Impact of Derivatives Trading on Emerging Capital Markets: A Note on Expiration Day Effects in India

THE WILLIAM DAVIDSON INSTITUTE
AT THE UNIVERSITY OF MICHIGAN








Impact of Derivatives Trading on Emerging
Capital Markets: A Note on Expiration Day
Effects in India






By: Sumon Bhaumik and Suchismita Bose










William Davidson Institute Working Paper Number 863
March 2007

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Impact of Derivatives Trading on Emerging Capital Markets:
A Note on Expiration Day Effects in India*




Suchismita Bose
ICRA Limited
(Associate of Moody’s Investor Services in India)
suchismita@icraindia.com

Sumon Kumar Bhaumik**
Brunel University,
IZA-Bonn, and
William Davidson Institute, Ann Arbor
Sumon.Bhaumik@brunel.ac.uk




Abstract:
The impact of expiration of derivatives contracts on the underlying cash market – on trading
volumes, returns and volatility of returns – has been studied in various contexts. We use an
AR-GARCH model to analyse the impact of expiration of derivatives contracts on the cash
market at the largest stock exchange in India, an important emerging capital market. Our
results indicate that trading volumes were significantly higher on expiration days and during
the five days leading up to expiration days (“expiration weeks”), compared with non-
expiration days (weeks). We also find significant expiration day effects on daily returns to the
market index, and on the volatility of these returns. Finally, our analysis indicates that it
might be prudent to undertake analysis of expiration day effects (or other events) using
methodologies that model the underlying data generating process, rather than depend on
comparison of mean and median alone.


Keywords: derivatives contracts, expiration day effect, India
JEL classification: G14


* The authors would like to thank John Hunter, Kyri Kyriacou and Menelaos Karanasos for helpful
discussions about expiration day effects, as well as GARCH models and their use in financial
economics. They remain responsible for all remaining errors.
** Corresponding author. Address: Economics and Finance, School of Social Sciences, Brunel
University, Marie Jahoda, Uxbridge UB8 3PH, UK. Phone: +44 1895 267247. Fax: +44 1895 269786.

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Impact of Derivatives Trading on Emerging Capital Markets:
A Note on Expiration Day Effects in India

1. Introduction
Even though the 1987 stock market crash in the United States was not attributed to futures and options
trading per se, there was some concern among regulators that programme trading and index arbitrage
that link the derivatives and cash markets to each other may have exacerbated the crisis (Edwards and
Ma, 1992, Chapter 11). By its very nature, arbitrage between the cash and (especially) futures markets
require investors to unwind positions in the latter market on the day of expiration of contracts, in
order to realise arbitrage profits. The consequent increase in the number of large buy and sell orders,
and the temporary mismatch between these orders, can significant affect prices and volatility in the
underlying cash market. Not surprisingly, regulators around the world have responded with a number
of measures aimed at reducing price volatility on account of the so-called expiration effect of index
derivatives.

The importance of expiration day effects on the cash market to regulators has, in turn, generated
interest on such effects within the research community. As a consequence, the impact of expiration of
futures and options contracts on the underlying cash market has been examined in a number of
contexts: Australia (Stoll and Whaley, 1997), Canada (Chamberlin, Cheung and Kwan (1989),
Germany (Schlag, 1996), Hong Kong (Bollen and Whaley, 1999; Kan, 2001, Chow, Yung and Zhang,
2003), Japan (Karolyi, 1996), Norway (Swidler, Schwartz and Kristiansen, 1994), Spain (Corredor,
Lechon and Santamaria, 2001), Sweden (Alkeback and Hagelin, 2004), the United Kingdom (Pope
and Yadav, 1992), and the United States of America (Stoll and Whaley, 1987, 1991; Hancock, 1993;
Chen and Williams, 1994). The nature of the impact of expiration of derivatives on underlying cash
prices remains an open question. For example, while Kan (2001) does not observe significant price
volatility and price reversal in Hong Kong, in the same market, Chow, Yung and Zhang (2003)
observe a negative price effect and some return volatility of cash prices on account of expiration day
effects. Similarly, while Chen and Williams (1994) found no effect of expiration on mean returns and
volatility of the underlying asset prices in the cash market, Chamberlin, Cheung and Kwan (1989)
found significant impact of derivatives contract expiration on both mean returns and volatility.


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Figure 1
Growth of the cash equity market at National Stock Exchange
1200
30000

1000
25000
i
es &
an
p
800
20000
m
o
n

c
e
d
d
illio
a
600
15000
t
e
b
i
s
tr
R
f
l
I
N
o
400
10000
er
b
m
u
200
5000
N
0
0
2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07
Financial year
No. of companies listed
No. of trades (million)
Turnover (INR billion)
Market Capitalisation (INR billion)

Note: 2006-07 figures correspond to the first two quarters, i.e., April-September.

Figure 2
Trends in the cash market at the National Stock Exchange
Closing index value and daily returns at National Stock Exchange
4000
10
3500
5
3000
s
e
g
ta
i
f
t
y 2500
n
0
e
N
r
c
e
2000
p
CNX
n
P
-5
i
& 1500
s
S
r
n
tu
1000
-10
Re
500
0
-15
1
7
1
2
8
2
1
67876385 93702
20129 37846
64172 81690
07516 245334
506593682770
943
36 36 36 36 37 37111
37 37 37 37 37552
37 37 37 37 37986
38 38 38 38 38420
38 38 38 38 38860
38
Trading day (June 1, 2000 - September 29, 2006)
Close
Returns


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Figure 3
Growth of the equity derivatives market at the National Stock Exchange

Turnover in NSE derivatives markets (Contracts)
2001-02
2002-03
2003-04
2004-05
Financial year 2005-06
2006-07
0
10
20
30
40
50
60
70
80
90
Number of contracts (millions)
Index Futures
Stock Futures
Index Options
Stock Options

Note: 2006-07 figures correspond to the first two quarters, i.e., April-September.

Turnover in NSE derivatives market (INR)
2001-02
r 2002-03
2003-04
2004-05
2005-06
Financial yea
2006-07
0
50
100
150
200
250
300
INR hundred billion
Index Futures
Stock Futures
Index Options
Stock Options

Note: 2006-07 figures correspond to the first two quarters, i.e., April-September.


In this paper, we examine the expiration day effects of derivatives at the National Stock Exchange
(NSE) in India. The Indian stock market has grown rapidly since its liberalisation in the early 1990s.
Since its inception in 1994, the market capitalisation at the NSE has grown by 828 percent; growth
since the turn of the century has been 412 percent. The growth in the derivatives segment of the
exchange, which was introduced in June 2000, has kept pace with the growth in the cash market.

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Between April 2002 and March 2006, the total turnover of the derivatives segment increased by 4,633
percent, while the average daily turnover increased by 4,587 percent. At the end of November 2006,
1098 companies were listed on the exchange, and 1014 of these stocks were regularly traded. The
meteoric growth of the cash and derivatives segments of the NSE is graphically highlighted in Figures
1-3. Of the 1098 listed securities, 123 act as underlying assets for futures and options contracts. In
addition, three indices are used as the underlying assets for futures and options trading at the
exchange. Details about the nature of these equity derivative contracts are reported in Table 1. In
November 2006, the latest month for which figures are available, the turnover in the derivatives
segment of the equity market was 342 percent of the corresponding turnover in the underlying cash
market. Most importantly, the Indian stock market experiences the “quadruple witching hour”. On the
last Thursday of every month, index futures and options as well as futures and options contracts on
individual securities expire.

Table 1
Derivatives contracts at National Stock Exchange




Futures on
Options on
Parameter
Index futures
Index options
individual
individual
securities
securities
Underlying
S&P CNX Niftya
S&P CNX Nifty

CNX ITb
CNX IT
123 securities
123 securities
CNX Bank Niftyc
Bank Nifty
Contract size
Minimum lot sizes are as follows:
Minimum lot sizes vary by security
S&P CNX Nifty 100
but the minimum value of a
CNX IT 50
contract at inception remains INR
CNX Bank Nifty 100
200,000.
The minimum value of a contract is
INR 200,000 at inception.
Trading cycle
3-month trading cycle: the near month (one), the next month (two) and the
far month (three).
Expiry day
Last Thursday of the expiry month. If the last Thursday is a trading holiday,
then the expiry day is the previous trading day.
Option type
-- European
-- American
Base price
Theoretical
Black-Scholes
Theoretical
Black-Scholes
futures price on
based theoretical futures price on
based theoretical
first day of
price on first
first day of
price on first
trading, and equal
day of trading,
trading, and
day of trading,
to the settlement
and equal to the
equal to the
and equal to the
price on all other
close price on
settlement price
close price on
days.
all other days.d
on all other
all other days.d
days.
Strike price
-- INR
10
-- Vary
by
intervals
security.
Price steps
INR 0.05
Price bands
Operating range
Operating range
Operating range
Operating range
of 10% of the
of 99% of the
of 10% of the
of 99% of the
base price
base price
base price
base price
Source:
National Stock Exchange (http://www.nseindia.com)

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Notes:
a) The S&P CNX Nifty is the market index for National Stock Exchange. The 50-stock index

that covers 22 sectors of the economy has 1995 as the base year, and 1000 as the base

value.
b) The CNX IT is a 20-stock index covering the information technology sector, and was

introduced on January 1, 1996, with base value of 100 with effect from May 28, 2004.
c) The CNX Bank Nifty is a 12-stock index covering 79% of the market capitalisation of the
banking sector, and was introduced on January 1, 2000, with base value of 1000.
d) The interest rate used to calculate the option price is the Mumbai Inter-Bank Offer Rate
(MIBOR). The closing price is calculated as follows: (i) If the contract is traded in the last
half an hour, the closing price shall be the last half an hour weighted average price. (ii) If
the contract is not traded in the last half an hour, but traded during any time of the day,
then the closing price will be the last traded price (LTP) of the contract. (iii) If the contract
is not traded for the day, the base price of the contract for the next trading day shall be the
theoretical price of the options contract arrived at based on Black-Scholes model of
calculation of options premiums.


Vipul (2005) uses data on 14 equity shares to examine expiration day effects in the Indian stock
market. The underlying stocks are selected in a manner that reflected a range of different liquidities
for the associated derivative products; the ratio of turnover in the derivatives market to turnover in the
underlying cash market ranged from 55 percent to 344 percent. Thereafter, the price, volatility and
volume of the underlying shares in the cash segment of the exchange 1 day prior to expiration (of
derivatives contracts), on the day of expiration and 1 day after expiration are compared with the
corresponding values of these variables 1 week and 2 weeks prior to the expiration days, using the
Wilcoxon matched-pairs signed-rank test. The study concludes that prices in the cash market are
somewhat depressed a day before the expiration of the derivatives contracts, and they strengthen
significantly the day after the expiration. However, for most of the shares, this does not tantamount to
price reversals. Finally, volumes are higher on expiration days than on the benchmark non-expiration
days.

We extend and improve upon the aforementioned research on expiration day effects in India in the
following two ways: First, we examine the expiration effects on the market index as opposed to prices
of individual stocks. This allows us to mitigate problems that might arise on account of information
that affect prices of individual stocks much more than a broader market index. Also, broad market
indices are much less likely to be affected by liquidity effects than prices of individual stocks. Further,
as evident from Figure 3, the turnover in the index derivatives markets is much greater than that in the
market for derivatives products associated with individual stocks, and therefore expiration day effects
is likely to be much more prominent for market indices than for individual stocks. Second, we use
generalised autoregressive conditional heteroskedasticity (GARCH) models to jointly model the data
generating process underlying the mean and variance of returns in the cash market over an extended
time period, thereby taking a more sophisticated approach to capturing the expiration day effect than
comparison of measures of central tendency and dispersion.


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Our results indicate that trading volumes were significantly higher on expiration days and during the
five days leading up to expiration days (“expiration weeks”), compared with non-expiration days
(weeks). We also find significant expiration day effects on daily returns to the market index, and on
the volatility of these returns. Finally, our analysis indicates that it might be prudent to undertake
analysis of expiration day effects (or other events) using methodologies that model the underlying
data generating process, rather than depend on comparison of mean and median alone.

The rest of the paper is as follows: In Section 2, we describe the data, and perform some basic tests
for expiration day effects. In Section 3, we discuss the GARCH models and the associated coefficient
estimates. Section 4 concludes.

2. Data and initial results
For our analysis, we use daily data for the market index for NSE – the “Nifty” – for the June 2000
through September 2006 period. The Nifty is a 50-stock market capitalisation weighted index whose
component companies cover 22 different industries. Currently, the stocks included in the Nifty
account for about 60 percent of market capitalisation of all NSE listed companies. Overall, we have
data for 1518 trading days, of which 76 were days on which derivatives contracts expired at the
exchange. We repeat all empirical exercises using a subset of this data, namely, for the February 2002
through September 2006 period. The significance of this sub-period is that foreign institutional
investors (FIIs) were allowed access to the derivatives segment of the exchange from February 2002.
Given that purchase and sell orders of FIIs currently account for 51 percent of the turnover in the cash
market, and reportedly a significant proportion of the turnover in the derivatives market, this
distinction is clearly important. The sub-sample accounts for 1121 trading days, of which 56 days
witnessed the quadruple witching hour.


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Figure 4
Comparative trading: Expiration day vs. control

Panel A
700
1.8
1.6
600
1.4
500
1.2
400
1
300
0.8
control
0.6
200
Trades (millions)
0.4
100
0.2
Ratio of expiration days to
0
0
-00 00
01
02
03
04
05
06
p-
r-01
p-
r-02
p-
r-03
p-
r-04
p-
r-05
p-
r-06
p-
Jun
ec-00
ec-01
ec-02
ec-03
ec-04
ec-05
Se D Ma Jun-01
Se D Ma Jun-02
Se D Ma Jun-03
Se D Ma Jun-04
Se D Ma Jun-05
Se D Ma Jun-06
Se
Month/Year
Expiration day
Control*
Ratio

Note: * Average of reported volume on Thursdays 1 and 2 weeks prior to expiration Thursday.

Panel B
180
2
160
1.8
140
1.6
1.4
120
1.2
100
1
80
0.8
60
0.6
Volume (Rs. billion)
40
0.4
20
0.2
Ratio of expiration day to control
0
0
0
1 01 01 01 2 02
3 03
04
5
5 05
6 06 06
n-0 p-00c-00r-0 n- p- c- r-0 n- p-02c-02r-0
r- n-04
c-04r-0 n-0 p- c-05r-0 n- p-
Ju
ep-03ec-03
ep-04
Se De Ma Ju Se De Ma Ju Se De Ma Jun-S D Ma Ju S De Ma Ju Se De Ma Ju Se
Month/Year
Expiration day
Control *
Ratio

Note: * Average of reported volume on Thursdays 1 and 2 weeks prior to expiration Thursday.


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The total number of trades executed in the cash segment of the exchange, and the ratio of the trades
concluded on expiration (Thurs)days to the trades concluded on a control category of non-expiration
days are highlighted in Panel A of Figure 4. The control category is the average of concluded trades
on Thursdays one and two weeks prior to the expiration Thursday. Three things are evident from the
figure: First, the numbers of trades on expiration days and the control category are closely correlated;
the correlation coefficient is 0.91. Second, as noted earlier in the paper, there was a significant
increase in the number of trades executed in the cash segment of the market over time. Not
surprisingly, therefore, the ratio of number of trades on the expiration day to the number of trades
included in the control category average (r) is close to unity, namely, 1.07. However, the null
hypothesis that r = 1 is rejected at the 1 percent level, the alternative hypothesis being r > 1. In other
words, in the cash market, the number of trades on the expiration day, on average, significantly
exceeds the average number of trades on the Thursdays of the previous two weeks of trading.

Panel B reports the impact of expiration of derivatives contracts on the volume of trade that is
measured in Indian rupees (INR or Rs.) billion. It is evident that the patterns and trends reported in
Panel B are very similar to those reported in Panel A. As in the case of number of trades, the volume
of trade increases significantly over time, and the volume of trade on expiration days is highly
correlated (0.92) with the volume of trade on the control days. The ratio of the volume of trade on
expiration days to the volume of trade on control days has an average of 1.13, and the null hypothesis
that this ratio equals 1 is rejected at the 1 percent level, when the alternative hypothesis is that the
ratio exceeds 1.

Table 2
Expiration day effects

Panel A: Growth rate of volumes (No. of shares traded)

Non-
Significance
Non-
Significance
Expiration
Expiration
expiration
for t- or z-
expiration
for t- or z-
day
week
day
statistic
week
statistic
June 2000 – September 2006
Mean
0.05
- 0.002
**
0.02
- 0.004
--
Median 0.09
0.001
***
0.30
-
0.002
**
Standard
0.25 0.24
--
0.30 0.22 ***
deviation
No. of obs.
76
1518

380
1214

February 2002 – September 2006
Mean
0.14
- 0.01
***
0.02
- 0.01
*
Median 0.16
-
0.002
***
0.03
-
0.003
**
Standard
0.15 0.21 **
0.24 0.20 ***
deviation
No.
of
obs.
56
1119 280
895



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