Year: 2024
Type: article
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.
Source: Financial economics insights.
DOI: 10.70088/6bb8rk74
Author: Xu Zhang
Institution: Universiti Sains Malaysia
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4404727037