an empirical investigation on the relationship between onshore and offshore rupee market gautam...

1

AN EMPIRICAL INVESTIGATION ON THE RELATIONSHIP BETWEEN ONSHORE AND OFFSHORE RUPEE MARKET

GAUTAM JAIN, PALANISAMY SARAVANAN, SHARAD NATH BHATTACHARYA

2

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

3

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

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2013-14

30

35

40

45

50

55

60

65

44.9 44.3 45.3

40.2

45.947.4

45.647.9

54.4

60.5

Rupee/USD

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2013-14

60

70

80

90

100

110

102.2497.63

104.75

93.34 90.94 93.5487.38

78.3272.32

Trend in NEER Index (Trade Weighted): Base 2004-05

NEER(36 currency weights)

4

FOREIGN EXCHANGE MARKETS AND THEIR DEPTHS

Net-net basis, daily averages in April, in billions of US dollars

Instrument199

8200

12004 2007 2010 2013

Foreign exchange instruments1,52

71,23

91,934 3,324 3,971 5,345

Spot transactions 568 386 631 1,005 1,488 2,046

Outright forwards 128 130 209 362 475 680Foreign exchange swaps 734 656 954 1,714 1,759 2,228

Currency swaps 10 7 21 31 43 54Options and other products 87 60 119 212 207 337

Global foreign exchange market turnoverForeign exchange trading currency wise scenario in billions of US dollars

Total

Spot transactions

Outright forwards

Foreign exchange swaps

Currency swaps

FX options

Total

5,345

2,046 680 2,228 54 337

USD 4,652

1,691 588 2,030 50 293

EUR 1,786

754 178 766 18 70

JPY 1,231

612 123 332 11 153

GBP

631 227 69 301 5 29

INR 53 15 24 10 0 3

Currency distribution of global foreign exchange market turnover in billions of US dollars

Currency

2004 2007 2010 2013

Share Rank Share Rank Share Rank Share Rank

USD

88.0

1 85.6

1 84.9 1 87.0 1

EUR

37.4

2 37.0

2 39.1 2 33.4 2

JPY

20.8

3 17.2

3 19.0 3 23.0 3

GBP

16.5

4 14.9

4 12.9 4 11.8 4

AUD 6.0

6 6.6

6 7.6 5 8.6 5

CHF 6.0

5 6.8

5 6.3 6 5.2 6

CAD 4.2

7 4.3

7 5.3 7 4.6 7

BRL 0.3

21 0.4

21 0.7 21 1.1 19

INR 0.3

20

0.7

19

1.0 15 1.0 20

Offshore trading of emerging market currencies, 2013

Offshore turnover (daily)

Offshore share in global

turnover

Share of regional financial centers

(Hongkong, Singapore)

UK USEuro zone

USD bn In percent

Emerging market currencies

678.7 67.4 - 29.9 16.4 4.6

Emerging Asian currencies

277.2 59.2 25.3 18.8 8.4 2.6

Chinese renminbi 86.1 72 43.7 18 5.8 1.5Hong Kong dollar 40.7 52.6 8.1 22.9 8.9 5.1Singapore dollar 48.8 65.4 11.4 27.8 15.5 3.7

Korean won 27.4 42.7 21.1 11.3 7.1 1.5Indian rupee 28 53 26.9 15.1 8.5 1.1

Source : BIS

5

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

6

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

7

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

8

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

9

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

10

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 TrendSeries

ADF test

statistic P-value

LnSpo

t Level -0.8301 0.3470

First

Differenc

e -8.460 0.000

LnND

F Level -0.8052 0.3656

First

Differenc

e -8.620 0.000

LnFW Level -0.8386 0.3510

First

Differenc

e -9.853 0.000

Series

ADF test

statistic P-value

LnSpot Level -2.8128 0.0589

First

Difference -8.481 0.000

LnNDF Level -2.7140 0.0740

First


LnFW Level -2.7440 0.0691

First


Series

ADF test

statistic P-value

LnSpot Level -2.2995 0.4312

First


LnNDF Level -2.1490 0.5135

First


LnFW Level -2.1351 0.5215

First


11

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 LogL LR FPE AIC SC HQ

0

722.389

3 NA

1.00E-

07 -10.4404 -10.398 -10.4232

1

1223.17

5

979.797

7

7.48E-

11 -17.6402 -17.5129 -17.5885

2

1256.86

4

64.9380

5

4.87E-

11 -18.0705

-

17.8583

8*

-

17.9843

0*

3

1262.25

9

10.2422

6*

4.77e-

11*

-

18.0907

1* -17.7938 -17.97

4

1264.07

2

3.38928

3

4.92E-

11 -18.059 -17.6772 -17.9039

5

1267.10

9 5.59061

4.99E-

11 -18.0451 -17.5784 -17.8554

6

1269.35

3 4.06457

5.13E-

11 -18.0196 -17.4681 -17.7955

7

1272.11

9

4.93004

4

5.22E-

11 -18.0017 -17.3654 -17.7431

8 1272.51

0.68664

7

5.51E-

11 -17.9494 -17.2282 -17.6563

Autocorrelation LM test (Spot market and NDF market)Lag length Criteria (Spot market and NDF market)

Lags LM-Stat Prob.

1 4.591102 0.3319

2 5.808291 0.2139

3 0.471402 0.9762

4 4.753419 0.3135

12


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

VAR Granger Causality/Block

Exogeneity Wald Tests

Sample: 1 146

Dependent variable: NDF

Excluded Chi-sq Df Prob.

Spot

11277.4

4 3 0

Dependent variable: spot


NDF

5.44584

4 3 0.1419

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

VAR Granger Causality (Spot market and NDF market)

13

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 67 th observation has maximum value of LR F-

statistic, which was verified by doing chow test and it was found that there is a break in the model at 67 th 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/Block

Exogeneity Wald Tests

Sample: 1 66



Spot

5948.24

1 3 0

Dependent variable: Spot


NDF 8.4936 3 0.0368

Sample: 68 146

Dependent variable:

NDF


Spot 4056.473 3 0

Dependent variable:

Spot


NDF 0.3327 3 0.9538

VAR Granger Causality (2008 (Aug.) to 2014 (Feb.) VAR Granger Causality (2002 (Jan.) to 2008 (Aug.)

14


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

15

RELATIONSHIP BETWEEN SPOT AND FORWARD MARKET


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

Lag length Criteria (Spot 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

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5


VAR Residual Serial Correlation LM

Tests

Null Hypothesis: no serial correlation

at lag order h

Lags LM-Stat Prob.

1 5.122888 0.2749

2 5.644696 0.2273

3 5.157891 0.2715

4 5.098557 0.2773

16


VAR Granger Causality/Block Exogeneity Wald Tests

Included observations: 142

Dependent variable: Forward Rate


Spot 1363.25 3 0



Forward Rate 13.1128 3 0.0044

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)

17

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/Block Exogeneity Wald

Tests


Excluded Chi-sq df Prob.

Spot 51900.05 3 0



Forward Rate 0.15067 3 0.9851

VAR Granger Causality/Block Exogeneity Wald

Tests



Spot 1847.01 3 0



Forward Rate 16.66 3 0.0008

VAR Granger Causality (2013 (Jan.) to 2014 (Feb.) ) VAR Granger Causality (2002 (Jan) to 2013 (Feb.) )

18


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

19

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

20

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


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

Lag length criteria

Autocorrelation LM Test

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

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5


21

RELATIONSHIP BETWEEN NDF AND FORWARD MARKET

VAR Granger Causality/Block Exogeneity Wald Tests


Dependent variable: NDF Rate


Forward 7.8459 3 0.0493

Dependent variable: Forward


NDF Rate 21.086 3 0.0001

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 p-value of 0.0493

VAR Granger Causality (NDF and Forward market)

22

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/Block Exogeneity

Wald Tests

Sample: 1 27




Forward Rate

1.48761

4 3 0.6851



NDF

6.02796

7 3 0.1103

VAR Granger Causality (2011 (Dec.) to 2014

(Feb.)) VAR Granger Causality/Block Exogeneity Wald

Tests

Sample: 28 50




Forward Rate 27.33781 3 0



NDF 18.73519 3

0.00

03

VAR Granger Causality (2011(Nov.) to 2010(Jan.))

23

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

24

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

25

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

26

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

27

REFERENCES

Behera, H., V. Narasimhan and K.N. Murty (2006), 'Relationship between exchange rate volatility and central bank intervention - An empirical analysis for India', South Asia Economic Journal, 9(1): 69-84

Case, K., J. Quigley and R. Shiller (2001), 'Comparing wealth effects: the stock market versus the housing market', Advances in Macroeconomics, 5(1): 1-32

Cheng, X., D. He and C. Shu (2013), 'Impact of the renminbi on Asian currencies: role of the offshore market', mimeo

Colavecchio R. and M. Funke (2008), 'Volatility transmissions between renminbi and Asia-Pacific onshore and offshore U.S. dollar futures', china economic review, 19: 635-648

Dua, P. and R. Ranjan (2009), 'Exchange Rate Policy and Modelling in India', RBI Development Research Group, 33: 1-117

28

REFRENCES

Ehlers, T. and F. Packer (2013), ‘FX and derivatives market in emerging economies and the internationalisation of their currencies’, BIS Quaterly Review, December

Goyal, R., R. Jain and S. Tewari (2013), ’Non Deliverable Forward and Onshore Indian Rupee Market: A Study on Inter-linkages’, RBI Working paper series, 11: 1-15

Guru, A. (2009), 'Non-Deliverable Forwards Markets for Indian Rupee: An Emprical Study‘, Indian Journal of Economics and Business, 8(2): 245-260

Hutchison, M., J. Kendall, G. Pasricha and N. Singh (2009), ‘Indian Capital Control Liberalisation: Evidence from NDF Markets, NIPFP Working Paper, 60

Ludwig, A. and T. Slok (2004), 'The relationship between stock prices, house prices and consumption in OECD countries', The B.E. Journal of Macroeconomics, 4(1): 1-28

29

REFRENCES

Ma, G., H. Corrine and R. N. McCauley (2004), ‘The Markets for Non- deliverable forwards in Asian currencies’, Bis Quaterly Review, June

Misra, S. and H. Behera (2006), ' Non-Deliverable Forward Exchange Market: An Overview', Reserve Bank of India Occasional Papers, 27(3): 25-55

Pedroni, P. (2004), ' Panel co-integration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis', Econometric Theory, 20: 597-625

Park, J. (2001), ‘Information flows between non-deliverable forwards (NDF) and spot markets: Evidence from Korean currency’, Pacific Basin Finance Journal, 9 (4): 363-377

Park, Y. and C. Song (2011), 'Renminbi internationalization: prospects and implications for economic integration in East Asia', Asian Economic Papers, 10 (3): 42-72

30

REFERENCES

Sharma, S. (2011), ' An Empirical Analysis of the relationship between currency futures and exchange rates volatility in India', RBI Working paper series, 1: 1-20

Toda, H. Y. and T. Yamamoto (1995), ' Statistical inference in vector autoregressions with possible integrated processes', Journal of Econometrics, 66(1-2): 225-250

Tripathy, R. (2013), 'Intervention in foreign exchange markets: the approach of the Reserve Bank of India', BIS Papers, 73: 169-176

Wang K.L., C. Fawson and M.L. Chen (2007), ‘Information Flows among Exchange Rate Markets: What Do We Learn From Non-Deliverable Forward Markets in Asia?’, Chaoyang University of Technology, October

31

THANK YOU !!

an empirical investigation on the relationship between onshore and offshore rupee market gautam...

Documents

spot market

foreign exchange markets

ndf market

currency markets

offshore market of korean

movement of exchange

offshore turnover

managed float exchange