An Empirical Method to Measure Stochasticity and Multifractality in Nonlinear Time Series

Chih-Hao Lin, Chia-Seng Chang and Sai-Ping Li

Papers from arXiv.org

Abstract: An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

Date: 2014-01
New Economics Papers: this item is included in nep-ets
References: Add references at CitEc
Citations:

Published in Phys. Rev. E88(2013) 062912

Downloads: (external link)
http://arxiv.org/pdf/1401.1292 Latest version (application/pdf)

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:arx:papers:1401.1292

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().