IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v288y2000i1p444-450.html
   My bibliography  Save this article

Modelling high-frequency economic time series

Author

Listed:
  • Tang, Lei-Han
  • Huang, Zhi-Feng

Abstract

The minute-by-minute move of the Hang Seng index (HSI) data over a 4-yr period is analysed and shown to possess similar statistical features as those of other markets. Based on a mathematical theorem (Pope, Ching, Phys. Fluids A 5 (1993) 1529), we derive an analytic form for the probability distribution function (PDF) of index moves from fitted functional forms of certain conditional averages of the time series. Furthermore, following a recent work by Stolovitzky and Ching (Phys. Lett. A 255 (1999) 11), we show that the observed PDF can be reproduced by a Langevin process with a move-dependent noise amplitude. The form of the Langevin equation can be determined directly from the market data.

Suggested Citation

  • Tang, Lei-Han & Huang, Zhi-Feng, 2000. "Modelling high-frequency economic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 288(1), pages 444-450.
    • Handle: RePEc:eee:phsmap:v:288:y:2000:i:1:p:444-450
    DOI: 10.1016/S0378-4371(00)00442-8
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437100004428
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/S0378-4371(00)00442-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Engle, Robert F. (ed.), 1995. "ARCH: Selected Readings," OUP Catalogue, Oxford University Press, number 9780198774327.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kian-Ping Lim & Melvin J. Hinich & Venus Khim-Sen Liew, 2005. "Statistical Inadequacy of GARCH Models for Asian Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 4(3), pages 263-279, December.
    2. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    3. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    4. J. James Reade & Ulrich Volz, 2009. "Too Much to Lose, or More to Gain? Should Sweden Join the Euro?," Economics Series Working Papers 442, University of Oxford, Department of Economics.
    5. Frank J. Fabozzi & Radu Tunaru & Tony Wu, 2004. "Modeling Volatility for the Chinese Equity Markets," Annals of Economics and Finance, Society for AEF, vol. 5(1), pages 79-92, May.
    6. Zura Kakushadze, 2016. "Volatility Smile as Relativistic Effect," Papers 1610.02456, arXiv.org, revised Feb 2017.
    7. Yanan Li & David E. Giles, 2015. "Modelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 20(2), pages 155-177, March.
    8. Petra Posedel, 2006. "Analysis of the Exchange Rate and Pricing Foreign Currency Options on the Croatian Market: the NGARCH Model as an Alternative to the Black-Scholes Model," Financial Theory and Practice, Institute of Public Finance, vol. 30(4), pages 347-368.
    9. Siem Jan Koopman & Eugenie Hol Uspensky, 2002. "The stochastic volatility in mean model: empirical evidence from international stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 667-689.
    10. Martha Misas & Maria Teresa Ramirez, 2004. "Long-run income and price elasticities of demand for Colombian nontraditional exports: a multivariate cointegration framework," Applied Economics, Taylor & Francis Journals, vol. 36(9), pages 931-938.
    11. Thomas C. Chiang & Jiandong Li, 2012. "Stock Returns and Risk: Evidence from Quantile," JRFM, MDPI, vol. 5(1), pages 1-39, December.
    12. Rama Cont & Jean-Philippe Bouchaud, 1997. "Herd behavior and aggregate fluctuations in financial markets," Science & Finance (CFM) working paper archive 500028, Science & Finance, Capital Fund Management.
    13. Tommaso Proietti, 2016. "Component-wise Representations of Long-memory Models and Volatility Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 668-692.
    14. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.
    15. Yiwen Cui & Lei Li & Zijie Tang, 2021. "Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(4), pages 322-336, April.
    16. Chandra, S. Ajay, 2009. "Testing the equality of error distributions from k independent GARCH models," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1245-1260, July.
    17. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    18. Payam MOHAMMAD ALIHA & Tamat SARMIDI & Fathin FAIZAH SAID, 2018. "Investigating The Impact Of Financial Innovation On The Volatility Of The Demand For Money In The United Stated In The Context Of An Arch/Garch Model," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 19-26, June.
    19. Teräsvirta, Timo, 2006. "An introduction to univariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 646, Stockholm School of Economics.
    20. Willy Alanya & Gabriel Rodríguez, 2019. "Asymmetries in Volatility: An Empirical Study for the Peruvian Stock and Forex Markets," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-18, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:288:y:2000:i:1:p:444-450. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.