Improved Estimation of Implied Volatility with Stacking-Blending Ensemble Model

In this chapter, we explore the capabilities of statistical models, machine learning (ML), and neural networks to estimate the implied volatility from cross-sectional observations of the S&P 500’s option price. We introduce increasing complexity into the models, starting with multiple linear regression and progressing to the utilization of ensemble stacking methods. The results obtained at level 0 of our analysis indicate that ensemble models, particularly those of the bagging and boosting types, exhibit superior fitting compared to linear models and artificial neural networks (ANN). Furthermore, in terms of overall performance, the ensemble stacking method (blending) outperforms the models fitted at level 0. Our analysis reveals that ensemble stacking methods are the most reliable models for estimating implied volatility. These findings underscore the importance of using ensemble techniques to improve the accuracy and reliability of volatility estimations from cross-sectional data. The results presented in this chapter provide valuable information for financial analysts and researchers seeking improved methodologies for volatility estimation in the context of financial markets, with implications for risk assessment and investment decision making. It should be noted that in the blending model we incorporated conformal prediction, yielding excellent results.