Exploring the Synergy Between Financial Econometrics and Computational Intelligence in Risk Modeling

Abstract

Financial econometrics and computational intelligence are two key fields used independently for financial data analysis and risk modeling. Financial econometrics employs statistical and mathematical models to analyze relationships between economic and financial variables, while computational intelligence focuses on complex and nonlinear data analysis for predicting financial risks and volatility. The synergy between these two approaches can enhance the accuracy of modeling and financial decision-making. This paper aims to examine the synergy between financial econometrics and computational intelligence in risk modeling and presents successful examples of its application in finance. This study employs a descriptive and narrative review of the existing literature. Relevant academic sources related to the applications of financial econometrics and computational intelligence in predicting volatility and financial risks were reviewed. Additionally, combined models of these two fields were analyzed, identifying their synergies and evaluating the challenges involved. The results demonstrate that combining financial econometrics and computational intelligence significantly improves the accuracy of financial forecasts, reduces systemic risks, and identifies complex patterns in financial data. Combined models, especially in volatile and unstable financial markets, outperform individual approaches. The combination of financial econometrics and computational intelligence can lead to enhanced risk modeling and financial decision-making. Despite challenges such as the need for large datasets and computational complexity, this synergistic approach holds significant potential for developing more accurate predictive models and improving risk management.