scholarly journals Market Efficiency and Nonlinear Analysis of Soybean Futures

2021 ◽  
Vol 13 (2) ◽  
pp. 518
Author(s):  
Tao Yin ◽  
Yiming Wang
Keyword(s):  
Market Efficiency ◽  
Long Range ◽  
Price Change ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Empirical Results ◽  
Tail Distribution ◽  
Financial Anomalies ◽  
Range Correlation ◽  
Rational Investment

In this paper, the multifractal detrended fluctuation analysis (MF-DFA) method is used to identify the multifractal structure of in the Chicago Board of Trade (CBOT) soybean futures and quantitatively describe the inefficiency and nonlinearity of the market. The data is the daily price of CBOT soybean futures from 3 January 2000 to 20 December 2019, with a total of 5025 trading days. The empirical results also show that the perspective based on MF-DFA can explain the market’s nonlinear, long-range correlation, predictability and other financial anomalies. At the same time, the prediction of price change direction and risk degree of the market are further studied. It is pointed out that multifractal characteristics are generated under the joint action of fat-tail distribution and long-range correlation. Investors can make use of these market characteristics to make arbitrage possible. Finally, based on the empirical results, some policy suggestions are put forward: strengthening rational investment education, strengthening supervision, reducing information asymmetry and other measures to improve market efficiency.

scholarly journals Comparative Multifractal Detrended Fluctuation Analysis of Heavy Ion Interactions at a Few GeV to a Few Hundred GeV

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Gopa Bhoumik ◽  
Argha Deb ◽  
Swarnapratim Bhattacharyya ◽  
Dipak Ghosh
Keyword(s):  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Heavy Ion ◽  
Projectile Energy ◽  
Fluctuation Analysis ◽  
Data Set ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Range Correlation ◽  
Multifractal Property ◽  
Detrended Fluctuation

We have studied the multifractality of pion emission process in16O-AgBr interactions at 2.1 AGeV  and  60 AGeV,12C-AgBr  and  24Mg-AgBr interactions at 4.5 AGeV, and32S-AgBr interactions at 200 AGeV using Multifractal Detrended Fluctuation Analysis (MFDFA) method which is capable of extracting the actual multifractal property filtering out the average trend of fluctuation. The analysis reveals that the pseudorapidity distribution of the shower particles is multifractal in nature for all the interactions; that is, pion production mechanism has inbuilt multiscale self-similarity property. We have employed MFDFA method for randomly generated events for32S-AgBr interactions at 200 AGeV. Comparison of expt. results with those obtained from randomly generated data set reveals that the source of multifractality in our data is the presence of long range correlation. Comparing the results obtained from different interactions, it may be concluded that strength of multifractality decreases with projectile mass for the same projectile energy and for a particular projectile it increases with energy. The values of ordinary Hurst exponent suggest that there is long range correlation present in our data for all the interactions.

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.

MULTIFRACTAL STRUCTURE OF PSEUDORAPIDITY AND AZIMUTHAL DISTRIBUTIONS OF THE SHOWER PARTICLES IN Au + Au COLLISIONS AT 200 A GeV

2008 ◽  
Vol 23 (18) ◽  
pp. 2809-2816 ◽  
Author(s):  
Y. X. ZHANG ◽  
W. Y. QIAN ◽  
C. B. YANG
Keyword(s):  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Correlation Property ◽  
Fluctuation Analysis ◽  
Analysis Method ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Range Correlation ◽  
Detrended Fluctuation

This paper analyzes the long-range correlation property and the corresponding multifractal structure of the distribution of shower particles in central Au + Au collisions at 200 A GeV by using the Multifractal Detrended Fluctuation Analysis method. The result shows that the pseudorapidity and azimuthal distributions of shower particles are multifractals in those collisions.

A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syed Ali Raza ◽  
Nida Shah ◽  
Muhammad Tahir Suleman ◽  
Md Al Mamun
Keyword(s):  
Financial Crisis ◽  
Housing Market ◽  
Market Efficiency ◽  
House Price ◽  
Fluctuation Analysis ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Run ◽  
Before And After

Purpose This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period. Design/methodology/approach This study uses the MF-DFA to analyze house price fluctuations. Findings The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result. Originality/value The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

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.

Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

2018 ◽  
Vol 29 (11) ◽  
pp. 1850109 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Price Change ◽  
Wavelet Coherence ◽  
Series Data ◽  
Primary Study ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

scholarly journals Evaluation of the Performance of CMIP5 Models to Simulate Land Surface Air Temperature Based on Long-Range Correlation

2021 ◽  
Vol 9 ◽  
Author(s):  
Shanshan Zhao ◽  
Wenping He ◽  
Tianyun Dong ◽  
Jie Zhou ◽  
Xiaoqiang Xie ◽  
...  
Keyword(s):  
Long Range ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Fluctuation Analysis ◽  
Distribution Characteristics ◽  
Scaling Exponents ◽  
Zonal Distribution ◽  
Range Correlation

The daily average land surface air temperature (SAT) simulated by 8 CMIP5 models historical experiments and that from NCEP data during 1960–2005, are used to evaluate the performance of the CMIP5 model based on detrended fluctuation analysis (DFA) method. The DFA results of NCEP data show that SAT in most regions of the world exhibit long-range correlation. The scaling exponents of NCEP SAT show the zonal distribution characteristics of larg in tropics while small in medium and high latitudes. The distribution characteristics of the zonal average scaling exponents of CMCC-CMS, GFDL-ESM2G, IPSL-CM5A-MR are similar to that of NCEP data. From the DFA errors of model-simulated SAT, the performance of IPSL-CM5A-MR is the best among the 8 models throughout the year, the performance of FGOALS-g2 is good in spring and summer, GFDL-ESM2G is the best in autumn, CNRM-CM5 and CMCC-CMS is good in winter. The scaling exponents of model-simulated SAT are closer to that of NCEP data in most areas of the mid-high latitude on the northern hemisphere. However, simulations of SAT in East Asia and Central North American are generally less effective. In spring, most models have better performance in Siberian (SIB), Central Asia (CAS) and Tibetan (TIB). SAT in Northern Europe area are well simulated by most models in summer. In autumn, areas with better performance of most models are Mediterranean, SIB and TIB regions. In winter, SAT in Greenland, SIB and TIB areas are well simulated by most models. Generally speaking, the performance of CMIP5 models for SAT on global continents varies in different seasons and different regions.

scholarly journals Long-Range Temporal Correlations, Multifractality, and the Causal Relation Between Neural Inputs and Movements

2011 ◽  
Author(s):  
Jianbo Gao ◽  
Yi Zheng ◽  
Jing Hu
Keyword(s):  
Factor Analysis ◽  
Long Range ◽  
Multifractal Analysis ◽  
Causal Relation ◽  
Fano Factor ◽  
Fluctuation Analysis ◽  
Movement Trajectory ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Reaching Task ◽  
Temporal Correlations

Understanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we perform systematic statistical and information theoretical analysis of neuronal firings of 104 neurons, and employ three different types of fractal and multifractal techniques (including Fano factor analysis, multifractal detrended fluctuation analysis (MF-DFA), and wavelet multifractal analysis) to examine whether neuronal firings related to movements may have long-range temporal correlations. We find that MF-DFA and wavelet multifractal analysis (but not Fano factor analysis) clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.

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