Information supervisory model for financial risk prevention and control based on twin-SVM

Qiong Kang

International Journal of Information Technology and Management, 2022, vol. 21, issue 2/3, 311-326

Abstract: Aiming at the problems of poor effect and low accuracy of traditional financial market risk prevention and control methods, a financial risk prevention and control information monitoring model based on double support vector machine is proposed. The improved support vector machine and asymmetric COvAR data were combined with covariance operation to reduce the actual tail risk overflow. Through financial aggregation and covariance tail data of current characteristic financial system, the spillover effect of financial risk is obtained. According to the extreme value statistical analysis theorem, it is determined that the current financial risk gradually obeys the extreme value. In order to verify the effectiveness of the method, the financial data from 2012 to 2018 provided by a bank were used as experimental samples to conduct simulation experiments. Experimental data show that the proposed method has lower boundary cost and higher market arbitrage, and has a strong market applicability.

Keywords: financial risk; dataset; support vector machine; SVM. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=123408 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijitma:v:21:y:2022:i:2/3:p:311-326

Access Statistics for this article

More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().