IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i1p209-216.html

The level crossing and inverse statistic analysis of German stock market index (DAX) and daily oil price time series

Author

Listed:
  • Shayeganfar, F.
  • Hölling, M.
  • Peinke, J.
  • Reza Rahimi Tabar, M.

Abstract

The level crossing and inverse statistics analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, να+, where Tα=1/να+ is the average waiting time for observing the level α again. We estimate the probability P(K,α), which provides us the probability of observing K times of the level α with positive slope, in time scale Tα. For analyzed time series, we found that maximum K is about ≈6. We show that by using the level crossing analysis one can estimate how the DAX and oil time series will develop. We carry out the same analysis for the increments of DAX and oil price log-returns (which is known as inverse statistics), and provide the distribution of waiting times to observe some level for the increments.

Suggested Citation

  • Shayeganfar, F. & Hölling, M. & Peinke, J. & Reza Rahimi Tabar, M., 2012. "The level crossing and inverse statistic analysis of German stock market index (DAX) and daily oil price time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 209-216.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:1:p:209-216
    DOI: 10.1016/j.physa.2011.07.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111005863
    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/j.physa.2011.07.037?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    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. Giraitis, Liudas & Leipus, Remigijus & Robinson, Peter M. & Surgailis, Donatas, 2004. "LARCH, leverage, and long memory," LSE Research Online Documents on Economics 294, London School of Economics and Political Science, LSE Library.
    2. Aggarwal, Raj & Mougoue, Mbodja, 1998. "Common Stochastic Trends among Asian Currencies: Evidence for Japan, ASEANs, and the Asian Tigers," Review of Quantitative Finance and Accounting, Springer, vol. 10(2), pages 193-206, March.
    3. Yao, Juan & Gao, Jiti & Alles, Lakshman, 2005. "Dynamic investigation into the predictability of Australian industrial stock returns: Using financial and economic information," Pacific-Basin Finance Journal, Elsevier, vol. 13(2), pages 225-245, March.
    4. Xue-Zhong He, 2003. "Asset Pricing, Volatility and Market Behaviour: A Market Fraction Approach," Research Paper Series 95, Quantitative Finance Research Centre, University of Technology, Sydney.
    5. Alessio Emanuele Biondo, 2018. "Order book microstructure and policies for financial stability," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 35(1), pages 196-218, March.
    6. Wei Deng & Jun Wang, 2014. "Nonlinear Behaviors of Tail Dependence and Cross‐Correlation of Financial Time Series Model," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    7. Luis A. N. Amaral & P. Gopikrishnan & Kaushik Matia & Vasiliki Plerou & H. E. Stanley, 2001. "Application of statistical physics methods and conceptsto the study of science & technology systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 9-36, April.
    8. Derksen, M. & Kleijn, B. & de Vilder, R., 2022. "Heavy tailed distributions in closing auctions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    9. Worthington, Andrew & Kay-Spratley, Adam & Higgs, Helen, 2005. "Transmission of prices and price volatility in Australian electricity spot markets: a multivariate GARCH analysis," Energy Economics, Elsevier, vol. 27(2), pages 337-350, March.
    10. Zouhaier Dhifaoui, 2022. "Determinism and Non-linear Behaviour of Log-return and Conditional Volatility: Empirical Analysis for 26 Stock Markets," South Asian Journal of Macroeconomics and Public Finance, , vol. 11(1), pages 69-94, June.
    11. Downing, Chris & Oliner, Stephen, 2007. "The term structure of commercial paper rates," Journal of Financial Economics, Elsevier, vol. 83(1), pages 59-86, January.
    12. Cotter, John, 2001. "Margin exceedences for European stock index futures using extreme value theory," Journal of Banking & Finance, Elsevier, vol. 25(8), pages 1475-1502, August.
    13. Thomas Theobald, 2015. "Agent-based risk management – a regulatory approach to financial markets," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 42(5), pages 780-820, October.
    14. Youwei Li & Xue-Zhong He, 2005. "Long Memory, Heterogeneity, and Trend Chasing," Computing in Economics and Finance 2005 113, Society for Computational Economics.
    15. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    16. Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.
    17. Y. Malevergne & D. Sornette, 2003. "Testing the Gaussian copula hypothesis for financial assets dependences," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 231-250.
    18. Kumar, A. & Wyłomańska, A. & Połoczański, R. & Sundar, S., 2017. "Fractional Brownian motion time-changed by gamma and inverse gamma process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 648-667.
    19. Ivan Jericevich & Murray McKechnie & Tim Gebbie, 2021. "Calibrating an adaptive Farmer-Joshi agent-based model for financial markets," Papers 2104.09863, arXiv.org.
    20. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:391:y:2012:i:1:p:209-216. 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.