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Asymmetric Information Arrival and the Short-Run Dynamics of Australian Dollar Volatility: a Mixture of Distributions Approach

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  • Terry Boulter
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    Abstract

    Contemporary commentators point to excess volatility within the FX market as an indicator of market inefficiency. It is thought that the excessive volatility is being driven by speculation. Policy options have emerged which focus on bounding volatility via government regulation of speculation. These options make implicit assumptions; one, that volatility is excessive and two, that it is speculation which is driving volatility. What is not sufficiently understood is the role public information arrival plays in terms of explaining returns and its volatility impact. It is the purpose of this paper to simply model Australian Dollar returns and volatility with public information arrival, which has been classified into categories so as to ascertain whether total information arrival or the arrival of specific types of information is related to changes in returns and volatility. We use an EGARCH model so as to pick up the asymmetric impacts of good and bad news. We find evidence from both a GARCH and EGARCH model that public information plays an important role in the determination of AUD returns and volatility and that good news impacts are less then negative ones. We also find that economic information in relation to full information set has a greater relationship to volatility. This has some interesting implications in terms of the volatility debate. Rather then regulating speculation, it may be more relevant to clarify information.

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    File URL: http://external-apps.qut.edu.au/business/documents/discussionPapers/2000/Boulter_73.pdf
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    Bibliographic Info

    Paper provided by School of Economics and Finance, Queensland University of Technology in its series School of Economics and Finance Discussion Papers and Working Papers Series with number 073.

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    Date of creation: 20 Jan 2000
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    Handle: RePEc:qut:dpaper:073

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    1. G. William Schwert, 1990. "Stock Volatility and the Crash of '87," NBER Working Papers 2954, National Bureau of Economic Research, Inc.
    2. Torben Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," NBER Working Papers 6961, National Bureau of Economic Research, Inc.
    3. Andrew K. Rose, 1994. "Are exchange rates macroeconomic phenomena?," Economic Review, Federal Reserve Bank of San Francisco, pages 19-30.
    4. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    5. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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    8. Bollerslev, Tim & Melvin, Michael, 1994. "Bid--ask spreads and volatility in the foreign exchange market : An empirical analysis," Journal of International Economics, Elsevier, vol. 36(3-4), pages 355-372, May.
    9. Ito, Takatoshi & Engle, Robert F. & Lin, Wen-Ling, 1992. "Where does the meteor shower come from? : The role of stochastic policy coordination," Journal of International Economics, Elsevier, vol. 32(3-4), pages 221-240, May.
    10. Stock, James H., 1987. "Measuring Business Cycle Time," Scholarly Articles 3425950, Harvard University Department of Economics.
    11. Bollerslev, Tim & Domowitz, Ian, 1993. " Trading Patterns and Prices in the Interbank Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 48(4), pages 1421-43, September.
    12. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
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    14. Stock, James H, 1987. "Measuring Business Cycle Time," Journal of Political Economy, University of Chicago Press, vol. 95(6), pages 1240-61, December.
    15. Collinge, Robert A., 1994. "Dampening exchange rate volatility: A micro alternative to macro policies," Journal of Policy Modeling, Elsevier, vol. 16(1), pages 113-118, February.
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    Cited by:
    1. Kathleen Goffey & Andrew Worthington, 2002. "Motor Vehicle Usage Patterns in Australia: A Comparative Analysis of Driver, Vehicle & Purpose Characteristics for Household & Freight Travel," School of Economics and Finance Discussion Papers and Working Papers Series 117, School of Economics and Finance, Queensland University of Technology.

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