Modified generalized multiscale sample entropy and surrogate data analysis for financial time series

2018 ◽  
Vol 92 (3) ◽  
pp. 1335-1350 ◽  
Author(s):  
Yue Wu ◽  
Pengjian Shang ◽  
Yilong Li
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Financial Time Series ◽  
Surrogate Data ◽  
Sample Entropy ◽  
Financial Time

scholarly journals Forecasting the volatility of Financial Time Series by Tree Ensembles

2019 ◽  
Vol 12 (3) ◽  
pp. 82-89
Author(s):  
O. S. Vidmant
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Capital Investment ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Time Interval ◽  
Short Term ◽  
Computing Power ◽  
Financial Time

The use of new tools for economic data analysis in the last decade has led to significant improvements in forecasting. This is due to the relevance of the question, and the development of technologies that allow implementation of more complex models without resorting to the use of significant computing power. The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. More stringent liquidity and transparency standards in relation to the financial sector also encourage participants to experiment with protective mechanisms and to create predictive algorithms that can not only reduce the losses from the volatility of financial instruments but also benefit from short-term investment manipulations. The article discusses the possibility of improving the efficiency of calculations in predicting the volatility by the models of tree ensembles using various methods of data analysis. As the key points of efficiency growth, the author studied the possibility of aggregation of financial time series data using several methods of calculation and prediction of variance: Standard, EWMA, ARCH, GARCH, and also analyzed the possibility of simplifying the calculations while reducing the correlation between the series. The author demonstrated the application of calculation methods on the basis of an array of historical price data (Open, High, Low, Close) and volume indicators (Volumes) of futures trading on the RTS index with a five-minute time interval and an annual set of historical data. The proposed method allows to reduce the cost of computing power and time for data processing in the analysis of short-term positions in the financial markets and to identify risks with a certain level of confidence probability.

The multiscale analysis between stock market time series

2015 ◽  
Vol 26 (06) ◽  
pp. 1550071 ◽  
Author(s):  
Wenbin Shi ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Financial Time ◽  
Cross Correlation Analysis

This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.

MULTISCALE ENTROPY ANALYSIS OF FINANCIAL TIME SERIES

2012 ◽  
Vol 11 (04) ◽  
pp. 1250033 ◽  
Author(s):  
JIANAN XIA ◽  
PENGJIAN SHANG
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Scale Factor ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Financial Time

The paper mainly applies the multiscale entropy (MSE) to analyze the financial time series. The MSE is used to examine the complexity of a quantified system. Based on MSE, we propose multiscale cross-sample entropy (MSCE) to analyze the complexity and correlation of two time series. By comparing with the results, we find that both results present remarkable scaling characterization and the value of each log return of financial time series decreases with a increasing scale factor. From the results of MSE, we also find that the entropy of the Europe markets is lower than that of the Asia, but higher than that of the Americas. It means the MSE can distinguish different areas markets. The results of MSCE show that financial plate have high synchrony with the plate of Electron, IT and Realty. The MSCE can distinguish the highly synchronous plates.

scholarly journals Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis

2019 ◽  
Vol 30 (5) ◽  
pp. 1407-1418 ◽  
Author(s):  
Dat Thanh Tran ◽  
Alexandros Iosifidis ◽  
Juho Kanniainen ◽  
Moncef Gabbouj
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Temporal Attention ◽  
Financial Time ◽  
Time Series Data Analysis

scholarly journals Detecting nonlinear dynamics using BDS test and surrogate data in financial time series

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Alex Neri Gutierrez ◽  
Alexis Rodriguez Carranza ◽  
Ana Gamarra Carrasco
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Financial Time Series ◽  
Surrogate Data ◽  
Financial Time ◽  
Bds Test
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