The Relationship between Spot and Futures
Index Contracts after the Introduction of
Electronic Trading on the Johannesburg Stock
Exchange
Owen Beelders1 and Jonathan Massey
Department of Economics
Emory University
Atlanta Ga 30322-2240
March 13, 2002
1 We
thank participants of the Third Annual Finance and Investment Conference of the African Center for Investment Analysis at the University of Stellenbosch, South Africa, in August 2001, for their comments on an earlier version of
this paper and we thank Francisco Parodi for his research assistance. All remaining
errors are ours.
Abstract
The objective of this paper is to analyze the interrelationship between the all
share, gold and industrial indexes of the Johannesburg Stock Exchange and
the corresponding index futures contracts traded on the South African Futures Exchange after the introduction of electronic trading. For the all share
and industrials contracts, we find quicker information transmission between
the spot and futures markets after the introduction of electronic trading.
In addition, the contemporaneous correlation between the markets increases
and the size and asymmetry of the volatiltiy spillovers is reduced. For the
gold contract, we find slower information transmission after the introduction
of electronic trading, but this result may be biased by the fact that the gold
contract was discontinued.
1
Introduction
The underlying principle of market efficiency is that new information is immediately reflected in asset prices. In practice market frictions such as transactions costs can impede the speed at which new information is reflected
in prices. However, automated trading systems have decreased transactions
costs and increased the speed at which new information can be reflected in
asset prices.
The objective of this paper is to analyze the speed of transmission of information flows between the Johannesburg Stock Exchange (JSE) and the South
African Futures Exchange (SAFEX) after three important innovations: first,
brokerage fees changed from fixed to flexible after the introduction of the new
Stock Exchange Control Act in November 1995; second, the Johannesburg
Equities Trading (JET) electronic trading system was introduced on the JSE
in June 1996, and third, the JSE indexes underlying the futures contracts
were redesigned to consist of a smaller basket of stocks. These innovations
should increase the speed of transmission of information shocks between the
spot and futures markets because electronic trading increases the speed at
which transactions can be executed, the reduced size of the basket of stocks
facilitates arbitrage and the cost of trading can be reduced by negotiating
brokerage fees.
We use two econometric methods to measure the speed at which information shocks are reflected in the spot and futures markets. First, using
the cost-of-carry relationship as a guide, we test for cointegration and estimate a vector error correction model (VECM) for the pre- and post-JET
sample periods. We then compare the speed at which a shock in one market
is reflected in the other using the variance decomposition. Second, we use
a bivariate EGARCH 1 model to measure the transmission of shocks to the
conditional variance. In particular, we are interested in testing the following
hypotheses: 1) futures markets respond more quickly to shocks than spot
markets because of margin trading; 2) the transmission of shocks between
the spot and futures markets is quicker after the introduction of the electronic trading system; 3) the contemporaneous correlation between the spot
and futures markets increases after the introduction of electronic trading; 4)
the spillover effects between the spot and futures markets are smaller in mag1
EGARCH is an acronym for exponential generalized autoregressive conditionally heteroskedastic.
1
nitude after the introduction of electronic trading because there is a larger
contemporaneous response.
We investigate the relationship between spot and futures markets for
three index futures contracts traded on the SAFEX: the all share index, the
gold index and the industrials index. The all share index is a value-weighted
market index. The gold index represents the largest resource sector on the
JSE and the industrials index represents the largest, aggregated non-resource
index. For the futures contracts based on the all share and industrial indexes
we find that the futures contracts do respond more quickly to shocks than the
spot markets and that the speed of transmission of shocks between the spot
and futures markets increases after the introduction of electronic trading.
The contemporaneous correlation between the spot and futures markets also
increases after the introduction of electronic trading and there is a reduction
in the magnitude and asymmetry of the spillover effects in the conditional
variance. These results do not hold for the futures contract based on the gold
index because the gold contract was discontinued in 1998 due to the lack of
trading volume.
The paper is organized as follows. In section 2 we discuss the costs and
benefits of automated trading and in section 3 we describe the electronic
trading system on the JSE. We discuss the data and methodology in section
4 and report the results of our analysis in section 5. We conclude with section
6.
2
The Costs and Benefits of Automated Trading
Prior to 1996, the Johannesburg Stock Exchange was characterized as a thinly
traded market: the turnover ratio (total value traded divided by total market
capitalization) averaged 6.4% per annum from 1990 to 1995 due to the highly
concentrated nature of the JSE. The 20 largest firms made up 46% of the
total market capitalization and the ten largest firms made up 30.2% of total
market capitalization. Some of the largest firms were also characterized by
pyramid structures that were designed to keep voting power in the hands of
a close-knit family group (Barr, Gerson and Kantor (1995)). Although the
problem of thin trading has been reduced due to the relaxation of exchange
controls on South African companies, it still exists for the medium and small
2
companies.
The main benefit of introducing electronic trading on the JSE is the
transparency of the order book: it provides depth for the more closely-held
and thinly-traded stocks. Of the more than 650 companies traded on the JSE
at the end of 1999, we expect to see the greatest benefit accruing to the midsize and small companies that are closely held and have been thinly traded.
The information intensity for these companies is low, trading volume is low
and transactions are infrequent. For floor traders, information about the
last trade of the thinly traded stocks is not very informative, the inactivity
of floor traders does not reveal any information about their intentions and
there is not much to be gained from conversation among traders. In contrast,
the information in a limit order book is more updated and would provide a
better indicator of market developments.
On the other hand, electronic trading has two drawbacks for the more
liquid stocks. First, the increased transparency provided by the limit order
book may cause block trades to migrate to other markets. This may lead to
a reduction in the trading volume of the larger firms on the JSE as volume
migrates to many of the foreign markets where they are dually listed. Biais,
Hillion and Spatt (1992) find empirical evidence of this cost where trades
migrated from the automated Paris Bourse to London where the publication
of transaction size can be delayed.
Second, electronic trading introduces anonymity between traders. This
can be a problem during “fast markets” when the information intensity is high
(Massimb and Phelps (1994)). When information intensity is high the benefit
shifts to floor trading because of the greater danger of adverse selection. Floor
traders develop reputation capital for fair trading and this becomes more
valuable when information intensity is high. Observation of other traders on
the floor as well as their body language provides additional information about
their future trades. The ability to change bid and ask prices is much easier on
the floor according to Massimb and Phelps (1994) because this can be done
by a simple hand signal and a brief verbal statement. In contrast, during
periods of high information intensity, the anonymity of electronic trading
creates opportunities for strategic trading and their is little protection against
adverse selection. Traders may reduce their limit orders in the order book
(Glosten (1994) or shorten the time span that an order is displayed in the
order book to protect themselves. Franke and Hess (1995) hypothesize that
traders preferences are revealed by the volume of activity on the floor relative
to the electronic system during periods of different information intensity. For
3
the Bund futures market they find that the electronic trading system has a
larger market share when volatility or information intensity is low.
Martens (1998) has also shown that for ‘identical’ Bund futures contracts
that are traded on the floor in London (LIFFE) and electronically in Germany
(DTB), volume migrates to the LIFFE when the information intensity is high
and volume migrates to the DTB when information intensity is low. When
information intensity low, there is less of a concern for adverse selection and
the anonymity of electronic trading is not as costly. On the other hand,
when information intensity is high reputation capital that has been built up
between traders on the floor of the LIFFE reduces the problem of adverse
selection. Studies by Pagano and Röell (1992) and De Jong et al (1995)
provide further support for this point. Madahavan (1992) reaches the same
conclusion using a theoretical approach; the anonymity of the continuous
auction (electronic) trading system leads to strategic behavior that distorts
prices and induces inefficiency unless dealers can impose sufficient sanctions
for traders to reveal their private information.
In conclusion, the limit order book of an automated trading system may
provide more of a market for the thinly traded stocks on the JSE, but the
increased transparency and anonymity may cause larger, more highly traded
companies to seek a listing elsewhere. Given that the futures contracts are
based on indexes that are heavily weighted towards the large, liquid companies, electronic trading may actually impede the flow of information between
the spot and futures markets.
3
The Introduction of Electronic Trading on
the JSE
In March 1996, the Johannesburg Stock Exchange introduced the Johannesburg Equities Trading (JET) system to improve the transparency for market
participants. Since the inception of the JSE, trading was conducted on a
trading floor using an open outcry system. The new electronic trading system was introduced over a three month period from March 8, 1996 to June 7,
1996. An additional benefit of the trading system are the security and fault
audit trails that greatly enhance investor protection.
The cornerstone of the order driven system is a central order book that
is open to members via their JET workstations. Dealers enter buy and sell
4
orders which are included anonymously in the summary display and the
aggregate of these orders make up the order book for all dealers to view.
Previously, the Stock Exchange Control Act, No.1 of 1985 (the Act) stipulated that stockbroking firms could only act as agents on behalf of clients
except under certain circumstances. The Stock Exchange Amendment Act,
November 1995 and the amendments to the JSE rules now allow members
the choice of dealing in a single or dual capacity. Dual trading capacity is
where a member acts as either an agent on behalf of, or deals as a principal
with a client. The member may therefore sell a client’s shares from its own
holding of shares. The market also provides for voluntary market makers
and in addition there is an Odd Lot Specialist who manages the market in
odd lots, automatically trading the odd lot orders at the next round lot sale
price. Round lots are the standard unit of trade (100 shares).
The JET system operates from 8:25am to 6:00pm Mondays to Fridays
(excluding public holidays) and the following sessions are applicable: preopening runs from 8:25am to 9:00am, the market opens at 9:00am, continuous
trading takes place from 9:00am to 4:00pm and after-hours or runoff trading
takes place from 4:00pm to 6pm. The transition from pre-open to open and
from open to runoff fluctuates randomly within a range which is currently set
at 5 minutes. This means that the market can open anywhere between 08h55
and 09h00; and can move to runoff any time between 16h00 and 16h05. The
fluctuation was introduced to reduce any chance of price ramping.
The trading sessions are organized as follows: during the pre-opening
session, the JET system accepts limit priced orders only. “At Market” priced
orders are not allowed. The JET system inserts orders into the queues of
bids and offers for the Main Board which are ranked in price/time priority.
Quotes are broadcast as orders are accepted during the pre-opening session.
The system also calculates and broadcasts projections of the price at which
each stock will open, based on the existing orders in the system and the
“opening price algorithm”. No orders are matched until the opening of the
market. During continuous trading, buying and selling orders at the same
price level are automatically matched. “Limit” priced, “At Market”, “Fill or
Kill”, “Hit and Take”, “Immediate or Cancel” are some of the order types
accepted, as well as orders for the Odd Lot and Special Terms books. The
run-off period can be used to report any arbitrage transactions to the system
using the Report Only facility.
The JSE has a Surveillance Department, responsible for the surveillance
of market activities and member activities to ensure compliance with the Act,
5
JSE rules, directives and gazettes. The Department also monitors the capital
adequacy requirements of members on a daily basis and conducts special
investigations. The JET system provides improved transparency, security
and full audit trails. Facilities also exist to “replay” the market which assists
the Surveillance Department when analyzing particular market events.
The main order book is the place where orders are routed, managed and
matched. Orders which are received by the system and not immediately
matched are stored in the order book and ranked in price/time priority. The
main order book shows bids and offers where the bid represents the aggregate
number of shares for each buying price level and the offer represents the
aggregate number of shares for each selling price level.
4
Model and Estimation Strategy
Our modelling strategy is motivated by the cost-of-carry relationship, an
arbitrage relationship that links the spot and futures markets. In the absence
of market frictions, the relationship holds exactly and new information is
reflected simultaneously in both the spot and futures markets. However,
given the presence of market frictions, we use the VECM to analyse the
speed at which shocks to the spot market are reflected in the futures market,
and vice versa, and we use the bivariate EGARCH model to analyze the
transmission of volatility between the two markets.
4.1
The Cost-of-Carry Relationship
In the absence of transactions costs, the following cost-of-carry (arbitrage)
relationship holds between spot and futures prices (French (1988)):
F (t, T ) = (1 + r(T, t))S(t) − D(T, t),
where F (t, T ) is the futures price at time t with maturity at time T , S(t) is
the spot price at time t, r(t, T ) is the T −t period riskless rate of return, where
T − t is the time until the expiration of the futures contract; and D(T, t) is
the value of the dividends paid over period from t to T by the stocks that
make up the index. Implicit in this relationship is the arbitrage process that
ensures that the equality holds via a cash-and-carry or reverse-cash-and-carry
transaction. When transaction costs are present, the relationship does not
hold exactly, but holds within arbitrage bounds around the true relationship.
6
Stoll and Whaley (1990) have found evidence that suggests that the cost-ofcarry relationship is sometimes violated outside of these bounds.
In our empirical application, we use the cost-of-carry relationship as motivation for testing for cointegration between the spot and futures contracts, i.e.
if both the spot and futures prices are integrated, the cost-of-carry relationship implies that the spot and futures prices may be cointegrated (Brenner
and Kroner (1995)).
4.2
The Vector Error Correction Model
The VECM has the following form for the spot and futures prices:
¶
¶ X
µ
¶ µ
¶
µ
µ
10
∆St−j
Rst
∆St
St−1
= Π0 + Π
+
+
Πj
∆Ft
Ft−1
∆Ft−j
Rf t
(1)
j=1
where St is the natural log of the spot price and Ft is the natural log of the
futures price that is closest to maturity, Π0 is a 2 × 1 vector of intercepts, Π
is a 2 × 2 matrix of error correction coefficients, the Πj are 2 × 2 matrices
of coefficients on the lagged first differences of the spot and futures prices,
and Rit , i = s, f, are the residuals of the spot and futures prices. First, we
test for unit roots using the augmented Dickey-Fuller (ADF) test (Said and
Dickey (1984)) and then we test for cointegration using the likelihood ratio
test of Johansen (1991). If the spot and futures prices are cointegrated, we
can use the VECM to analyze the speed at which shocks to the spot market
are reflected in the futures market, and vice versa, by using impulse response
analysis or the variance decomposition.
In the absence of market frictions, new information should be reflected
simultaneously in both the spot and futures market and neither Π nor any
of the Πj should contain significant coefficients. However, due to market
frictions we expect to find a lagged adjustment to new information, and due
to the availability of margin trading, we expect the futures market to lead
the spot market (Chan (1992)). After the introduction of the JET trading
system, we expect to find that new information is reflected more quickly in
both spot and futures prices.
4.3
The Bivariate EGARCH Model
After estimating the VECM in (1), we save the residuals, (Rst, Rf t ), for use in
the bivariate-EGARCH model. We use the bivariate-EGARCH model of
7
Koutmos and Booth (1995) because it captures the transmission of volatility between the two markets and the contemporaneous correlation between
the two indexes. In addition, it improves the precision of the parameter
estimates by extending the seemingly unrelated estimation method to the
case that includes conditional heteroskedasticity (Bollerslev (1990)). The
EGARCH structure also provides a number of benefits over other models
of the conditional variance. It captures the asymmetric effect of ‘good news’
(positive returns) and ‘bad news’ (negative returns) on the conditional variance and does not require restrictions on the parameters to ensure a positive
conditional variance.
The bivariate-EGARCH model consists of the following equation for the
conditional mean,
Ri,t = φi,0 + φi,1 Ri,t−1 + εi,t ,
(2)
where i = s, f and we include a lagged return to mop up any serial correlation
that may still be present. We assume that the joint conditional distribution
of the two errors is bivariate normal, i.e.
¶
µ
εf,t
|Ft−1 ∼ N(0, St ),
(3)
εs,t
where Ft−1 is the information set containing information through period t−1,
and the diagonal elements of St are given by the following equation for the
conditional variance,
σ 2i,t = exp (αi,0 + αi,1 (|zj,t−1 | − E |zj,t−1 |) + αi,2 zi,t−1
¡
¢ ¢
,
+αi,3 (|zj,t−1 | − E |zj,t−1 |) + αi,4 zj,t−1 + γ i ln σ 2i,t−1
(4)
σ i,j,t = ρi,j σ i,t σ j,t , i 6= j,
(5)
where zi,t = εi,t /σ i,t is the standardized innovation and E |zj,t−1 | =
and the off-diagonal elements are given by
p
2/π,
for i, j = s, f .
The conditional variance at time t is given in (4). Each of the four αi,j
coefficients plays a specific role: αi,1 captures the effect of the magnitude of a
8
lagged innovation on the conditional variance and αi,2 captures the effect of
the sign of a lagged innovation on the conditional variance, i.e. the leverage
effect (Black (1976), Christie (1982)). This is shown by taking the partial
derivative of (4) with respect to the standardized innovation:
∂σ 2i,t /∂zi,t = 1 + αi2 for zi > 0
∂σ 2i,t /∂zi,t = 1 − αi2 for zi < 0.
(6)
If αi2j is negative and statistically significant, then we obtain an asymmetric
response, i.e. if both αi2j and zi,t−1 are negative, the effect of the shock will
be larger than if αi2 is positive. The relative importance of the asymmetry
can be measured by the ratio, |−1 + αi2j | / |1 + αi2j |. The remaining two
αij , j = 3, 4, capture the size and sign effect of a shock to market j on market
i. The last term in the conditional variance contains the lagged conditional
variance where the coefficient γ i is a measure of persistence. Following Booth
et al. (1997) we use the γ i ’s to calculate the half-life of a shock using the
formula ln(0.5)/ ln(γ i ), i.e. the half-life of a shock is the number of days it
takes a shock to decay to half its initial level. Finally, we assume that the
correlation coefficient is constant in (5).
To accomodate the change to the JET trading system in June 1996, we
replace each coefficient in (4) by αij + δ ij and we replace ρ in (5) by ρ + δ
for the period after the introduction of the electronic trading system, i.e. we
allow each coefficient in the conditional second moment to change after the
introduction of the JET trading system. Because we are using daily data,
we expect to find greater contemporaneous correlation between the spot and
futures markets and less volatility transmission between the spot and futures
markets after the introduction of the JET trading system.
The log-likelihood of the system is given by,
L(θ) = −(1/2)
T
X
¡
¢
2 ln(2π) + ln kSt k + ε0t St−1 εt ,
(7)
t=1
where T is the number of observations, θ is the parameter vector, εt is the
2 × 1 vector of innovations at time t and St is the 2 × 2 time-varying covariance matrix. We maximize the log-likelihood function using the Broyden, Fletcher, Goldfarb and Shanno (BFGS) algorithm. The parameter vector θ has dimension (2 × 14 + 2) ×1 and consists of two vectors, (φi,0 , φi,1 ,
αi,0 , αi1 , ..., αi,4 , γ i , δ i0 , ..., δ i6 ), with dimension 14 × 1 for the spot and the
9
futures contract and the two remaining parameters are the correlation coefficient and the coefficient of the dummy, δ. Because the true distribution
of the errors is unknown, we estimate the model using quasi maximum likelihood estimation (QMLE) and we use the White (1980) robust covariance
matrix estimator for inference.
4.4
Data
The data in this study consists of daily closing prices for the value-weighted
all share, industrial, and gold indexes for both the spot and futures markets.
The starting date of the sample is August 1, 1990 and the ending date is
August 31, 2000 for the all Share index, February 14, 2000 for the Industrial
Index and March 17, 1998 for the gold index, the date when the gold futures
contract was discontinued. The data were extracted from historical data files
that are available on the SAFEX web page (www.safex.co.za). Only nearby
futures contracts are used because they are liquid enough to justify examination. The futures market closed at 4:30pm, 30 minutes after the closing
of the spot market, during the pre-JET period although trading sometimes
continued to 5:30pm. During the post-JET period the SAFEX opening time
is 8:30am, 30 minutes before the spot market, and the closing time is 5:30pm.
A possible explanation for finding a weak linkage between these two markets
is the differences in closing times.
During the pre-JET period that terminates at the end of 1995, the all
share (ALSI), industrial (INDI) and gold indexes (GLDI) consisted of approximately 536, 324 and 40 stocks, respectively. In the post-JET sample,
these three indexes were replaced by the ALSI40, INDI25 and GLDI10 indexes where the number at the end of each contract denotes the number of
stocks in the index. Given the smaller basket of highly traded stocks in each
index we expect a closer relationship between the spot and futures markets
after the introduction of JET.
We report summary statistics for the returns of all three indexes in the
pre-JET and post-Jet sample periods in Table 1. In addition, we report the
results of tests of skewness and kurtosis, serial correlation and conditional
heteroskedasticity. We use the GMM estimator to obtain standard errors for
the tests of skewness and kurtosis because it is robust to conditional heteroskedasticity in the returns (Pagan (1996)). The key empirical regularities
for the three series are that they all have excess kurtosis, all have serial correlation and conditional heteroskedasticity. The returns of the gold index spot
10
and futures prices are large and negative for the post-JET period because
this period coincided with a severe downward trend in the gold price.
5
5.1
Results
VECM
We report the unit root tests and tests for cointegration in Table 2. We
include unit root tests on the levels and first differences of the natural log of
the spot and futures prices where we include 10 lagged dependent variables in
the ADF test. We find that each of the spot and futures prices is integrated of
order 1, i.e. the levels are non-stationary and the first differences or returns
are stationary.
For the Johansen likelihood ratio (LR) test for cointegration, we also
include 10 lags in the VECM. We find that all six pairs of spot and futures
prices, pre- and post-JET, are cointegrated at the 1% level of significance.
The results in the pre-JET period are consistent with the results of Ferret
and Page (1998) who also analyzed the cointegration of the spot and futures
contracts in the pre-JET period. Given the cointegrating relationships, we
can now turn to the variance decompositions of the vector error correction
model.
We report the variance decompositions of the six models in Figures 1(a)
through (f) where we focus on interpreting the transmission of a shock in the
spot market to the futures market, and vice versa. The market that experiences the shock is placed first in the ordering for the variance decomposition.
We use the interpretation in Hamilton (1994, p. 324), for the variance decompositions, i.e., the variance decomposition of variable i converges to the
percentage of the variance that can be explained by a shock to variable j.
Figure 1(a) contains the variance decompositions of the spot and futures
prices of the All Share Index after a one standard deviation shock to the spot
price. In the pre-JET sample, the effect of a shock to the spot price decays
slightly over time and explains about 80% of the variance of the spot price
after 10 days. The effect of the shock to the spot price has an immediate
effect on the futures price and accounts for slightly less than 40% of the
variance in the futures price. In addition, there is no decay in the effect
across the 10-day horizon. In the post-JET period, a shock to the spot price
has a much bigger initial effect on the futures price: 80% of the variance is
11
explained from day one and there is no decay after 10 days. This result is
consistent with our expectation that electronic trading leads to a closer link
between the spot and futures markets.
Turning to Figure 1(b), we consider a shock to the futures price of the All
Share Index and its effect on the spot and futures prices. Here we find a big
difference between the pre- and post-JET behavior of the variance decompositions. Before the introduction of JET, a shock to the futures price initially
explains about 40% of the variance in the spot price and then increases to
70% after 10 trading days. However, after the introduction of JET, a shock
to the futures price has a much bigger initial effect of 80% and then continues
to increase to 95%. Once again, this result is consistent with our expectations that the electronic trading system has increased the contemporaneous
response and the speed of information transmission between the markets.
Figures 1(c) and (d) contain the variance decompositions of the gold index. In Figure 1(c) we see that a shock to the spot market explains about
80% of the variance in the futures market with no decay over the 10-day
horizon, prior to the introduction of JET. However, in the post-JET period
we find that the initial response is only 60% and increases to 90% after 10
days. These results are not consistent with an improvement in the information transmission between the spot and futures markets. In Figure 1(d) we
find that the transmission of a shock from the futures to the spot market
has also deteriorated and the variance decomposition has shifted down. In
the pre-JET period, the initial effect of a shock to the futures market explained 80% of the variance in the spot market and this increases to slightly
more than 90% after 10 days. However, in the post-JET period, the initial
response is only 60% and after 10 days, only 70% is explained. These results
are consistent with the fact that the gold index contract was discontinued in
1998 due to the lack of trading volume.
Figures 1(e) and 1(f) contain the variance decompositions of the Industrial
index. In Figure 1(e) we find little difference between the transmission of a
shock in the spot market to the futures market, however in Figure 1(f) we
find that a shock to the futures market has a uniformly stronger effect on
the spot market. In the pre-JET period, we find that a shock to the futures
market initially explains 50% of the variance in the spot market and this
increases to 85% after 10 days. In the post-JET period, the initial response
is stronger: it begins at 60% and increases to 95% after 10 days.
We conclude that the introduction of the electronic trading system has
had a positive effect on the speed of information transmission between the
12
spot and futures markets for the contracts based on the all Share index and
the industrial Index. There was no benefit to the contract based on the
gold index although this result may simply reflect the fact that the contract
was unsuccessful and was discontinued in 1998. These results are consistent
with Ferret and Page (1998) who analyzed the pre-JET period, i.e. there is
bi-directional feedback between both spot and futures markets for all three
contracts.
5.2
The Bivariate EGARCH Model
The parameter estimates of the bivariate EGARCH model for the all share
index are contained in Table 3. We find that neither the LB test of the standardized residuals or squared residuals are significant thus confirming that
the model captures the serial correlation and conditional heteroskedasticity
that is present in the data. The likelihood ratio test statistic of the null hypothesis that each of the dummy coeficients equals zero is highly significant.
Prior to the introduction of the JET trading system, we find that the spot
contract on the all share index has no asymmetry in its conditional variance
with respect to its own shocks, but the shocks from the futures market do
have an asymmetric effect. The effect of a negative shock to the futures
market on the conditional variance of the spot market is 50% bigger than a
positive shock. In addition, the coefficient of the lagged conditional variance
is 0.9478 and implies that shocks to the spot market have a half-life of 13
days. Turning to the futures contract, we find significant asymmetry with
respect to its own shocks, but no volatility spillovers from the spot market.
The coefficient on the lagged conditional variance implies that shocks to the
futures market have a half-life of 6 days.
When we consider the coefficients of the dummies on the conditional
variance that allow for parameter changes after the introduction of electronic
trading, we find that shocks to the spot market have an asymmetric effect
on the variance of the spot contract and the asymmetric effect of the futures
market on the spot market has been considerably reduced. The effect of
a negative shock from the futures market on the spot market has a been
reduced from 1.5 to 1.01. For the futures market, we find a reduction in the
asymmetry of the effect of its own shocks on its own conditional variance and
shocks to the spot market now do have an effect on the conditional variance
of the futures market. Finally, we find that the contemporaneous correlation
between the two markets has increased from 0.6657 in the pre-JET period to
13
0.8514 in the post-JET period.
In summary, the introduction of electronic trading has led to a much
stronger contemporaneous correlation, a reduction in the spillovers from the
futures to the spot market and an increase in the spillovers from the spot to
the futures market.
The parameter estimates of the bivariate EGARCH model for the industrial index are contained in Table 4. We find that neither the LB test of the
standardized residuals or squared residuals are significant thus confirming
that the model captures the serial correlation and conditional heteroskedasticity that is present in the data. The likelihood ratio test statistic of the null
hypothesis that each of the dummy coeficients equals zero is highly significant. For the spot contract there is no asymmetric effect for its own shocks
and no spillovers from the futures market before or after the introduction of
the JET system. For the futures contract,we find no asymmetric effect of its
own shocks prior to the introduction of electronic trading, but we do find a
small asymmetric effect from the spot market after the introduction of electronic trading. In the post-JET period, the futures contract has significant
asymmetric effects from its own shocks and the asymmetric effect of its own
shocks on the spot market are eliminated.
For both the spot and futures contracts we find a significant increase in
the coefficient of the lagged conditional variance. For the spot contract, the
coefficient increases from 0.6501 to 0.9706 while for the futures contract it
increases from 0.8251 to 0.9754. Finally, we find that the contemporaneous
correlation increases from 0.7552 to 0.8055.
The general conclusions that we can draw from this section are that the
contemporaneous correlation between the spot and futures markets increases
after the introduction of electronic trading and there is a reduction in the
magnitude of the asymmetric spillovers. We did not estimate the model for
the gold index because of the estimates would be biased by the fact that the
gold index futures contract was discontinued.
6
Conclusion
The objective of this paper was to analyze the interrelationship between the
all share, gold and industrials indexes of the JSE and their corresponding
index futures contracts on the SAFEX. We find that the three indexes and
their futures prices are cointegrated. In addition, using a VECM we find
14
that after the introduction of electronic trading the spot and futures markets
respond more quickly to their own shocks and shocks from the other market.
In particular, using a bivariate-EGARCH model we find that the contemporaneous corelation increases, the spillover and asymmetric effects decrease
in size. Despite the fact that the indices are heavily weighted towards the
large, highly traded stocks on the JSE, the electronic trading system does
not hamper the price response as suggested by some of the theories in section
2.
Future analysis of the introduction of JET can be conducted at a microstructure level by looking at the changes in the volume of trading of small
and large companies as well as their bid-ask spreads. If electronic trading is
good for small companies, we expect to see an increase in trading volume and
a decrease in their bid-ask spreads, but if it is bad for large companies, we
expect to see a decline in volumes and an increase in their bid-ask spreads.
In addition, for the large companies that are listed in London and/or New
York we expect to see a migration of block trades away from the JSE. Finally,
it would be better to analyze the relationship between the spot and futures
markets using intra-day as opposed to a daily data.
References
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Walk Model and South African Share Prices,” South African Journal of
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15
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16
Table 1: The Descriptive Statistics of the Daily Returns
Pre-JET
Mean
Std. Dev.
Skewness
Kurtosis
LB (10)
LB2 (10)
Sample Size
All Share
Spot
Futures
0.0462 0.0499
1.0024 1.2351
-0.6513 1.2620
22.746c 14.966c
14.622 10.077
218.81c
3.805
1404
1404
Gold
Spot
Futures
0.0104 0.0076
2.3743 2.4748
0.3832 0.3358
2.390c 2.1045c
11.391 5.106
48.880c 19.692c
1404
1404
Industrials
Spot
Futures
0.0709 0.0704
0.7962 1.0439
-0.3478 -0.2211
6.997c 4.6125c
50.877c 17.566a
203.92c 52.834c
1404 1404
0.0229
1.4962
-1.2394
12.121c
30.710c
274.30c
1061
-0.2233
2.0608
0.4924
2.068c
18.375b
70.449c
443
0.0212
1.9899
0.0738
44.742c
31.281c
237.24c
928
Post-JET
Mean
Std.Dev
Skewness
Kurtosis
LB(10)
LB2 (10)
Sample Size
0.0128
1.7371
-1.1503
10.194c
34.534c
222.24c
1061
-0.2288
2.4140
-0.0559
2.412c
12.865
65.482c
443
0.0196
1.7900
-0.5959
6.142c
17.837a
356.26c
928
a b c
, , denote significance at the .10, .05 and .01 level, respectively.
The mean, standard deviation, skewness and kurtosis and their standard errors
are estimated using the GMM estimator because it is robust to the presence
of conditional heteroskedasticity.
LB(.) and LB2 (.) are the Ljung Box tests for serial correlation in the returns
and squared returns, respectively.
JET is the acronym for Johannesburg Equities Trading electronic trading system that was
introduced on the Johannesburg Stock Exchange from March to June 1996.
17
Table 2: Unit Root Tests and Tests for Cointegration
All Share
Spot
Futures
Gold
Spot
Futures
Pre-Jet
Industrials
Spot
Futures
-2.727
-2.840
-1.994
-1.964
-2.566
-2.601
Changes -11.345c
-11.602c
-11.937c
-10.830c
-10.830c
-11.468c
LR Test
19.41b
Levels
32.128c
46.25c
Post-JET
Levels
-1.660
-1.824
-3.082
-3.069
-1.135
-1.391
Changes -9.187c
-9.073c
-7.513c
-7.513c
-8.706c
-8.864
LR Test
57.648c
32.35c
39.634c
The LR Test refers to the likelihood ratio test for cointegration following
Johansen (1991).
The null of no-cointegration is tested against the alternative of cointegration.
The pre-Jet period refers to the period from August 1990 to May 31, 1996
and the post-Jet period from June 10, 1996 to the respective ending date
of each contract.
18
Table 3: The Parameter Estimates of the Bivariate EGARCH Model
for the All Share Index
Spot
Futures
Parameter Pre
Dummy
Pre
Dummy
φi,0
0.0389b
(0.0212)
0.0402a
(0.0314)
φi,1
0.0868c
(0.0227)
0.0122
(0.0278)
αs,0
0.0124
(0.0151)
0.0353a
(0.0280)
0.0678a
(0.0418)
0.0233
(0.0632)
αs,1
0.081
(0.0685)
0.0979
(0.0967)
0.4629c
(0.1703)
-0.3823b
(0.1895)
αs,2
0.1265b
(0.0547)
-0.2207c
(0.0818)
-0.1771c
(0.0715)
0.1718a
(0.1092)
γ
0.9478c
(0.0246)
-0.0475
(0.0542)
0.8906c
(0.0627)
0.0008
(0.8200)
αs,3
0.2872c
(0.0843)
-0.1181
(0.1133)
-0.1210
(0.1025)
0.3346c
(0.1245)
αs,4
-0.2083c
(0.0581)
0.2019c
(0.0791)
0.1010
(0.1007)
-0.2245a
(0.1474)
ρ12
0.6657c
(0.0433)
0.1857c
(0.0440)
Note: Robust standard errors in parenthesis.
a b c
, , denote significance at the 10%, 5% and 1% level, respectively.
19
Table 3 (continued): Misspecification Tests for the All Share Index
Spot
Futures
LR Test
6598.205c
LB(10)
7.177
10.878
LB2 (10)
6.941
4.283
LB(.) and LB2 (.) are the Ljung Box tests for serial correlation in the
standardized residuals and squared residuals, respectively.
The LR Test denotes the likelihood ratio test statisic of the hypothesis that
all the coefficients of the dummies equal zero, i.e. there is no change in the
parameters of the EGARCH model after the introduction of electronic trading.
20
Table 4: The Parameter Estimates of the Bivariate EGARCH Model
for the Industrial Index
Spot
Futures
Parameter Pre
Dummy
Pre
Dummy
φi,0
-0.0233
(0.0203)
-0.0268
(0.0277)
φi,1
0.1134c
(0.0251)
0.0765c
(0.0257)
αs,0
-0.1936b
(0.0922)
0.2255c
(0.1001)
0.0288
(0.0369)
0.0009
(0.0488)
αs,1
0.4738a
(0.0970)
-0.3331b
(0.1620)
0.1568c
(0.0729)
-0.1421a
(0.1026)
αs,2
-0.0347
(0.0605)
0.0275
(0.1100)
-0.0160
(0.0482)
-0.1021a
(0.0773)
γ
0.6501c
(0.0938)
0.3205c
(0.0973)
0.8251c
(0.0675)
0.1503b
(0.0733)
αs,3
0.0639
(0.1218)
-0.0646
(0.1757)
0.1825∗∗∗
(0.0774)
-0.0810
(0.0998)
αs,4
-0.0062
(0.0655)
-0.0758
(0.1287)
-0.0636a
(0.0483)
0.1156a
(0.0716)
ρ12
0.7552c
(0.0263)
0.0503a
(0.0382)
Note: Robust standard errors in parenthesis.
a b c
, , denote significance at the 10%, 5% and 1% level, respectively.
21
Table 4 (continued): Misspecification Tests for the Industrial Index
Spot
Futures
LR Test
5882.932c
LB(10)
10.921
7.399
LB2 (10)
4.125
13.195
LB(.) and LB2 (.) are the Ljung Box tests for serial correlation in the
standardized residuals and squared residuals, respectively.
The LR Test denotes the likelihood ratio test statisic of the hypothesis that
all the coefficients of the dummies equal zero, i.e. there is no change in the
parameters of the EGARCH model after the introduction of electronic trading.
22