The FTSE 100 is a market-capitalisation weighted index representing the performance of the 100 largest UK-domiciled blue chip companies, which pass screening for size and liquidity. The index represents approximately 84.35% of the UK’s market capitalisation and is suitable as the basis for investment products, such as funds, derivatives and exchange-traded funds. The FTSE 100 Index also accounts for 8.02% of the world’s equity market capitalisation (based on the FTSE All-World Index as at 30 June 2011).
FTSE 100 constituents are all traded on the London Stock Exchange’s SETS trading system.
The indices are managed according to a transparent and public set of index rules,
and overseen by an independent committee of leading market professionals. The
committee ensures that the rules are correctly applied and adhered to. Regular
index reviews are conducted to ensure that a continuous and accurate representation
of the market is maintained.
World Stock market,Dow Jones Futures,FTSE Futures,Nikkei,CAC,DAX,Nasdaq future,Live Gold future,Crude,Silver,Copper future,SGX NIFTY Futures,Indian Stock Market,Asian stock market ,American stock market,European stock market,live quotes.
Major Global Index Future Live ( Refresh every 5 second)
Note :
Please wait 10 second for data upload. If Data Doesn't Upload in 10 Seconds please try another link :http://liveworldmarket.com
Monday, August 22, 2011
About the Dow Jones Industrial Average
The Dow Jones Industrial Average (DJIA) is a price-weighted index of 30 blue-chip U.S.
companies representing nine economic sectors including financial service, technology,
retail, entertainment and consumer goods. The leadership position of the component
stocks in the DJIA tends to result in an extremely high correlation of the DJIA to broader
U.S. indexes, such as the S&P 500 Index providing additional opportunities.
companies representing nine economic sectors including financial service, technology,
retail, entertainment and consumer goods. The leadership position of the component
stocks in the DJIA tends to result in an extremely high correlation of the DJIA to broader
U.S. indexes, such as the S&P 500 Index providing additional opportunities.
Sunday, August 21, 2011
Disclaimer of Liveworldmarket blog
Note : liveworldmarket.blogspot.com is a blog of NSTIPS ADVISORY (a sole proprietorship)
By accessing this blog, you have read, understood and agree to be legally bound by the terms of the following disclaimer and user agreement:
This blog has taken due care and caution in compilation of data for its web site. The market view expressed on this blog are in no way a guarantee either express or implied . However, this blog does not guarantee the accuracy, adequacy or completeness of any information and is not responsible for any errors or omissions or for the results obtained from the use of such information.This blog especially states that it has no financial liability whatsoever to any user on account of the use of information provided on its website.
MODELING THE JOINT DYNAMICS OF COUNTRY AND WORLD VOLATILITY
The univariate models we employed above do not allow for time-varying
conditional covariance between the country and world returns. If the
conditional covariance changes systematically with the introduction of
stock index futures, then our previous results may be biased.
In this section, we will address this problem by estimating the joint
dynamics of each country’s return with the world-market return in a multivariate
GARCH framework that allows for time-varying conditional covariance.
Because we wished to capture the dynamic interaction between
world-market volatility, country-specific volatility, and conditional covariance,
we used the BEKK specification of Engle and Kroner (1995).9
Unlike certain other well-known multivariate GARCH models, the BEKK
model allows conditional variances and covariances to influence each
other.
conditional covariance between the country and world returns. If the
conditional covariance changes systematically with the introduction of
stock index futures, then our previous results may be biased.
In this section, we will address this problem by estimating the joint
dynamics of each country’s return with the world-market return in a multivariate
GARCH framework that allows for time-varying conditional covariance.
Because we wished to capture the dynamic interaction between
world-market volatility, country-specific volatility, and conditional covariance,
we used the BEKK specification of Engle and Kroner (1995).9
Unlike certain other well-known multivariate GARCH models, the BEKK
model allows conditional variances and covariances to influence each
other.
THE EFFECT OF FUTURES TRADING ACTIVITY
In the previous section, we tested whether there appears to be any structural
change in the underlying market at the time of futures introduction.
In this section, we will test whether there appears to be a relationship,
after the futures have been listed, between the level of future trading activity
and the volatility of the underlying index.
Our approach is based on that of Bessembinder and Seguin (1992).
Using an autoregressive integrated moving average (ARIMA) model, they
decomposed the time series of futures trading volume and open interest
into expected and unexpected components. Bursts of trading activity
stimulated by unexpected price changes should be picked up in the unexpected
component, while the expected component should reflect the
“background” level of futures trading. They found that market volatility
was positively related to the unexpected components of volume and open
interest, reflecting the positive effect of volatility on volume, but that
market volatility was negatively related to the expected component, suggesting
an underlying stabilizing influence.
We followed a similar procedure using futures market trading volume
and open-interest data from 17 of our 25 countries for which volume and
open interest data were available. First, we analyzed the volume and openinterest
time series from each country to select an ARIMA model that
appeared to fit the data reasonably well. Restricting our attention to models
with five or less autoregressive lags and five or less moving average
lags, we selected, on the basis of the autocorrelation structure, a different
model for each time series.
change in the underlying market at the time of futures introduction.
In this section, we will test whether there appears to be a relationship,
after the futures have been listed, between the level of future trading activity
and the volatility of the underlying index.
Our approach is based on that of Bessembinder and Seguin (1992).
Using an autoregressive integrated moving average (ARIMA) model, they
decomposed the time series of futures trading volume and open interest
into expected and unexpected components. Bursts of trading activity
stimulated by unexpected price changes should be picked up in the unexpected
component, while the expected component should reflect the
“background” level of futures trading. They found that market volatility
was positively related to the unexpected components of volume and open
interest, reflecting the positive effect of volatility on volume, but that
market volatility was negatively related to the expected component, suggesting
an underlying stabilizing influence.
We followed a similar procedure using futures market trading volume
and open-interest data from 17 of our 25 countries for which volume and
open interest data were available. First, we analyzed the volume and openinterest
time series from each country to select an ARIMA model that
appeared to fit the data reasonably well. Restricting our attention to models
with five or less autoregressive lags and five or less moving average
lags, we selected, on the basis of the autocorrelation structure, a different
model for each time series.
VOLATILITY EFFECTS OF FUTURES LISTING
Empirical Framework for Univariate Modeling
We begin our analysis by modeling the time series of excess country returns
net of the world-market portfolio as a univariate GARCH process.
Australia 2 January 1980–31 December 1997 779 3572
Austria 20 November 1987–31 December 1997 1162 1334
Belgium 2 January 1990–31 December 1997 941 1037
Canada 2 January 1973–31 December 1997 2740 3516
Chile 2 January 1987–31 December 1997 879 1741
Denmark 10 December 1979–31 December 1997 2476 2037
Finland 2 January 1987–31 December 1997 333 2424
France 9 July 1987–31 December 1997 330 2270
Germany 21 November 1977–31 December 1997 3215 1771
Japan 4 January 1980–31 December 1997 2098 2298
Hong Kong 2 January 1973–31 December 1997 3263 2888
Hungary 2 January 1991–31 December 1997 1056 674
Israel 2 January 1992–31 December 1997 928 527
Italy 2 January 1973–31 December 1997 5507 780
Korea 3 January 1990–31 December 1997 1540 398
Malaysia 2 January 1980–31 December 1997 3902 508
Netherlands 3 January 1983–31 December 1997 1402 2313
Norway 3 January 1983–31 December 1997 2418 1333
Portugal 1 January 1993–31 December 1997 853 376
South Africa 10 April 1985–31 December 1997 1136 1891
Spain 6 January 1987–31 December 1997 1238 1501
Sweden 2 January 1986–31 December 1997 311 2694
Switzerland 1 July 1988–31 December 1997 590 1792
United Kingdom 2 January 1973–31 December 1997 2871 3485
United States 2 January 1973–31 December 1997 2340 3967
This framework is parsimonious, which allowed us to capture many of
the salient features of the data, and to partially account for movements
in the world market in a model with relatively few parameters. Later, we
would estimate a multivariate GARCH model that would allow us a richer
model of the joint dynamics of country-specific and world-market returns.
Following Pagan and Schwert (1990) and Engle and Ng (1993), the
first step in our univariate GARCH analysis was to remove from the time
series any predictability associated with lagged returns or day-of-the-week
effects. For each country, the following regression was estimated:
where Rt is the daily return on the country’s stock index and RWt is the
daily return on the World Market Index on day t, RWt1 is the lagged return on the World Market Index, and DAYj are day-of-the-week dummies
for Tuesday through Friday.
We used the excess return relative to the World Market Index as our
dependent variable and the lagged World Market Index return as an independent
variable in an effort to remove the effect of worldwide price
movements on volatility.6 It should be noted that because of differences
in time zones, different markets line up differently with the world-market
return. This makes it difficult to compare directly the coefficients of the
first-stage regression. For example, if the U.S. market is influenced by
Asian markets, this will be reflected through the contemporaneous market
return on the left-hand side of the regression equation. On the other
hand, if the Asian markets are influenced by the US, this will be reflected
through the lagged market portfolio.
We begin our analysis by modeling the time series of excess country returns
net of the world-market portfolio as a univariate GARCH process.
Australia 2 January 1980–31 December 1997 779 3572
Austria 20 November 1987–31 December 1997 1162 1334
Belgium 2 January 1990–31 December 1997 941 1037
Canada 2 January 1973–31 December 1997 2740 3516
Chile 2 January 1987–31 December 1997 879 1741
Denmark 10 December 1979–31 December 1997 2476 2037
Finland 2 January 1987–31 December 1997 333 2424
France 9 July 1987–31 December 1997 330 2270
Germany 21 November 1977–31 December 1997 3215 1771
Japan 4 January 1980–31 December 1997 2098 2298
Hong Kong 2 January 1973–31 December 1997 3263 2888
Hungary 2 January 1991–31 December 1997 1056 674
Israel 2 January 1992–31 December 1997 928 527
Italy 2 January 1973–31 December 1997 5507 780
Korea 3 January 1990–31 December 1997 1540 398
Malaysia 2 January 1980–31 December 1997 3902 508
Netherlands 3 January 1983–31 December 1997 1402 2313
Norway 3 January 1983–31 December 1997 2418 1333
Portugal 1 January 1993–31 December 1997 853 376
South Africa 10 April 1985–31 December 1997 1136 1891
Spain 6 January 1987–31 December 1997 1238 1501
Sweden 2 January 1986–31 December 1997 311 2694
Switzerland 1 July 1988–31 December 1997 590 1792
United Kingdom 2 January 1973–31 December 1997 2871 3485
United States 2 January 1973–31 December 1997 2340 3967
This framework is parsimonious, which allowed us to capture many of
the salient features of the data, and to partially account for movements
in the world market in a model with relatively few parameters. Later, we
would estimate a multivariate GARCH model that would allow us a richer
model of the joint dynamics of country-specific and world-market returns.
Following Pagan and Schwert (1990) and Engle and Ng (1993), the
first step in our univariate GARCH analysis was to remove from the time
series any predictability associated with lagged returns or day-of-the-week
effects. For each country, the following regression was estimated:
where Rt is the daily return on the country’s stock index and RWt is the
daily return on the World Market Index on day t, RWt1 is the lagged return on the World Market Index, and DAYj are day-of-the-week dummies
for Tuesday through Friday.
We used the excess return relative to the World Market Index as our
dependent variable and the lagged World Market Index return as an independent
variable in an effort to remove the effect of worldwide price
movements on volatility.6 It should be noted that because of differences
in time zones, different markets line up differently with the world-market
return. This makes it difficult to compare directly the coefficients of the
first-stage regression. For example, if the U.S. market is influenced by
Asian markets, this will be reflected through the contemporaneous market
return on the left-hand side of the regression equation. On the other
hand, if the Asian markets are influenced by the US, this will be reflected
through the lagged market portfolio.
Launch Dates for Index Futures Contracts
Launch Dates for Index Futures Contracts
Country Underlying Index Launch Date
United States Value Line 24 February 1982
S&P 500 21 April 1982
Australia All Ordinaries 16 February 1983
United Kingdom FT-SE 100 3 May 1984
Canada TSE 300 16 January 1984
Brazil BOVESPA 14 February 1986
Hong Kong Hang Seng 6 May 1986
Japan (SIMEX) Nikkei 225 3 September 1986
(Osaka) OSE 50 9 June 1987
(Osaka) Nikkei 225 3 September 1988
(Tokyo) Topix 3 September 1988
New Zealand Barclay Share January 1987
Sweden OMX 3 April 1987
Finland FOX 2 May 1988
Netherlands AEX 24 October 1988
France CAC 40 9 November 1988
Denmark KFX 7 December 1989
South Africa All Share 30 April 1990
Switzerland SMI 9 November 1990
Germany DAX 23 November 1990
Chile IPSA December 1990
Spain IBEX 35 14 January 1992
Austria ATX 7 August 1992
Norway OBX 4 September 1992
Belgium BEL 20 29 October 1993
Italy MIB 30 28 November 1994
Hungary BSI 31 March 1995
Israel Maof 25 27 October 1995
Malaysia KLCI 15 December 1995
Korea KOSPI 200 3 May 1996
Portugal PSI-20 20 June 1996
Russia RTS March 1997
Venezuela IBC 5 September 1997
Poland WIG20 16 January 1998
Greece FTSE/ASE-20 27 August 1999
Country Underlying Index Launch Date
United States Value Line 24 February 1982
S&P 500 21 April 1982
Australia All Ordinaries 16 February 1983
United Kingdom FT-SE 100 3 May 1984
Canada TSE 300 16 January 1984
Brazil BOVESPA 14 February 1986
Hong Kong Hang Seng 6 May 1986
Japan (SIMEX) Nikkei 225 3 September 1986
(Osaka) OSE 50 9 June 1987
(Osaka) Nikkei 225 3 September 1988
(Tokyo) Topix 3 September 1988
New Zealand Barclay Share January 1987
Sweden OMX 3 April 1987
Finland FOX 2 May 1988
Netherlands AEX 24 October 1988
France CAC 40 9 November 1988
Denmark KFX 7 December 1989
South Africa All Share 30 April 1990
Switzerland SMI 9 November 1990
Germany DAX 23 November 1990
Chile IPSA December 1990
Spain IBEX 35 14 January 1992
Austria ATX 7 August 1992
Norway OBX 4 September 1992
Belgium BEL 20 29 October 1993
Italy MIB 30 28 November 1994
Hungary BSI 31 March 1995
Israel Maof 25 27 October 1995
Malaysia KLCI 15 December 1995
Korea KOSPI 200 3 May 1996
Portugal PSI-20 20 June 1996
Russia RTS March 1997
Venezuela IBC 5 September 1997
Poland WIG20 16 January 1998
Greece FTSE/ASE-20 27 August 1999
Introduction to World Market Future
The world’s first stock index futures contract was the Value Line contract,
introduced by the Kansas City Board of Trade on February 24, 1982.
Today, stock index futures and options trade in markets all over the world,
with new contracts launched nearly every year. Table 1 reports launch
dates for thirty nations that introduced stock index futures between 1982
and January, 1998. In addition, plans are underway for exchange-listed
index futures in many other nations, including India, Indonesia, Czech
Republic, Slovakia, Turkey, and others.
As exchange-traded stock index futures and other derivatives become
more pervasive in the world’s financial markets, it is increasingly important
to understand the effect of derivatives trading on the underlying
markets. The previous literature on the effects of stock index futures
trading has focused primarily on developed markets, and it is unclear to
what extent these results are applicable to less-developed markets. Moreover,
the existing research has come to conflicting conclusions regarding
the effect of futures trading on volatility. While some authors have found
that volatility appears to increase with the introduction of futures, others
have found no significant effect, and still others have found that volatility
decreases.1
In this site, we examine the time series properties of stock indexes
in twenty-five countries in order to investigate the impact of stock index
futures listing and subsequent trading activity on the volatility structure
of the underlying cash market.We examined this issue in two ways. First,
we tested for structural changes at the time of futures listing by comparing
properties of the returns series before and after listing. Second, we
tested whether volatility in the post-listing period is related to futures
market volume and open interest. The results of both tests showed that
futures trading is associated with increased volatility in the United States
and Japan, but this was not the case in virtually every one of the other
twenty-three countries. In some countries, there is no robust, significant
effect, and in many others, futures trading is associated with lower
volatility.
pointed out by Hodges (1992), Mayhew (1999), and others, most of these
theories predicted that volatility can increase or decrease with the introduction
of futures depending on the underlying assumptions, or depending
on the parameter values used in the models. Due to the large number
of competing theoretical models with overlapping and ambiguous predictions,
we are reluctant to interpret our results as favoring any particular
model. Perhaps futures markets influence cash markets through multiple,
offsetting channels. Perhaps futures play a more-important stabilizing
role in markets that lack alternative stabilization mechanisms.
introduced by the Kansas City Board of Trade on February 24, 1982.
Today, stock index futures and options trade in markets all over the world,
with new contracts launched nearly every year. Table 1 reports launch
dates for thirty nations that introduced stock index futures between 1982
and January, 1998. In addition, plans are underway for exchange-listed
index futures in many other nations, including India, Indonesia, Czech
Republic, Slovakia, Turkey, and others.
As exchange-traded stock index futures and other derivatives become
more pervasive in the world’s financial markets, it is increasingly important
to understand the effect of derivatives trading on the underlying
markets. The previous literature on the effects of stock index futures
trading has focused primarily on developed markets, and it is unclear to
what extent these results are applicable to less-developed markets. Moreover,
the existing research has come to conflicting conclusions regarding
the effect of futures trading on volatility. While some authors have found
that volatility appears to increase with the introduction of futures, others
have found no significant effect, and still others have found that volatility
decreases.1
In this site, we examine the time series properties of stock indexes
in twenty-five countries in order to investigate the impact of stock index
futures listing and subsequent trading activity on the volatility structure
of the underlying cash market.We examined this issue in two ways. First,
we tested for structural changes at the time of futures listing by comparing
properties of the returns series before and after listing. Second, we
tested whether volatility in the post-listing period is related to futures
market volume and open interest. The results of both tests showed that
futures trading is associated with increased volatility in the United States
and Japan, but this was not the case in virtually every one of the other
twenty-three countries. In some countries, there is no robust, significant
effect, and in many others, futures trading is associated with lower
volatility.
pointed out by Hodges (1992), Mayhew (1999), and others, most of these
theories predicted that volatility can increase or decrease with the introduction
of futures depending on the underlying assumptions, or depending
on the parameter values used in the models. Due to the large number
of competing theoretical models with overlapping and ambiguous predictions,
we are reluctant to interpret our results as favoring any particular
model. Perhaps futures markets influence cash markets through multiple,
offsetting channels. Perhaps futures play a more-important stabilizing
role in markets that lack alternative stabilization mechanisms.
Suggestions/Feedback
SUGGESTION/FEEDBACK Your suggestion/feedback is important for us to improve upon the services of public need. Please send email: contact@niftysuretips.com .Thanks.
Subscribe to:
Posts (Atom)
