654
-17.7431
8
1272.51
0.686647
5.51E-11
-17.9494
-17.2282
-17.6563
Lags
LM-Stat
Prob.
1
4.591102
0.3319
2
5.808291
0.2139
3
0.471402
0.9762
4
4.753419
0.3135
11
RELATIONSHIP BETWEEN SPOT MARKET AND NDF MARKET
Inverse Roots of AR Characteristic Polynomial
VAR Granger Causality (Spot market and NDF market)
1.5
VAR Granger Causality/Block Exogeneity
1.0
Wald Tests
0.5
Sample: 1 146
0.0
Dependent variable: NDF
Excluded
Chi-sq
Df
Prob.
Spot
11277.44
3
0
Excluded
Chi-sq
Df
Prob.
NDF
5.445844
3
0.1419
-0.5
-1.0
Dependent variable: spot
-1.5
-1.5

-1.0
-0.5
0.0
0.5
1.0
1.5
Model is stable since all the roots are within unit circle. From the upper panel of Table, we see that we can reject the null of no
causality from spot to NDF. From the lower panel we see that we cannot reject the null of no causality from NDF to spot, at the
5% Significance level
12
RELATIONSHIP BETWEEN SPOT MARKET AND NDF MARKET

It is customary to check for the structural break in the model. Therefore the system of VAR (p) model was created and tested with
Quandt Andrews break point test with 5% trimmed data. The result shows that 67th observation has maximum value of LR Fstatistic, which was verified by doing chow test and it was found that there is a break in the model at 67th observation (2008 (Sept))

The split analysis has given an interesting observation that for the period 2008 (August) to 2014 (Feb) there is two way causal
relationship since the P-value is 0 and 0.0368 but for the period 2002(Jan) to 2008 (Aug) there is only unidirectional causal
relationship i.e. from spot to NDF but not vice-versa
VAR Granger Causality (2008 (Aug.) to 2014 (Feb.)
VAR Granger Causality (2002 (Jan.) to 2008 (Aug.)
VAR Granger Causality/Block Exogeneity
Sample: 68 146
Wald Tests
Dependent variable: NDF
Sample: 1 66
Dependent variable: NDF
Excluded
Chi-sq
Df
Prob.
Spot
5948.241
3
0
Dependent variable: Spot
Excluded
Chi-sq
Df
Prob.
NDF
8.4936
3
0.0368
Excluded
Chi-sq
Df
Prob.
Spot
4056.473
3
0
Excluded
Chi-sq
Df
Prob.
NDF
0.3327
3
0.9538
Dependent variable: Spot
13
RELATIONSHIP BETWEEN SPOT MARKET AND NDF MARKET
 On 2008 (Sept) Lehman Bros. collapsed and the inter-connected world was in financial crisis with the value of
currencies falling and Indian rupee also started feeling pressure till RBI intervened
 The slew of measures taken in 2011-2012 by RBI has created constraints in the domestic forward market and has
therefore propelled market participants to take position in the off-shore market. It can be observed that the global
Turnover of Indian rupee has increased from 23.6 billion U.S. dollars in 2007 to 52.8 billion U.S. dollars in 2013
i.e. an increase of 123% which is a very huge increase as compared to all the emerging markets except china. This
recent increase in the depth of the market can be the reason of its influence to the spot market
14
RELATIONSHIP BETWEEN SPOT AND FORWARD MARKET
• T-Y approach was followed and lag length of 3 was selected because at this lag length there is no autocorrelation and model is also stable since all the roots are within unit circle
Lag length Criteria (Spot and forward market)
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
Lag
LogL
LR
FPE
AIC
SC
HQ
0
722.799
NA
1.24E-07
-10.2241
-10.1823
-10.2071
1
1114.875
767.4678
5.06E-10
-15.7287
-15.60324*
-15.67773*
2
1118.125
6.269443
5.11E-10
-15.7181
-15.509
-15.6331
3
1125.11
13.27621*
4.90e-10*
-15.76042*
-15.4676
-15.64144
0.0
VAR Residual Serial Correlation LM Tests
Null Hypothesis: no serial correlation at lag
-0.5
order h
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
Lags
LM-Stat
Prob.
1
5.122888
0.2749
2
5.644696
0.2273
3
5.157891
0.2715
4
5.098557
0.2773
1.5
15
RELATIONSHIP BETWEEN SPOT AND FORWARD MARKET
From the upper panel of results, we see that we can reject the null of no causality from spot to Forward market. From
the lower panel we see that we can reject the null of no causality from Forward to spot, at the 5% Significance level. This
implies that spot and forward market both has an impact on each other
VAR Granger Causality (Spot and forward market)
VAR Granger Causality/Block Exogeneity Wald Tests
Included observations: 142
Dependent variable: Forward Rate
Excluded
Chi-sq
Df
Prob.
Spot
1363.25
3
0
Excluded
Chi-sq
Df
Prob.
Forward Rate
13.1128
3
0.0044
Dependent variable: Spot
16
RELATIONSHIP BETWEEN SPOT AND FORWARD MARKET
 The split period analysis shows that the till 2012-2013 financial year there was a bidirectional causal relationship
between the two markets but now in the last one year the relationship is unidirectional i.e. causal relationship
exists only from spot to forward rate
 VAR (p) model was tested for structural break points and for this purpose Quandt Andrews break point test with
5% trimmed data was performed. It was found that there is a break point in the series at an observation number
13 i.e. 2013 (Feb). This observation was further verified by chow test and it was concluded that there is a break in
the model at 13th observation.The study was further divided into two parts
VAR Granger Causality (2013 (Jan.) to 2014 (Feb.) )
VAR Granger Causality (2002 (Jan) to 2013 (Feb.) )
VAR Granger Causality/Block Exogeneity Wald Tests
VAR Granger Causality/Block Exogeneity Wald Tests
Dependent variable: Forward Rate
Dependent variable: Forward Rate
Excluded
Chi-sq
df
Prob.
Spot
51900.05
3
0
Dependent variable: Spot
Excluded
Chi-sq
df
Prob.
Spot
1847.01
3
0
Chi-sq
df
Prob.
Dependent variable: Spot
Excluded
Chi-sq
df
Prob.
Forward Rate
0.15067
3
0.9851
Excluded
17
RELATIONSHIP BETWEEN SPOT AND FORWARD MARKET
 The result is perfectly explaining the fact that the steps taken by RBI after the U.S. downgrade in 2011 August
which prompted decline in emerging market currencies has helped to curb the Volatility and unidirectional
speculative pull
 RBI took measures which has helped to prevent the spill over effect from forward market to spot market. That is
why in the period 2013-2014 there is no causality relationship from forward market to spot market which was
present in the earlier period
 The causal relationship from spot market to forward market exists because of the technical reason as the
underlying asset in forward is U.S. dollar which is apparently the future spot price. This major step is one of the
reasons that there is structural break in the model
18
MEASURES TAKEN BY RBI AIMED AT CURBING SPECULATIVE
ATTACKS
 Rebooking of cancelled forward contracts involving the rupee booked by residents to hedge transactions has not
been permitted
 The facility for importers availing themselves of the past performance facility was reduced to 25 per cent of the
average of actual import/export turnover of the previous three financial years or the actual import/export
turnover of the previous year, whichever is higher

Transactions undertaken by Authorised Dealers (ADs) on behalf of clients are for actual remittances/delivery
only and cannot be cancelled/cash settled
 Rebooking of cancelled forward contracts booked by FIIs is not permitted
 The Net Overnight Open Position Limits (NOOPL) and intra-day open position/daylight limit of AD banks has
been reduced
 Positions taken by banks in currency futures/options cannot be offset by undertaking positions in the OTC
market.
 The NOOPL of the banks as applicable to the positions involving the rupee as one of the currencies would not
include positions taken by banks on the exchanges
19
RELATIONSHIP BETWEEN NDF AND FORWARD MARKET
 T-Y approach was followed and lag length of 3 was selected because at this lag length there is no auto-correlation
and model is also stable since all the roots are within unit circle
Lag length criteria
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
Lag
LogL
LR
FPE
AIC
SC
HQ
0
881.1313
NA
1.57E-08
-12.2955
-12.2541
-12.2787
1
1120.934
469.5429
5.79E-10
-15.5935
-15.46916*
-15.543
2
1129.437
16.41204*
5.44e-10*
-15.65646*
-15.4493
-15.57227*
3
1133.378
7.495614
5.44E-10
-15.6556
-15.3656
-15.5378
0.0
Autocorrelation LM Test
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
Lags
LM-Stat
Prob.
1
9.36283
0.0526
2
9.39938
0.0519
3
9.82052
0.0436
4
5.50074
0.2397
1.5
20
RELATIONSHIP BETWEEN NDF AND FORWARD MARKET
The results shows that there exists a causal relationship from NDF market to Forward market since P-value is
0.0001 but the causal relationship from forward market to NDF market is significant just at the margin with pvalue of 0.0493
VAR Granger Causality (NDF and Forward market)
VAR Granger Causality/Block Exogeneity Wald Tests
Included observations: 142
Dependent variable: NDF Rate
Excluded
Chi-sq
Df
Prob.
Forward
7.8459
3
0.0493
Excluded
Chi-sq
Df
Prob.
NDF Rate
21.086
3
0.0001
Dependent variable: Forward
21
RELATIONSHIP BETWEEN NDF AND FORWARD MARKET
 VAR (p) model was tested for structural break points and for this purpose Quandt Andrews break point test with
5% trimmed data was performed. It was found that there are 9 break points in the model at an observation
number 12, 27, 33,42,43,47,48,66,84,121. These observations were further verified by chow test and it was
concluded that break points do exist at these places
VAR Granger Causality (2011 (Dec.) to 2014 (Feb.))
VAR Granger Causality (2011(Nov.) to 2010(Jan.))
VAR Granger Causality/Block Exogeneity Wald
VAR Granger Causality/Block Exogeneity Wald Tests
Tests
Sample: 28 50
Sample: 1 27
Included observations: 23
Included observations: 23
Dependent variable: NDF
Dependent variable: NDF
Excluded
Chi-sq
df
Prob.
Forward Rate
1.487614
3
0.6851
Dependent variable: Forward Rate
Excluded
Chi-sq
df
Prob.
Forward Rate
27.33781
3
0
Chi-sq
Df
Prob.
Dependent variable: Forward Rate
Excluded
Excluded
Chi-sq
df
Prob.
NDF
6.027967
3
0.1103
0.000
NDF
18.73519
3
3
22
RELATIONSHIP BETWEEN NDF AND FORWARD MARKET
•
The results clearly shows that there exists no causality between the NDF market and Forward market in the period of
2011(Dec) to 2014(Feb.). This again is due to the fact that RBI intervention in the forward market has restricted many
speculative players to take position in the Forward market
•
From 2011(Nov) to 2010(Jan) and it was found that there was causal relationship between the NDF market and
Forward market. This is the period before RBI intervened hence in this period there was flow of information from Off-
shore market to domestic market and vice-versa
•
From 2009 (Dec.) to 2008 (Sept.) i.e. when the world was in crisis after the collapse of Lehman and it was found there
exists causal relationship between the NDF market and Forward market
•
For 2007(March) to 2004(March)This period shows that no causal relationship exists between NDF and Forward
market as P-value > 0.05, therefore enough evidence is not there to not to reject the Null Hypothesis of no causality
23
CONCLUSION
•
It was found that the relationship between all the three markets is quite dynamic owing to the policy measures
taken by RBI to curb the volatility in 2012
•
The split analysis has given an interesting observation that for the period 2008 (August) to 2014 (Feb) there is two
way causal relationship but for the period 2002(Jan) to 2008 (Aug) there is only unidirectional causal relationship
i.e. from spot to NDF but not vice-versa. On 2008 (Sept) Lehman Bros. collapsed and the inter-connected world
was in financial crisis with the value of currencies falling and rupee also started feeling pressure till RBI intervened.
This was structural change in the market which is observation number 67 rightly predicted in this analysis
•
The measures taken by RBI has created constraints in the domestic forward market and has therefore propelled
market participants to take position in the off-shore market. It can be observed that the global Turnover of Indian
rupee has increased from 23.6 billion U.S. dollars in 2007 to 52.8 billion U.S. dollars in 2013 i.e. an increase of 123%
which is a very huge increase as compared to all the emerging markets except china
24
CONCLUSION
•
This recent increase in the depth of the market can be the reason of its influence to the spot market. Again in
2012 many restrictions were put in place by RBI to wipe out the speculative attacks, this has also helped to
protect the domestic spot market against external shock. This can be the reason that why there is no causal
relationship in this period (2013-2014) from forward market to the spot market
•
However the relationship between NDF and forward market is more dynamic than other markets because of the
evolving nature of these markets. Till 2007 there was no causal relationship between the markets but it was only
after the expansion of both the markets with increase in turnover and evolution of more sophisticated products
that the markets started passing information to each other. But in the period 2012-2014 there was again no causal
relationship again owing to the measures taken by RBI to curb the speculative attacks by market participants in
2012
25
LIMITATIONS AND FUTURE DIRECTION
 In this study the period under consideration is from 2002 to 2014 i.e. 12 years.
 However period under study can be extended and analysis can be done by breaking the period into sub-periods
as per the business cycle. The difference in the Tax rates between India and Singapore can also be the influencing
factor, as in India corporate tax stands at 34% whereas in Singapore the maximum tax rate is 17%, and can be
considered in analysis
26
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THANK YOU !!
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