Hedge Effectiveness of Index Futuresshodhganga.inflibnet.ac.in/bitstream/10603/85435/14/14_chapter...

Hedge Effectiveness of Index Futures

Dhanya K. A. “Hedge effectiveness and attitude of individual derivative traderstowards hedging – A study with special reference to selected financial derivatives” Thesis. Department of Commerce & Management Studies, University of Calicut, 2013

Chapter 5 – Hedge Effectiveness of Index Futures

242

Chapter Five


Introduction

Hedge Efficiency of Index Futures – ECM


Summary


243

INTRODUCTION

Indices also play a significant role in Indian futures market. This chapter tries to analyse

the hedge effectiveness of some selected index futures traded at NSE. Out of the seven indices,

three indices with past six year‟s data are selected for the study. They are 1. S&P CNX Nifty 2.

Bank Nifty and 3. CNX IT. Other five indices are of recent origin and hence ignored. This

chapter is divided into two sections:

A) Hedge efficiency of index futures – Error correction model

B) Hedge effectiveness of index futures

A) HEDGE EFFICIENCY OF INDEX FUTURES –

Error Correction Model

All the three indices selected are analyzed separately using Error Correction Model to

know the hedge efficiency. Summary of analysis and results along with their interpretations are

exhibited in the following pages:

1. S&P CNX Nifty

Daily closing prices of S&PCNX Nifty futures and its spot prices are taken for a period

of six years starting from April 2006 to March 2012. Each year‟s hedge effectiveness is

calculated separately. Impact of futures on spot and spot on futures has to be analyzed. But

before proceeding further it is necessary to see whether the series is stationary or not.

Test of Stationarity

Dickey-Fuller unit root test is being used to test the stationarity. First difference of the

series (Yt-1) is taken and regressed with (Yt) to know the stationarity of the original series. Null

hypothesis is “Series are non-stationary”. Results of test are exhibited below:


244

Table 5.1: Unit Root Test of S&P CNX Nifty – Original Series

Nifty

Futures 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -53.841 -20.729 -24.756 29.259 -83.788 -83.982

βf .015 .005 .005 -.004 .015 .016

t value 1.415 .533 .649 -.424 1.390 1.158

p value .158 .595 .517 .672 .166 .248

Nifty Spot

α -47.211 -15.950 -22.598 32.702 -83.383 -78.119

βf .014 .004 .004 -.005 .015 .014

t value 1.337 .451 .610 -.511 1.397 1.114

p value .183 .652 .542 .609 .164 .266

Source: Compiled from SPSS

Above table shows the Dicky-Fuller test results of both Nifty futures and Nifty spot.

Constant value (α), Beta values, T test value, p value of significance etc. are exhibited.

Dicky- Fuller table value at 1% level of significance is 2.58. Here, in all cases calculated value is

less than the table value 2.58 and hence accept H0. p value also explains the same result. Since in

all cases p value is greater than .01, null hypothesis is accepted at 1% level of significance. Thus

it can be concluded that the original time series of both futures and spot prices are non stationary.

As the series need to be stationary for doing regression, first difference of the series is

taken and tested for stationarity using Dicky-Fuller. Null hypothesis is “Series are non

stationary”. Results of test are exhibited below:


245

Table 5.2: Unit Root Test of S&P CNX Nifty – First Difference Series Nifty

Futures 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -1.352 -5.318 7.410 -9.016 -1.781 2.710

βf 1.024 .958 1.001 .985 1.030 .976

t value 16.031 15.219 15.472 15.155 16.259 15.392

p value .000 .000 .000 .000 .000 .000

Nifty Spot

α -1.231 -4.882 7.028 -8.828 -1.657 2.475

βf .939 .911 .961 .968 .993 .942

t value 14.730 14.495 14.875 14.904 15.680 14.811

p value .000 .000 .000 .000 .000 .000


First difference regression shows that the series are stationary. Here in all cases

calculated value is higher than Dickey-Fuller table value of 2.58 and hence reject null hypothesis

„series are non stationary‟ at 1% level of significance. p value for all cases is less than .01 which

also supports the same result that null hypothesis is rejected. Hence it can be concluded that the

series are stationary at first difference.

Regression Using Stationary Series

Now the original series has been made stationary with first difference. This stationary

series is being used for further analysis. Both futures on spot and spot on futures need to be

regressed.

Regression of S&P Nifty Futures on S&P Nifty Spot

Here S&P Nifty futures is taken as dependent and S&P Nifty spot as independent and

regression is carried out. Results are given below


246

Table 5.3: Results of Regression of Nifty Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -.202 -.121 .259 -.168 -.066 .076

βs 1.080 1.054 1.059 1.024 1.021 1.031

t value 91.090 108.686 135.286 172.078 98.824 106.260

p value .000 .000 .000 .000 .000 .000


From the above analysis following regression equation can be drawn:

Futures = α +βs*Spot.

Next step is to know whether the series are cointegrated or not. For this Engle-Granger

test of cointegration is used.

Test of Cointegration

Cointegration test is done based on the residuals of regression equation. Engle-Granger

has used Dickey-Fuller to test the cointegration. Based on the estimated values of futures and

actual futures value, the residuals or errors are calculated. Dickey-Fuller unit root test is applied

to these residuals to know whether the series are cointegrated or not. Hence first difference of

residuals is taken and regressed with original series to test the hypothesis “Series are not

cointegrated”. Results are given below:

Table 5.4: Results of Test of Cointegration of Nifty Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α 26.719 29.060 15.805 12.648 64.918 35.025

βf .092 .108 .125 .117 .595 .230

t value 3.508 3.712 3.685 3.642 10.430 5.733

p value .001 .000 .000 .000 .000 .000



247

Result of cointegration shows that in all the cases the null hypothesis is rejected at 1%

level of significance since the p value is less than .01. Hence the residual of futures on spot

shows that the series are cointegrated. Same is the case for all the years selected for the study.

When the series are cointegrated Error Correction Model can be applied which considers

residuals as an independent variable on which the original series will be dependent. Here, futures

are taken as dependent and ECM is applied by taking both spot price and residuals of futures

price as independent variables. Thus a multiple regression is carried out. Results are given

below:

Table 5.5: Results of ECM - Nifty Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α 27.081 29.632 5.809 13.157 65.450 35.351

βspot 1.075 1.049 1.059 1.020 1.013 1.026

βresidual .093 .110 .125 .121 .600 .232

R2

.973 .981 .987 .992 .983 .981

p valuespot .000 .000 .000 .000 .000 .000

p valueresidual .001 .000 .000 .000 .000 .000


Error Correction Model is applied and now the equation of futures on spot will be

changed as follows.

∆ Ft = α0 + β1 ∆ St + β2ut-1 + εt

By applying the ECM model values to the equation, the estimated future value is

obtained. Error term εt can be obtained by finding the difference between estimated and actual

future prices. The variance of this futures error is taken as a variable for calculating the hedge

ratio. Now, the same procedure is to be done with S&P Nifty spot.


248

Regression of S&P Nifty Spot on S&P Nifty Futures

Here S&P Nifty spot is taken as the dependent variable and S&P Nifty futures is taken as

independent variable. Result of regression is given below:

Table 5.6: Results of Regression of S&P Nifty Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α .222 .203 -.333 .235 .109 -.117

βf .899 .929 .932 .969 .955 .949

t value 91.090 108.686 135.286 172.078 98.824 106.260

p value .000 .000 .000 .000 .000 .000


From the above analysis regression equation can be drawn as follows:

Spot = α +βs*Futures.

Next step is to know whether the series are cointegrated or not. For this Engle-Granger

test of cointegration is being used.

Test of Cointegration

Null hypothesis is set as “series are not cointegrated”. Results of Engle-Granger

cointegration are given below:

Table 5.7: Results of Test of Cointegration of Nifty Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -17.473 -17.773 -4.605 -9.424 -87.936 35.025

βs .048 .051 .043 .068 .362 .230

t value 2.529 2.445 2.342 2.616 7.575 5.733

p value .012 .015 .031 .009 .000 .000



249

Result of cointegration shows that in all cases the null hypothesis is rejected at 5% level

of significance. Hence the residuals of spot on futures show that the series are cointegrated. Now

as series are cointegrated, ECM can be applied. Here spot prices are taken as dependent and

ECM is applied by taking both futures prices and residuals of spot prices as independent

variables. Thus a multiple regression is carried out. Results are given below:

Table 5.8: Results of ECM - Nifty Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -17.615 -18.008 -4.608 -9.712 -88.392 -34.665

βfutures .901 .931 .932 .971 .960 .953

βresidual .049 .052 .044 .070 .363 .134

R2

.972 .980 .987 .992 .980 .980

p valuefutures .000 .000 .000 .000 .000 .000

p valueresidual .012 .015 .031 .009 .000 .000


Error Correction Model is applied and now the equation of spot on futures will be

changed as follows.

∆ St = α0 + β1 ∆ Ft + β2ut-1 + εt

By applying the ECM model estimated spot value is obtained. By finding the difference

between estimated and actual spot prices, error εt can be calculated. Covariance of futures errors

and spot errors is required to calculate the hedge ratio.


250

Hedge Ratio and Hedge Efficiency

Using the errors obtained from ECM, the hedge ratio is calculated. Variance of spot

prices and futures prices and their covariance are used to find the hedge efficiency. Results of

analysis are given below:

Table 5.9: Hedge Ratio and Hedge Efficiency of S&P CNX Nifty Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

Var (εf) 492.40 702.82 2398.7 122.41 25.26 117.69

Var (εs) 958.15 1649 3202.83 245.01 41.50 241.72

Cov (εf,εs) 670.4 1046 2754.6 169.78 10.71 155.15

Hedge Ratio 1.36 1.48 1.148 1.38 .4239 1.32

Var (hedge) 22438.42 131740 24985 43469 41335 13284

Var (unhedge) 137750.5 501296.3 749566 278390.2 127785.5 107592.5

Hedge

Efficiency .8371 .7372 .9667 .8438 .6765 .8765


The above table exhibits the hedge ratio and hedge effectiveness of S&P Nifty for the

past six financial years. Hedge efficiency was more in 2008-09 i.e. .9667 followed by 2011-12

.8765. Hedge efficiency was least in the year 2010-11 i.e. .6765. In all the cases, hedge

efficiency seems to be more than 60%. On an average overall hedge efficiency of S&P Nifty

futures is found to be .8229. From this it can be inferred that S&P CNX Nifty is an efficient tool

which provides good coverage of risk and hence can be considered as an efficient hedge tool.

In the same way the hedge efficiency of other two indices are also analyzed to know the

extent of hedge efficiency exhibited by NSE index futures.


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2. Bank Nifty

Daily closing prices of Bank Nifty futures and its underlying, Bank Nifty spot are taken

for a period of six years starting from April 2006 to March 2012. Null hypothesis is “Series are

non stationary”. Results of test are exhibited below:

Table 5.10: Unit Root Test of Bank Nifty – Original Series

Bank Nifty

Futures 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -38.272 -18.541 -89.117 67.610 -97.103 -110.120

βf .008 .003 .014 -.006 .010 .010

t value 1.008 .387 1.347 -.684 1.032 .927

p value .314 .699 .179 .495 .303 .355

Bank Nifty

Spot

α -36.821 -14.041 -86.335 70.069 -97.130 -110.079

βf .008 .003 .014 -.006 .010 .010

t value .986 .330 1.326 -.740 1.029 .938

p value .325 .742 .186 .460 .304 .349


Above table shows the Dicky-Fuller test results of both Bank Nifty futures and Bank

Nifty spot. Constant value (α), Beta values, T test value, p value of significance etc. are

exhibited. Dicky-Fuller table value at 1% level of significance is 2.58. Here in all cases

calculated value is less than the table value 2.58 and hence accept H0. p value also explains the

same result. Since in all cases p value is greater than .01, null hypothesis is accepted at 1% level

of significance. Thus it can be concluded that the original time series of both futures and spot are

non stationary.


252

First difference is taken and tested. Null hypothesis is “Series are non stationary”. Results

of test are exhibited below:

Table 5.11: Unit Root Test of Bank Nifty – First Difference Series

Bank Nifty

Futures 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -1.785 -7.218 10.081 -19.676 -8.507 6.443

βf .850 .807 .932 .893 .949 .909

t value 13.433 13.055 14.425 13.865 15.012 14.349

p value .000 .000 .000 .000 .000 .000

Bank Nifty

Spot

α -1.908 -6.904 9.811 -18.718 -8.297 5.972

βf .839 .784 .921 .855 .917 .882

t value 13.285 12.725 14.269 13.351 14.518 13.935

p value .000 .000 .000 .000 .000 .000


Result shows that the series are stationary. As calculated value is higher than Dickey-

Fuller table value of 2.58 reject null hypothesis „series are non stationary‟ at 1% level of

significance. p value for all cases is less than .01 which also supports the same result. Hence it

can be concluded that the series are stationary at first difference. Bank Nifty futures are taken as

dependent and Bank Nifty spot as independent and regression is carried out.

Table 5.12: Results of Regression of Bank Nifty Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -.120 -.257 -.148 -.217 -.246 -.056

βs 1.008 1.031 1.016 1.020 1.006 1.011

t value 87.924 116.781 166.441 151.719 113.967 116.189

p value .000 .000 .000 .000 .000 .000



253

First difference of residuals is taken and regressed with original series to test the

hypothesis “series are not cointegrated”. Results are given below:

Table 5.13: Results of Test of Cointegration – Bank Nifty Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α 17.168 38.502 21.530 -24.125 12.434 29.377

βf .434 .175 .238 .151 .234 .306

t value 8.171 4.839 5.681 4.355 5.767 6.626

p value .000 .000 .000 .000 .000 .000



of significance since the p value is less than .01. Hence the residual of futures on spot shows that

the series are cointegrated. Same is the case of all the years selected for the study. Now futures

are taken as dependent and ECM is applied by taking both spot prices and residuals of futures as

independent variables. Thus a multiple regression is carried out. Results are given below:

Table 5.14: Results of ECM – Bank Nifty Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α 17.387 38.553 21.750 25.023 12.551 29.529

βspot .999 1.030 1.012 1.015 1.011 1.007

βresidual .439 .175 .241 -.156 .237 .308

R2

.976 .984 .992 .990 .983 .985

p valuespot .000 .000 .000 .000 .000 .000

p valueresidual .000 .000 .000 .000 .000 .000


Bank Nifty spot is taken as the dependent variable and Bank Nifty futures are taken as



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Table 5.15: Results of Regression of Bank Nifty Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α .188 .366 .057 .434 .404 -.045

βf .961 .952 .976 .970 .976 .971

t value 87.924 116.781 166.441 151.719 113.967 116.189

p value .000 .000 .000 .000 .000 .000


Cointegration is done using residuals of spot. Null hypothesis is set as “series are not

cointegrated”. Results of Engle-Granger cointegration are given below:

Table 5.16: Results of Test of Cointegration –Bank Nifty Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -34.994 -24.661 -20.286 -17.234 -74.750 -60.747

βs .181 .071 .151 .074 .302 .218

t value 4.870 2.927 4.427 2.857 6.644 5.510

p value .000 .004 .000 .005 .000 .000



of significance or 5% level of significance. Hence the residual of spot on futures shows that the

series are cointegrated. Now as series are cointegrated ECM can be applied. Results are given

below:

Table 5.17: Results of ECM – Bank Nifty Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -35.167 -24.723 -20.400 -17.836 -75.930 -61.139

βfutures .965 .071 .978 .973 .969 .975

βresidual .181 .953 .152 .076 .306 .220

R2

.972 .983 .992 .990 .984 .984

p valuefutures .000 .004 .000 .000 .000 .000

p valueresidual .000 .000 .000 .004 .000 .000



255



prices and futures prices and their covariance are used to get the hedge efficiency.

Table 5.18: Hedge Ratio and Hedge Efficiency of Bank Nifty Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

Var (εf) 205.79 1286.82 286.86 448.87 569.77 444.43

Var (εs) 699.84 3616.29 604.68 1201.96 525 646. 39

Cov (εf,εs) 30.95 2062.36 380.69 686.13 385.19 440.02

Hedge Ratio .1504 1.6 1.327 1.52 .676 .99

Var (hedge) 496899 858002.6 145413 478632 125206 8835.58

Var

(unhedge) 690016.3 2206212 1259273 1672957 1228182 989427.58

Hedge

Efficiency .2798 .6110 .8845 .7139 .8980 .9910


The above table exhibits the hedge ratio and hedge effectiveness of Bank Nifty for the

past six financial years. It is clear that that hedge efficiency was more in 2011-12 i.e. .9910

followed by 2010-11 .8980. Hedge efficiency was least in the year 2006-07 i.e. .2798. There is

an increasing trend in the hedge efficiency over the years. On an average overall hedge

efficiency of Bank Nifty futures is found to be .7297. Hence it can be concluded that Bank Nifty

also seems to be an efficient tool of hedge as it has showed a steady increase in its hedge

coverage and in almost all years the ratio is above 60 except the initial year of its launching.


256

3. CNX IT

Daily closing prices of CNX IT futures and its underlying CNX IT spot are taken for a

period of six years starting from April 2006 to March 2012. Null hypothesis is “Series are non

stationary”. Results of test are exhibited below:

Table 5.19: Unit Root Test of CNX IT – Original Series CNX IT

Futures 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -50.492 -47.063 -26.334 -15.174 -84.248 -96.877

βf .011 .008 .006 .005 .014 .015

t value 1.249 2.308 .990 1.620 1.406 1.944

p value .213 .021 .323 .106 .161 .052

CNX IT

Spot

α -46.090 -47.062 -25.348 -14.995 -91.535 -100.495

βf .010 .008 .006 .005 .015 .016

t value 1.195 2.323 .977 1.589 1.459 1.994

p value .233 .021 .329 .113 .146 .047


Above table shows the Dicky-Fuller test results of both CNX IT futures and CNX IT spot.

Constant value (α), Beta values, T test value, p value of significance etc. are exhibited. Dicky-

Fuller table value at 1% level of significance and infinite d.f is 2.58. Here in all cases calculated

value is less than the table value 2.58 and hence accept H0. p value also explains the same result.

Since p value is greater than .01 in all cases, null hypothesis is accepted at 1% level of

significance. Thus it can be concluded that the original time series of both futures prices and spot

prices are non stationary.

First difference is taken and tested. Null hypothesis is “Series are non stationary”. Results

of test are exhibited below:


257

Table 5.20: Unit Root Test of CNX IT – First Difference Series

CNX IT

Futures 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -3.047 -.014 5.807 -4.190 -4.439 -.463

βf 1.067 1.051 .981 .940 1.080 .993

t value 16.732 23.719 15.161 20.624 17.216 22.227

p value .000 .000 .000 .000 .000 .000

CNX IT

Spot

α -2.973 .030 5.736 -4.217 -4.500 -.556

βf 1.021 1.031 .065 .951 1.093 .988

t value 15.981 23.214 14.747 20.850 17.460 22.070

p value .000 .000 .000 .000 .000 .000


The first difference regression shows that the series are stationary. Here, calculated value

is higher than Dickey-Fuller table value of 2.58 in all cases and hence reject null hypothesis

„series are non stationary‟ at 1% level of significance. p value for all cases is less than .01 which

also supports the same result that null hypothesis is rejected. Hence it can be concluded that the

series are stationary at first difference.

CNX IT futures are taken as dependent and CNX IT spot as independent and regression is

carried out. Results are given below:

Table 5.21: Results of Regression of CNX IT Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -.065 -.040 -.013 .123 .150 -.009

βs 1.028 .995 1.018 .970 .958 .987

t value 74.358 115.805 109.484 110.979 92.509 141.365

p value .000 .000 .000 .000 .000 .000



258

First difference of residuals is taken and regressed with original series to test the

hypothesis “series are not cointegrated”. Results are given below:

Table 5.22: Results of Test of Cointegration - CNX IT Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α 46.439 -6.773 8.546 -18.653 -24.536 -19.024

βf .348 .331 .153 .136 .089 .204

t value 7.186 7.026 4.447 4.187 3.425 5.253

p value .000 .000 .000 .000 .000 .000



of significance since the p value is less than .01. Hence the residual of futures on spot shows that

the series are cointegrated. Same is the case for all the years selected for the study. Now futures

are taken as dependent and ECM is applied by taking both spot prices and residuals of futures as

independent variables. Thus a multiple regression is carried out. Results are given below:

Table 5.23: Results of ECM - CNX IT Futures on Spot

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α 46.689 -6.485 8.545 -15.338 -24.542 -18.673

βspot 1.035 1.020 1.018 .920 .957 1.005

βresidual .350 .323 .153 .118 .089 .201

R2

.965 .967 .982 .945 .973 .981

p valuespot .000 .000 .000 .000 .000 .000

p valueresidual .000 .000 .000 .000 .000 .000


By applying the ECM model, estimated future value is obtained. By finding the

difference between estimated and actual future prices, error term εt can be obtained. The


259

variance of this futures error is taken as a variable for calculating the hedge ratio. Now the same

procedure is to be done with CNX IT spot.

Here CNX IT spot is taken as the dependent variable and CNX IT futures is taken as


Table 5.24: Results of Regression of CNX IT Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α .185 .033 -.098 .043 -.021 .037

βf .931 .969 .963 .992 1.014 .989

t value 74.358 115.805 109.484 110.979 92.509 141.365

p value .000 .000 .000 .000 .000 .000


Cointegration is done using residuals of spot. Null hypothesis is set as “series are not

cointegrated”. Results of Engle-Granger cointegration are given below:

Table 5.25: Results of Test of Cointegration - CNX IT Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -27.609 -42.606 -6.620 -25.814 22.119 -15.345

βs .085 .286 .056 .722 .226 .271

t value 3.302 6.460 2.649 11.656 5.660 6.224

p value .001 .000 .009 .000 .000 .000



of significance or 5% level of significance. Hence the residual of spot on futures shows that the

series are cointegrated. Now as series are cointegrated, ECM can be applied. Results are given

below:


260

Table 5.26: Results of ECM - CNX IT Spot on Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

α -27.647 -41.813 -6.620 -25.743 22.120 -15.144

βfutures .930 .938 .963 1.018 1.015 .975

βresidual .086 .280 .056 .710 .226 .266

R2

.959 .966 .981 .963 .975 .982

p valuefutures .000 .000 .000 .000 .000 .000

p valueresidual .001 .000 .009 .000 .000 .000


By applying the ECM model values to the equation the estimated spot value is obtained.

By finding the difference between estimated and actual spot prices error εt can be calculated.



prices and futures prices and their covariance are used for hedge efficiency.

Table 5.27: Hedge Ratio and Hedge Efficiency of CNX IT Futures

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

Var (εf) 302.16 261.42 381.81 7437.71 857.4 384.32

Var (εs) 1552.5 785.54 1248.63 717.89 258.42 232.27

Cov (εf,εs) 646.18 384.69 653.75 2300.76 447.49 247.65

Hedge Ratio 2.138 1.47 1.71 .309 .5219 .644

Var (hedge) 518294.45 67901 428766 598417.68 56406.73 27173

Var (unhedge) 384696.21 271253.8 810472.4 1247662.44 249973.6 213028

Hedge

Efficiency -.3473 .7496 .4709 .5203 .7743 .8724



261

The above table exhibits the hedge ratio and hedge effectiveness of CNX IT for the past

six financial years. It can be noted that hedge efficiency was more in 2011-12 i.e. .8724 followed

by 2010-11, .7743. Hedge efficiency was least in the year 2006-07 i.e. -34.73%. A sudden

decline from .74 to .47 is noted in the year 2008-09, which may be the effect of economic

slowdown which affected IT industries badly. On an average overall hedge efficiency of CNX IT

futures is found to be .5067. From the analysis it can be concluded that CNX IT futures shows

less stability in hedge coverage. It is still an efficient tool as it expresses good coverage during

recent years but its effectiveness is questionable.

Hedge Efficiency Ratios of Index Futures

From the above analysis it is clear that index futures are efficient tool of hedge as in most

of the years all the indices expressed good coverage. Following figure shows the average hedge

efficiency ratio of three indices over the past six years.

Fig. 5.1: Hedge Efficiency of Index Futures

Source: Result of secondary data analysis

83%

73%

97%

84%

68%

88%

28%

61%

88%

71%

90%99%

-34%

75%

47%52%

77%

87%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

S&P CNX Nifty

Bank Nifty

CNX IT


262

During the six years under study with three indices, out of 18 cases notable deficiency

was found only two times. Bank Nifty has hedge coverage of only 28% in 2006-07 and CNX IT

had hedge coverage of -34% in 2006-07. In all other cases, indices‟ performance shows that they

are efficient tool for hedge. Over the last six years average hedge efficiency of the three indices

can be summarised as below:

CNX IT - 50.67%

Bank Nifty - 72.97%

S&P CNX Nifty - 82.29%

Hence S&P CNX Nifty seems to have the highest hedge percentage followed by Bank

Nifty and CNX IT.

B) HEDGE EFFECTIVENESS OF INDEX FUTURES

This section tries to analyse the effectiveness of hedge efficiency ratios by comparing

efficiency ratios with standard ratio of 80% to 120% fixed by SFAS 133.

Table 5.28: Near Month Hedge Ratio of Index Futures & Standard Ratios -

Comparison

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 Average

S&P CNX

Nifty .8371 (.7372) .9667 .8438 (.6765) .8765 82.29%

Bank Nifty (.2798) (.6110) .8845 (.7139) .8980 .9910 72.97%

CNX IT (-.3473) (.7496) (.4709) (.5203) (.7743) .8724 50.67%

Source: Secondary data


263

Table 5.28 shows that out of past six years, only two times hedge efficiency of S&P CNX

Nifty was found to be ineffective. But Bank Nifty futures were ineffective in three years out of

six years. CNX IT exhibits ineffective hedges in almost all years except 2011-12.

Global financial crisis has mainly affected IT industry and this may be the reason why

CNX IT has become ineffective in almost all the six years except in 2011-12. On comparing the

average ratios of six years , CNX IT and Bank Nifty has efficiency ratio less than standard ratio

of 80% but S&P CNX Nifty seems to be effective with a ratio above standard.

SUMMARY

This chapter analysed the hedge efficiency and effectiveness of three popular and oldest

indices among the seven indices traded on NSE. S&P CNX Nifty, Bank Nifty and CNX IT were

the three indices selected for the study. Other five indices are of recent origin and hence were

ignored. ECM is applied to assess the hedge efficiency ratios. These ratios are compared with

standard ratios set by SFAS 133. Result shows that S&P CNX Nifty is effective while the other

two indices are ineffective in hedge coverage.

Next chapter deals with assessment of the attitude and behaviour of financial derivative

traders. It helps to find out whether there is any gap between hedge effectiveness and the attitude

of individual traders towards hedge. If so, necessary steps should be taken to curb this gap and to

promote hedge.

Hedge Effectiveness of Index Futuresshodhganga.inflibnet.ac.in/bitstream/10603/85435/14/14_chapter...

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