Implied Volatility Forecasting for American Options Based on Random Forest Regressor, Linear Regression Model

Abstract

The research investigates the effectiveness of the Random Forest Model in accurately capturing the volatility of American options, a critical aspect of financial market analysis. Making use of a comprehensive dataset consisting of various parameters such as option prices, strike values, volume, and open interest, the research conducts thorough pre-processing tasks. This involves intricate procedures including feature engineering to extract meaningful predictors, handling of missing values, and ensuring uniform data standardization to facilitate model training. The study proceeds to train random forest models on the accurately processed dataset. Subsequently, the performance of these models is evaluated on a distinct test set to gauge their predictive capabilities accurately. The evaluation involves a comparative analysis between the Random Forest Model and a benchmark Linear Regression Model, employing widely accepted metrics like R^2 and MAPE. The findings underscore the outstanding performance of the Random Forest Model, showing enhanced accuracy and significantly reduced errors compared to the linear regression counterpart, which means Random Forest Model performs better. Furthermore, the study explores deeper into dissecting the strengths and weaknesses inherent in the Random Forest Model, shedding light on its potential applications and limitations in real-world financial scenes. By elucidating these aspects, the research provides valuable insights for practitioners in the field of financial trading and risk management. These findings serve as a significant contribution towards addressing the myriad challenges encountered in financial markets, empowering stakeholders with enhanced decision-making capabilities and more robust risk management strategies.