Reconstructing a complex financial network using compressed sensing based on low-frequency time series data

Jingjian Si, Jinsheng Zhou, Xiangyun Gao, Wang Ze, Wu Tao and Yiran Zhao

Finance Research Letters, 2022, vol. 49, issue C

Abstract: Financial time series data are often used to construct financial complex networks for studying price volatility transmission, risk diffusion and asset portfolio and so on. High frequency Network can provide more effective information for exploring network structure and more accurate research on network evolution rules. The motivation of this paper is to construct high frequency networks using low frequency data when high frequency data is unavailable, with improvement of compressed sensing method. Results show that the network reconstructed by compressed sensing is closer to the high frequency network. In conclusion, compressed sensing can be applied to solve financial practice problem.

Keywords: Compressed sensing; Complex network; Network reconstruction; Time series (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612322003221
Full text for ScienceDirect subscribers only

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:finlet:v:49:y:2022:i:c:s1544612322003221

DOI: 10.1016/j.frl.2022.103097

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().