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Market Architecture and Nonlinear Dynamics of Australian Stock and Futures Indices

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  • Heather M. Anderson
  • Farshid Vahid

Abstract

This paper studies the All Ordinaries Index in Australia, and its futures contract known as the Share Price Index. We use a new form of smooth transition model to account for a variety of nonlinearities caused by transaction costs and other market/data imperfections, and given the recent interest in the effects of market automation on price discovery, we focus on how the nonlinear properties of the basis and returns have changed, now that floor trading in the futures contract has been replaced by electronic trading.

Suggested Citation

  • Heather M. Anderson & Farshid Vahid, 2001. "Market Architecture and Nonlinear Dynamics of Australian Stock and Futures Indices," Australian Economic Papers, Wiley Blackwell, vol. 40(4), pages 541-566, December.
    • Handle: RePEc:bla:ausecp:v:40:y:2001:i:4:p:541-566
    DOI: 10.1111/1467-8454.00141
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    References listed on IDEAS

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    1. Balke, Nathan S & Fomby, Thomas B, 1997. "Threshold Cointegration," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 627-645, August.
    2. Lin Shinn-Juh & Stevenson Maxwell, 2001. "Wavelet Analysis of the Cost-of-Carry Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(1), pages 1-17, April.
    3. Alex Frino & Terry Walter & Andrew West, 2000. "The lead–lag relationship between equities and stock index futures markets around information releases," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(5), pages 467-487, May.
    4. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    5. Martin Martens & Paul Kofman & Ton C. F. Vorst, 1998. "A threshold error-correction model for intraday futures and index returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 245-263.
    6. Anderson, Heather M, 1997. "Transaction Costs and Non-linear Adjustment towards Equilibrium in the US Treasury Bill Market," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(4), pages 465-484, November.
    7. Anderson, Heather M. & Vahid, Farshid, 1998. "Testing multiple equation systems for common nonlinear components," Journal of Econometrics, Elsevier, vol. 84(1), pages 1-36, May.
    8. Miller, Merton H & Muthuswamy, Jayaram & Whaley, Robert E, 1994. "Mean Reversion of Standard & Poor's 500 Index Basis Changes: Arbitrage-Induced or Statistical Illusion?," Journal of Finance, American Finance Association, vol. 49(2), pages 479-513, June.
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    Cited by:

    1. Canto, Bea & Kräussl, Roman, 2007. "Electronic trading systems and intraday non-linear dynamics: An examination of the FTSE 100 cash and futures returns," CFS Working Paper Series 2007/20, Center for Financial Studies (CFS).
    2. Sylwia Nowak, 2008. "How Do Public Announcements Affect The Frequency Of Trading In U.S. Airline Stocks?," CAMA Working Papers 2008-38, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Assaf, Ata, 2006. "The stochastic volatility in mean model and automation: Evidence from TSE," The Quarterly Review of Economics and Finance, Elsevier, vol. 46(2), pages 241-253, May.
    4. Gourishankar S Hiremath & Bandi Kamaiah, 2010. "Nonlinear Dependence in Stock Returns: Evidences from India," Journal of Quantitative Economics, The Indian Econometric Society, vol. 8(1), pages 69-85, January.
    5. Nowak, Sylwia & Anderson, Heather M., 2014. "How does public information affect the frequency of trading in airline stocks?," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 26-38.
    6. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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