Integrating Machine Learning and Traditional Models for Financial Risk Quantification

Xu Zhang

Financial Economics Insights, 2024, vol. 1, issue 1, 50-61

Abstract: This study explores the integration of machine learning techniques with traditional financial risk measurement models to enhance the accuracy and robustness of risk quantification. By employing models such as Value at Risk (VaR) and GARCH alongside machine learning algorithms like Random Forest and Neural Networks, the research demonstrates improved prediction accuracy across various market conditions. The findings highlight the advantages of an integrated approach, which not only provides a comprehensive framework for financial risk assessment but also bridges the gap between theoretical models and practical applications. This work contributes to the evolving landscape of financial risk management by offering insights into effective model integration, thereby paving the way for future research in advanced risk quantification strategies.

Keywords: financial risk; machine learning; model integration; Value at Risk (VaR); GARCH (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://soapubs.com/index.php/FEI/article/view/159/169 (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:axf:feiaaa:v:1:y:2024:i:1:p:50-61

Access Statistics for this article

More articles in Financial Economics Insights from Scientific Open Access Publishing
Bibliographic data for series maintained by Yuchi Liu ().