Multivariate multiscale distribution entropy of financial time series

Yali Zhang and Pengjian Shang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 515, issue C, 72-80

Abstract: The complexity of time series has become necessary to understand the dynamics of the control system. In this paper, the multivariate multiscale distribution entropy (MMSDE) is introduced as a new method to assess the complexity of dynamical systems such as financial systems, physiological systems, etc. Distribution entropy (DE) takes full advantage of the information hidden in the state–space by the estimation of the probability density of distances among vectors. Based on this, MMSDE can quantify the complexity of multivariate time series from multiple time scales. We test the performance of this method with simulated data. Results show that MMSDE has less dependence on parameters and the test of short time series is very effective. As for the real data, we explore the high dimensional series that are composed of opening price, closing price, volume of stocks data including US stock indices and Chinese stock indices. MMSDE can quantify the change in the complexity of the stock market data. In addition, we get richer information from MMSDE and gain some features about the difference between the U.S. and Chinese stock indexes.

Keywords: Multivariate multiscale distribution entropy; Simulated data; Financial time series; Complexity (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437118313104
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:515:y:2019:i:c:p:72-80

DOI: 10.1016/j.physa.2018.09.180

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().