The temporal price relationship between the stock index futures and the underlying stock index : evidence from Malaysia

The stock index futures was introduced in Malaysia in December 1995 with the launching of the futures contract on the Kuala Lumpur Stock Exchange Composite Index. Due to its recentness in the country, many issues pertaining to this equity derivatives instrument have not been explored. Thus, the development of stock index futures opens many opportunities for research in this area. This study examines the temporal relationship between the price of the Kuala Lumpur Stock Exchange Composite Index futures contract (FKLI) and its underlying stock index, the Kuala Lumpur Stock Exchange Composite Index (KLSE CI). The five-year period under study is split into three subperiods to observe the price co-movement pattern under different volatility levels. The study finds that futures market tends to lead the spot market by one day during the periods of stable market, and there is a mixed lead-lag relationship between the two markets during the period of highly volatile market.

Modelling regulatory change V's volume of trading effects in HSIF and HSI volatility

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Floor trading and screen trading : distinctions for practitioners

The creation of an electronic limit order book is discussed as the basis for distinguishing between the floor trading and screen trading of derivative instruments. Distinguishing between FTP and ETP in terms of market transparency allows investors to contemplate the trade-off between the 2 platforms. Distinguishing between FTP and ETP in terms of memory preservation allows practitioners to contemplate the different experiences when analyzing floor data and screen data. A comparable set of floor and screen data is used to examine the impact on the trading dynamics and price discovery of LIFFE's FTSE 100 index futures market when trading is automated on LIFFE CONNECT. The dynamics in the quote change equation is shortened when moving from the floor to screen sample. Using the model's measure of trade informativeness, it is found that in 4 out of 5 daily sub-samples, screen trades are more than twice as informative as floor trades. Variability within a system of equations is explained more by order size history than trade size history.

Measuring the summary informativeness of orders and trades

Improved preservation of order flow history from the automation of derivative trading platforms suggests that traders are potentially learning from the recent history of both order and trade parameters. Consequently, a model to measure price discovery should encapsulate the dynamic interaction between the price-size coordinates of orders and trades. The Hasbrouck (1991) model is extended to measure the summary informativeness of order size and trade size. The two models are used to test for price discovery improvements in the FTSE 100 index futures market from order flow consolidation post deletion of its E-mini counterpart. The informativeness of trades has declined sharply, while the informativeness of orders has risen significantly in the post deletion sample.

Is carbon emissions trading profitable?

The European Carbon Emissions Trading Scheme introduced in 2005 has led to both spot and futures market trading of carbon emissions. However, despite seven years of trading, we have no knowledge on how profitable carbon emissions trading is. In this paper, we first test whether carbon forward returns predict carbon spot returns. We find strong evidence on both in-sample and out-of-sample predictability. Based on this evidence, we forecast carbon spot returns using both carbon forward returns and a constant. We consider a mean-variance investor and a CRRA investor, and show that they have higher utility and can make more statistically significant profits by following forecasts generated from the forward returns model than from a constant returns model.

Market makers v's the general public : a first look at S & P500 futures trade data

This paper considers 15 minute records of trading volume and traded prices coinciding with the reporting intervals required by the Commodity Futures Trading Commission. Records are extracted from trade records for market trade and also two way trade between market makers (CT1) and the general public (CT4) from January 1994 to June 2004. Futures price records are matched with S&P500 cash index price records. Simultaneous volatility models are specified and estimated to test trading volume to futures volatility lead/lag effects and also futures volatility to cash index volatility lead/lag effects. As we disaggregate from the market records to CT1 and CT4 records and further into year to year samples volume to futures volatility leading effects and also futures volatility to cash volatility leading effects dominate. The results raise important issues for risk management and dynamic hedging models employing intra-day trader data. A number of important issues for further analysis are also raised in this paper.

Dispersion of information or market behaviour : general public trading in S & P 500 index futures

This paper considers 15 minute records of trading volume and traded prices coinciding with the reporting intervals required by the Commodity Futures Trading Commission. Records are extracted from trade records for two way trade between market makers (CTI1) and the general public (CTI4) from January 1994 to June 2004. Futures price records are matched with S&P500 cash index price records. Simultaneous volatility models are specified and estimated to test trading volume to futures volatility lead/lag effects and also futures volatility to cash index volatility lead/lag effects. There is evidence that existing theoretical models of the general public trading behaviour do not explain such behaviour in these very actively traded markets. These effects can depend more on market conditions than what is suggested in theoretical models.

Simultaneous volatility transmissions and spillover effects: U.S. and Hong Kong stock and futures markets

Contemporaneous transmission effects across volatilities of the Hong Kong Stock and Index futures markets and futures volume of trade are tested by employing a structural systems approach. Competing measures of volatility spillover, constructed from the overnight U.S. S&P500 index futures, are tested and found to impact on the Hong Kong asset return volatility and volume of trade patterns. The examples utilize intra-day 15-min sampled data from this medium-sized Asia Pacific equity and derivative exchange. Both the intra- and inter-day patterns in the Hong Kong market are allowed for in the estimation process.

Realized volatility and correlation in grain futures markets: testing for spillover effects

Fluctuations in commodity prices are a major concern to many market participants. This paper uses realized volatility methods to calculate daily volatility and correlation estimates for three grain futures prices (corn, soybean, and wheat). The realized volatility estimates exhibit properties consistent with the stylized facts observed in earlier studies. According to daily realized correlations and regression coefficients, the spot returns from the three grain futures are positively related. The realized estimates are then used to evaluate the degree of volatility transmission across grain futures prices. The impulse response analysis is conducted by fitting the vector autoregressive model to realized volatility and correlation estimates, using the bootstrap method for statistical inference. The results indicate that rich dynamic interactions exist among the volatilities and correlations across the grain futures markets.

The trading dynamics of close-substitute futures markets : evidence of margin policy spillover effects

Volatility spillover is well documented among closely related securities. I investigate the relationship between margin policy and trading dynamics of the Nikkei 225 index futures markets of Osaka Securities Exchange (OSE) and Singapore Exchange (SGX). I find that OSE’s margin policy influences trading dynamics across both markets, although it is the less liquid SGX market that performs price discovery. This suggests that policy markers of close substitute markets should coordinate, or at least communicate policy intentions due to policy spillover. SGX’s market design facilitates price discovery, suggesting that a microstructure framework capable of overcoming the liquidity entry barrier is of interest to any futures exchange contemplating contract proliferation.

Solving the halloween indictor puzzle : market efficiency still reigns

Prior research by Bouman and Jacobsen (2002) document unusually high monthly returns over the period November-April for both United States (U.S.) and foreign stock markets and label this phenomenon the Halloween effect. The implication is that the Halloween effect represents an exploitable anomaly, which has negative implications for stock market efficiency. We extend this research to the S&P 500 futures contract and find no evidence of an exploitable Halloween effect over the period April 1982 through April 2003. Re-examining Bouman and Jacobsen’s empirical results for the U.S., we find that two outliers drive their results. These two outliers are associated with the “Crash” in October 1987 and collapse of the hedge fund Long-Term Capital Management in August 1998. After inserting a dummy variable to account for the impact of the two identified outliers, the Halloween effect disappears.

Stock market efficiency withstands another challenge : solving the “sell in May/buy after Halloween” puzzle

Examining the years 1970 to 1998, Bouman and Jacobsen (2002) document unusually high monthly returns during the November-April periods for both United States (U.S.) and foreign stock markets and label this phenomenon the Halloween effect. Their research suggests that the Halloween effect represents an exploitable anomaly and has negative implications for claims of stock market efficiency.

Re-examining Bouman and Jacobsen’s empirical results for the U.S. reveals that their results are driven by two outliers, the “Crash” of October 1987 and the collapse of the hedge fund Long-Term Capital Management in August 1998. After inserting a dummy variable to account for the impact of the two identified outliers, the Halloween effect becomes statistically insignificant. This anomaly is not economically exploitable for U.S. equity markets. We extend the research to the S&P 500 futures contract and find no evidence of an exploitable Halloween effect over the period April 1982-April 2003.

Optimal hedge ration with moving least squares - an empirical study using Indian single stock futures data

The use of commodity, currency and stock index futures to hedge risky exposures in the underlying assets is well documented in financial literature. However single stock futures are a relatively new addition to the family of futures and as such, academic research on its use as a hedging tool is relatively thin. In this study we have explored the efficacy of two different methodological approaches that may be applied when hedging a long position in the underlying stock with a single stock future. We use daily trading data covering years 2002 to 2007 from the Indian market, where single stock futures have been really thriving in terms of volume of trade, to extract the optimal hedge ratios using both static OLS as well as 30-day, 60-day and 90-day moving least squares. The method of moving least squares has been in use by market practitioners for some time primarily as a trend analysis and charting tool. Our results indicate that the moving least squares approach outperforms the static OLS in terms of the hedging efficiency, which has been measured by the root mean square hedging error.