Implied Volatility, Option Valuation, and Long Straddles: A Study of Theoretical Valuation Accuracy and Volatility-Based Trading Around Biotech Catalyst Events

Abstract

This thesis examines systematic misestimation of implied volatility (IV) and whether this yields inefficiencies in option valuation, in the context of catalyst events for smaller biotech stocks listed on the NASDAQ and NYSE, considering a period from June 2017 to December 2024. Catalyst events, such as clinical trials and regulatory decisions, introduce substantial uncertainty in stock price fluctuations, with IV — a critical parameter in option valuation — often differing greatly from realized volatility (RV) around these binary events. Through a mixed-methods analytical framework, this study combines heuristic comparisons of implied versus realized volatility with an empirical assessment of theoretical valuation models’ price estimation accuracy against observed market prices (Black-Scholes, Merton’s Jump-Diffusion, Heston’s Stochastic Volatility, and a combined Stochastic Volatility Jump-Diffusion model), and econometric backtests on volatility-based option strategy profitability. This research uncovers persistent IV misestimations, emphasizing the shortcomings of select theoretical valuation models under event-driven volatility. Empirical results indicate the superiority of simpler valuation models over more complex approaches incorporating stochastic volatility, which demonstrate reduced accuracy due to reliance on multiple unobservable parameters before an option is traded. However, despite systematic IV misestimations, the volatility-based long straddle strategy emerges as loss-generating due to practical market frictions. This study fills a critical literature gap by explicitly examining IV dynamics and theoretical option valuation performance in a uniquely uncertain and underexplored market segment, offering important implications for theoretical model refinement and practical trading strategies.