Fractality and Multifractality in a Stock Market's Nonstationary Financial Time Series

被引:2
|
作者
Jung, Nam [1 ]
Le, Quang Anh [1 ]
Mafwele, Biseko J. [1 ]
Lee, Hyun Min [1 ]
Chae, Seo Yoon [1 ]
Lee, Jae Woo [1 ,2 ,3 ]
机构
[1] Inha Univ, Dept Phys, Incheon 22212, South Korea
[2] Inha Univ, Inst Nat Basic Sci, Incheon 22212, South Korea
[3] Inha Univ, Inst Adv Computat Sci, Incheon 22212, South Korea
关键词
Multifractality; Financial market; Stock market; Econophysics; Detrended fluctuation analysis; CROSS-CORRELATION ANALYSIS; DETRENDED FLUCTUATION ANALYSIS; LONG-RANGE CORRELATIONS; VARYING HURST EXPONENT; MOVING AVERAGE; MULTISCALING PROPERTIES; GENERALIZED DIMENSIONS; HIERARCHICAL NETWORK; EMPIRICAL PROPERTIES; WAVELET-TRANSFORM;
D O I
10.3938/jkps.77.186
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
A financial time series, such as a stock market index, foreign exchange rate, or a commodity price, fluctuates heavily and shows scaling behaviors. Scaling and multi-scaling behaviors are measured for a nonstationary time series, such as stock market indices, high-frequency stock prices of individual stocks, or the volatility time series of a stock index. We review the fractality, multi-scaling, and multifractality of the financial time series of a stock market. We introduce a detrended fluctuation analysis of the financial time series to extract fluctuation patterns. Multifractality is measured using various methods, such as generalized Hurst exponents, the generalized partition function method, a detrended fluctuation analysis, the detrended moving average method, and a wavelet transformation.
引用
收藏
页码:186 / 196
页数:11
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