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Analysis of financial time series using multiscale entropy based on skewness and kurtosis

Meng Xu and Pengjian Shang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 490, issue C, 1543-1550

Abstract: There is a great interest in studying dynamic characteristics of the financial time series of the daily stock closing price in different regions. Multi-scale entropy (MSE) is effective, mainly in quantifying the complexity of time series on different time scales. This paper applies a new method for financial stability from the perspective of MSE based on skewness and kurtosis. To better understand the superior coarse-graining method for the different kinds of stock indexes, we take into account the developmental characteristics of the three continents of Asia, North America and European stock markets. We study the volatility of different financial time series in addition to analyze the similarities and differences of coarsening time series from the perspective of skewness and kurtosis. A kind of corresponding relationship between the entropy value of stock sequences and the degree of stability of financial markets, were observed. The three stocks which have particular characteristics in the eight piece of stock sequences were discussed, finding the fact that it matches the result of applying the MSE method to showing results on a graph. A comparative study is conducted to simulate over synthetic and real world data. Results show that the modified method is more effective to the change of dynamics and has more valuable information. The result is obtained at the same time, finding the results of skewness and kurtosis discrimination is obvious, but also more stable.

Keywords: Financial time series; Multiscale entropy (MSE); Skewness; Kurtosis; Coarse-graining (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1543-1550