IDEAS home Printed from https://ideas.repec.org/p/arx/papers/0709.2423.html
   My bibliography  Save this paper

Effectiveness of Measures of Performance During Speculative Bubbles

Author

Listed:
  • Filippo Petroni
  • Giulia Rotundo

Abstract

Statistical analysis of financial data most focused on testing the validity of Brownian motion (Bm). Analysis performed on several time series have shown deviation from the Bm hypothesis, that is at the base of the evaluation of many financial derivatives. We inquiry in the behavior of measures of performance based on maximum drawdown movements (MDD), testing their stability when the underlying process deviates from the Bm hypothesis. In particular we consider the fractional Brownian motion (fBm), and fluctuations estimated empirically on raw market data. The case study of the rising part of speculative bubbles is reported.

Suggested Citation

  • Filippo Petroni & Giulia Rotundo, 2007. "Effectiveness of Measures of Performance During Speculative Bubbles," Papers 0709.2423, arXiv.org.
    • Handle: RePEc:arx:papers:0709.2423
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/0709.2423
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. D. Sornette, 2003. "Critical Market Crashes," Papers cond-mat/0301543, arXiv.org.
    2. Ausloos, M, 2002. "Empirical Analysis of Time Series," MPRA Paper 28700, University Library of Munich, Germany.
    3. Anders Johansen & Didier Sornette, 2000. "The Nasdaq crash of April 2000: Yet another example of log-periodicity in a speculative bubble ending in a crash," Papers cond-mat/0004263, arXiv.org, revised May 2000.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Schuhmacher, Frank & Eling, Martin, 2011. "Sufficient conditions for expected utility to imply drawdown-based performance rankings," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2311-2318, September.

    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. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Can log-periodic power law structures arise from random fluctuations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 228-250.
    2. C. Vladimir Rodríguez-Caballero & Mauricio Villanueva-Domínguez, 2022. "Predicting cryptocurrency crash dates," Empirical Economics, Springer, vol. 63(6), pages 2855-2873, December.
    3. Stephan Schwill, 2018. "Entropy Analysis of Financial Time Series," Papers 1807.09423, arXiv.org.
    4. Wosnitza, Jan Henrik & Denz, Cornelia, 2013. "Liquidity crisis detection: An application of log-periodic power law structures to default prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3666-3681.
    5. Marcel Ausloos, 2013. "Econophysics: Comments on a Few Applications, Successes, Methods and Models," IIM Kozhikode Society & Management Review, , vol. 2(2), pages 101-115, July.
    6. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Shocks in financial markets, price expectation, and damped harmonic oscillators," Papers 1103.1992, arXiv.org, revised Sep 2011.
    7. D'Arcangelis, Anna Maria & Rotundo, Giulia, 2021. "Herding in mutual funds: A complex network approach," Journal of Business Research, Elsevier, vol. 129(C), pages 679-686.
    8. 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.
    9. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.
    10. Mahla Afghahi & Farzaneh Nassirzadeh & Davood Askarany, 2024. "Exploring the impact of customer concentration on stock price crash risk," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    11. Johansen, Anders, 2003. "Characterization of large price variations in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 157-166.
    12. X. F. Jiang & T. T. Chen & B. Zheng, 2013. "Time-reversal asymmetry in financial systems," Papers 1308.0669, arXiv.org.
    13. Qian, Xi-Yuan & Song, Fu-Tie & Zhou, Wei-Xing, 2008. "Nonlinear behaviour of the Chinese SSEC index with a unit root: Evidence from threshold unit root tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 503-510.
    14. Cornelis A. Los & Rossitsa M. Yalamova, 2004. "Multi-Fractal Spectral Analysis of the 1987 Stock Market Crash," Finance 0409050, University Library of Munich, Germany.
    15. Shiryaev, Albert N. & Zhitlukhin, Mikhail N. & Ziemba, William T., 2014. "Land and stock bubbles, crashes and exit strategies in Japan circa 1990 and in 2013," LSE Research Online Documents on Economics 59288, London School of Economics and Political Science, LSE Library.
    16. Ludovic Tangpi & Shichun Wang, 2023. "Optimal Bubble Riding with Price-dependent Entry: a Mean Field Game of Controls with Common Noise," Papers 2307.11340, arXiv.org.
    17. Zhou, Wei-Xing & Sornette, Didier, 2003. "Evidence of a worldwide stock market log-periodic anti-bubble since mid-2000," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 330(3), pages 543-583.
    18. Li Lin & Didier Sornette, 2015. ""Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets," Papers 1510.08162, arXiv.org.
    19. Hüsler, A. & Sornette, D. & Hommes, C.H., 2013. "Super-exponential bubbles in lab experiments: Evidence for anchoring over-optimistic expectations on price," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 304-316.
    20. Devi, Sandhya, 2021. "Asymmetric Tsallis distributions for modeling financial market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).

    More about this item

    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:arx:papers:0709.2423. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.