Forecasting Volatility and Analyzing Importance Using Dual Machine Learning Algorithms and Elastic Net Regression

This study delves into the influence of exchange rate volatility, employing an innovative dual machine learning (DML) algorithm for causal effect estimation combined with elastic net regression. This methodology adeptly navigates the complexities of high-dimensional data and variable covariance, selecting optimal model parameters through cross-validation to mitigate overfitting risks. Furthermore, heterogeneity analysis within this study sheds light on the varied impacts of exchange adjustments across different export product types and target, offering nuanced insights into the dynamics. By blending theoretical exploration with empirical analysis, this research contributes significantly to the advancement of theory and its application in decision, providing a robust framework for addressing volatility challenges.