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False Euro (FEUR) exchange rate correlated behaviors and investment strategy

Author

Listed:
  • M. Ausloos

    (GRASP and SUPRAS, B5, Sart Tilman, 4000 Liège, Belgium)

  • K. Ivanova

    (Department of Meteorology, Pennsylvania State University, University Park, PA 16802, USA)

Abstract

We have searched for correlations and anticorrelations with respect to currencies as CHF, DKK, JPY, and USD in order to understand the EUR behavior. In order to do so we have invented a false euro (FEUR) dating back to 1993 and have derived simulated exchange rates of the FEUR. Within the Detrended Fluctuation Analysis (DFA) statistical method we have obtained the power law behavior describing the rms. deviation of the fluctuations as a function of time. We have compared the time-dependent exponent for these four exchange rates, and observe the role of the DEM, and the other currencies forming the EUR. A simple investment strategy based on the local DFA technique shows one can obtain appreciable gains, even taking into account some modest transaction fee. We compare the time dependent α exponent of the DFA for various exchange rates as in a correlation matrix for estimating respective influences.

Suggested Citation

  • M. Ausloos & K. Ivanova, 2001. "False Euro (FEUR) exchange rate correlated behaviors and investment strategy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 20(4), pages 537-541, April.
    • Handle: RePEc:spr:eurphb:v:20:y:2001:i:4:d:10.1007_s100510170235
    DOI: 10.1007/s100510170235
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    Citations

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    Cited by:

    1. K. Ivanova & M. Ausloos, 2001. "False EUR exchange rates vs. DKK, CHF, JPY and USD. What is a strong currency?," Papers cond-mat/0103033, arXiv.org.
    2. T. Di Matteo & T. Aste & M. M. Dacorogna, 2003. "Using the Scaling Analysis to Characterize Financial Markets," Papers cond-mat/0302434, arXiv.org.
    3. Boilard, J.-F. & Kanazawa, K. & Takayasu, H. & Takayasu, M., 2018. "Empirical scaling relations of market event rates in foreign currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1152-1161.
    4. Ausloos, M. & Bronlet, Ph., 2003. "Strategy for investments from Zipf law(s)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 30-37.
    5. Ausloos, Marcel, 2016. "Modelling and measuring the irrational behaviour of agents in financial markets: Discovering the psychological solitonAuthor-Name: Dhesi, Gurjeet," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 119-125.
    6. Kartono, Agus & Febriyanti, Marina & Wahyudi, Setyanto Tri & Irmansyah,, 2020. "Predicting foreign currency exchange rates using the numerical solution of the incompressible Navier–Stokes equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    7. Celeste, Valerio & Corbet, Shaen & Gurdgiev, Constantin, 2020. "Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 310-324.
    8. Nepal, Rabindra & Yadav, Miklesh Prasad & Katoch, Rupinder & Gupta, Himani & Kumar, Atul, 2024. "Co-movement between carbon emissions and forex market: A tale of COVID-19 outbreak and Russia-Ukraine invasion," Resources Policy, Elsevier, vol. 90(C).

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