Relevant bibliographies by topics / Stock index futures Australia

Academic literature on the topic 'Stock index futures Australia'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Stock index futures Australia"

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Cummings, James Richard, and Alex Frino. "Tax Effects on the Pricing of Australian Stock Index Futures." Australian Journal of Management 33, no. 2 (December 2008): 391–406. http://dx.doi.org/10.1177/031289620803300209.

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Frino, Alex, and Andrew West. "The Lead-Lag Relationship Between Stock Indices and Stock Index Futures Contracts: Further Australian Evidence." Abacus 35, no. 3 (October 1999): 333–41. http://dx.doi.org/10.1111/1467-6281.00049.

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Cummings, James Richard, and Alex Frino. "Index arbitrage and the pricing relationship between Australian stock index futures and their underlying shares." Accounting & Finance 51, no. 3 (August 16, 2010): 661–83. http://dx.doi.org/10.1111/j.1467-629x.2010.00365.x.

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Sim, Ah-Boon, and Ralf Zurbreugg. "Intertemporal volatility and price interactions between Australian and Japanese spot and futures stock index markets." Journal of Futures Markets 19, no. 5 (August 1999): 523–40. http://dx.doi.org/10.1002/(sici)1096-9934(199908)19:5<523::aid-fut2>3.0.co;2-6.

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Galati, Luca, Alex Frino, and Alexander Webb. "LIQUIDITY OF FUTURES MARKETS OVER THE LAST QUARTER OF A CENTURY: TECHNOLOGY & MARKET STRUCTURE VERSUS ECONOMIC INFLUENCES." Applied Finance Letters 11, no. 1 (May 24, 2022): 52–65. http://dx.doi.org/10.24135/afl.v11i1.547.

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This study examines the major technological and market forces that have acted on the liquidity of futures markets over almost the last quarter of a century – equivalent to Professor Robert Webb’s tenor as Editor-in-Chief at the Journal of Futures Markets. We examine the impact of electronic trading replacing open outcry, the impact of high-frequency trading and co-located trading, compare the liquidity impacts of these developments with the impact of major economic events, including the Global Financial Crisis and Covid-19 Pandemic. Using a stock index futures contract traded on Australian futures exchanges as an example, we find that technological advances have had a statistically significant but almost imperceptible influence on measures of liquidity of Australian futures contracts. In contrast, economic crises, and crashes such as the Global Financial Crash and the Covid-19 crash have had a massive and sustained impact on the liquidity of futures markets. Our results suggest that liquidity effects from technological innovations, while important, remain dwarfed by those from extreme outlier events.
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Vuong, Ngoc Bao, and Yoshihisa Suzuki. "Does Fear has Stronger Impact than Confidence on Stock Returns? The Case of Asia-Pacific Developed Markets." Scientific Annals of Economics and Business 67, no. 2 (2020): 157–75. http://dx.doi.org/10.47743/saeb-2020-0009.

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Employing data from Australia, Hong Kong, and Japan over the period between January 2004 to December 2017, this study investigates the relationship between investor sentiment and stock returns. We analyze two reversed sentiment indicators, namely Consumer Confidence Index (CCI) and Volatility Index (VIX), in two conversing situations: low and high sentiment. The empirical evidence suggests that sentiment has a significant link with concurrent returns, but its influence seems to wipe out quickly as the little to no return predictability is detected. More importantly, we find that “investor fear gauge” (VIX) generates a more significant contemporaneous effect on market returns than investor confidence. The impact on future returns, on the contrary, is inconclusive since low CCI and VIX dominate the opposite ones most of the time.
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Gardner, C., S. D. Frusher, R. B. Kennedy, and A. Cawthorn. "Relationship between settlement of southern rock lobster pueruli, Jasus edwardsii, and recruitment to the fishery in Tasmania, Australia." Marine and Freshwater Research 52, no. 8 (2001): 1271. http://dx.doi.org/10.1071/mf01032.

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Puerulus catches on artificial collectors were measured monthly at four sites around Tasmania from 1991 to April 2000, with the aim of predicting future changes in recruitment to the fishery. Support for the potential of catch-rate prediction in Tasmania was provided at the two sites that have overlap of several years between indices of puerulus settlement and indices of the abundance of recruits to the fishery. At Bicheno, on the northeast coast, correlations between annual puerulus index and commercial catch rates were highly significant, with a lag of 5 years (P< 0.01). Similar interannual trends in puerulus index and estimates from a stock-assessment model of the biomass of recruits to the fishery provided additional support for a link with puerulus index. A 5-fold interannual variation in puerulus index detected at Bicheno, with a peak in 1995, was preceded by 3 years of relatively low puerulus catch. The peak in puerulus index appears to lead to an increase in the abundance of sublegal males in research sampling 3 years later. Correlation between annual measures of puerulus index and catch rate also appeared significant at King Island (P= 0.06) although data at this site had less contrast.
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Brennan, Michael J., and Eduardo S. Schwartz. "Arbitrage in Stock Index Futures." Journal of Business 63, S1 (January 1990): S7. http://dx.doi.org/10.1086/296491.

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Liu, Yixuan. "Correlation Analysis between Stock Index and Spot Index." Frontiers in Business, Economics and Management 5, no. 1 (September 1, 2022): 94–97. http://dx.doi.org/10.54097/fbem.v5i1.1472.

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Since the reform and opening up, with the continuous strengthening of China's economic strength, the continuous improvement of the financial market, and the increasing appeal of investors to avoid market risks. Since China's first stock index futures contract was listed and traded, the research on the relationship between this financial derivative and the corresponding spot market has been a hot spot in academic circles. It refers to the hot events triggered by the futures market, and it has also been widely concerned by the media, the government and the people. China's financial derivatives market is still in its infancy, the market system is not yet perfect, and the professional quality of institutions and individuals engaged in trading still lags far behind that of investors in western mature markets. How to improve the futures index market mechanism and maintain the stable operation of the market deserves the government's in-depth consideration. China's financial futures market supervision authorities should continue to vigorously promote the improvement and development of China's stock index futures market. Specific measures include: speeding up the introduction of institutional investors to participate in futures trading and cultivating mature market trading subjects; Establish an investor suitability management system to lower the entry threshold of the stock index futures market; Perfecting the risk management system of the futures market will provide the necessary risk barrier for the healthy and stable development of China's financial market.
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Lu, Xunfa, Kai Liu, Kin Keung Lai, and Hairong Cui. "The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test." Entropy 23, no. 9 (September 6, 2021): 1172. http://dx.doi.org/10.3390/e23091172.

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Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between WTI crude oil futures market and Chinese stock index futures market or US stock index futures market. The relationship in mean between WTI crude oil futures prices and S&P 500 stock index futures, or CSI 300 stock index futures is weakening. Meanwhile, there is a decreasing dynamic conditional correlation between the WTI crude oil futures market and Chinese stock index futures market or US stock index futures market after the breakpoint in the price series. The Chinese stock index futures are less affected by short-term fluctuations in crude oil futures returns than US stock index futures.
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Dissertations / Theses on the topic "Stock index futures Australia"

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Tilakaratne, Chandima University of Ballarat. "Stock market predictions based on quantified intermarket influences." University of Ballarat, 2007. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12798.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Doctor of Philosophy
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Tilakaratne, Chandima. "Stock market predictions based on quantified intermarket influences." University of Ballarat, 2007. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15394.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Doctor of Philosophy
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Tilakaratne, Chandima University of Ballarat. "A neural network approach for predicting the direction of the Australian stock market index." University of Ballarat, 2004. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12804.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Master of Information Technology by Research
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4

Tilakaratne, Chandima. "A neural network approach for predicting the direction of the Australian stock market index." University of Ballarat, 2004. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15397.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Master of Information Technology by Research
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5

Wan, Hon-kuen Francis. "The Hong Kong stock index futures market /." [Hong Kong] : University of Hong Kong, 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12334868.

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Wan, Hon-kuen Francis, and 溫漢權. "The Hong Kong stock index futures market." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31263926.

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Kalogeropoulou, Joanna. "Arbitrage in the FTSE 100 index futures." Thesis, Brunel University, 1998. http://bura.brunel.ac.uk/handle/2438/5396.

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This thesis presents five empirical papers investigating the issue of arbitrage trading of the FTSE 100 stock index futures. The first paper explores the effects of nonsynchronous trading on the spot index and develops a new technique as well as improving current methodologies for removing them. Studies in U. S. have shown that if the problem of non-synchronous trading is severe, the reported spot index is not reliable affecting the correct pricing of futures contracts. The second paper investigates the elasticity of supply of arbitrage in the futures market and the ability of the spot and the futures markets to respond to new information. It shows that arbitrage trading is initiated when spot prices largely drift apart from the futures prices. In addition, the futures prices tend to uncover new information before the spot prices, although this relationship is not stable over time. The analysis incorporates all possible channels of information to the -markets, which previous research fails to consider. The third paper analyses the behaviour of the deviation of the actual futures price from its theoretical value. Although this deviation is seen to have decreased its size over the years, it is still significant and persistent. Furthermore, it cannot be explained by the tax-timing option on pricing the futures or the effects of nonsynchronous trading. The fourth paper examines the presence, size and frequency of the profitability of the observed arbitrage opportunities by applying different transactions costs bounds to account for different classes of traders. After applying trading simulations arbitrage profitability is found to be frequent and significant, despite the fact that its size has decreased over the years. Finally, the thesis concludes with the fifth empirical paper which investigates the impact of futures trading on the spot and futures market volatility. It finds that arbitrage increases spot and futures price volatility but a volatile market brings the two markets closer on the whole, the thesis shows that although profitable arbitrage opportunities are not present in the long-run, they are not quickly removed in the short-run, allowing the spot and futures prices to drift apart.
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Holmes, Richard Roland. "The economics of stock index futures : theory and evidence." Thesis, Brunel University, 1993. http://bura.brunel.ac.uk/handle/2438/5391.

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This thesis aims to provide detailed investigation into the role and functioning of the FTSE-100 stock index futures contract, by examining four interrelated issues. Chapter 1 reviews the literature, demonstrating that stock index futures can increase investor utility by offering hedging and investment opportunities. Further, the price discovery role of futures is discussed. Chapter 2 investigates the risk return relationship for the FTSE-100 contract within a CAPM framework. While CAPM adequately explains returns prior to October 1987, post-crash the contract is riskier and excess returns and a day of the week effect are evident. Chapter 3 examines the impact of futures on the underlying spot market using GARCH, which allows examination of the link between information and volatility. While spot prices are more volatile post-futures, this is due to more rapid impounding of information. The view that futures destabilise spot markets and should be subject to further regulation is questioned. Chapter 4 examines futures market efficiency using the Johansen cointegration procedure and variance bounds tests which are developed here. Results suggest futures prices provide unbiased predictions of future spot prices for 1, 2 and 4 months prior to maturity of the contract. For 3, 5 and 6 months prior to maturity the unbiasedness hypothesis does not hold. Chapter 5 discusses the major role of futures; hedging. Hedge ratios and hedging effectiveness are examined in relation to duration and expiration effects. Hedge ratio stability is also examined. Finally, hedging strategies based on historical information are examined. Results show there are duration and expiration effect, hedge ratios are stationary and using historical information does not greatly reduce hedging effectiveness. The FTSE-100 contract is shown to be a highly effective means by which to hedge risk. Chapter 6 provides a summary and concluding remarks concerning the relevance of the research carried out here.
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Luo, Wu-chang. "The predictability of stock index futures markets in Taiwan." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423215.

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Yadav, Pradeep Kumar. "Studies on stock index futures pricing : a UK perspective." Thesis, University of Strathclyde, 1992. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21498.

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There has been considerable interest among market participants, market regulators and academics in the pricing of stock index futures contracts. Academic research in this area has been motivated by several considerations. First, the utility of these contracts for risk allocation and price discovery depends on the efficiency with which they are priced relative to the underlying index. Second, it has been widely believed that they have adverse impact on price dynamics in the stock market. Third, and most important, stock index futures offer the possibility of directly studying the economics of arbitrage in the context of market microstructure. This dissertation extends the theoretical framework on stock index futures pricing in two directions. First, within the static cost of carry framework, it generalises the forward pricing formula by allowing for cash market settlement procedures. Second, it shows that in the presence of arbitrage related transaction costs, the time series of stock index futures "mispricing" can be modelled as a threshold autoregressive (TAR) process, a piecewise linear autoregressive process in which the process parameters are path dependent. The TAR model is potentially attractive for many financial applications and this dissertation appears to be the first use of the TAR model in finance. This dissertation also provides substantial and significant new empirical evidence relevant to the theoretical issues involved. Inter-alia, it analyses several important aspects not adequately examined in past research, and it utilises the unique microstructural features of the London stock market to explore several major theoretical issues. The empirical analysis is based mainly on about four years of "time and sales" transactions data from the London International Financial Futures Exchange together with synchronous hourly cash index data.
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Books on the topic "Stock index futures Australia"

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Stock index options and futures. London: McGraw-Hill, 1992.

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Merrick, John J. Portfolio insurance with stock index futures. [Philadelphia]: Federal Reserve Bank of Philadelphia, 1987.

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Regulierungen auf Stock Index Futures Märkten. Frankfurt am Main: Lang, 1993.

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Sutcliffe, Charles M. S. Stock index futures: Theories and international evidence. London: Chapman & Hall, 1993.

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Merrick, John J. Hedging with mispriced futures. [Philadelphia]: Federal Reserve Bank of Philadelphia, 1987.

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How to make money in stock index futures. New York: McGraw-Hill, 1985.

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Exchange, London International Financial Futures. FT-SE 100 Stock Index futures and options. London: LIFFE, 1986.

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Goetzmann, William N. Index funds and stock market growth. Cambridge, MA: National Bureau of Economic Research, 1999.

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Securities and Exchange Board of India. Prof. J. R. Varma Group on Risk Containment Measures in the Indian Stock Index Futures Market. Report of the Prof. J.R. Varma Group on Risk Containment Measures in the Indian Stock Index Futures Market. [S.l.]: SEBI, 1998.

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Byrne, B. Thomas. The stock index futures market: A trader's insights and strategies. Chicago, Ill: Probus Pub. Co., 1987.

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Book chapters on the topic "Stock index futures Australia"

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Labuszewski, John W., and Brett Vietmeier. "Stock Index Futures Fundamentals." In The CME Group Risk Management Handbook, 137–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118266564.ch5.

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Salinger, Michael A. "Stock Market Margin Requirements and Volatility: Implications for Regulation of Stock Index Futures." In Regulatory Reform of Stock and Futures Markets, 23–40. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2193-1_4.

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Liu, Xiaoxue, and Cuiping Dong. "The Empirical Study on the Intraday Interaction Relationship between Stock Index Futures and Stock Index." In Communications in Computer and Information Science, 407–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23065-3_59.

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Yi, Qianwei, and Ziying Liang. "The Information Efficiency of Stock Index Futures in China." In Lecture Notes in Electrical Engineering, 1041–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40081-0_88.

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Wang, Yuanzheng, Yajing Xu, and Peng Liu. "The Co-integration and Causality Relationship Research of Stock Index Futures IF1006 and HS300 Stock Index." In Communications in Computer and Information Science, 541–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23065-3_78.

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Markose, Sheri, Edward Tsang, and Hakan Er. "Evolutionary Decision Trees for Stock Index Options and Futures Arbitrage." In Genetic Algorithms and Genetic Programming in Computational Finance, 281–308. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0835-9_14.

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Wu, Sen, Bin Chen, and Deying Xiong. "Topological Properties of Stock Index Futures Based on Network Approach." In LISS 2013, 135–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40660-7_19.

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Lei, Tao, Cheng Zeng, and Bin Li. "A Study on the Stock Index Futures Arbitraging and Hedging Model." In Advances in Intelligent and Soft Computing, 843–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27866-2_103.

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Zhou, Zhou. "Development and Problems of Stock Index Futures and Margin Trading and Short Selling in China." In The Chinese Stock Market Volume I, 313–67. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137391100_5.

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Floros, Christas, and Dimitrios V. Vougas. "Index Futures Trading, Information and Stock Market Volatility: The Case of Greece." In Derivatives and Hedge Funds, 118–39. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137554178_6.

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Conference papers on the topic "Stock index futures Australia"

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Li, Shiwei. "Research on the stock index futures arbitrage: Price boundary and ETF tracking of stock index futures." In 2010 2nd IEEE International Conference on Information Management and Engineering. IEEE, 2010. http://dx.doi.org/10.1109/icime.2010.5478052.

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Zhang Jianfeng, Zhang Li, and Chang Qing. "The effect of stock index futures to stock market volatility." In 2011 International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM). IEEE, 2011. http://dx.doi.org/10.1109/icssem.2011.6081227.

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Fang, Zheng, and Rong Da Chen. "The Influence from Stock Index Futures to Stock Market Volatility." In 2011 Fourth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2011. http://dx.doi.org/10.1109/bife.2011.132.

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Zhou, Bei, and Chong Wu. "The dynamic relationships between stock index futures and stock index markets: Evidence from China." In 2014 International Conference on Management Science and Engineering (ICMSE). IEEE, 2014. http://dx.doi.org/10.1109/icmse.2014.6930401.

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Zhu Jinyu and Diao Jiewen. "Price discovery in index futures and spot market in China: Based on HS300 stock index futures." In 2011 International Conference on Business Management and Electronic Information (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/icbmei.2011.5914877.

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Guo, Xicai. "Index Futures and Spot Index Volatility: Evidence from China Stock Market." In 2011 Fourth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2011. http://dx.doi.org/10.1109/bife.2011.69.

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Chen, Shao-gang, and Xing Gu. "Research on overconfidence influence of stock index futures hedge." In 2014 International Conference on Management Science and Engineering (ICMSE). IEEE, 2014. http://dx.doi.org/10.1109/icmse.2014.6930379.

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Xiang-hong, Zhu. "Study on the risk forecast of stock index futures." In 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE). IEEE, 2010. http://dx.doi.org/10.1109/icife.2010.5609411.

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Dai, Hezhong, Yichi Zhang, and Dapeng Wang. "Price Prediction of Stock Index Futures Based on SVM." In 2011 Fourth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2011. http://dx.doi.org/10.1109/bife.2011.96.

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Ruan, Luning, and Zhen Liu. "The study on the linkages between the stock index futures market and the stock index market: The empirical analysis of the HS300 index futures market." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5882508.

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Reports on the topic "Stock index futures Australia"

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Ahn, Dong-Hyun, Jacob Boudoukh, Matthew Richardson, and Robert Whitelaw. Behavioralize This! International Evidence on Autocorrelation Patterns of Stock Index and Futures Returns. Cambridge, MA: National Bureau of Economic Research, July 1999. http://dx.doi.org/10.3386/w7214.

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