Multifractal detrended fluctuation analysis: Practical applications to financial time series

2016 ◽  
Vol 126 ◽  
pp. 63-88 ◽  
Author(s):  
James R. Thompson ◽  
James R. Wilson
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Practical Applications ◽  
Financial Time ◽  
Detrended Fluctuation

scholarly journals Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1157
Author(s):  
Faheem Aslam ◽  
Saima Latif ◽  
Paulo Ferreira
Keyword(s):  
Time Series ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

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

2017 ◽  
Vol 28 (02) ◽  
pp. 1750028 ◽  
Author(s):  
Yang Yujun ◽  
Li Jianping ◽  
Yang Yimei
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Stock Indices ◽  
Generalized Hurst Exponent ◽  
Detrended Fluctuation

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.

Multifractal detrended fluctuation analysis of SENSEX fluctuation in the Indian stock market

2010 ◽  
Vol 88 (8) ◽  
pp. 545-551 ◽  
Author(s):  
Srimonti Dutta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Detrended Fluctuation Analysis ◽  
Nonstationary Time Series ◽  
Anomalous Behaviour ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Indian Stock Market ◽  
Detrended Fluctuation

The fluctuation of SENSEX in the Indian stock market for the period Jan 2003–Dec 2009 is studied using the multifractal detrended fluctuation analysis (MFDFA) approach. The effect of the fall in the stock market in 2008 is also investigated. The data exhibits that the nonstationary time series of SENSEX fluctuations are multifractal in nature. An increase in the degree of multifractality prior to the anomalous behaviour in the SENSEX values is also observed. The increase in the degree of correlation for the period 2007–2009 is also responsible for the meteoric rise and the catastrophic fall in the values of SENSEX.

MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS OF ELECTRIC LOAD SERIES

Fractals ◽  
2015 ◽  
Vol 23 (02) ◽  
pp. 1550010 ◽  
Author(s):  
XIAOHUI YUAN ◽  
BIN JI ◽  
YANBIN YUAN ◽  
YUEHUA HUANG ◽  
XIANSHAN LI ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Quadratic Function ◽  
Fluctuation Analysis ◽  
Electric Load ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Range Correlation ◽  
Detrended Fluctuation ◽  
Multifractal Nature

Multifractal detrended fluctuation analysis (MF-DFA) method is applied to analyze the daily electric load time series. The results of the MF-DFA show that there are three crossover timescales at seven days, 15 days and 365 days approximately in the fluctuation function. Also we find that these fluctuations have multifractal nature with long range correlation behavior. The multifractal singularity spectrum of the daily electric load series has been fitted by the quadratic function model. Comparing the MF-DFA results of the original load series with those of shuffled and surrogate series, it concludes that the multifractal characteristics of the daily electric load time series is due to both broadness of the probability density function and long-range correlation, and the long-range correlation is dominant.

scholarly journals A probe into the Multifractal Behaviour of Total Ozone Time Series through Detrended Fluctuation Analysis

2021 ◽  
Author(s):  
Sombit Chakraborty ◽  
Surajit Chattopadhyay
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Total Ozone ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Order Polynomial ◽  
Local Trend ◽  
Detrended Fluctuation ◽  
Daily Total

Abstract The present study reports a multifractal detrended fluctuation analysis of total ozone time series. Considering daily total ozone concentration (TOC) data ranging from 2015 to 2019, we have created a new profile by subtracting the trend. Subsequently we have divided the profile \({X}_{i}\) into non intersecting segments of equal time scale varying from 25 to 30. Fitting a second order polynomial, we have eliminated the local trend from each segment and thereafter we have computed the detrended variance. Finally the multifractal behaviour has been identified and the singularity spectra has helped us in obtaining the generalised Hurst exponent which in this case has come out to be greater than 0.5.

Multifractal detrended fluctuation analysis of soil radon (222Rn) and thoron (220Rn) time series

2021 ◽  
Vol 328 (1) ◽  
pp. 425-434
Author(s):  
Muhammad Rafique ◽  
Javid Iqbal ◽  
Kashif Javed Lone ◽  
Kimberlee Jane Kearfott ◽  
Saeed Ur Rahman ◽  
...  
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Soil Radon ◽  
Detrended Fluctuation

scholarly journals Target Detection within Sea Clutter Based on Multifractal Detrended Fluctuation Analysis

2012 ◽  
Vol 4 ◽  
pp. 259-262 ◽  
Author(s):  
Zhan Xu ◽  
Jian Wei Wan ◽  
Gang Li ◽  
Fang Su
Keyword(s):  
Time Series ◽  
Target Detection ◽  
Detrended Fluctuation Analysis ◽  
Hurst Parameter ◽  
Fluctuation Analysis ◽  
Sea Clutter ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Potential Applications ◽  
Detrended Fluctuation ◽  
Better Than

We present the multifractal detrended fluctuation analysis (MFDFA) for target detection within sea clutter. The multifractal character of the sea clutter time series is discussed. The great hurst parameter differences between sea clutter and target by the detrended fluctuation analysis are available. Experimental results of IPIX datasets show that the proposed method performs better than that based on fluctuation analysis (FA). This type of analysis is promising an efficient framework for analysis of sea radar signals with several potential applications.

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