Cross-correlation complexity and synchronization of the financial time series on Potts dynamics

Jie Wang and Jun Wang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 541, issue C

Abstract: The Potts dynamic system is investigated to reproduce and analyze the fluctuation and noises of the financial markets in the present paper. A random financial agent-based model is developed by applying the stochastic dynamic systems to uncover the cross-correlation complexity and synchronization between different markets. In attempt to verify the rationally of the established model, the descriptive statistical analysis and cross-correlation of return behaviors between different series are exhibited by comparing with the actual market returns, SSE, SZSE, HSI, N225, GDAX and IXIC. Furthermore, the multiscale cross-sample entropy (MSCE) analysis and the multiscale generalized complexity synchronization (MGCS) analysis are employed to measure the cross-correlation between different series at multiple scales, where the MGCS method is firstly introduced to verify the synchrony behaviors between simulative series and actual series. Empirical results reveal that the feasibility and effectiveness of the proposed financial model, and exhibit the cross-correlation and synchronization behaviors among the return series from the perspective of complexity analysis.

Keywords: Potts model; Synchronization; MSCE analysis; MGCS analysis; Financial time series (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318424

DOI: 10.1016/j.physa.2019.123286

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