Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

Yang Yujun, Li Jianping () and Yang Yimei
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Yang Yujun: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China†School of Computer Science and Engineering, Huaihua University, Huaihua 418008, P. R. China‡Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent, Control Technology, Huaihua 418008, P. R. China
Li Jianping: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
Yang Yimei: #x2020;School of Computer Science and Engineering, Huaihua University, Huaihua 418008, P. R. China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 02, 1-19

Abstract: This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

Keywords: Rényi entropy; multiscale; MFDFA; multiproperty; financial time series; fluctuation characteristics (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117500280

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