Statistical and nonlinear analyses of return volatility dynamics on energy futures

Guochao Wang, Shenzhou Zheng () and Jun Wang
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Guochao Wang: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China
Shenzhou Zheng: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China
Jun Wang: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 11, 1-26

Abstract: The energy markets, as important parts of global financial markets, have been regarded as complex nonlinear systems. The researches on the return volatility of energy futures are of great significance for grasping the law of operations and measuring the risks of markets. This paper introduces two novel volatility statistics — volatility two-component range intensity (VTRI) and cumulative volatility two-component range intensity (CVTRI) into energy futures markets to investigate the volatility dynamics of eight important energy futures. These two statistics combine the volatility shortest passage time (or volatility duration) with the volatility maximum change intensity and the volatility cumulative change intensity. Then, some statistical properties including probability distribution and tail power-law distribution of VTRI series and CVTRI series for eight energy futures are studied by some statistical analyses methods. Moreover, various nonlinear analytical methods are used to explore some nonlinear behaviors including correlation behavior, similarity behavior and multifractal property of VTRI series and CVTRI series.

Keywords: Return volatility dynamics; volatility statistic; statistical properties; nonlinear behaviors; energy markets (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500840

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