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A geometric analysis of nonlinear dynamics and its application to financial time series

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  • Isao Shoji
  • Masahiro Nozawa

Abstract

A geometric method to analyze nonlinear oscillations is discussed. We consider a nonlinear oscillation modeled by a second order ordinary differential equation without specifying the function form. By transforming the differential equation into the system of first order ordinary differential equations, the trajectory is embedded in $R^3$ as a curve, and thereby the time evolution of the original state can be translated into the behavior of the curve in $R^3$, or the vector field along the curve. We analyze the vector field to investigate the dynamic properties of a nonlinear oscillation. While the function form of the model is unspecified, the vector fields and those associated quantities can be estimated by a nonparametric filtering method. We apply the proposed analysis to the time series of the Japanese stock price index. The application shows that the vector field and its derivative will be used as the tools of picking up various signals that help understanding of the dynamic properties of the stock price index.

Suggested Citation

  • Isao Shoji & Masahiro Nozawa, 2020. "A geometric analysis of nonlinear dynamics and its application to financial time series," Papers 2012.11825, arXiv.org.
  • Handle: RePEc:arx:papers:2012.11825
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    References listed on IDEAS

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    1. Fan J. & Zhang C., 2003. "A Reexamination of Diffusion Estimators With Applications to Financial Model Validation," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 118-134, January.
    2. Chiarella, Carl & Hung, Hing & T, Thuy-Duong, 2009. "The volatility structure of the fixed income market under the HJM framework: A nonlinear filtering approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2075-2088, April.
    3. Gois, Sandra R.F.S.M. & Savi, Marcelo A., 2009. "An analysis of heart rhythm dynamics using a three-coupled oscillator model," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2553-2565.
    4. Wang, Weiming & Cai, Yongli & Ding, Zuqin & Gui, Zhanji, 2018. "A stochastic differential equation SIS epidemic model incorporating Ornstein–Uhlenbeck process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 921-936.
    5. Grudziński, Krzysztof & Żebrowski, Jan J, 2004. "Modeling cardiac pacemakers with relaxation oscillators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 153-162.
    6. Beccar-Varela, Maria P. & Mariani, Maria C. & Tweneboah, Osei K. & Florescu, Ionut, 2017. "Analysis of the Lehman Brothers collapse and the Flash Crash event by applying wavelets methodologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 162-171.
    7. Che, Yanqiu & Liu, Bei & Li, Huiyan & Lu, Meili & Wang, Jiang & Wei, Xile, 2017. "Robust stabilization control of bifurcations in Hodgkin-Huxley model with aid of unscented Kalman filter," Chaos, Solitons & Fractals, Elsevier, vol. 101(C), pages 92-99.
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