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Chapter-II
Review of Literature
48
Table No.II.1. REVIEW OF LITERATURE
NO Year Topic of the study Authors Objective of the study Period Statistical -
Tools
Findings
Relationship between Futures and Spot Market
1 1997 Index futures trading and stock
return volatility of Midcap 400
index futures
Tina. M.
Galloay and
James M. Miller
To investigate the index futures trading
and stock return volatility of Midcap 400
index futures
1991 -
1992,
Skinners
methodology
Changes observed in the risk and liquidity
for the mid cap 400 stocks derive from
market wide changes unrelated to the introduction of midcap 400 index and index
futures
2 1997 Prudent margin levels in the Finnish stock index futures
market
G. Geoffrey Booth, John
Paul
Broussard,
Teppo Martikainen
To examine the behavior of Finland’s stock index futures intraday and daily
price movement
1988 -1994 SWARCH, GASRCH,
GARCH models
It may also want to consider the establishment of price limit and to ensure
that brokers regulatory monitor their
customer’s margin, such action will improve
Finnish option markets margin setting process and thereby increasing the viability
of the Finnish futures markets
3 1997 Futures market performance Guarantees
Rojer Craine To derive the market value of the futures market performance guarantee and
present estimates of the value of the
exchanges exposure.
1987-1995 Black’s option pricing formula
At beginning and the end of the month the performance guarantee was fairly priced.
Estimates of the under pricing are sensitive
to the assumptions about the underlying
distribution of returns.
4 1998 Profitability and arbitrage Kee-Kong
Bae, Kalok Chan and
Yan- Cheung
To investigate the profitability and
arbitrages between stock index futures and stock index option in Hong Kong
market.
1st October
1993 to 30th June 1994
Regression model Relationship between the likelihood of an
arbitrage opportunities and the size of bid-ask spreads in the futures and option
markets
5 1998 Linear and non linear granger causality between the index
futures and the cash market in
Abhay Abhyankar
To tie together of Dwyer, Locke and Yu (1996) and explore further the nature of
the non linear of causal relationship
between the index futures and the cash
March92, June 92,
Sept92
Back and Brock test, Granger
Causality test, E-
After using an E-GARCH filter, the contemporaneous correlation between the
index futures and the cash index is high, the
linear lead lag relationship persists even
49
U.k market in U.k GARCH filter after the return series were adjusted for
persistence in volatility
6 1999 Trading costs and price discovery
across stock index futures and
cash markets
Minho Kim,
Andrew C.
Szakmary and Thomas V.
To examine price leadership among the
cash index underlying and futures
contracts, using an approach pioneered by Stoll and Whallay
January
1986 to
July 1991
Johansen
cointegration and
VAR
The major markets index in the MMI has
the highest predictive power over the others
and is least explained by the others.
7 1999 Price discovery and causality in
the Australian share price index futures markets
Joshua
Turkinton and David Walsh
To address the extend and timing of lead
lag relationship between share price index futures and the underlying spot
index
3rd January
1995 to 21st December
1995
ARMA model
and simple Granger causality
test.
The price discovery time of the true price,
following an information shock, depends on whether the shock is an own market shock
or another markets shocks
8 1999 Mispricing of index futures contracts and short sales
constraints
Joseph K.W.Fung
and Paul
Draper
To examine if changes in shorts sales constraints affects the extent to which
index futures contracts are mispriced
January 1994 to
March
1996
Multiple regression
Traders establish positions that don’t cover all the transaction cost. Ex-post arbitrage
profit suggested that traders establish
position that doesn’t cover all the transaction cost.
9 1999 Lead –lag relationship between
the spot markets and stock index evidence from Korea
Jae H. Min
and Mohammad
Najand
To investigate the relationship between
futures and spot markets, both in terms of return and volatility utilizing the nearly
incepted futures markets in Korea
3rd may
1996 to 16th October
1996
SEM, VAR Neither KOSPI 200 nor futures contracts
leads the other during the June contracts lead the other during the September nor
December contracts periods
10 1999 Transaction cost, short sale restriction and futures market
efficiency in Korea
Gerald. D. Gay and Dae.
Y.Jung
To examine the price discovery performance of Korean stock exchange
contracts
from 3rd May to 12th
May 1998
GARCH, A substantial portion of the under pricing can be explained by transaction cost,
however a high incidence of mispricing did
remain after accounting for the level of
transaction cost, faced the lowest cost trader group- the KSE exchange members
11 2000 Intraday volatility component in
FTSE- 100 stock index futures
Alan
E.H.Speight, David G.
McMillan
To investigate intraday volatility
component in FTSE- 100 stock index futures
January
1992 to June 1995
GARCH, ARCH,
RCH-LM test and BDS tests
It indicated full decay of a shock to the
transitory components parameter estimates is statistically insignificant at the half day
frequency
50
12 2000 The lead lag relationship between
equities and stock index futures
market around information releases
Alex Frino,
Terry Walter
and Andrew West
To investigated the lead lag relationship
between equities and stock index futures
market of Australian stock exchange and Sydney stock exchange
August
1995 to 31st
December 1996
ARIMA approach The lead lag relationship between return on
stock index and stock index futures are
influenced by the release of the macroeconomic and stock specific
information
13 2000 Price discovery occur for internationally traded firms and
how did international stock price
adjust to an exchange rate shock
Joachim Grammig,
Michael and
Christian
Schlag
To address two questions such as where did price discovery occur for
internationally traded firms and how did
international stock price adjust to an
exchange rate shock
19998-1999
Cointegration and vector error
correction models
Home market largely determines the random walk components of the
international value of firms along with the
independent role of exchange rate shocks to
affect prices in the derivatives markets.
14 2001 The index futures markets and the
efficiency of screen trading in Germany and Korea
Laurence
Copeland Sally-A Jones
To make a study on the index futures
markets and the efficiency of screen trading in Germany and Korea
1984 to
1994
Mok, Lam and Li
Procedure
The relative frequency of price maxima and
maxima is far greater than is consistent with a random walk in all cases.
15 2001 Intraday price formation in US
equity index markets
Joel
Hasbrouck
To empirically investigate in the price
discovery of US equity index market in the new environment
1998-2000 Cointegration,
VECM and Var model
For the S&P 500 and Nasdaq 100 index,
price discovery was dominated by futures trading,
16 2001 Modeling linkage between
Australian financial futures markets
Sang Bae
Kim, Francies In and
Christopher
To make an understanding of the nature
of cross market linkage,the interaction is an essential consideration of investors
and policy makers
January
1988 to December
1999
E-GARCH model Australian financial futures markets are
strongly linked in the sense that they have developed dynamic second moment
interactions.
17 2001 The cash settlement and price discovery in futures market in
USA.
Leo Chan and Donald Lien
To examine the effects of cash settlement ability of the futures market to predict
futures spot price.
September 1977 to
December
1998
Vector Auto regression model
with Error
correction
It was found that the feeder cattle futures contract improved its price discovery
function after the cash settlement was
adopted.
18 2002 End of an era? The futures of stock option.
Steven. M. Van Putten
and Edward
D. Graskamp
To present all most all topics related to futures markets and to analyze the
technical aspects of electronic trading
2002 Conceptual The movement of option stock market in the last one decades were clearly analyzed and
explained in such a way that demographic
trend, financing and leverage performance.
51
19 2002 Intra-day price discovery process
between the Singapore Exchange
and Taiwan Futures Exchange
Mathew
Roope and
Ralf Zurbruegg
To analyze the intra-day price discovery
process between the Singapore Exchange
and Taiwan Futures Exchange
January
11th 1999 to
31st June 1999
ECM, Granger
and ARIMA
models
Singapore index futures play a role in price
discovery significantly greater than that of
the TAIFEX futures.
20 2002 Introduction of CUBES on the
Nasdaq-100 index spot –futures pricing relationship
Alexande A.
Kurov and Donnis J.
Lasser
To examine the pricing relationship
between NASDAQ -100 futures and the underlying index
1st July to
20th October
1991
Autoregressive
and regression Model
Both the average magnitude of futures
mispricing and the frequency of boundary violations fall after the introduction of
cubes.
21 2002 Pricing efficiency of the S&P 500 index markets
Quentin C. Chu and
Wen- Liang
Gideon
To examine the price efficiency and arbitrage opportunities between S&P
depository receipts and the S&P 500
index futures
2002-2001 VAR model Found a surprisingly close price relationship between SPDR’s and the S&P500 index
futures.
22 2002 Short term dynamic linkage between NSE Nifty and
NASDAQ composite in India and US
K. Kiran Kumar and
Chiranjith Mukhopadyay
To empirically investigate the short term dynamic linkage between NSE Nifty in
India and NASDAQ composite in US
1999-2001 ARMA-GARCH model
The Granger Causality result indicated unidirectional Granger Causality running
from the US stock market to the Indian stock market. The previous day time returns
of both NASDAQ composite and NSE Nifty
had significant impact on the NSE.
23 2003 Dynamic relationship between South Asian and developed
equity market
Asjeet S. Lamba
To analyse the dynamic relationship between South Asian and developed
equity markets
July 1997-February
2003
Multivariate cointegration,VE
CM
The Indian market was influenced by the large developed equity market including the
US, UK and Japan and this influence had
strengthen during the period of January 2000-february 2003
24 2003 Price discovery for NYSE stocks Haiwei Chen,
Honghui Chen etal
To investigate the effects of trading halts
on price discovery for NYSE stocks
1992 Cointegration,
VECM
The degree of benefit from trading halt
depends on the types of news and significance of the news items.
25 2003 Time variation in Beta in India. Ajay Shah
and Syed Abuzar
Moonis
To tested time-variation in Beta in India. 1 May
1996 to 30 March
2000
kalman filter
model and bivariate GARCH
model
Contributed a dynamic hedging strategy, in
which hedge ratios were frequently adjusted in the listing of new information will
perform better compared to a static strategy.
52
26 2003 Price discovery and volatility
spill over in index futures market
–some evidence from Mexico
Maosen
Zhong Ali F.
Darrat and Rafael Otero
To investigated the price discovery and
volatility spill over on index futures
market in Mexico
15th April
1999 to 24th
July 2002
EGARCH model The newly established futures market in
Mexico was a useful price discovery
vehicle, although futures trading had also been a source of instability for spot market.
27 2003 Price discovery in the U.S option
market
Yusif E.
Simaan
To investigate the price discovery
process on the most actively traded option that were listed on all five stock
five stock option exchanges
2000 Cointegration,
VECM
Newly exchanges which are electronically
equipped that is ISE was the leader in providing the most informative quotes
28 2003 Price discovery in hybrid markets on the London markets.
Hung Neng Lai
To provide evidence that while SETS and dealers both contributed to the price
discovery process and to understand the
role of SETS in the price discovery process
first three months of
year 2002
Regression The price during the trading ours tends to shift after a SETS trade more than a trader
trade. The results showed that non FTSE -
100 stocks are similar to those on FTSE -100 stocks.
29 2003 Price discovery for Mexican
shares
George M.
and Carlos B. Tabora
To find the level and accuracy of price
discovery in Mexican shares.
2000-2002 LOP, and Error
Correction Model
They found that when deviations from LOP
occur that call for error correction , usually with the next trading session, much of the
correction made during ensuring trading in
new York rather than in Mexico city
30 2004 Information content of extended trading for index futures
exchange
Louis.T.W.Cheng, Li.Jiang
and etal
To investigate the information content of extended trading for index futures
exchange in Hong Kong
November 20th 1998 to
May 31st
2000
Weighted period contribution,
GARCH
Pre-open futures innovations had a positive impact on overnight returns, and pre-open
futures innovations had a positive impact on
overnight returns.
31 2004 Forward pricing function of the
Australian equity index futures
contracts
Irena
Ivanovic and
Peter Howley
To investigate the extent to which
Australian stock index futures prices with
varying terms to maturity are unbiased estimator of spot index values.
1983 to
2001
Johansen
cointegration
method, OLS,VCEM.
Speculative opportunities seem to exist for
the six- nine and twelve months spreads and
that they do not convey unbiased signals about the futures of the spot price.
32 2004 Lead lag relationship between
equity and stock index futures market and its variation around
information release-empirical
Kedar Nath
Mukherajee and K. Mishra
To investigate the lead lag relationship
between the spot and future markets in India
April to
September 2004
VAR model,
Granger Causality test,
A symmetric spill over among the stock
return volatility in Indian spot and future markets, the leading role of futures market
wouldn’t strengthen even for major
53
evidence from India markets-wide information releases
33 2004 Resiliency ability of the
underlying spot markets in Hong
Kong
Andy.C.N.Ka
n
To provide an empirical analysis for the
impact of the HSI futures trading on the
resiliency ability of individual HSI constituents stock in the Hong Kong
stock index
6th may
1980 to 5
may 1992
Regression model Cross sectional model was only
significantly positive in the intervals of one
year before and after the introduction of the HSI futures markets.
34 2004 Price discovery in the Hang Seng Index markets
Raymond W. and Yiuman
Tse
To extend the understanding of information processing by investigating
how information is transmitted in the
HongKongmarkets
November 12th 1999 to
June 28
2002
Multivariate GARCH model
The futures market is the main driving force in the price discovery process, followed by
the index.
35 2004 Price dynamics in the regular and E-mini futures markets
Alexander Kurov and
Dennis.J. Lasser
To examine the price dynamics in the S&P 500 and Nasdaq-100 index futures
contracts.
May 7 2001 to
September 7 2001
VECM The order flow is more informative in the Nasdaq-100 market than in the S&P 500
market.
36 2004 Price discovery in the Athens
derivatives exchange
Dimitris
F.Kenourgios
To examine the informational linkage
between the FTSE/ASE-20 stock index and its three months index futures
contracts and the role in price discovery
August
1999 to June 2002
Johansen
cointegration, vector error
correction and
Wald test models
Futures contracts could be used as price
discovery vehicles and it indicated that important role of futures markets in the
Greek capital markets
37 2005 Index futures trading and spot price volatility in emerging
markets
Spyros.I.Spyrou
To empirically investigate whether the introduction of futures trading leads to
increase volatility and uncertainty in the
underlying markets for an important European emerging equity market that is
Athens stock Exchange
September 2003
GARCH Neither current nor lagged futures trading activity was statistically significant in the
volatility equation. It can be concluded in
such way that the overall implication of this result is that the ASE futures trading did not
seem to destabilize spot markets.
38 2006 Does an index futures split enhance trading activity and
hedging effectiveness of the
Lars Norden To investigate whether an index futures split affects the trading activity at the
futures market and the hedging efficiency
October 24th 1994 to
June 29th
bivariate GARCH model
No evidence that the futures split significantly affects the relative futures bid-
ask spread. Futures trading volume had
54
futures contracts of the futures contracts with respect to
the underlying index stocks
2001 increased significantly because of the split
39 2006 Transaction tax and market quality of the Taiwan stock index
futures
Robin K. Chou and
George
H.K.Wang
To make different study by insetting two aspects, focused mainly on the impacts of
the tax cut on the markets quality of the
TAIFEX itself and they examined the
behavior of transaction tax revenue before and after the tax rate reduction
May 1st 1999 to
April 30th
2001
Indicator regression
approach
suggested by
Huang and Stoll
Effects of tax reduction on trading volume showed that the negative coefficients can be
interpreted as the short run estimates of the
elasticity of trading volume with respects to
the bid-ask spread for the futures contracts.
40 2006 Lead lag relationship of return
and volatilities among the KOSPI 200spot, futures and option
markets.
Jangkoo
Kang, Chang Joo Lee and
Soonhee Lee
To empirically investigate the intraday
price change relations in the KOSPI200 index markets, the KOSPI 200 futures
market and the KOSPI 200 option
market.
1 October
2001 to 30 December
2002
Black-Scholes
model
Estimation of the lead lag relation of
volatilities indicated that the realized volatilities of the KOSPI200 stock index
volatilities by around 5 minutes.
41 2007 Econometric analysis of the lead lag relationship between India’s
NSE Nifty and its derivatives contracts
Sathya saroop Debasish
To offer a unique contribution in examining lead lag relationship between
NSE nifty index and the futures and option contracts
from July 2000 June
2008
Cointegration and ARMA models.
The call and put markets broad move together but there is a tendency for the call
option price to react more quickly than the put option price. relative transaction cost are
a major determinants of the lead lag
relationship.
42 2007 The contribution of Indian index futures to price formulation in the
stock markets
Suchismita Bose
To examine whether price in the Indian stock index futures markets contribute to
the pricing process in the stock markets
March 2002 to
September
2006
Johansen cointegration,
Vector error
correction model
The futures markets response faster to the previous period’s deviation from the long
run equilibrium. Arbitrage trading is more
prevalent than momentum trading in the spot markets
43 2007 Effect of futures trading on the
distribution of spot index returns
M. Illueca
and J.A.Lafuente
To make an investigation on the effect of
futures trading on the distribution of spot index returns in Spanish.
Jan 17
2000 to Dec 20,
2002
ARIMA and
GARCH model
Futures trading activity is a significant
variable to explain the density function of spot returns conditional to spot trading
volumes.
55
44 2007 Stock index futures prices and
Asian Financial Crisis
Taufiq
Hassan,
Shamsher Mohammed,
Mohammad
Ariff
To investigate stock index futures prices
and Asian Financial Crisis
1996 to
December
201
Keim and
Madhavan’s
(1996) method
Liquidity constraints and the absence of
foreign institutional participation and
restrictions on domestic institutional investors to enter in to the market and
domestic investors’ inefficiency also raised
doubts to the growth of stock index futures
market in an emerging markets
45 2008 Price discovery and arbitrage
efficiency of Indian equity futures
and cash markets
Kapil Gupta
and
Balwinder Singh
To examine the price discovery and
arbitrage efficiency of an emerging
capital that is India, to empirically reveal the weather futures and cash markets
have strong and stable long run relation
April 2003
to March
2007
Johansen
cointegration
procedure,VECM and Egranger
causality
Strong and stable long run co movement
between two markets which suggested both
long run equilibrium and maturity data price coverage’s, Indian equity future market
dominates the information assimilation
process in the Indian capital market
46 2008 Dynamic interaction among mutual funds flows, stock market
return and volatility
Thenmozhi and Manish
Kumar
To examine whether the information on mutual fund flows can be used to predict
the changes in market returns and volatility.
January 2001to
April 2008
EGARCH model, VAR model
There was significant positive correlation between returns and sales fund flows but a
significant negative correlation was observed in the code of net fund flows.
47 2008 Dynamic relationship between
stock returns trading volume and volatility from the evidence of
Indian stock market
Brajesh
Kumar and Priyanka
Singh
To address so far four important issues
such as what kind of relationship existed between trading volume and returns
2000 to
2008
OLS and VAR
modeling, GARCH model
Very strong evidence that in Indian market
and further it supported by the variance decomposition. In case of unconditional
volatility and trading volume, they found
positive comperenious relationship between
trading volume and unconditional volatility
48 2008 Limits to stock index arbitrage by examining S&P 50 futures and
SPDRS
Nivine Richi, Robert T.
Daigler and Kimberly
C.Gleason
To examine the potential limit of arbitrage regarding the S&P 500 cash
index and whether the standard and poor depository receipts could be used to price
and execute arbitrage opportunities with
the S&P 500 futures contracts
1998 to 2002
cost of carry model
Mispricing exists for both the S&P 500 index and SPDR relative to the futures
contracts. Volatility and the time that arbitrage opportunities persist support the
existence of limit to arbitrage.
56
49 2008 The efficiency of Greek stock
index futures market
Christos
Floros and
Dimitrios V. Vougas
To address the issue of cointegration
between Greek spot and futures market
1999-2001 Granger two step
analyses, VEC
model
Both spot and futures are Cointegrated,
implying market efficiency, current spot
price adjust to the long run difference between itself and futures prices, futures
lead spot return.
50 2009 Persistent mispricing in a recently opened emerging index futures
markets
David G. McMillan and
Numan Ulku
To show in the early days of the futures markets and in the absence of informed
traders the disposition effect is visible in
the movement of futures prices.
March 2005 to
October
2005
Cost of carry model, MTAR
model, LSTR
model and
Newey- West procedure
Quicker adjustment back to equilibrium when the change in the basis is positive and
when the change in the future price is
greater in absolute value than the change in
the index value, this study shed light on how the interaction between informed arbitragers
51 2009 Lead lag relationship between the spot index and futures price for
the Turkish derivatives exchange
Ulkem Basdas
To revisit the lead lag relationship in such a way that whether futures price
lead the spot price for ISE30 and
compare the forecasting abilities of many
models
February 4 2005to may
9 2008
ECM, ECM with COC, ARIMA,
and VAR model
The superiority of ECM over the other models proved that the lead lag relation
included explanatory power to model the
path of services rather than series alone.
52 2009 The impacts of index futures on the index spot markets of Indian
markets
Y.P.Singh and Megha
Agrwal
To investigate the nature and strength of relationship between Nifty spot and index
January 2004-2007
Granger Causality
Futures return Granger causes Nifty spot index while the reverse was not true, futures
lead the spot market for Nifty.
53 2009 Information memory and pricing
efficiency of futures markets
Kapil Gupta
and
Balwinder singh
To examine the information
dissemination efficiency of Indian equity
futures markets
January
2003 to
December 2006
GARCH,
EGARCH,
ARMA,
GARCH model results implied that every
price change response asymmetrically to the
positive and negative news in the markets and leverage effects is persistent in the
Indian equity futures markets..
54 2009 Risk transmission from futures to spot markets without data
stationarity in Turkey’s market
Alper Ozum and Erman
Erbaykal
To detect risk transmission from futures to spot markets without data stationarity
in Turkey’s market
January 2, 2006 to
March 25,
2008
ARDL models There is a cointegration relationship between spot and futures returns. According
to the results there is no causality
relationship exists between the two markets.
57
55 2010 Individual index futures investors
destabilize the underlying spot
market
Martin. T.
Boli,
Christian.A. Salm , Berdd
To investigate the impact of the
introduction of index futures trading in
Poland on the conditional return volatility of the underlying stock index markets
1st
November
1994 to 31st December
2007
Markov-
Switching
GARCH model
Introduction of index futures trading in
Poland did not lead to an increase in
volatility of the underlying stock markets.
56 2010 Relationship between index futures margin trading and
securities leading in China
Pagat Dare Brayan, Yang
Tie Chang
and Patrick
Phua
To investigate the relationship between index futures margin trading and
securities leading in China.
2008-2010 Conceptual A list of trading securities and collaterals for the trial was published by the Shanghai
stock exchange and Shenzhen stock
exchange was the another important aspects
which encouraged the futures markets movements in China.
57 2010 Index arbitrage and the pricing relationship between Australian
price index futures and their
underlying shares
James Richard
Cummings
and Alex
Frino
To extend Brailsford and Hodgsons (1997) analysis of stock index futures
pricing based on the Australian All
Ordinary share price index contracts
1st January 2002 to 15th
December
2005
Regression The efficiency of the arbitrage mechanism is improved by increasing the level of liquidity
in the stock markets, thereby increasing
strengthening the most vulnerable point
relied upon to maintain the price linkage between stock index futures and their
underlying shares.
58 2010 Price discovery and investor’s structure in stock index futures
Martin. T. Bohl,
Christian A.
Salm and
Michael Schumppli
To investigate whether the dominance of presumably unsophisticated individual
investors in the futures market impairs
the informational contribution of futures
trading
16th January
1998 to
June 30th
2009
Vector Error Correction
model with a
multivariate
DCC-GARCH extension
Under the dominance of presumably unsophisticated individual investors in the
futures markets, price discovery occurs
mainly in the spot markets, which is
dominated by foreign and domestic institutional investors.
Determinants of Futures market
1 2002 Volatility, open interest volume
and arbitrage by using evidence from the S&P 500 futures market
Stephen
P.Ferris,Hun Y.Park and
Kwangwoo
Park
To empirically examine the dynamic
interactions and causal relations between arbitrage opportunities and a set of
endengeours variable in the standard and
poor 500 index futures markets.
November
1993 to June 1998
VAR DISD, DOI,
DVOL, and PRER
The level of open interest is not directly
affected by the increase in volatility, open interest in the S&P 500 index futures is a
useful proxy for examining the flow of
capital in to or out of the market given
pricing error information shocks.
58
2 2002 The determinants of derivatives
by Australian companies
Hoa Nguyen
and Robert
Faff
To investigate the factors that determine
the use of derivatives by Australian
corporations.
1999 and
2000
Tobit model Found a positive relationship between firms
size and the likely hood of derivatives
usage.
3 2003 Informational content of trading
volume and open interest-an
empirical study of stock option market in India
Sandeep
Srivastave
To examine the role of open interest and
trading volume from the stock option
market in determining the price of underlying shares at cash market
November
2002 to
February 2003
GARCH model The presence of option market improves the
price discovery in the underlying assets
markets, open interest being more significant as compared to trading volume
4 2004 Impact of open interest and trading volume in option market
on underlying cash market
evidence form Indian option
market
Kedar Nath Mukherjee
and R.K.
Mishra
To empirically investigate the impact of a few non price variables such as open
interest and trading volume from option
market in the price index like Nifty index
in underlying market.
June 2001 to June
2004.
Multiple regression and
Granger causality
tests
Open interest based predictors are significant in predicting the spot price index
in underlying cash markets in both the
periods
5 2004 Informational role of open interest in futures markets
Jian Yang, David a.
Bessler and Hung-Gay
Fung
To test two hypotheses such as whether there is any long run equilibrium
relationship between futures price levels and open interest and whether futures
price move with open interest in the long
run or the other way around
1991 to 2002
Johansen cointegration and
error correction model
open interest and the futures price share common long-run information for storable
commodities but not for non storable commodities, all futures prices cause open
interest while open interest did not cause
futures price in the long run
6 2005 Hang seng index futures open interest and its relationship with
the cash market
Hongyi Chen, Laurence
Fung and Jim
To study the Hang seng index futures open interest and its relationship with the
cash market
2000-2004 Correlation, regression
Open interest and cash market turnover are positively correlated, the level and volatility
of index were not statistically significant
7 2007 Price and open interest in Greece Stock index Futures Market
Christos Floros
To make an investigation on price and open interest in Greece Stock index
Futures Market
1999 to 2001
GARCH One can use the information of open interest to predict futures price in the long run for
FTSE/ ASE20,
8 2007 Volatility and autocorrelation in European futures markets
Epaminontas Katsikas
To make a study on volatility and autocorrelation in European futures
markets
2000-2006 Generalized error distribution
During the period of high volatility auto correlation is statistically zero, volatility
itself is an asymmetric function of past error
in the sense that negative errors exert considerably higher impact on volatility
than positive ones.
59
9 2007 Volatility characteristics and
transmission effects in the Indian
stock index and index futures markets
Suchismita
Bose
To investigate the nature of volatility of
returns in the Indian stock index and
stock index futures market and tried to estimate the extend of spillovers
experienced within the two markets
June 200 to
March
2007
GARCH models NSE index and its futures return volatility
had no tendency to drift upward indefinitely
with time, but in fact had a normal or mean level to which they ultimately revert.
10 2008 Mispricing, price volatility, volume and open interest of stock
futures and their underlying
shares
Vipul To investigate the relationship between mispricing, price volatility, volume and
open interest of stock futures and their
underlying shares in Indian futures
markets.
January 2002 to 30th
November
2004
Cointegration and VAR
An increase in the volatility of the futures is followed by an increase in the volatility of
their underlying for the next1-2 days,
Mispricing does not consistently lead or lag
any other variable.
11 2008 Tax effects on the pricing of
Australian stock index futures
James
Richard Cummings
and Alex
Frino
To adapt and extend the frame work
adapted by Cannavan, Finn and Gray (2004) to infer the value of cash
dividends
1st January
2002 to 15th December
2005
Regression
Analysis.
The cost of financing the set of shares of
underlying index provides a mild tax shield, the accumulated tax dividends are
incompletely valued and the franking credits
are worth at least fifty percent of their face
value relative to futures pay off
12 2008 The determinants of the decisions to use financial derivatives in the
lodging industry
Amrik Singh and Arun
Upneja
To investigate the determinants of the decisions to use financial derivatives in
the lodging industry
2000to 2004
profit model with a binary variable
Both the market to book ratio and the leverage ratio to be significantly affecting
the decision to hedge, implying that hospitality firms with higher opportunities
and higher leverage are more likely to use
derivatives.
13 2009 Futures trading and volatility of S&P CNX Nifty index
P.Sakthivel and
B.Kamaiah
To investigate whether futures trading activity affects spot market volatility or
not
1st July 2000 to
February
28th 2008
ARCH, GARCH, GJRGARCH
Unexpected open interest had positive and significant effects on spot market volatility
but estimated coefficients of expected open
interest were negative
14 2009 The impact of volatility
derivatives on S&P500 volatility
Paul Dawson
and Sotiris. K.
Staikouras
To examine the impact of the volatility
derivatives trading on the S&P 500 index
January 3rd
2000 to
May 30th 2008
GARCH Under normal market conditions volatility
derivatives trading contributed to lowering
the underlying assets, the one set of the volatility derivatives trading has lowered the
volatility of both the cash market index and
60
reduced the impact of shocks to volatility.
15 2010 Relationship between open interest, volume and volatility in
Taiwan futures markets
Stephane. M. Yen and
Ming. Hsiang
Chen
To find the relationship among any variable from an ex- ante perceptive that
is out of sample forecasting performance
21st July 1998 to 31st
December
2007
EGARCH, GJR, APARCH,
GARCH and
IGARCH
Significant in sample relationship among the futures daily volatilities, the lagged total
volume and the lagged total open interest
16 2010 Measuring speculative and hedging activities in futures
markets
Julia. J. Lucia and Angel
Pardo
To identify who trades futures from objective market activity data that is
readily available in every derivative
market in the world namely volume of trading and the open interest
March 2000 and
December
2006
Ratios The ratio of volume to absolute change in open interest, regardless of them being
positive or negative imply that the opening
of new positions out numbers the liquidation of old positions
17 2010 Volatility persistence and trading volume in an emerging futures
market
Pratap Chandra Pati
and Prabina
Rajib
To make an attempt to investigate volatility persistence and trading volume
-evidence from NSE Nifty stock index
futures
January 1st 2004 to
December3
1st 2008
ARMA-GARCH model
The evidence of time varying volatility which exhibits clustering high resistance
and predictability in the Indian futures
markets.
18 2010 Cash trading and index futures price volatility
Jinliang Li To examine the effects of cash markets liquidity on the return volatility of stock
index futures
1980 through
2005
GARCH model S&P 500 index futures are less sensitive to cash marketing trading liquidity relative to
NYSE composite index futures.
19 1995 Long term stock return volatility for accounting and valuation of
equity derivatives
Anadrew W. Alford and
James R.
Boatsman
To examine empirically the prediction of long term return volatility where long
term volatility was computed using
monthly stock return over five years
1990-1994 Kolmogorov-Smirnov
goodness of fit
test
If data to compute a historical forecast did not exist the picking comparable firms on
the basis of industry and firms’ size works
best.
20 2002 Futures trading, information and
spot price volatility of NSE50
index futures contracts
M.
Thenmozhi
To examine change in the volatility of
Nifty index due to the introduction of
Nifty futures
15th June
1998 to 26th
2002
GARCH model Though the futures lead the spot market
returns by one day, the exact day by which
the futures lead the spot markets returns was not identified as the study was using daily
returns due to lack of data in terms of
minute-by minute or hourly return
61
21 2003 Do futures and option trading
increase stock market volatility
Premalatha
Shenbagaram
an
To assess the impact of introducing index
futures and 0ption contracts on the
volatility of the underlying stock index in India
October
1995 to
December 2002
GARCH,
EGARCH models
Derivatives introduction had no significant
impact on spot market volatility. The
introduction of new stock index futures or options contracts in emerging markets like
India will stabilize stock market
22 2005 Derivatives trading and volatility of Indian stock market
Ash Narayan Sah and G.
Omkarnath
To understand whether the Indian stock markets show some significant changes
in the volatility after the introduction of
derivatives trading
April 1998 to March
2005
ARCH, GARCH model
When surrogate index taken in to consideration S&P Nifty showed decline in
volatility while BSE sensex exhibited rise in
volatility.
23 2007 Asymmetric response of volatility to news in Indian stock market
Puja Padhi To investigate the effect of the introduction of stock index futures on the
volatility of the spot equity market and to test the impact of the introduction of the
stock index futures contracts
1995to 1st June 2007
GARCH, EGARCH
There is decrease in the volatility in case of Nifty where as there is increase of volatility
in the case of Nifty junior after the introduction of futures in the derivative
market
24 2007
A model of moment methods for exotic volatility derivatives
Claudio Albanese and
Adel Osseiran
To make a model of moment methods for exotic volatility derivatives.
2004-2005 Jumps stochastic volatility and
regime switching
Volatility derivatives were particularly well suited to be treated with moment methods
25 2008 Volatility persistence and the feedback trading hypothesis from
Indian evidence
Vasilieios Kallinterakis
and Shikha
Khurana
To produce an original contribution to the finance literature by investigating the
relationship between feedback trading
and volatility from a markets
evolutionary perspective
1992 -2008 Sentana and Wadhwani -
Model
Both the level and the nature of volatility from the significance of volatility manifest
themselves independently from the
significance of feedback trading
26 2008 Derivative trading and the
volume volatility link in the
Indian stock market
S. Bhaumik,
M.Karanasos
and A. Kartsaklas
To investigate the issue of temporal
ordering of the range based volatility and
volume in the Indian stock market
1995-2007 Bivariate dual
momery model,
AR-FI –GARCH
The introduction of futures trading leads to
a decrease in spot volatility, the migration of
some speculators to option markets on the listing of options was accompanied by a
decrease in trading volume in the underlying
security.
27 2009 Extension of stochastic volatility equity models with Hull- White
Lech.A.Grzelak,
To combine a arbitrage free Hull-white interest rate model in which the
2002-2008 Hull- White interest rate
Although the model was so attractive, because of its square root volatility
62
interest rate process Cornelis.W.D
osterlee and
Sacha Van
Weeren
parameters were consistent with market
price of caps and swaptions.
process structure. It was unable to generate extreme
correlations. Numerical experiment for
different hybrid product that under the same
plain vanilla prices the extended stochastic volatility model gave different prices than
the Heston model.
28 2010 Is an introduction of derivative trading cause-increased
volatility?
Mayank Joshipura
To use simple approach to test the change in volatility by measuring changes in
relative volatility of the stocks on
introduction of futures and options
trading using Beta as a relative measure of volatility
July 2001 to June
2008
GARCH model The effect of introduction of derivatives trading on average daily excess return of
underlying stocks and portfolios
Reviews on Risk reduction through futures market
1 1991 Time varying optimal hedge ratio
on futures markets
Robert J.
Myers
To compare two approaches such as
moving sample variances and covariance
of past prediction errors for cash and futures prices
June 1977
to May
1983
GARCH model The GARCH Model performed only
marginally better than a simple constant
hedge ratio estimates
2 1992 A study on an alternative
approach for determining hedge ratio for futures contracts
Allan
Hodgson and Okunev
Examined whether hedge ratio change for
increase level of risk aversion
1st July
1985 to 29th September
1986
Mean Gini coeffi
Figlewsiki and Kwan and Yip
approaches cient,
The hedge ratio for moderate to strongly
risk averse investors are much more volatile than for low of risk aversion.
3 1995 Estimated hedge ratio and examined the hedging
effectiveness of the FTSE-100
stock index futures contracts
Phil Holmes To examine stock index futures hedging, July 1984 to
June1992
Optimal hedge ratio
The introduction of the FTSE-100 futures contracts has given port folio managers a
valuable instrument by which to avoid risk
even hedge ratio are non constant near time.
4 1998 The future duration on the basis of convexity hedging method
Robert T. Daigler and
Mark Copper
To explain the theory on fixed income securities hedging and its implications
through the comparison of two models
1993-1996 Good man-Vijayaragavan
model
Duration convexity hedge ratio successfully against changes in interest rates without the
need to dynamically alter the hedge ratio
63
5 1999 Fractional cointegration and
futures hedging
Donald Lien
& Yiu Kuen
Tse
To investigate fractional cointegration
and futures hedging by using NSA
futures daily data
Jan. 1989
to August
1997
EC- GARCH
Model, VAR, EC,
FIEC
The hedge ratio of the EC Model was
consistently larger than that of FIEC Models
6 2000 Futures hedging when the
structure of the underlying assets
changes
Manolis G.
Kavussanos
and Nikos k. Nomikos
To investigate futures hedging when the
structure of the underlying assets changes
1985 to
1998
OLS, VECM and
time varying
GARCH Model
The new index would have a more
homogeneous structure than the BFI and will
consists of shipping route which were strongly correlated with each other
7 2001 E. arbitrage approach on hedging a derivative securities and
incomplete market
Dimitris Bertsimas
Leonid Kogm
etal
To make hedge ratio by applying E-arbitrage Approach on derivatives in
incomplete markets.
2000 E-arbitrage Approach
The replication error of the optimal replication strategy could be used as a
quantitative measure for the degree of market
incompleteness
8 2001 Shrimp futures markets as price discovery and hedging
mechanism
Leigh .J. Maynard,
Samhancock
and Heath Hoagland
To analyze the performance of shrimp futures markets as price discovery and
hedging mechanism
1994 to June 1998
Cointegration,
GARCH
Shrimp futures markets were ineffective hedging tools for many shrimp verities during
the period examined
9 2002 Recent developments in futures
hedging
Donald Lien
and Y.K Tse
To make a study on the analysis on recent
developments in futures hedging
1996-2000 Mean-Gini
approach, Conventional
hedging, varying
hedge ratios.
The optimal hedge strategy that minimizes
lower partial moment may be sharply different from the minimum variance hedge
ratio strategy
10 2002 Multi period hedging with futures contracts
Aaron Low, Jayaram
Muthuswamy
etal
To Study multi period hedging with futures contracts
September 1989 to
June 1995
GARCH The hedging strategy which was used in this study performed well than other hedging
strategies on an-ex-ante basis
11 2003 Measuring hedge effectiveness John M.
Charnes and
Paul Koch
Studying on measuring hedge
effectiveness for FAS133 compliance
Conceptual The researchers classified the 80-125 rules
to establish guide lines for acceptable levels
of risk reduction
64
12 2003 Risk management with
derivatives by dealers and market
quality in Government bond markets
Narayan Y.
Nayik and
Prdeep K. Yadhav
To address four questions like they
analyze the extent of selective market
risk taking by government bond dealers and spot –risk on a day to day basis
August
1994 to
December 1995
Regression model Futures market played a healthy role that
could potentially improve spot market
quality by enabling efficient management of the headable components of spot risk
13 2003 How much do firm’s hedge with
derivatives
Wayne Guay,
S.P Kotari
To Examine the hypothesis that final
derivatives were an economically important component of corporate risk
management
2000-2001 Financial
Analysis
The magnitude of the derivative positions
held by most firms was economically small in relation to their entity level risk exposure
14 2004 Multivariate GARCH hedge ratio and hedging effectiveness in
Australian futures markets
Wenling Yang and
David E.
Allen
To compared the hedging effectiveness of conditional and unconditional hedge
ratios using a risk return comparison and
utility maximization.
1992 to 2000
GARCH, OLS,VEC,
Cointegration,VA
R
The VECM hedge ratio performs better than VAR hedge ratio in terms of variance
reduction
15 2004 The relationship between hedge ratio and hedging horizon- A
simultaneous estimation
Sheng- Syan Chen, Cheng-
Few Lee and Keshab
Shrestha
To estimate the effects of hedging horizon length on the optimal hedge ratio
and effectiveness in greater detail
1995-2000 mean–Gini coefficient and
Generalised semivariance
The short run hedge ratio is significantly less than the naive hedge ratio, long run
hedge ratio is consistent with the empirical results obtained by Geppert indicated that if
the hedge horizon is long.
16 2004 Markov Regime switching approach for hedging stock
indices
Amir Alizadeh and
Nikos
Nomikos
To apply Markov regime switching approach for hedging stock indices
1984 to 2001
Markov Regime Switching
models,
GARCH,ECM
By using MRS models markets agents might be able to obtain superior gains, measured in
terms of variance reduction and increase in
utility
17 2004 Optimal hedge ratio and hedging efficiency
SVD Nageswara
Rao and Sajay
etal
To investigated the optimal hedge ratio and hedging efficiency of Indian
derivatives market
1st January 2002 to 28th
March
2002
KHM, JSE model, FBM
methodology
with black-Schole model
Returns on hedged positions using FBM ratio should be significantly higher, the mean
return estimated using BSM and FBM
methodology are not statistically different.
18 2005 Structurally sound dynamic index
futures hedging
Paul Kofman
and Patrict Mcglenchy
To evaluate a simple dynamic hedging
scheme that conditions on continuous changes, as well as on discrete changes
1994 to
July 2003
GARCH, ARCH
test, ROC hedge ratio
For a perfect hedge scenario (HSI), there is
very little evidence of any dynamic hedging strategy. Significantly outperforming the
buy and hold hedging.
65
19 2005 Risk and hedging-do credit
derivatives increase bank risk
Norvald
Instefjord
To investigate whether financial
innovation of credit derivatives made
banks exposed to credit risk
1998-2003 Geometric
Brownian Motion
Model
The financial innovation in the credit
derivatives market might increase bank risk,
particularly those that operated in highly elastic credit market segment
20 2006 Optimal hedge ratio by using
constant, time varying and the Kalman Filter approach
Abdulnasser
Hatemi-J and Eduardo Roca
To calculated the optimal hedge ratio by
using constant, time varying and the Kalman Filter approach
1988-2001 Constant, Time
Varying and the Kalman Filter
approach
The optimal hedge ratio calculated based on
the time varying model implied that futures contracts, at least deserves consideration as a
possible hedging instruments for a portfolio
consisting of Australian equity
21 2006 Hedging and value at risk Richard D.F.Harris
and Jain Shen
To make a study on hedging by using value at risk methodology.
1994 to 2004
Minimum Variance
Hedging, Skewness and
Kurtosis
Minimum Value at risk hedge ratios are generally lower than minimum variance
hedge ratios, the estimated minimum value at risk hedge ratio are generally lower than
the corresponding minimum variance hedge
ratios
22 2007 Robustly hedging variable annuities with Guarantees under
Jump and volatility risks
T.F. Coleman, Y.kim, Y.Li
etal
To compute and evaluate hedging effectiveness of strategies using either the
underlying or standard options as
hedging instruments
1994-2002 Black-Scholes Model.
The risk maximization hedging using underlying as the hedging instrument
outperform the delta hedging strategies
23 2007 The rationales for corporate
hedging and value implication
Kevin Aretz,
Sohneke M.
Bartram Gunter Dufey
To provide a comprehensive and
accessible overview of the existing
rationales for corporate risk management in hedging
2007 Conceptual Found that corporate hedging may increase
from value by reducing various transaction
cost. By reducing cash flow volatility, firms face a lower probability of defaults and thus
have to bear lower expected cost of
bankruptcy.
24 2007 The hedging for multi period down side risk in the presence of
jump dynamics and conditional
heteroskedastisity
Ming- Chih Lee and Jui-
Cheng Hug
Analysis on the hedging for mutiperiod down side risk in the presence of jump
dynamics and conditional
heteroskedastisity
1996-1999 ARCH, ARJI model, ARMA,
VaR
The multi period hedging strategy out performs the one period strategy for all
cases.
66
25 2007 Relationship between hedging
ratio and hedging horizon using
Walvet analysis
Donald Lien
and Keshab
Shrestha
To empirically analysis the relationship
between hedging ratio and hedging
horizon using Walvet analysis
1982 to
1997
OLS Hedge
Ratio, Walvet
Hedge Ratio, ECM
Both error correction and walvet hedge
ratios are larger than the minimum variance
hedge ratio. In terms of performance, error correction hedge ratio performs well for
shorter hedging horizons
26 2008 Hedging effectiveness of the Athens stock index futures
contracts
Manolis G. Kavussanos
To investigate hedging effectiveness of the Athens stock index futures contracts
1999 to June 2004
VECM-GARCH and VECM-
That two stock index futures contracts on ADEX served their risk management
function through hedging
27 2008 The art market-creating art derivatives
Olivia Ralevski
To make a study on hedging in the art market-creating art derivatives
2008 Conceptual Art derivatives could revolutionize the art market by offering a simpler and easier way
to manage the risk and return of art
28 2008 Optimal hedge ratio and hedging effectiveness of stock index
futures
Saumitra N. Bhaduri and
S. Raja Sethu
Durai
To give an overview of the competing models in calculating optimal hedge ratio
5th August 2005 to 19th
September
2005
OLS, VAR, VECM, DVEC-
GARCH,
GARCH
The time varying hedge ratio derived from DVEC-GARCH model gave a higher means
returns compared to other counter parts. The
simple OLS strategy that performs well at the shorter time horizon
29 2008 Estimated hedge ratio and
investigated the effective of hedge ratio on S&P 500 stock
index futures contracts
Dimitris
Kenourgios, Aristeidis
Samitas and
Panagiotis
Drosos
To estimate hedge ratio by using
different model specification and calculate minimum variance hedge ratios
July 1992
to June 2002
OLS, ECM,
GARCH
The Error Correction specification out
performs all the other models since it has the smallest value of the above measures,
the error correction model is the appropriate
method for estimating optimal hedge ratio
since provides better results than the conventional OLS Method , the ECM with
GARCH model.
30 2008 Corporate hedging for foreign risk in India
Anuradha, Sivakumar
and Runa
Sarkar
To provide a perspective on managing the risk that firms face due to fluctuating
exchange rate.
2008 Conceptual Indian companies are actively hedging their foreign exchange risk with forward,
currency and interest rates swaps and
different types of options such as call, put,
cross currency and range barrier options.
67
31 2008 Dynamic hedging performance
with the evaluation of
multivariate GARCH models from KOSTAR index futures
Gyu-Hyen
Mioon, Wei-
Choun Yu and Chung-
Hyo Hong
To make a bridge to the gap of the
application and evaluation of various
GARCH models in the in sample and out of sample dynamic hedging
June 1st,
2007 to
November 8th 2007
OLS model,
Bivariate
GARCH Models, CCC GARCH
All dynamic hedging model outperform the
conventional model in the out of sample
period and using the mean- variance utility function, dynamic hedging models remain
desirable even though they considered
transaction cost induced by daily portfolio
rebalances
32 2008 The effectiveness of dynamic
hedging of selected European
stock index futures
Jahangir
Sultan,
Mohammed S. Hasan
To examine the hedging effectiveness of
stock index futures market in France,
Germany, Netherlands and the U. K for minimizing the exposure from holding
positions in the underlying stock markets
1990-2006,
1999- 2006
GARCH model,
OLS regression,
Dynamic hedging strategy should be the
choice of a hedging method for large
investors looking to minimize the risk of their sophisticated bets
33 2009 Optimum hedge ratio in the Indian equity futures market
Kapil Gupta and
Balwinder
Singh
To investigate the optimum hedge ratio in the Indian equity futures market over
the period
2003 to 2009
VAR, VECM. EGARCH and
TARCH
Hedging through index futures reduces port folio variances by approximately 96%where
as in the case of individual stocks, it varied
from stocks from stocks
34 2009 Determination of closing prices and hedging performances with
stock indices futures
Hsiu-Chuan Lee, Cheng-
Yi Chien and Tzu- Haiang
To examines the impact of the determination of stock closing prices on
futures prices efficiency and hedging effectiveness with stock indices futures
4th January to 4th
December 2003
CCCGARCH The determination of stock closing prices affects markets efficiency as the futures
markets close and hedging effectiveness with stock indices futures
35 2009 A Copula based regime switching
GARCH model for optimal futures hedging
Haiang-Tai
Lee
To apply a Copula based regime
switching GARCH model for calculating optimal futures hedging
1991 to
2007
GARCH model The copula- based regime- switching
varying correlation GARCH model performed more efficiently in future hedging
with more flexibility in the distribution
specification.
36 2010 Hedging performance and stock market liquidity- evidence from
the Taiwan futures market
Hsiu-Chuan Lee and
Cheng –
Chene
To make a study on hedging performance and stock market liquidity of the Taiwan
futures market
2006 to 2008
OLS Model, GARCH Model,
CCC GARCH
The hedge ratio is related to stock market liquidity and the stock market liquidity
would affect the dynamic relationship
between stock and futures prices
68
37 2010 Dynamic hedge ratio for stock
index futures by applying
threshold VECM
Ming-Yuan
Leon Li
To investigate dynamic hedge ratio for
stock index futures by applying threshold
VECM
1996 to
2005
VECM, OLS, The study support the superiority of the
threshold VECM is enhancing hedging
effectiveness for emerging markets
38 2010 Optimal value at risk hedging
strategy under bivariate regime
switching ARCH frame work
Kuang-Liang
Chang
To make an analysis on the optimal value
at risk hedging strategy under bivariate
regime switching ARCH frame work
1998 to
2006
SWARCH,
GASRCH,
GARCH model
SWARCH model is better than GARCH
model in predicting the dynamic behavior
and distribution shape of spot and futures returns.
69
DETAILED REVIEW OF LITERATURE
2.1. INTRODUCTION
Review of literature is a body of knowledge that aims to review the important
aspects of current knowledge in critical way. It has an ultimate goal of bringing the
reader up to date with present literature on a topic and makes a foundation for another
study that may be needed in the same area. Through the review of literature collects
information from the research field to support specific argument or writing about
particular study. It is the bridge between existing knowledge and the knowledge what
is to be explored. Literature review process may encourage the researcher to start
empirical work on a particular topic and it paves the way for right direction for the
research.
In this study, the researcher collected about 250 studies in the different area of
derivatives. Then studies which are so close to the objective are selected, reviewed
thoroughly and classified them in to three groups such as studies related to
relationship between spot and futures market, reviews on determinants of futures
market and literature about the risk reduction of futures market through hedging
process. To make a clear-cut path for the in depth research work on the area studies
on various period, different nations and multiple contexts were reviewed here. In the
first section of the chapter empirical researches on long term, short term and causality
relationship between spot and futures markets by using different methodologies have
been reviewed and included. The influence of different variables on futures market
has been critically reviewed. Finally, studies on hedge ratio and the efficiency of
futures market to reduce the risk is also included in the another section of the chapter.
The gap for the further study is placed in the last section.
70
2.2. REVIEWS ON RELATIONSHIP BETWEEN FUTURES AND SPOT
MARKET.
1. Tina. M. Galloay and James M. Miller (1997) investigated the index futures
trading and stock return volatility of Midcap 400 index futures. This study presented
new evidence on the relation between index futures trading and volatility in the equity
market using the S&P Midcap 400 stock index and Midcap 400 index futures. Daily
data and trading volume data were obtained from separate period such as pre index
period that is before June 1991, interim period which includes 175 trading after June
5th 1991 but before February 13
th 1992 and post futures which includes after February
13th 1992. To determine changes in return volatility, Skinners methodology was
employed. The analysis indicated that the documented decrease in return volatility for
the Midcap 400 stocks is simply a reflection of a decrease in return volatility that
affected all medium capitalization stocks.
2. G. Geoffrey Booth, John Paul Broussard, Teppo Martikainen and Vesa
Puttonen (1997) made a study on prudent margin levels in the Finnish stock index
futures market. The purpose of this study was to examine the behavior of Finland’s
stock index futures intraday and daily price movement and to incorporate the
observed external price behavior in an assessment of the Finnish futures markets
current initial and variation margin setting practices. Sample period of the study
began on 2nd
May 1988 and ended on December 5th
1994. Two different types of
intraday futures return such as minimal returns and the minimal and maximal returns
with in a day irrespective of the closing price were constructed. Empirical result of
estimating equations and minimal and maximal return indicated a close coherence
between actual and fitted observations.
3. Rojer Craine (1997) valued the futures market performance Guarantees. This
study derived the market value of the futures market performance guarantee and
presented estimates of the value of the exchanges exposure on the nearby S&P 500
contract during October 1987 market crash. This paper employed the econometrics
model to assess whether the probability is economically important or not. It was
illustrated the valuation technique by estimating the value of the exchanges
performance guarantee on the nearby contracts on December S&P 500 futures
contracts in October 1987. Black’s option pricing formula was applied for call option
71
valuation. The result showed that the implied variances from the November option,
although high by historical standards are an order of magnitude smaller than the G-K
estimates.
4. Kee-Kong Bae, Kalok Chan and Yan- Leung Cheung (1998) investigated the
profitability and arbitrage by dividing the analysis in to three parts in which first part
revealed arbitrage profitability, the second part was examined arbitrage profitability
based on quotations information and in third part transaction prices were used. This
study obtained data from Hong Kong Futures Exchange for Hang Sang futures index
and option contracts for the sample period from 1st October 1993 to 30
th June 1994.
The authors compared the results to examine the effectiveness of the approach that
evaluated arbitrage opportunity based on transaction price and it takes into account
the impact of bid- ask cost through estimated spread. Results showed that the
frequency of mispricing opportunities varies across different approaches in a pattern
similar to before the percentage violation are the highest for transaction prices, lower
for feasible transaction prices and the lowest for bid-ask quotations.
5. Abhay and Abhyankar (1998) made an investigation on linear and non linear
Granger Causality. The main purpose of this study was to tie together of Dwyer,
Locke and Yu (1996) and explore further the nature of the non linear of causal
relationship between the index futures and the cash market in U.K. Back and Brock
test, Granger Causality test and ARMA model were used in its empirical analysis as
tools to reveal the objectives. The data set consisted of intraday price histories for four
FTSE 100 index futures contracts maturing in March 92, June 92, Sept 92 and the
FTSE 100 index recorded minutes by minutes during 1992. The FTSE cash index
series exhibited high positive auto correlation at the first lag in each period with
statistically significant positive autocorrelation up to lag 6 during some futures
contracts periods. The results of the linear Granger Causality test based on the
multivariate regression index using both raw and AR filtered cash index return
indicated that a high degree of contemporaneous correlation between the cash and
futures contracts.
6. Minho Kim, Andrew C. Szakmary and Thomas V. Schwarz (1999) studied
trading costs and price discovery across stock index futures and cash markets. The
authors used the impulse response function to examine how an innovation in one
72
markets transmits across different markets. Transaction prices on the S&P 500, the
NYSE composite and the MMI futures contracts from January 1986 to July 1991 were
selected as sample. Johansen Cointegration and Vector Autoregressive techniques
were also applied as the tools for the analysis. The Trace and Maximal Eigen value
test indicated that there is no Cointegration relationship among the stock index futures
series of the S&P 500, NYSE index and major markets index. For VAR estimation,
results imply that in predicting unexpected movements among stock index futures
contracts, the S&P index futures has the highest predictive power.
7. Joshua Turkinton and David Walsh (1999) made an investigation on price
discovery and causality in the Australian share price index futures markets. This study
aimed to address the extend and timing of lead lag relationship between share price
index futures and the underlying spot index. The sample period of the study ran from
3rd
January 1995 to 21st December 1995 where the sample was drawn every 5
minutes. Simple Cost and Carry method, Cointegration test, ARMA model and simple
Granger Causality test were employed for the analysis of the study. The causality tests
results indicated that bi-directional causality among the variables and authors found
that an index shop appears to induce a very large response in the futures.
8. Joseph K.W.Fung and Paul Draper (1999) made an empirical analysis on
mispricing of index futures contracts and short sales constraints. The authors analyzed
the mispricing of the Hong Kong Hang Seng index futures contracts. Time stamped
transaction data of the Hang Seng index futures contracts for the period 1st April 1993
to 30th
September 1996 were obtained, minutes by minutes index prices and
annualized month end dividend yield for the same period from Hang Seng index
services were used in the empirical analysis. The empirical results revealed that
traders establish positions that don’t cover all the transaction cost. The result of
regression using mispricing as the dependent variable for both zero and number level
transaction cost was reported here.
9. Jae H. Min and Mohammad Najand (1999) investigated the lead –lag
relationship between the spot markets and stock index evidence from Korea. The
authors attempted to investigate the relationship between futures and spot markets,
both in terms of return and volatility utilizing the nearly incepted futures markets in
Korea. Dynamics Simultaneous Equation Models (SEM) and Vector Auto Regression
73
Models (VAR) were employed in the analysis part of this study. The authors used 10
minutes intraday data from 3rd
May 1996 to 16th October 1996 for the KOSPI 200
index. Simultaneous equation models results indicated that in the early inception of
Korean futures markets, the futures markets lead the spot markets by at least 30
minutes. The Wald statistics also indicated the model is well specified and there is a
strong relationship between the futures and spot markets. 10. Gerald. D. Gay and
Dae. Y.Jung (1999) had investigated a further look at transaction cost, short sale
restriction and futures market efficiency. The authors had taken Korean stock index
futures as the sample for the study. The aim of the study was to examine the price
discovery performance of Korean stock exchange contracts. The sample period of the
study started from 3rd
May to 12th May 1998. Daily credit depository rates to proxy
for the applicable financing rates also obtained for the analysis. Time varying market
volatility was estimated by using GARCH (1, 1) model. Results on nearby contracts
indicated that longer dated contracts are relatively more underpriced. It also suggested
that there may risk premia associated with longer dated contracts that is not captured
by the cost of carry model.
11. Alan E.H.Speight, David G. McMillan and Owain A.P Gwilyan (2000)
investigated intraday volatility component in FTSE- 100 stock index futures. This
study applied many models to intraday U.K stock index futures market return data at
various frequencies in an effort to determine whether permanent and transitory
component can be explicitly identified, in such data and whether the persistence of
short run volatility diminishes as the intraday frequencies decreases. The data sets
contain all trades on FTSE-100 stock index futures contracts from January 1992 to
June 1995. GARCH, RCH-LM test and BDS tests were employed in its empirical
analysis and the GARCH model specifically remaining diagnostic indicated the
presence of residual ARCH structure at all frequencies other than half day. Both
GARCH model and Wald tests results include complete dissipation of the transitory
component by the half day frequency. Only the GARCH model is adequately
capturing return dependency at the half day frequency following the dissipation of
transitory volatility.
12. Alex Frino, Terry Walter and Andrew West (2000) investigated the lead lag
relationship between equities and stock index futures market around information
74
releases by using data from the Australian stock exchange and Sydney stock
exchange. Share price index futures contracts on the Sydney futures exchange and
Australian stock index exchange were taken as the data for the period of 1st August
1995 to 31st December 1996. The empirical results implied that both adjustment for
infrequent trading work as expected, although there is some evidence that the
midpoint index adjustment may perform better. This study provided evidence that the
lead lag relationship between return on stock index and stock index futures are
influenced by the release of the macroeconomic and stock specific information.
13. Joachim Grammig, Michael and Christian Schlag (2000) addressed two
questions such as where did price discovery occur for internationally traded firms and
how did international stock price adjust to an exchange rate shock? Three large
German firms like Daimler Chrysler, Deutsche Telekom and SAP were analyzed to
find the answers for these questions. Cointegration and Vector Error Correction
Models were used for the analysis. A highly frequency sample of quotes from both
locations along with the dollar euro exchange rate were considered as the data. The
evidence suggested the structure of international equity market that had the home
market largely determine the random walk components of the international value of
firms along with the independent role of exchange rate shocks to affect prices in the
derivatives markets.
14. Laurence Copeland Sally-Ann Jones and KinLam (2001) made a study on the
index futures markets and the efficiency of screen trading in Germany and Korea. In
this study, the authors took more direct approach to measuring efficiency by
addressing many questions. Its empirical work, non-parametric tests were based on
the Arc sine Law which involves comparing the theoretical distribution implied by an
intraday random walk with the empirical frequency distribution of the intraday
high/low times were implemented. Real time transaction price and volume data for
three months futures on the FTSE-100 traded on LIFFE, the CAC-40 traded originally
on the MATIF, now on MONEP, the DAX in Germany and the KOSPI-100 in Korea
were taken as data sets for the study. The study period ran from 1984 to 1994.
Empirical study results indicated that the relative frequency of price maxima and
maxima is far greater than is consistent with a random walk in all cases.
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15. Joel Hasbrouck (2001) studied on intraday price formation in US equity index
markets. This study empirically investigated in the price discovery of US equity index
market in the new environment where the mirror of index with exchange traded funds,
electronically traded markets, small denomination futures contracts and a family of
sector ETF that break the index into nine components. This paper assessed the
importance of the step by step development of US equity markets by considering the
NASDAQ 100 index, EFT futures contracts and S&P 500 index as the sample for the
analysis. Cointegration, Vector Error Correction Model and VAR Models result
suggested that for the S&P 500 and NASDAQ 100 index, price discovery was
dominated by futures trading. The S&P 500 sector funds were EFTs that were
constructed on industry lines and could be used to replicate the overall index.
16. Sang Bae Kim, Francies In and Christopher Viney (2001) investigated
modeling linkage between Australian financial and futures markets. This study made
an attempt to empirically analyze the dynamic interdependence and volatility linkage
between the Australian stock, bond and money market futures contracts traded on the
Sydney futures exchange using a Multivariate E-GARCH representation. The data set
of the study consists of daily settlement price for each contract obtained from the
Sydney Futures Exchange for the period from 4th January 1988 to 23
rd December
1999. In the initial stage, the authors examined the raw futures markets data and the
Univariate (GARCH (1,1), again the diagnostic test suggested by Eagle and Nag was
employed to check whether there is a potential asymmetry of volatility response to
past innovations. The empirical results concluded that there exists a strong multi-
directional influences among all three markets.
17. Leo Chan and Donald Lien (2001) examined the cash settlement and price
discovery in futures market in USA. The effects of cash settlement ability of the
futures market to predict futures spot price was thoroughly examined here. Vector
Auto Regression model with Error Correction was applied to analyze the data. They
collected cash and futures price data from September 1977 to December 1998 from
the commodity system Inc. Tuesday cash price and nearby futures price data were
taken for the analysis. It was found that the feeder cattle futures contract improved its
price discovery function after the cash settlement was adopted.
76
18. Steven. M. Van Putten and Edward D. Graskamp (2002) made an
investigation on the topic end of an era- the futures of stock option. They aimed to
present all most all topics related to futures markets and to analyze the technical
aspects of electronic trading. The movements of option stock market in the last one
decade were clearly analyzed and explained in such a way that demographic trend,
financing and leverages are performed well. The implication of futures and option
market also were explained. This study was ultimately theoretical and conceptual.
They concluded this study with aspiration and hope to a dawn of new era in futures
option.
19. Mathew Roope and Ralf Zurbruegg (2002) analyzed the intra-day price
discovery process between the Singapore Exchange and Taiwan Futures Exchange.
January 11th
1999 to 31st June 1999 were taken as the period of the study. Three
separate techniques such as Error Correction Model, Gonzalo and Granger (1995)
Methodology and ARMA Model were applied for the analysis. Test of erogeneity
results indicated that there is bidirectional relationship between Singapore futures
markets and Taiwan futures markets. Finally it was suggested that the majority of
information is impounded first in Singapore and therefore will tend to be the more
informational efficient market. In the case of regulatory regime in Singapore has
helped to establish it as the dominant market for trading in Taiwan index futures.
20. Alexande A. Kurov and Donnis J. Lasser (2002) investigated the effect of the
introduction of CUBES on the Nasdaq-100 index spot –futures pricing relationship.
This study used tick by tick transaction data for Nasdaq-100 futures and 15s interval
data for the Nasdaq-100 index from July 1st to October 20
th 1991. The entire sample
period was divided in to two sub periods about eight months each such as before the
introduction of cubes and after the introduction of cubes. To compute the mispricing
series futures prices are synchronized with the spot index value using a MIN SPAN
procedure suggested by Harris, Mclnish and Wood (1995) was applied. On the basis
of this result it was clear that simulated arbitrage trades becomes much riskier in the
post cube periods and introduction of cubes had reduced the effective transaction cost
needed to form the spot futures market arbitrage portfolios.
21. Quentin C. Chu and Wen- Liang Gideon Hsieh (2002) investigated price
efficiency of the S&P 500 index markets. The aim of the study was to examine the
77
price efficiency and arbitrage opportunities between S&P depository receipts and the
S&P 500 index futures. Through the empirical analysis, the authors defined the
occurrence of boundary violation as a series of same side violations so that any two
adjacent violations in the same occurrence occur within 20 minutes interval. Ex-ante
results between the index and futures revealed that only large price deviations and
transaction cost levels yields profitable ex-ante arbitrage profits. The study found a
surprisingly close price relationship between SPDR’s and the S&P500 index futures.
22. K. Kiran Kumar and Chiranjith Mukhopadyay (2002) made a comparative
study on short term dynamic linkage between NSE Nifty and NASDAQ composite in
India and US to empirically investigate the short term dynamic linkage between NSE
Nifty in India and NASDAQ composite in US during the period of 1999-2001 by
using intra daily data which determine the day time and overnight returns. The authors
employed two stages GARCH Model and ARMA-GARCH Model to capture the
mechanism by which NASDAQ composite daytime return and volatility had an
impact on not only the mean but also on the conditional volatility of Nifty overnight
returns. The Granger Causality result indicated unidirectional Granger Causality
running from the US stock market to the Indian stock market. Further it found that the
previous day time returns of both NASDAQ composite and NSE Nifty had significant
impact on the NSE Nifty over night returns.
23. Asjeet S. Lamba (2003) analyzed the dynamic relationship between South Asian
and developed equity market for analyzing the short and long run relationship
between each of equity market in the South Asian Region and the major developed
equity markets during July 1997-February 2003. For India, daily data on the CNX
Nifty50, for Pakistan and Srilanka, daily data on the Karachi 100 and the specific
developed equity market include in the analysis were France, Germany, Japan, UK,
and US. Using a Multivariate Cointegration frame work and Vector Error Correction
Model the authors found that the Indian market was influenced by the large developed
equity market including the US, UK and Japan.
24. Haiwei Chen, Honghui Chen and Nicholas Valerio (2003) investigated the
effects of trading halts on price discovery for NYSE stocks. In this study, intraday
data from the institute for the study of security market was used for the year 1992.
The stocks which are listed continuously on the NYSE during the entire year were
78
collected as the data for the empirical analysis. To address the potential bid ask spread
induced bias arising from using trading data, the midpoint of quoted bid and ask
prices was used to measure prices on each day. It was found that the degree of benefit
from trading halt depends on the types of news and significance of the news items.
Trading halts can be beneficial when some significant news items already hit the
market and investors need more time to digest the impact on price.
25. Ajay Shah and Syed Abuzar Moonis (2003) tested time-variation in Beta in
India. There are two approaches on time variation beta such as kalman filter model
and bivariate GARCH model in this study. The data sets of the study contained daily
return on the BSE for 50 highly liquid stocks and the NSE50 index for the period
from 1st May 1996 to 30
th March 2000. To measure the improvement on fit over the
conventional OLS beta market model, they used two measures, the coefficients of
determination and the variances of the errors. The empirical results showed a
tendency for beta to be mean reverting and showed little evidence of beta as a random
walk process.
26. Maosen Zhong Ali F. Darrat and Rafael Otero (2003) had investigated the
price discovery and volatility spill over in index futures market of Mexico. The
authors tested the hypothesis with daily data from Mexico in the context of EGARCH
model that also incorporated possible cointegration between the futures and spot
markets. The study covered the period from 15th
April 1999 to 24th
July 2002. IPC
index futures were the sample of the research. The analysis revealed that the newly
established futures market in Mexico was a useful price discovery vehicle, although
futures trading had also been a source of instability for spot market.
27. Yusif E. Simaan (2003) analyzed price discovery in the U.S option market. The
aim of the study was to investigate the price discovery process on the most actively
traded option that was listed on all five stock option exchanges. Based on real time
feeds from the option price reporting authority in January 2002 the researcher
analyzed both the quotes and trades on the 50 most actively traded stock option. They
measured the Hasbrouck (1995) information by using the second by second quotes
book and the link between price discovery and other market conditions also were
analyzed. This study found that newly exchanges which are electronically equipped
that is ISE, was the leader in providing the most informative quotes.
79
28. Hung Neng Lai (2003) made a study on price discovery in hybrid markets on the
London markets. This study attempted to provide evidence that while SETS and
dealers both contributed to the price discovery process and to understand the role of
SETS in the price discovery process. The sample included the trade records and the
transcripts of the limit order book during the first three months of year 2002. 171
stocks were taken and FTSE-100 was the main concentration of the study. Regression
was used to analyze the data. The study found that the price during the trading hours
tends to shift after SETS trade more than to a trader’s trade. The results showed that
non FTSE -100 stocks are similar to those on FTSE -100 stocks.
29. George M. and Carlos B. Tabora (2003) made a study on the topic price
discovery for Mexican shares. It was a comparative study on NYSE and Bolsa. The
study considered price discovery as a matter of degree of accuracy. Daily closing data
series from both the markets were taken into consideration for the analysis. In its
analysis part they analyzed the variance of daily stock price changes in the two
markets were compared, investigated market leadership through temporary departure
from LOP, and Error Correction Model also were applied. They found that when
deviations from LOP occur that call for Error Correction , usually with the next
trading session, much of the correction made during ensuring trading in new York
rather than in Mexico city.
30. Louis.T.W.Cheng, Li.Jiang and Renne W.Y.Ng (2004) made a study on
information content of extended trading for index futures exchange. In this study the
authors employed the S&P 500 and Hang Seng London reference index to control for
a possible spillover effects. Minutes by minute’s quotes of the HSI from Hang Seng
index services limited and HSIF transaction data from the Hong Kong Exchange were
obtained for the period of 20th
November 1998 to 31st May 2000. Weighted Period
Contribution (WPC) was used to measuring the price discovery in the extended
trading sessions. Futures return innovations from the post close trading sessions were
extracted by using a GARCH (1, 1) model. The explanatory power of the futures
returns innovations of the post- close and pre-open sessions on over night spot returns
were examined and information content of extended futures trading results showed
that pre-open futures innovations had a positive impact on overnight returns.
80
31. Irena Ivanovic and Peter Howley (2004) examined the forward pricing function
of the Australian equity index futures contracts. This study investigated the extent to
which Australian stock index futures prices with varying terms to maturity are
unbiased estimator of spot index values and examined Australian equity futures
contracts with six different terms of maturity and investigated the relationship
between futures and spot values. The settlement prices of futures contracts and spot
prices of Sydney futures Exchange and its corresponding spot price were taken for the
period of 1983 to 2001. The OLS, Johansen Cointegration and Vector Error
Correction Model were employed in the empirical analysis and found that Australian
equity index futures price are Cointegrated with the subsequent spot values for one,
two, three, six, nine and twelve months to maturity.
32. Kedar Nath Mukherajee and K. Mishra (2004) made an empirical study on the
topic lead lag relationship between equity and stock index futures market and its
variation around information release from India. The main objective of the study was
to investigate the lead lag relationship between the spot and future markets in India,
both in terms of return and volatility. Intraday price histories for the nearby contract
of Nifty index futures, Nifty cash index and also the price of some specific component
stocks during April to September 2004 were considered for the analysis. VAR model
and the Granger Causality test among the return series of the spot and the future
markets results indicated that on the volatility spill over among the spot and future
market in India and also revealed that a symmetric spill over among the stock return
volatility in Indian spot and future markets.
33. Andy.C.N.Kan (2004) studied Resiliency ability of the underlying spot markets
in Hong Kong after the introduction of index futures contracts. The aim of the study
was to provide an empirical analysis for the impact of the HSI futures trading on the
resiliency ability of individual HSI constituents stock in the Hong Kong stock index
which is the important financial markets in the Asian Pacific region. A cross sectional
regression model was employed in the study for investigation after controlling some
important factors. Daily stock price and return from 6th May 1980 to 5
th May 1992 of
HSI were taken for the analysis. Results of regression model in the four different
sampling intervals indicated that the increase in the liquidity ratios of the HSI
81
constituent stocks is significantly greater than that of the non-constituents stocks from
the pre-futures to the post futures periods after controlling other relevant factors.
34. Raymond W. and Yiuman Tse (2004) made a study on price discovery in the
Hang Seng Index markets by using index futures and the tracker fund. The objective
of the study was to extend of their understanding of information processing by
investigating how information is transmitted among the Hong Kong Hang Seng index
markets. They also examined the volatility spillover effects of the three markets via a
multivariate GARCH model. Minute by minute data of the Hang Seng index from
November 12th 1999 to June 28
th 2002 were taken in to consideration. The result of
Gonzalo and Granger model showed that the futures market is the main driving force
in the price discovery process, followed by the index. Multivariate GARCH model
indicated that the volatility of the index and futures market spill over to each other to
the strong effects from the futures to the index markets.
35. Alexander Kurov and Dennis.J. Lasser (2004) analyzed price dynamics in the
regular and E-mini futures markets. The purpose of the study was to examine the
price dynamics in the S&P 500 and Nasdaq-100 index futures contracts. This study
employed trade data for the regular and E-mini S&P 500 and Nasdaq-100 futures
from May 7th
2001 to September 7th
2001. The researcher included the total E-mini
trade series in the VECM and in the calculation of information shares. Information
shares statistics results supported the notion that general characteristics that are
inherent to the electronics trading mechanism and available to all traders. It was
calculated the cumulative impulse response functions to initiate traders by forecasting
the VECM after the unit shocks to one of the CTI price series and all trade series and
findings suggested that the order flow is more informative in the Nasdaq-100 market
than in the S&P 500 market.
36. Dimitris F.Kenourgios (2004) studied the price discovery in the Athens
derivatives exchange. The purpose of the study was to examine the informational
linkage between the FTSE/ASE-20 stock index and its three months index futures
contracts. Johansen cointegration, Vector Error Correction and Wald test models were
applied here for its estimation. Price data on the stock index and three months
FTSE/ASE-20 index futures contracts from Athens stock exchange and Athens
derivatives exchanges for the period from August 1999 to June 2002 were considered.
82
The findings suggested that both the markets are Cointegrated, there is bi-directional
relationship between both markets and there is informational linkage among them and
futures contracts could be used as price discovery vehicles in the Greek capital
markets.
37. Spyros.I.Spyrou (2005) investigated index futures trading and spot price
volatility on the basis of emerging markets. The aim of this article was to empirically
investigate whether the introduction of futures trading leads to increase volatility and
uncertainty in the underlying markets for an important European emerging equity
market that is Athens stock Exchange. For empirical analysis daily closing prices for
the main markets index, the FTSE/ASE 20 for the period September 2003 were
employed. The results from GARCH (1, 1) model indicated that all coefficients are
significantly for both periods, when both coefficients are slightly increased for the
post futures periods and Alpha is slightly increased and C1 is slightly reduced. Wald
test results revealed that there is no statistically significant effect on volatility
following the introduction of futures trading.
38. Lars Norden (2006) made an investigation on the topic does an index futures
split enhance trading activity and hedging effectiveness of the futures contracts. All
futures contracts with the omex- index as underlying securities at OM from October
24th 1994 to June 29
th 2001 were considered for the study. Bivariate GARCH model
was applied to obtain a measure of the optimal futures hedge ratio and the estimation
results for stock index return revealed that there is strong evidence of conditional
heteroskedastisity in both the stock index and the futures.
39. Robin K. Chou and George H.K.Wang (2006) investigated transaction tax and
market quality of the Taiwan stock index futures. The intraday futures price rates of
the TAIFEX from May 1st 1999 to April 30
th 2001 were selected for the analysis.
Generalized method of movement procedure and instrumental variable method were
applied to estimate the parameters. Exploratory data analysis results supported the
argument by the transaction tax opponents that the imposition of a transaction tax
would reduce markets liquidity. It was observed that tax reduction may bring in more
liquidity and this in turn would bring in even more liquidity.
40. Jangkoo Kang, Chang Joo Lee and Soonhee Lee (2006) made an empirical
investigation of the lead lag relationship of return and volatilities among the KOSPI
83
200 Spot, Futures and Option markets. This study empirically investigated the
intraday price change relations in the KOSPI 200 index markets, the KOSPI 200
futures market and the KOSPI 200 option market by taking the sample from 1st
October 2001 to 30th December 2002. The correlation between the stock index return
and the futures returns between the stock index return and the implied forward returns
are smaller. This revealed that option and futures markets lead the spot markets by
around 5 minutes, while the spot markets lead the futures markets to a much weaker
degree of around 5 minutes.
41. Sathya Saroop Debasish (2007) made a study on an econometric analysis of the
lead lag relationship between India’s NSE Nifty and its derivatives contracts. High
frequency data for the NSE Nifty stock index futures from July 2000 to June 2008
was taken as the sample for the study. Empirical results showed that the NSE Nifty
stock index hourly returns have significant first order positive auto correlation and the
series matched with calls and puts separately showed consistent serial correlation
structure. Cointegration and ARMA models were employed in the analysis part.
Findings evidenced on the lead lag relationship between the NSE Nifty index and the
NSE Nifty index futures. Overall, it was clear that the futures market generally lead
the index by up to one hour.
42. Suchismita Bose (2007) investigated the contribution of Indian index futures to
price formulation in the stock markets. The authors analyzed Indian stock index and
Indian futures price returns for the period of March 2002 to September 2006. In order
to examine that the index futures price provide any information that contribute to the
adjustment process of the stock index, daily closing prices of the futures contracts on
the S&P CNX Nifty index and the underlying index values were taken for the
analysis. The cross correlation matrices indicated that futures markets leading the spot
markets with a day lag, while the reverse was not true. This study showed that the
futures markets information showed the price discovery of the underlying Nifty is
marginally higher than what Nifty contributes to its futures price discovery.
43. M. Illueca and J.A.Lafuente (2007) made an investigation on the effect of
futures trading on the distribution of spot index returns. Data on the lbex 35 spot and
futures markets were provided by MEFFRV for the period January 17th
2000 to
December 20th
2002 was taken in to consideration. ARIMA and GARCH model were
84
employed to accomplish the objectives. The empirical findings for the Spanish market
revealed that futures trading activity is a significant variable to explain the density
function of spot returns conditional to spot trading volumes. The results confirmed
that futures markets significantly contribute to the price discovery process regardless
the day of the week.
44. Taufiq Hassan, Shamsher Mohammed, Mohammad Ariff and Annuar M.D
Nassir (2007) investigated stock index futures prices and Asian Financial Crisis. The
author’s referred to the Asian Financial crisis in July 1997 as the East Asian Region to
introduce stock index futures contracts. For the data of KLCI index and KLCI index
futures contracts were used. The sample period of the study was from January 1996 to
December 2001. They examined whether derivatives trading by either a domestic or a
foreign investors have any influence on these prices. Findings indicated that after
financial crisis, the stock market was extremely volatile and many legal restrictions
were imposed on the capital market which makes the arbitrage very risky. Keim and
Madhavan’s (1996) method was used to define permanent and temporary price
impacts associated with a domestic institution which suggests large temporary price
impacts.
45. Kapil Gupta and Balwinder Singh (2008) studied the price discovery and
arbitrage efficiency of Indian equity futures and cash markets with an objective to
empirically reveal that whether futures and cash markets have strong and stable long
run relation, which markets serves as a sources for information during short run and
how mispricing behave during the contact cycle by employing high frequency data
of Nifty index and fifty individual stocks from April 2003 to March 2007.The
research work applied Johansen Cointegration procedure, Vector Error Correction
Model, Granger Causality Methodology and Vector Auto Regression methodology.
This study found strong and stable long run co movement between two markets which
suggested both long run equilibrium and maturity data price coverage’s. During short
run, significant violation of equilibrium relationship had been observed.
46. Thenmozhi and Manish Kumar (2008) conducted a study on dynamic
interaction among mutual funds flows, stock market return and volatility with a
purpose of examining whether the information on mutual fund flows can be used to
predict the changes in market returns and volatility by using daily market index of
85
S&PCNX Nifty index from January 2001to April 2008. The conditional return
variances of the S&PCNX Nifty index were estimated using the EGARCH model and
the VAR model was also employed. The major findings indicated a strong positive
concurrent relationship between stock market return and mutual fund purchase, sales
and net. It was found that a positive relationship exists between stock market returns
and mutual fund flows, stock market volatility and mutual fund flows.
47. Brajesh Kumar and Priyanka Singh (2008) investigated the dynamic
relationship between stock returns trading volume and volatility from the evidence of
Indian stock market. This study addressed so far four important issues such as what
kind of relationship existed between trading volume and returns? Do trading volume
and returns exhibits dynamic relationship? What kind of relationship exists between
trading volume and price volatility and does there exists ARCH effect in the stock
return. Their data set consisted of all the stocks of S&PCNX Nifty index for the
period of 2000 to 2008. The study investigated the relationship between trading
volume and return and dynamic relationship using OLS and VAR modeling approach.
Mixed distribution hypothesis also was tested using GARCH model. Their findings
indicated evidence of positive contemporaneous correlation between absolute price
changes and trading volume in Indian stock markets.
48. Nivine Richi, Robert T. Daigler and Kimberly C.Gleason (2008) made a study
on the limits to stock index arbitrage by examining S&P 500 futures and SPDRS.
Authors attempted to examine the potential limit to arbitrage regarding the S&P 500
cash index and whether the S&P depository receipts could be used to price and
execute arbitrage opportunities with the S&P 500 futures contracts. Intraday futures
price and volume data of three months of high volatility from July 2002, September
1998, October 2002 and three months mid-level volatility and low months volatility
from 1998 to 2002 were employed. The application of cost of carry model was
employed to obtain fair futures value for both the S&P 500 cash index and the SPDR.
Empirical results offered insight into the limits to arbitrage regarding the S&P 500
futures, even given relatively high transaction cost.
49. Christos Floros and Dimitrios V. Vougas (2008) analyzed the efficiency of
Greek stock index futures market by addressing the issue of cointegration between
Greek spot and futures market over the period of 1999-2001. The short run efficiency
86
was examined by several Error Correction Models and long run efficiency was tested
through Johansen Cointegration approach. 525 daily observations on the FTSE/ASE
20 stock index and stock index futures contracts, 415 daily observations on the
FTSE/ASE mid 40 stock index and index futures contracts were considered. Granger
two step analyses indicated that both spot and futures are Cointegrated, implying
market efficiency. The results of VEC model for both FTSE/ASE 20 and FTSE-ASE
mid 40 indicated that futures lead spot return and it is confirmed that futures markets
are informally more efficient than underlying stock market in Greece.
50. David G. McMillan and Numan Ulku (2009) made a study on persistent
mispricing in a recently opened emerging index futures markets. The data set of this
study consists of five minutes values of the ISE30 spot index and index futures. The
sample covered the period from March 2005 to October 2005 and from November
2005 to February 2006 and provided with robustness also. Cost of Carry Model,
MTAR Model, LSTR Model and Newey- West procedure were employed to analyze
the data series and the results of MTAR Cointegration test revealed that quicker
adjustment back to equilibrium when the change in the basis is positive and when the
change in the future price is greater in absolute value than the change in the index
value.
51. Ulkem Basdas (2009) investigated the lead lag relationship between the spot
index and futures price for the Turkish derivatives exchange by using ISE30 and
compare the forecasting abilities of ECM, ECM with COC, ARIMA, and VAR model
considering the data from February 4th
2005 to May 9th
2008. The series of futures
prices on ISE 30 index was gathered from the Turk DEX Website and the spot value
also collected from the same source and for the same period. The Ganger causality
test results indicated that the log of spot price significantly Granger cause log of
futures but not vice versa.
52. Y.P.Singh and Megha Agrwal (2009) investigated the impacts of Indian index
futures on the index spot markets to understand the nature and strength of relationship
between Nifty spot and index and Nifty futures to determine the direction of flow of
information between Nifty spot index and Nifty futures and to establish a causal
relationship between return of Nifty spot and return of Nifty futures. Data sets of the
study consisted of closing price histories of Nifty futures and Nifty spot index for a
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period of January 2004-2007. In order to analyze the lead lag relationship between
Nifty and its futures return series, cross correlation coefficients between Nifty spot
return and Nifty futures for 10 lead lags were calculated. This result indicated that
futures lead the spot market for Nifty.
53. Kapil Gupta and Balwinder Singh (2009) investigated information memory and
pricing efficiency of futures markets to examine the information dissemination
efficiency of Indians equity futures markets which is expected to provide important
policy implications of regulatory bodies and help to improve the knowledge base of
market participants. The weak form efficiency of three indices and 84 individual stock
futures permitted for trading futures and option segments of NSE was examined for
the period from January 2003 to December 2006 by considering daily closing prices
of futures contracts. GARCH and EGARCH econometrics models results implied that
previous information shock plays significant role in the return generation process.
54. Alper Ozum and Erman Erbaykal (2009) detected risk transmission from
futures to spot markets without data stationarity in Turkey’s market. The authors used
the Bond test to examine cointegration and Toda and Yamamoto test to analyze
causality between spot and futures markets. Daily time series of spot and futures
prices of the US dollar /Turkish lira Exchange rate from January 2nd
, 2006 to March
25th 2008 was employed for empirical analysis. The Unrestricted Error Correction
Model and Auto Regressive Distribution Lag (ARDL) models were applied to
distinguish long and short relationship. Bound test which was used to examine
cointegration results showed that there is a cointegration relationship between spot
and futures returns and there is informational efficiency in Turkish foreign exchange
markets.
55. Martin. T. Boli, Christian.A. Salm and Berdd Wilfling (2010) investigated the
individual index futures investors destabilize the underlying spot market by applying
Markov- Switching GARCH model. The sample periods ran from November 1st 1994
to December 31st, 2007 for the WIG 20 and the WIG from December 31
st 1994 to
December 31st 2007 and for the WIG 80 from 31
st December 1999 to 31
st December
2007. The empirical results denoted that the coefficient sums are less than one for all
stocks returns time series across both regimes.
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56. Pagat Dare Brayan, Yang Tie Chang and Patrick Phua (2010) investigated the
relationship between index futures margin trading and securities leading in China. The
milestone of the China’s equity market was the announcement released on January 8th
2010 stated that council has given in principal approval to the trial implementation of
stock index futures trading, margin trading and securities lending in China. Stock
index futures trading were launched on April 16th
2010. This was a purely theoretical
study and here the authors mentioned that many questions about the proposed regime
for trading activities remain and are yet to be addressed by the Chinese regulators.
57. James Richard Cummings and Alex Frino (2010) examined index arbitrage and
the pricing relationship between Australian price index futures and their underlying
shares. This study analyzed the pricing efficiency of SFE SPI 200 index futures. The
independence of the absolute mispricing on the ex ante estimate of interest rate
volatility implied from interest rate option prices were investigated. The date series
describes the time, price and volume of each trade and the prices of the best available
bids and offers from 1st January 2002 to 15
th December 2005 were taken into
consideration for the analysis. Auto Regressive Regression Coefficients were
uniformly positive and significant which indicated a high degree of persistence in the
mispricing series.
58. Martin. T. Bohl, Christian A. Salm and Michael Schumppli (2010) had
investigated price discovery and investor structure in stock index futures with an aim
to understand whether the dominance of presumably unsophisticated individual
investors in the futures market impairs the informational contribution of futures
trading by taking daily closing prices for the WIG 20 index and daily settlement price
for the WIG 20 futures contracts from 16th January 1998 to June 30
th 2009. This study
used Vector Error Correction model with a Multivariate DCC-GARCH extension.
Estimation results suggested that the futures market does not fully perform the
expected price discovery function. Further, there was evidence of bidirectional
information flows and causality. Results revealed that futures price reacts more to
perturbations, implying a quicker correction of disequilibria.
2.3. REVIEWS ON DETERMINATES ON FUTURES MARKET
1. Stephen P.Ferris,Hun Y.Park and Kwangwoo Park (2002) made an
investigation on volatility, open interest ,volume and arbitrage by using evidence from
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the S&P 500 futures market for empirically examining the dynamic interactions and
causal relations between arbitrage opportunities and a set of endogenous variable in
the S&P 500 index futures markets by using daily S&P 500 stock index spot data
from November 1993 to June 1998. Four variables in VAR system as VAR DISD,
DOI, DVOL, and PRER were estimated here. It was found that the level of open
interest is not directly affected by the increase in volatility. Pricing error plays a
critical role in linking volatility and the level of open interest and open interest in the
S&P 500 index futures is a useful proxy for examining the flow of capital in to or out
of the market.
2. Hoa Nguyen and Robert Faff (2002) made a study on the determinants of
derivatives by Australian companies. The primary aim of this study was to investigate
the factors that determine the use of derivatives by Australian corporations. The
authors formed their sample by examining the notes to the financial reports of the 500
largest Australian companies that are listed on the Australian stock exchange for the
financial year of 1999 and 2000. In this study they have conducted three levels of
analysis in which basic univariate tests, a logistic model and the Tobit model were
employed to investigate the partial impacts of the same set of independent variables
on the decisions of how much derivatives to be used. This study found a positive
relationship between firm’s size and the likely hood of derivatives usage. Tobit results
revealed that leverage is the most important factor in determining the extent of
derivatives use.
3. Sandeep Srivastave (2003) made a study on the topic informational content of
trading volume and open interest-an empirical study of stock option market in India to
examine the role of certain non price variables namely open interest and trading
volume from the stock option market in determining the price of underlying shares at
cash market. For the analysis call option and put option open interest and volume
based predictors were used. The sample of the study includes daily data on 15
individual stocks which were most liquid stock option in the NSE option market from
November 2002 to February 2003 and it was found that these predictors have
significant explanatory power with open interest being more significant as compared
to trading volume.
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4. Kedar Nath Mukherjee and R.K. Mishra (2004) studied on impact of open
interest and trading volume in option market on underlying cash market evidence
form Indian option market. The objective of the study was to empirically investigate
the impact of a few non price variables such as open interest and trading volume from
option market in the price index like Nifty index in underlying cash market in India.
The study used daily data relating to price index in underlying cash market, open
interest and trading volume from June 2001 to December 2001 and January 2004 to
June 2004. The results of the Multiple Regression and Granger causality tests
confirmed that the open interest based predictors are significant in predicting the spot
price index in underlying cash markets in both the periods.
5. Jian Yang, David a. Bessler and Hung-Gay Fung (2004) investigated the
informational role of open interest in futures markets. The authors examined the long
run relationship between open interest and futures prices. Five futures contracts on
storable physical commodities and two stock indices, three non storable physical
commodities and one non storable financial future contract were selected from the
period 1991 to 2002. Johansen Cointegration and Error Correction Model were
employed and the empirical result showed that open interest and the futures price
share common long-run information for storable commodities but not for non storable
commodities. It was found that in the case of S&P 500 stock indexes, bidirectional
long term causality between futures prices and open interest rather than a
unidirectional causality.
6. Hongyi Chen, Laurence Fung and Jim Wong (2005) had studied the Hang seng
index futures open interest and its relationship with the cash market. In the analysis
two adjusted open interest indicators such as the de-trend open interest position and
the ratio of open interest to cash market turnover were calculated. Hang Seng futures
open interest and its underlying cash turn over were taken as the data for the study.
They analyzed the correlation between open interest and cash market turnover, open
interest and selling turn over and open interest and index volatility. It was found that
open interest and cash market turnover are positively correlated, the level and
volatility of index were not statistically significant and there was no clear-cut
relationship between open interest and short selling turn over. Analysis with ratio and
decomposed trend also showed positive relationship with open interest.
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7. Christos Floros (2007) made an investigation on price and open interest in Greece
Stock index Futures Market with an aim to provide further case study of interesting
country Greece to go beyond GARCH, Johansen and Granger Causality econometrics
techniques. 525 daily nearby observations on the FTSE/ASE 20 stock index futures
contracts from August 1999 to August 2001 and 415 daily nearby observations on the
FTSE/ASE40 stock index futures contracts from January 2000 to August 2001 were
taken into consideration for the analysis. The results of cointegration relationship
between daily price and open interest for Greek futures markets showed that open
interest as a proxy in the conditional variance helps in explaining the GARCH effects
in futures markets return.
8. Epaminontas Katsikas (2007) made a study on volatility and autocorrelation in
European futures markets. The Generalized Error Distribution was applied in its
empirical analysis by considering daily figures for the stock index futures of France,
Germany and the U.K as the data for the study. Evidence suggested that index futures
return in Europe markets behave similarly in the sense that auto correlation and
volatility are linked in a non linear fashion. The model implied that during the period
of high volatility auto correlation is statistically zero.
9. Suchismita Bose (2007) attempted to understand the volatility characteristics and
transmission effects in the Indian stock index and index futures markets by using
daily data for the market index of NSE-S&P CNX Nifty for the period from June
2000 to March 2007. U.S Dow Jones Industrial average returns was also included in
the analysis. The empirical results indicated that NSE index and its futures return
volatility had no tendency to drift upward indefinitely with time, but in fact had a
normal or mean level to which they ultimately revert. In the case of volatility
transmission, it was found strong bidirectional volatility spillovers between the
markets implying that the price and returns dynamics in one market are capable of
explaining much of the movement in the other.
10. Vipul (2008) investigated the relationship between mispricing, price volatility,
volume and open interest of stock futures and their underlying shares in Indian futures
markets. The sample data was selected on the basis of average volume based rank of
the stock futures from 1st January 2002 to 30
th November 2004. The daily volatility
for the futures and underlying shares was computed using Parkinson’s formula and it
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showed that the variance of daily returns can be estimated more efficiently using the
extreme value estimator. The results indicated that any increase or decrease in
mispricing did not lead to the significant change in volatility, volume or open interest
for any of the futures or the underlying shares.
11. James Richard Cummings and Alex Frino (2008) made an investigation on the
tax effects on the pricing of Australian stock index futures. To adapt and extend the
frame work adopted by Cannavan, Finn and Gray (2004) data for 1st January 2002 to
15th December 2005 have been taken. S&P/ASX 200 stock index values, time-
stamped approximately 30 seconds apart, were also considered. Daily series for the
overnight cash, 30, 90 and 180 days bank accepted bill rates had taken from the RB of
Australia. In the Australian markets, the timing option held by stock holders to
different capital gains and realize capital losses possibly accentuates the reduction in
the effective financing cost brought about by the tax deductibility of interest on loans.
12. Amrik Singh and Arun Upneja (2008) investigated the determinants of the
decisions to use financial derivatives in the lodging industry. Making distinction
between hedging and speculation is important because of the potential impact of
derivatives on firm cash flows and earnings volatility. All publically traded lodging
firms in the S&P composite data base were chosen for this study based on their 4 digit
standard industrial from 2000 to 2004. Annual and quarterly financial statement data,
as well as geographic statement data were obtained from S&P composite data base.
Results suggested that a comparison of derivatives users and non users on various
firms characterizes that proxy for incentives to hedge. The significant findings on
information asymmetry indicate that firms with large analyst have less incentive to
hedge.
13. P.Sakthivel and B.Kamaiah (2009) made a study on futures trading and
volatility of S&P CNX Nifty index to investigate whether futures trading activity
affects spot market volatility or not. The daily closing price of Nifty and trading
volume and open interest for Nifty index futures were collected from 1st July 2000 to
February 28th
2008. This study found that GARCH specification more appropriate
than the standard statistical models and the results of GARCH model revealed that
estimated coefficients of unexpected trading futures volume was positive and
significant which indicated that there is a positive relationship between spot market
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volatility and unexpected trading volume in Nifty futures markets. The results of
GJRGARCH model indicated a positive and highly statistically significant.
14. Paul Dawson and Sotiris. K. Staikouras (2009) made an investigation on the
impact of volatility derivatives on S&P500 volatility. The aim of the study was to
examine the impact of the volatility derivatives trading on the S&P 500 volatility
index to offer a fresh perspective on the issue of spot market volatility. The sample of
the study consisted of daily data from January 3rd
2000 to May 30th 2008. GARCH (1,
1) estimation was applied in its analysis and found the most appropriate structure.
When the whole period was split into the pre and post event date intervals the results
provided a useful insight. Empirical result indicated that under normal market
conditions volatility derivatives trading contributed to lowering the underlying assets.
15. Stephane. M. Yen and Ming. Hsiang Chen (2010) investigated the relationship
between open interest, volume and volatility in Taiwan futures markets to find the
relationship among any variable from an ex- ante perceptive that is out of sample
forecasting performance. The volatility forecasting performance of all five models
such as EGARCH, GJR, APARCH, GARCH and IGARCH were compared with or
without lags in total markets volume or total open interest included as predictable
variables. Daily closing prices, total trading volume and open interest for the Taiwan
stock exchanges, electricity sector futures and insurance sector futures from 21st July
1998 to 31st December 2007 were collected as sample for the study. VAR model was
applied to find relationship between each pair of three variables and found that
significant relationship. These asymmetric GARCH models such as EGRCH, GJR,
and APGARCH as well as the standard GARCH and IGARCH models results
indicated that the significance of in sample relationship among the futures daily
volatilities, the lagged total volume and the lagged total open interest.
16. Julia. J. Lucia and Angel Pardo (2010) made a study on measuring speculative
and hedging activities in futures markets from volume and open interest data. This
study attempted to provide critical assessment of speculative and hedging positions.
Three of the most actively traded stock index futures contracts such as S&P 500
futures contracts, Nikkei 225 futures and Eurex DAX index futures were selected for
the study. Daily figures of trading volume and open interest for the futures contracts
with the three underlying indices with maturity dates in the month of March, June,
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September and December between March 2003 and December 2006 has been taken
into consideration for the analysis. They tested the three ratios offer similar
information about the evaluation of speculation/hedging demand over time. The result
of positive cross correlation coefficient is particularly relevant to the aim of this study.
It was found that the ratio of volume to absolute change in open interest, regardless of
them being positive or negative imply that the opening of new positions out numbers
the liquidation of old positions.
17. Pratap Chandra Pati and Prabina Rajib (2010) made an attempt to investigate
volatility persistence and trading volume in an emerging futures market. This study
had taken evidence from NSE Nifty stock index futures and daily futures price and
trading volume from January 1st 2004 to December 31
st 2008 were taken as the data
for study. The results of F-statistics and LM test indicated the presence of ARCH
effect and time varying conditional heteroskedasticity in Nifty futures returns.
ARMA- GARCH and asymmetry ARMA-GARCH model were also applied and
found that the evidence of time varying volatility which exhibits clustering high
resistance and predictability in the Indian futures markets.
18. Jinliang Li (2010) made an analysis on cash trading and index futures price
volatility with an aim to examine the effects of cash markets liquidity on the return
volatility of stock index futures. The GARCH model was employed here to examine
the secular liquidity components in the daily stock index futures volatility. A quarterly
time series of the average commission rate for NYSE trading from 1980 to 2005 was
constructed and turnover of all NYSE stocks for the same period also was estimated.
Empirical findings indicated that the quarterly innovation to turn over does not
possess explanatory power to the daily volatility of the futures in the corresponding
quarter.
19. Anadrew W. Alford and James R. Boatsman (1995) predicted long term stock
return volatility for accounting and valuation of equity derivatives. The purpose of the
study was to examine empirically the prediction of long term return volatility where
long term volatility was computed using monthly stock return over five years. The
authors used monthly stock returns to compute futures because monthly returns were
approximately normally distributed while daily and weekly returns were not. They
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presented the distributions of P-value from Kolmogorov-Smirnov Goodness of fit test
of normality over the forecast period of the sample.
20. M. Thenmozhi (2002) made a study on futures trading, information and spot
price volatility of NSE 50 index futures contracts to examine if there was any change
in the volatility of Nifty index due to the introduction of Nifty futures and whether
movement in the futures price provides productive information regarding subsequent
movement in index prices. For the analysis daily closing price returns of NSE 50
index was considered for the period 15th June 1998 to 26
th 2002. Volatility had been
measured using standard deviation and GARCH model. The lead lag relationship
between spot and index futures were estimated by using ordinary least square and two
stages least square regression. The lead lag analysis showed that futures had little or
no memory effect and infrequent trading was virtually absent in future market. It was
concluded that the futures lead the spot market returns by one day.
21. Premalatha Shenbagaraman (2003) made research on the topic do futures and
option trading increase stock market volatility with the objective to assess the impact
of introducing index futures and option contracts on the volatility of the underlying
stock index in India. Daily closing prices for the period October 1995 to December
2002 for the CNX Nifty, Nifty Junior, Nifty futures contract volume and open interest
were taken from NSE website. The authors used GARCH model, EGARCH model of
Nelson (1991), the GARCH mode with t. distribution and GJR-GARCH Model of
Glosten. The empirical results of the study revealed that derivatives introduction had
no significant impact on spot market volatility.
22. Ash Narayan Sah and G. Omkarnath (2005) made a study on derivatives
trading and volatility of Indian stock market. This study tried to understand whether
the Indian stock markets show some significant changes in the volatility after the
introduction of derivatives trading and also examined whether decline or rise in
volatility can be attributed to introduction of derivatives alone or due to some macro
economic reasons. The study used daily data like S&P Nifty, Junior Nifty, NSE 200
and S&PCNX 500, BSE Sensex-BSE 100, BSE 200 from the period April 1998 to
March 2005. Autoregressive conditional Heteroskedastic (ARCH) model was applied
to achieve the stated objective. The study concluded that the impact of the
introduction of the futures and options of the volatility of the underlying markets was
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negligible as evident from the magnitude of the coefficient of the futures and options
dummies.
23. Puja Padhi (2007) investigated asymmetric response of volatility to news in
Indian stock market to examine the effect of the introduction of stock index futures
on the volatility of the spot equity market and to test the impact of the introduction of
the stock index futures contracts. The study used a GARCH model which is modified
along the lines of GJR- GARCH and EGARCH model, especially to take into
account the link between information and volatility. In the analysis, the dataset
comprises daily closing observations of the spot index rates for the aforementioned
markets from June 1995 to September 2006 for Nifty index and 7th June 2003 to 1
st
June 2007 for Nifty Junior. This study provided the evidence that there is not much
change in the volatility pattern after the introduction of futures in the Indian stock
market.
24. Claudio Albanese and Adel Osseiran (2007) made a model of moment methods
for exotic volatility derivatives. In this study the author gave an operator algebraic
treatment of the problem based on Dyson Expansions and Moment Methods and
discussed applications to exotic volatility derivatives. The methods were quite
flexible and allowed for a specification of the underlying process which was semi
parametric or even non parametric, including state- dependent local volatility, jumps
stochastic volatility and regime switching. The authors found that volatility
derivatives were particularly well suited to be treated with moment methods. The
authors considered a number of exotics such as variance knockouts, conditional
corridor variance swaps, gamma swaps and variance swaptions and gave valuation
formulas in detail.
25. Vasilieios Kallinterakis and Shikha Khurana (2008) investigated volatility
persistence and the feedback trading hypothesis from Indian evidence to produce an
original contribution to the finance literature by examining the relationship between
feedback trading and volatility from a markets evolutionary perspective, and to test
internationally established facts regarding feedback trading in an Indian markets
contexts. In order to test the feedback trading with the Senatana and Wadhwani
Model, the authors applied conditional variance. The daily closing prices from the
BSE 30, BSE 100 and BSE 200, and S&PCNX Nifty 50 from 1992 to 2008 were
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taken in to consideration. The empirical result indicated that positive feedback
trading is evident throughout the period from 1999. Volatility was found to maintain
significant asymmetries in most of the period under examination.
26. S. Bhaumik, M.Karanasos and A. Kartsaklas (2008) had conducted a study on
derivative trading and the volume volatility link in the Indian stock market to
investigate the issue of temporal ordering of the range based volatility and volume in
the Indian stock market. It was estimated the two main parameters for driving the
degree of persistence in the two variables and their respective uncertainties using a
bivariate constant conditional correlation generalized ARCH model that is fractional
integration in both the Auto a regressive and Variance specification. They estimated
the bivariate AR-FI –GARCH Model with lagged value of one variable included in
the mean equation of the other variables by using data set comprised 2814 daily
trading volumes and price of the NSE index from 1995 November to 2007 January.
It was found that during the period the impact of number of traders on volatility was
negative, introduction of option trading may have weakened and the impact of
volume had on volatility through the information route.
27. Lech.A.Grzelak, Cornelis.W.Dosterlee and Sacha Van Weeren (2009) made
extension of Stochastic Volatility Equity Models with Hull- White Interest Rate
Process. In this study the author presented a flexible multifactor stochastic volatility
model which included the term structure of the stochastic interest rates. Their aim
was to combine arbitrage free Hull-white interest rate model in which the parameters
were consistent with market price of caps and swaptions. The study has shown the
Schobel-Zhu-Hull White Model belongs to the category of affine jump diffusion
process and they compared the model to the Heston-Hull-White hybrid model with
an indirectly implied correlation between the equity and interest rate. They had found
that even though the model was so attractive because of its square root volatility
structure it was unable to generate extreme correlations.
28. Mayank Joshipura (2010) made a study on the topic is an introduction of
derivative trading cause-increased volatility? The aim of the study was to use simple
approach to test the change in volatility by measuring changes in relative volatility of
the stocks on introduction of futures and options trading using Beta as a relative
measure of volatility by using the data from July 2001 to June 2008. The researchers
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selected 12 different derivatives from the NSE and the changes in volatility of daily
stock return for one year period to derivative introduction and one year after the
derivative introduction was separately examined. The results showed that the effect
of introduction of derivatives trading on average daily excess return of underlying
stocks and portfolios.
2.4. REVIEWS ON RISK REDUCTION THROUGH FUTURES MARKET
1. Robert J. Myers (1991) estimated time varying optimal hedge ratio on futures
markets. This study attempted to compare two approaches such as moving sample
variances and covariance of past prediction errors for cash and futures prices and
GARCH model was used for estimating time varying optimal hedge ratios on futures
markets. All data were the Mid-Week closing price and the sample period ran from
June 1977 to May 1983. Separate bivariate GARCH model was estimated for cash
and May futures price, and for cash and December futures price. Preliminary results
suggested that a GARCH (1, 1) model, with one lag on the squared prediction errors
and one lag on past conditional covariance metrics, provided an adequate
representation of wheat price volatility.
2. Allan Hodgson and Okunev (1992) made a study on an alternative approach for
determining hedge ratio for futures contracts. The authors examined whether hedge
ratio change for increase in level of risk aversion or not. The authors created a port
folio by buying an underlying assets and selling futures contracts on the basis of
Figlewsiki and Kwan and Yip approaches. For the empirical analysis Associated
Australian Stock Exchange All Ordinary Index and the Share Price Index Futures
daily return was calculated for the period 1st July 1985 to 29
th September 1986.
Empirical results indicated that for low level of risk aversion, the hedge ratios are
significantly different to those of a mean variance hedge ratio. It was also confirmed
that as investors become more risk averse when they adopt different hedge ratios to
those of a mean variance investors.
3. Phil Holmes (1995) estimated hedge ratio and examined the hedging effectiveness
of the FTSE-100 stock index futures contracts. This study examined the performance
of ex ante hedge ratio is compared to that of the one to one hedge and the optimal
hedge. FTSE-100 stock index futures contracts from July 1984 to June 1992 of one
and two week’s duration were used for the analysis. Among many approach, two
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approaches like annually estimated ex post hedge ratio and a more dynamic strategy
based on hedge ratio estimation using the rolling regression procedure were employed
here. The empirical results demonstrated that all three hedged port folios such as the
ex post minimum variance hedge ratio, the hedge portfolio based on previous years
minimum variance hedge ratio and the beta hedge ratio achieved substantial risk
reduction compared to being unhedged.
4. Robert T. Daigler and Mark Copper (1998) made a study on the future duration
on the basis of convexity hedging method. This study explained the theory on fixed
income securities hedging and its implications through the comparison of two models.
This study developed a duration convexity hedge ratio and compared the hedging
effectiveness of this hedge ratio to the Good Man-Vijayaragavan (1987, 1989), two
instruments hedge ratio and the typical one instrument duration hedge ratio. The three
based models were compared for a specific set of characteristics of the cash bond and
yield for the cash bond and futures contracts. It was revealed that the resultant hedge
ratio for the long term instrument is larger than necessary to hedge the duration and
convexity of the cash bond.
5. Donald Lien & Yiu Kuen Tse (1999) investigated fractional cointegration and
futures hedging by using NSA futures daily data. In this article, the authors compared
the effectiveness of the hedge ratio estimated from the regression, VAR, EC and FIEC
models. They examined the performance of the hedge ratios with respect to the
different hedge horizon. The period of the study was from January 1989 to August
1997. Daily closing values of the spot index and settlement price of the futures
contracts were used. The estimation results for the variance equations supported the
existence of conditional heteroscedasticity for both spot index and the futures price.
The futures price exhibited strong variance persistence than the spot index.
6. Manolis. G. Kavussanos and Nikos k. Nomikos (2000) investigated futures
hedging when the structure of the underlying assets changes. Constant and time
varying hedge ratios were estimated for different periods, corresponding to revisions
in the composition of the BFI and their performance was compared over sub periods
and across routes. This study covered the period from 1985 to 1998 and the total
period was broadly identified corresponding to different faring composition of the
underlying index. The data set consists of weekly spot and futures price which was
100
nearest to maturity. Minimum hedge risk was estimated and in its methodology OLS,
VECM and time varying GARCH model were employed to analyze the data sets.
They found that OLS hedge ratio was outperformed the other hedges in 24 cases out
of 33 for the remaining 9 cases, the VECM-GARCH hedges provided higher variance
reduction.
7. Dimitris Bertsimas Leonid Kogm and Andrew .W. Lo (2001) applied an E.
arbitrage approach on hedging a derivative securities and incomplete market. This
research projected a method for replicating derivative securities in dynamically
incomplete markets. Using stochastic dynamic programming, the authors constructed
a self financing dynamic portfolio strategy that is best to approximate an arbitrary
payoff function in a mean-squared sense. This study provided on explicit algorithm
for computing strategies which can be formidable challenge despite market
completeness.
8. Leigh .J. Maynard, Samhancock and Heath Hoagland (2001) analyzed the
performance of shrimp futures markets as price discovery and hedging mechanism.
The objective of the study was to test the hypothesis that persistent arbitrage
opportunities do not exists even in thinly traded futures markets and to determine if
the potential profits from arbitrage have economic significance. The analysis relied on
a panel of weekly whole sale cash price data for thirteen commercial Inc. and weekly
closing price data provided by the Minneapolis Grain Exchange. The study period ran
from November 1994 to June 1998. The empirical results showed that if a future price
series reflects all available information used in predicting forward price, one would
expect it is to be leading indicator of related cash prices.
9. Donald Lien and Y.K Tse (2002) made a study on the analysis on recent
developments in futures hedging. Various methods like Conventional hedging, time
varying hedge rations and minimum variance hedge ratios were applied in the study.
The empirical results formed that the optimal hedge ratio based on the extended
mean-Gini approach for low level of risk aversion are similar to the minimum
variance hedge ratio. For the higher level of risk aversion, the extended Mean –Gini
approach hedge ratio generally differ from the minimum variance hedge ratio, with no
regularity in their relative size. Non parametric time variant hedge ratio which was
proposed by Lien and Tse (2000) was empirically proved here. The results indicated
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that a hedger who is willing to absorbed small losses but otherwise extremely cautious
about the large losses, the optimal hedge strategy that minimizes lower partial
moment may be sharply different from the minimum variance hedge ratio strategy.
10. Aaron Low, Jayaram Muthuswamy, Sudipto Sakar and Eric Terry (2002)
studied multi period hedging with futures contracts. In order to find hedge risk, main
financial instrument like futures contracts is used. In this study the authors examined
the hedging problem when futures prices obey the cost of carry model. The Nikkei
225 index futures contracts on SIMEX was used to hedge a portfolio of the
components stocks of this index and in the second part hedging is found on a spot
position in fuel oil using the high sulphur fuel oil contract on SIMEX. The sample
extended from September 1986 to April 1996 for Nikkei 225 index data and from
February 1989 to June 1995 for the high sulpher fuel oil data. The dynamic cost of
carry hedge model, the conventional hedge and the cointegrated price hedge were
used. It was found that the hedging strategy that is the cost of carry model performed
well than other hedging strategies on an-ex-ante basis, further the effectiveness of the
hedging was increased with its duration.
12. John M. Charnes and Paul Koch (2003) made a study on measuring hedge
effectiveness for FAST 133 compliance. In this study the authors outlined a basic
frame work for assessing anticipated hedging effectiveness. The frame work of the
study was based on a two part operational definitions that distinguishes between the
potential effectiveness of a hedging relationship and the attained effectiveness of a
selected hedge position. This study made an argument on hedging and speculation. A
hedging strategy involves choosing a hedging instrument and an appropriate hedge
ratio to accomplish the risk management. Various measures of effectiveness of hedge
ratio also were computed here. It is intended to measure the ability of the hedging
instrument in generating off setting changes in the fair value of the unhedged items. It
was argued that the ratio does not fully measure the degree to which the hedger has
effectively reduced risk.
13. Narayan Y. Nayik and Prdeep K. Yadhav (2003) conducted a research on the
topic risk management with derivatives by dealers and market quality in Government
bond markets. They investigated the relation between the selective market risk-taking
activity of dealers and market quality in the price effect of capital constraints for the
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period from August 1994 to December 1995. They analyzed the close of business
reports of 15 dealer firms who were separately capitalized and subsidiaries of well
known banking houses by applying Regression Model and found that dealers engage
extensively in selective market risk taking throughout that dealers use future market to
a great extent when the cost of hedging was lower, large dealers carried a great
amount of risk on their books and hedged the changes in their spot risk less compared
to smaller dealers.
14. Wayne Guay, S.P Kotari (2003) investigated how much do firm’s hedge with
derivatives. The authors examined the hypothesis that final derivatives are an
economically important component of corporate risk management. For a random
sample of 234 large nonfinancial corporations, the authors presented detailed
evidence on the cash flows and market values sensitivities of financial derivatives
portfolios to extreme changes in the underlying assets price. This study estimated an
upper bond on the dollar amount of cash flows that a firm would derive from its
derivatives portfolio. This empirical result suggested that the magnitude of the
derivative positions held by most firms was economically small in relation to their
entity level risk exposure.
15. Wenling Yang and David E. Allen (2004) made an analysis on multivariate
GARCH hedge ratio and hedging effectiveness in Australian futures markets. This
study aimed to estimate hedging ratios derived from four specifications such as an
Ordinary Least Square based model, Bivariate Auto Regression, Vector Error
Correction Model and Diagonal-Vech Multivariate Generalized Auto Regressive
Conditional Heteroskedasticity Model with Time Varying Conditional Covariance.
Index values for all ordinary index of Australian market and the share price index
futures contracts on all ordinary index from the period of 1992 to 2000 were taken in
to consideration. As expected and the line with Gosh (1993), the hedge ratio estimated
from VECM is greater than that obtained from the VAR model.
16. Sheng- Syan Chen, Cheng- Few Lee and Keshab Shrestha (2004) made an
empirical analysis of the relationship between hedge ratio and hedging horizon- A
simultaneous estimation of the effects of hedging horizon length on the optimal hedge
ratio and effectiveness in greater detail by using 25 different futures contracts and
different hedging horizon. They considered the only minimum variance hedge ratio.
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The authors found that most of the studies ignore the effects of hedging horizons
length of the optimal hedge ratios and hedging effectiveness. It is important to note
that all these studies considered the minimum variance hedge ratio instead of other
hedging ratios based on expected utility, extended Mean–Gini coefficient and
generalized semi variance. In sample analysis results indicated that the short run
hedge ratio is significantly less than the naive hedge ratio.
17. Amir Alizadeh and Nikos Nomikos (2004) applied a Markov regime switching
approach for hedging stock indices. This study described a new approach for
determining time varying minimum variance hedge ratio in stock index futures
markets by using the Markov Regime Switching Models. In this study the authors
developed a procedure that generated hedge ratios were regime dependent and change
as market conditions change. Hedging effectiveness of this model both in sample and
out of sample was tested and performance of regime switching hedge was compared
to GARCH and Error Correction Models. Using a multivariate extension of the
Markov Regime Model, they found that the relationship between spot and futures
return in the S&P 500 and FTSE 100 market was regime dependent. Weekly time
series of the FTSE-100 and S&P futures and spot indices for the period 1984 to 2001
were taken as the variable for the analysis. They calculated hedge ratios based on the
OLS model, Error Correction Model and GARCH Model and found that MRS
hedging strategies outperformed other models in terms of in sample port folio
variance reduction.
18. SVD Nageswara Rao and Sajay Kumar Thakur (2004) investigated the optimal
hedge ratio and hedging efficiency of Indian derivatives market. The authors had
made an attempt to estimate optimal hedge ratio based on KHM methodology using
JSE model as the bench mark for the futures. To estimate optimal hedge ratio for
options FBM methodology with Black-Schole Model has been used as the bench
mark. High frequency data for the period from 1st January 2002 to 28
th March 2002
for index futures and options had been taken in to consideration for the analysis of
hedging of Nifty price risk. In its analysis, the authors compared Herbest, Kare and
Marshall Methodology with Johanson and Stein methodology. Optimal hedge ratio
estimated by using HKM methodology was better and statistically significant at 95%
confidence level.
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19. Paul Kofman and Patrict Mcglenchy (2005) made a study on structurally sound
dynamic index futures hedging. The objective of this study was to evaluate a simple
dynamic hedging scheme that conditions on continuous changes, as well as on
discrete changes in the relationship between unhedged portfolio and futures returns.
The study used the main stock index the Hang Seng Commerce and industry Index
(HSI) and its companion derivatives contracts Hang Seng Index Futures (HSIF) as
well as two sub indices such as the Hang Seng Commerce and Industry index and the
Hang Seng Finance Index (HSFI) for the periods from January 1994 to July 2003. To
estimate the volatility cluster a GARCH specifications was estimated for the full
sample of HSI returns and the conditional standard deviation. This study found that
for a perfect hedge scenario in (HSI) and there is very little evidence of any dynamic
hedging strategy significantly outperforming the buy and hold hedging strategy.
20. Norvald Instefjord (2005) made a study on risk and hedging-do credit
derivatives increase bank risk? The main objective of the study was to investigate
whether financial innovation of credit derivatives made banks exposed to credit risk.
The research work investigated the suggestion that credit derivatives are important for
hedging and securitizing credit risk, and thereby likely to enhance the sharing of such
risk. Geometric Brownian Motion Model was applied for the analysis. The analysis
identified two effects of credit derivatives innovations such as they enhance risk
sharing as suggested by the hedging argument and acquisition of risk more attractive.
The key findings of the research were the financial innovation in the credit derivatives
market might increase bank risk, particularly those that operated in highly elastic
credit market segment.
21. Abdulnasser Hatemi-J and Eduardo Roca (2006) calculated the optimal hedge
ratio by using constant, time varying and the Kalman Filter approach. This study
proposed and demonstrated a procedure based on the Kalman Filter approach. The
study used Australian price index for equity markets and the share price index for
Australian futures markets for the period of 1988-2001. Daily data were used for a
total of 3586 observations. Johansen Cointegration test and Eagle Granger test
showed that there is cointegration between variables and proceeded to estimate the
time varying parameters by using Kalman Filter procedure. Test result showed that
the null hypothesis of constant parameter model was strongly rejected in favor of the
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alternative hypothesis of a time varying model. Further this study found that the
returns in the futures market had been greater than returns in the stock markets over
the time period.
22. Richard D.F.Harris and Jain Shen (2006) made a study on hedging and value at
risk. This study considered the consequences of minimum variance hedging in two
alternative frame works that implicitly incorporate portfolio skewness and kurtosis.
The effectiveness of the minimum variance hedging strategy was investigated by
considering both in sample and out sample performance. Daily returns provided by
Reuters for 10 developed markets currencies were measured against GBP for a period
1994 to 2004. Analysis of minimum variance hedging revealed that although it
reduced portfolio standard deviation, in many cases, it tends to increase left skewness
and increases kurtosis.
23. T.F. Coleman, Y.kim, Y.Li and M. Patron (2007) conducted a research on
robustly hedging variable annuities with Guarantees under Jump and volatility risks.
The authors focused on computing and evaluating hedging effectiveness of strategies
using either the underlying or standard options as hedging instruments. In this study,
the researchers compared discrete risk minimization hedging using the underlying
with that of using liquid standard options. The authors proposed to compute the risk
minimization hedging using standard options by jointly modeling the underlying price
dynamics and the Black-Scholes at the money implied volatility explicitly. The
performance of hedging strategies under jump and volatility risk could be analyzed
here. It was found that the risk maximization hedging using underlying as the hedging
instrument outperform the delta hedging strategies.
24. Kevin Aretz, Sohneke M. Bartram Gunter Dufey (2007) investigated the
rationales for corporate hedging and value implication. The research work aimed to
provide a comprehensive and accessible overview of the existing rationales for
corporate risk management in hedging which can lower the probability of future
financial distress and enable the firm to decrease its expected tax burden. It was found
that corporate hedging may increase from value by reducing various transaction cost.
By reducing cash flow volatility, firms face a lower probability of defaults and thus
have to bear lower expected cost of bankruptcy and financial distress. Further
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corporate risk management can reduce fluctuations in pre- tax income and thus lower
the tax burden of firms if corporate income is subject to convex tax schedule.
25. Ming- Chih Lee and Jui-Cheng Hug (2007) made an analysis on the hedging for
multi period down side risk in the presence of jump dynamics and conditional
heteroskedastisity. In order to compare the hedging effectiveness of one period and
multi period zero VaR hedge ratios, the authors constructed the portfolio implied by
the computed hedge ratio for each hedging period and calculated the mean and
variance in order to obtain the value at risk of hedge port folio returns over the
sample. Futures hedging in the S&P 500 futures market daily price over the period
1996 to 1999 were considered for the study. The VaR of multi period hedge ratios
result indicated that the multi period hedging strategy outperforms the one period
strategy for all cases.
26. Donald Lien and Keshab Shrestha (2007) made an empirical analysis of the
relationship between hedging ratio and hedging horizon using Walvet analysis. 23
different futures contracts where the futures prices were associated with nearest to
maturity contracts had been analyzed here. Whole data set for the empirical analysis
were taken from Data Stream for the period started from 1982 to 1997. Analysis
results revealed that for the financial assets such as stock indices and currencies, both
spot and futures markets are to be highly liquid and therefore the variance of spot and
futures returns are likely to be closed to each other. It was found that in general both
Error Correction and Walvet Hedge Ratios are larger than the minimum variance
hedge ratio and in terms of performance; Error Correction Hedge Ratio performs well
for shorter hedging horizons.
27. Manolis G. Kavussanos and D. Visvikis (2008) investigated hedging
effectiveness of the Athens stock index futures contracts. The data set used for the
analysis consists of weekly and daily cash and futures prices of the FTSE/ATHEX 20
markets from September 1999 to June 2004 and weekly and daily cash and futures
prices of the FTSE/ATHEX mid 40 markets from February 2000 to June 2004.
VECM-GARCH and VECM-GARCH-X model were employed as the model for
estimation. The results for the FTSE/ATHEX-20 market in sample hedge ratio, based
on both daily and weekly data indicated that time varying hedge ratios estimated from
the VECM-GARCH model over performed the constant hedge ratio based on the VR
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criterion. For daily data, the conventional model for the FTSE/ ATAEX-20 market
emanated from the VECM, where as for the FTSE/ ATHEX mid-40 market the
conventional OLS model was appropriate. In the case of out of sample hedge ratio, in
the FTSE/ATHEX mid -40 market the VECM-GARCH-X model had the worst
performance for weekly and daily data compared with the alternative constant
conventional and VECM hedging strategies.
28. Olivia Ralevski (2008) made a study on hedging the art market-creating art
derivatives. The objective of the study was to explore the opportunity for derivative
product in art. In order to create a true hedge for art, derivatives with art as the
underlying should be developed. The authors proposed a model for a total return art
swaps would allow investors to protect themselves against movement in the art
market. The need for tradable art indexes which were crucial for the successful
creation of art derivatives also had been discussed.
29. Saumitra N. Bhaduri and S. Raja Sethu Durai (2008) made a study on optimal
hedge ratio and hedging effectiveness of Indian stock index futures. This study
focused on estimating optimal hedge ratio for stock index futures in India and
compared its hedging effectiveness. Daily data on NSE stock index futures and S&P
CNX Nifty index for the period from 4 September 2000 to 4 August 2005 had been
considered for this study. The Regression Method, Bivariate VAR method, the Error
Correction Model and Multivariate GARCH method were adopted in its methodology
to calculate optimal hedge ratio. They checked the robustness of the result also.
Optimal hedge ratio results by using OLS Regression, VAR model, VEC model and
Multivariate GARCH model clearly showed the advantage and demerits of each
model and they claimed that bivariate GARCH model is the apt model which
eliminated and corrected the problems of the former models almost. Hedging
effectiveness of the stock index futures for the same period was estimated here and
they tested the effectiveness with 1,5,10 and 20 horizons. The results revealed that
within sample mean return the bench mark Naive strategy has significantly lesser
mean return than compared to all other strategies.
30. Dimitris Kenourgios, Aristeidis Samitas and Panagiotis Drosos (2008)
estimated hedge ratio and investigated the effective of hedge ratio on S&P 500 stock
index futures contracts. The hedging performance of the S&P 500 futures contracts
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was examined using closing prices on weekly basis data relating to the period July
1992 to June 2002. GARCH, EGARCH and ECM with GARCH errors were
employed here. The empirical results of the analysis could be concluded that in terms
of risk reduction the error correction model is the appropriate method for estimating
optimal hedge ratio since it provides better results than the models such as
conventional OLS Method, the ECM with GARCH errors, the GARCH model and the
EGARCH model.
31. Anuradha, Sivakumar and Runa Sarkar (2008) made a study on the topic
corporate hedging for foreign risk in India. This study aimed to provide a perspective
on managing the risk that firms face due to fluctuating exchange rate. Authors
analyzed almost all regulations and policies regarding the foreign exchange risk in
India. It was found that a statistical significant association between the absolute value
of exposure and the absolute value of the percentage use of foreign currency
derivatives and prove that the use of derivatives in fact reduce exposure. It was
claimed that anecdotal evidence that the pricing policy is the most popular means of
hedging economic exposures.
32. Gyu-Hyen Mioon, Wei-Choun Yu and Chung-Hyo Hong (2008) investigated
dynamic hedging performance with the evaluation of multivariate GARCH models
from KOSTAR index futures. Authors’ provided the practical simple rolling OLS
model which is very rarely discussed in the literature as an alternative model.
Conventional hedging strategy assumes that the investors hold one unit in long
position in the spot stock market. Price of nearest futures contracts of KOSTAR index
spot and futures from November 8, 2005 through November 8, 2007 were taken into
consideration. To perform model estimation, forecasting and evaluation, the data
period was divided into two samples such as from 8th
November, 2005 to 31st May,
2007 that is in sample and out of sample from June 1st, 2007 to November 8
th 2007.
GARCH and its family members like DVED and CCC GARCH also were employed
here. It was surprised to see that the OLS conventional constant hedge model
performed well and is only inferior to the metrics-diagonal model. During the out of
sample period the principal component GARCH model is superior to other model.
33. Jahangir Sultan, Mohammed S. Hasan (2008) had made a study on the
effectiveness of dynamic hedging of selected European stock index futures. This
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paper examined the hedging effectiveness of stock index futures market in France,
Germany, Netherlands and the U. K for minimizing the exposure from holding
positions in the underlying stock markets. They analyzed the effect of long run
relationship between the spot index and future index on hedging effectiveness. They
estimated optimal hedge ratio by using Bivariate Error Correction model with a
GARCH effects structure. They applied conventional and other advanced model to
find out the optimal hedge ratio and they argued that Bivariate GARCH model is
giving the highest optimal hedge ratio value. For the analysis the authors used weekly
data for the period of 1990-2006 for Netherland and U.K, and from 1999- 2006 for
Germany. The OLS regression results showed that the largest coefficient is found in
the case of France and lowest for U.K. The evidence of cointegration is consistent
with the literature for Australia, Germany, Japan and U.K. The result of variance
reduction in within sample period showed that in the case of France the dynamic
hedging model performs better when compared with a naive hedging strategy but fails
compared with the traditional OLS method.
34. Kapil Gupta and Balwinder Singh (2009) investigated the optimum hedge ratio
in the Indian equity futures market over the sample period January 2003 to December
2009. The scope of the study had been restricted to examine whether equity futures
contracts traded in India provide optimum hedging benefits. If, yes which statistical
methodology can help hedger to compute optimal hedge ratio so that they can
minimize port folio variance to minimum level at minimum trading as well as
transaction cost to execute such strategy which would result in increased portfolio
value? The sample size of the study had been restricted to three indices such Nifty,
Bank Nifty and CBXIT and 84 individual stocks. Six econometrics procedures were
employed to investigate an optimal hedge ratios which presumes a stable and strong
long run relationship between two markets and the hedging effectiveness would
depend up on the coefficient. The results confirmed that both markets observe stable
and strong co-movement over the contracts cycle. Hedge ratio estimated through
VAR methodology were the lowest for three indices are compared to those estimated
through other methodologies and the time varying hedge ratios estimated through
GARCH, EGARCH OR TARCH methodologies were the highest.
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35. Hsiu-Chuan Lee, Cheng-Yi Chien and Tzu- Haiang Lian (2009) investigated
on determination of closing prices and hedging performances with stock indices
futures. This study empirically examined the impact of the determination of stock
closing prices on futures prices efficiency and hedging effectiveness with stock
indices futures. Daily closing prices for three futures indices like Taiwan stock
exchange index, Taiwan stock exchange finance sector index and Taiwan stock
exchange electric sector index and the corresponding underlying stock indices from
4th January to 4
th December 2003 were considered as the sample for the study. The
Bivariate Error Correction model with CCC GARCH (1, 1) structure suggested by
Kroner and Sultan (1993) was applied to examine the hedging effectiveness for the
futures indices. The empirical findings indicated that the determination of stock
closing prices affects markets efficiency as the futures markets close and hedging
effectiveness with stock indices futures.
36. Haiang-Tai Lee (2009) applied a Copula based regime switching GARCH model
for optimal futures hedging. This article developed a regime switching Gambel-
Clayton (RSGC) copula GARCH model for dealing the draw backs of the regime
switching GARCH model. In the empirical analysis, corn, oats and wheat nearby
futures contracts traded in the CBOT and COCOA nearby futures contracts traded in
the NYBOT were investigated for the period from January 1991 to December 2007
and the spot and futures data were on Wednesday closing price and empirical results
of out of sample hedging effectiveness showed that RGCS exhibits good hedging
performance in terms of variance reduction. The copula- based regime- switching
varying correlation GARCH model performed more efficiently in future hedging with
more flexibility in the distribution specification.
37. Hsiu-Chuan Lee and Cheng –Chene (2010) made a study on hedging
performance and stock market liquidity- evidence from the Taiwan futures market by
using the data from Taiwan stock exchange. The Taiwan weighted stock index prices,
the number of transactions and trading volumes in shares and dollar were taken into
consideration. This study aimed to examine the impact of stock market liquidity on
the hedging performance of stock index futures. It was found that the conditional OLS
model reduces the hedge ratio volatility better than the OLS and GARCH model. The
study period covered from 2nd
January 2006 to 20th
December 2008. Hedging strategy
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can be evaluated and compared using three different performance metrics like
variance, semi variance and lower partial moment (LPM). The empirical results
indicated that stock markets liquidity contains information useful for predicting the
optimal hedge ratio and enhance the hedging performance during a bear markets.
38. Ming-Yuan Leon Li (2010) investigated dynamic hedge ratio for stock index
futures by applying threshold VECM. The authors employed rolling estimation to
include recent market information and continuously repeat the work of model
estimation and conducting a robust test of out of sample hedging performance for
various alternatives. The study conducted out sample hedging effectiveness test
through a rolling estimation process. This study covered the period from 3rd
January
1996 to 30th December 2005. OLS and VECM were applied here for estimating the
optimal hedge ratio through non-threshold system compared to OLS estimation. The
empirical result indicated that the setting without threshold setting over estimate or
under estimate the relative size of the standard error of the spot position to the futures
position for the outer region. Finally it revealed that the risk of spot position through
the VECM, the setting without threshold was smaller than for the setting with a
threshold.
39. Kuang-Liang Chang (2010) made analysis on the optimal value at risk hedging
strategy under bivariate regime switching ARCH frame work. This study used
bivariate switching Auto Regressive Conditional Heteroskedastisity Model which
extends from Hamilton and Susmel’s (1994) setting to calculate the optimal value at
risk hedge ratio. The aim of the work was to market hedgers precisely control the
down side risk that may happen to portfolio in the future holding period through
applying regime switching model. Daily closing prices of spot and futures indexes of
Taiwan Futures Exchange for the period July 1998 to December 2006 were
considered for the analysis. GARCH estimation results indicated that the persistence
volatility in spot and futures market is very strong. The in sample hedging results
under GARCH model showed the similarity to those of SWARCH model.
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2.5. RESEARCH GAP
* It is found that almost all studies have considered only one or two variables to
assess the futures market for a particular period. Conclusions were drawn based on
that variable alone without including other important variables from futures market.
Conclusions drawn are not confirmed through robustness between periods.
* The variables considered in these studies provide different results in different
periods about the same market.
* Many studies were carried out to find the best econometric model for estimation,
but not for testing the informational efficiency.
* Studies are focused on determining the effectiveness of hedge ratio and not the
optimum hedge ratio for individual stocks from futures market.
* Causality between spot and futures returns was determined but not the causality
between price series.
* All studies have used data from pre financial crisis period; it is rare from post
financial crisis period.
* Studies have considered the data from later years and not from introduction of
derivatives in India.
* Studies have considered the near month data period together for the entire study
period without considering the structural breaks.
In order to fill the above said research gap, this study frames four objectives
which are mainly concentrating on the development of the futures market in India,
comparing the linkage between spot and futures market, to find the determinants of
futures market and analysis the risk reduction efficiency of futures market in India.
