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Inverse statistics in the foreign exchange market

Author

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  • Jensen, M.H
  • Johansen, A
  • Petroni, F
  • Simonsen, I

Abstract

We investigate intra-day foreign exchange (FX) time series using the inverse statistic analysis developed by Simonsen et al. (Eur. Phys. J. 27 (2002) 583) and Jensen et al. (Physica A 324 (2003) 338). Specifically, we study the time-averaged distributions of waiting times needed to obtain a certain increase (decrease) ρ in the price of an investment. The analysis is performed for the Deutsch Mark (DM) against the US$ for the full year of 1998, but similar results are obtained for the Japanese Yen against the US$. With high statistical significance, the presence of “resonance peaks” in the waiting time distributions is established. Such peaks are a consequence of the trading habits of the market participants as they are not present in the corresponding tick (business) waiting time distributions. Furthermore, a new stylized fact, is observed for the (normalized) waiting time distribution in the form of a power law Pdf. This result is achieved by rescaling of the physical waiting time by the corresponding tick time thereby partially removing scale-dependent features of the market activity.

Suggested Citation

  • Jensen, M.H & Johansen, A & Petroni, F & Simonsen, I, 2004. "Inverse statistics in the foreign exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(4), pages 678-684.
    • Handle: RePEc:eee:phsmap:v:340:y:2004:i:4:p:678-684
    DOI: 10.1016/j.physa.2004.05.024
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    Cited by:

    1. Guglielmo D'Amico & Filippo Petroni, 2012. "Weighted-indexed semi-Markov models for modeling financial returns," Papers 1205.2551, arXiv.org, revised Jun 2012.
    2. Michele Caraglio & Fulvio Baldovin & Attilio L. Stella, 2021. "How Fast Does the Clock of Finance Run?—A Time-Definition Enforcing Stationarity and Quantifying Overnight Duration," JRFM, MDPI, vol. 14(8), pages 1-15, August.
    3. Zou, Yongjie & Li, Honggang, 2014. "Time spans between price maxima and price minima in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 303-309.
    4. G. D'Amico & F. Petroni & F. Prattico, 2013. "Semi-Markov Models in High Frequency Finance: A Review," Papers 1312.3894, arXiv.org.
    5. Restocchi, Valerio & McGroarty, Frank & Gerding, Enrico, 2019. "The stylized facts of prediction markets: Analysis of price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 159-170.
    6. Guglielmo D'Amico & Filippo Petroni, 2013. "Multivariate high-frequency financial data via semi-Markov processes," Papers 1305.0436, arXiv.org.
    7. Zhou, Wei-Xing & Yuan, Wei-Kang, 2005. "Inverse statistics in stock markets: Universality and idiosyncracy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 433-444.

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