Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis

Ying, Yibin; Xin-tian, Zhuang; Jin, Xiu

doi:10.1016/j.physa.2009.02.026

Cited by 177 publications

(50 citation statements)

References 16 publications

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“…Using rolling window, they found that Shenzhen stock market was becoming more and more efficient by analyzing the change of Hurst exponent. Yuan et al (2009) analyzed the Shanghai stock price index daily returns using DFA method, and found there are two different types of sources for multi-fractality in time series, namely, fat-tailed probability distributions and non-linear temporal correlations. It was found that when the stock price index rises and falls sharply, a strong variability is clearly characterized by the generalized Hurst exponents.…”

Section: Literature Surveymentioning

confidence: 99%

Is Hurst Exponent Value Useful in Forecasting Financial Time Series?

Mitra

2012

ASS

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We estimated Hurst exponent of twelve stock index series from across the glove using daily values of for past ten years and found that the Hurst exponent value of the full series is around 0.50 confirming market efficiency. But the Hurst exponent value is found to vary widely when the full series is split into smaller series of 60 trading days. Later, we tried to find relationship between Hurst exponent value and profitable trading opportunity from these smaller series and found that periods displaying high Hurst exponent have potential to yield better trading profits from a moving average trading rule.

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Section: Literature Surveymentioning

confidence: 99%

Is Hurst Exponent Value Useful in Forecasting Financial Time Series?

Mitra

2012

ASS

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“…Following that, Kantelhardt et al (2002) extended DFA into multifractal detrended fluctuation analysis (MFDFA) which enables the multifractal behavior of data to be detected, and by studying their shuffled and surrogate time series and comparing them with the results of the original series, the sources of multifractality can be investigated (Jafari et al 2007; Kimiagar et al 2009;Lim et al 2007;Niu et al 2008;Pedram and Jafari 2008;Telesca et al 2004). MFDFA has been used to study time series in geophysics (Kantelhardt et al 2003;Kavasseri and Nagarajan 2005;Koscielny-Bunde et al 2006), physiology (Dutta 2010;Makowiec et al 2006Makowiec et al , 2011, financial markets (Oswiecimka et al 2005;Yuan et al 2009), and the exchange rates of currencies (Norouzzadeh and Rahmani 2006a, b;Oh et al 2012;Wang et al 2011a, b). Multifractals describe the dynamic characteristics of systems more carefully and comprehensively, and characterize their properties both locally and globally.…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis of the drought area in seven large regions of China from 1961 to 2012

Hou

Feng

Yan

et al. 2017

Meteorol Atmos Phys

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Based on a monthly drought index, the Standardized Precipitation Index (SPI), we investigate the multifractality of monthly drought areas in seven major regions in China from 1961 to 2012 using multifractal detrended fluctuation analysis. The results show that multifractality is evident in the monthly time series for all seven regions, but its strength varies between areas. From the numerical results, we further examine the stationarity and persistence of the time series in the seven drought areas. The characteristics of the variance of big and small fluctuations are also analyzed. The characteristics of multifractal spectra are used to distinguish the features of the singularity of all the data, as well as the large and small fluctuations and the spread of the changes of fractal patterns, and so on, in the monthly drought area time series for the different regions. Finally, we investigate the possible source(s) of multifractality in the drought series by random shuffling as well as surrogating the original series for each region.

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“…But as the values of the Hurst exponent in this case are not significantly far from 0.5, they might have a hint of randomness and this certainly adds to the uncertainty in the forecasting of the present three stock market indices. Study also reveals that both SENSEX and NSE data are multifractal in nature [8][9][10][11][12][13][14][15][16][17][18][19][20]. These two observations point out the possibility of multi-periodic or/and pseudo-periodic behaviour of the SEN-SEX and NIFTY indices.…”

Section: Introductionmentioning

confidence: 91%