Evaluating an Enhanced Monte Carlo Framework for Bull Contract Valuation and Risk Assessment: A GARCH-T Empirical Study

Abstract

Traditional Monte Carlo simulations have been widely used in the field of financial derivatives pricing owing to their capacity in handling complex path dependent structures. However, they are limited in their ability to capture volatility clustering and fat-tailed distributions in financial markets. This study proposes an enhanced approach to assist in pricing derivative warrants with mandatory redemption features by integrating GARCH models and t-distribution assumptions into the Monte Carlo framework. To validate its superiority in capturing volatility clustering and fat-tailed phenomena, the results are compared against those obtained from conventional Monte Carlo simulations. Empirical results indicate that this method improves predictive accuracy and risk measurement, providing a more precise depiction of price dynamics and serving as a more reliable tool for derivative valuation and risk management. The framework contributes to the broader field of computational finance by offering a robust tool for the valuation of complex derivative products and serves as a foundational reference for future research on structured products in emerging markets.