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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
Hedge Effectiveness of Index Futures
Introduction
Hedge Efficiency of Index Futures – ECM
Hedge Effectiveness of Index Futures
Summary
Chapter 5 – Hedge Effectiveness of Index Futures
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:
Chapter 5 – Hedge Effectiveness of Index Futures
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:
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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
Source: Compiled from SPSS
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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.
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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
Source: Compiled from SPSS
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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.
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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.
Chapter 5 – Hedge Effectiveness of Index Futures
251
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
Source: Compiled from SPSS
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.
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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
Source: Compiled from SPSS
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
Result of cointegration shows that in all 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 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
Source: Compiled from SPSS
Bank Nifty spot is taken as the dependent variable and Bank Nifty futures are taken as
independent variable. Result of regression is given below:
Chapter 5 – Hedge Effectiveness of Index Futures
254
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
Source: Compiled from SPSS
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
Source: Compiled from SPSS
Result of cointegration shows that in all cases the null hypothesis is rejected at 1% level
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
Source: Compiled from SPSS
Chapter 5 – Hedge Effectiveness of Index Futures
255
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 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
Source: Compiled from SPSS
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.
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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:
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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
Source: Compiled from SPSS
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
Result of cointegration shows that in all 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. 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
Source: Compiled from SPSS
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
Chapter 5 – Hedge Effectiveness of Index Futures
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
independent variable. Result of regression is given below:
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
Source: Compiled from SPSS
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
Source: Compiled from SPSS
Result of cointegration shows that in all cases the null hypothesis is rejected at 1% level
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:
Chapter 5 – Hedge Effectiveness of Index Futures
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
Source: Compiled from SPSS
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.
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 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
Source: Compiled from SPSS
Chapter 5 – Hedge Effectiveness of Index Futures
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
Chapter 5 – Hedge Effectiveness of Index Futures
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
Chapter 5 – Hedge Effectiveness of Index Futures
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.
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