We uncover momentum and reversal patterns in half-day option returns that persist for up to at least 20 business days, with economic magnitudes of 0.22% to 0.45% per half-day. Specifically, returns show strong momentum within the same period (e.g., intraday-to-intraday) but reverse sharply...

The literature on derivative pricing in illiquid markets has mostly focused on computing optimal hedging controls, but empirical microstructure studies show that large order flow generates persistent and predictable price effects. Therefore, these controls can themselves induce endogenous market...

We develop a unified cost-benefit framework that allows for a better understanding of nature conservation and climate policies under risk and uncertainty. We derive modified Hotelling rules from a social planner’s welfare optimization. They reveal four forces that jointly determine market...

Put options are known to be priced unusually high in the market, which we refer to as the overpriced put puzzle. This study proposes a quantum model (QM) that can explain such high put option prices as fair prices. Starting from a stochastic differential equation of stock returns, we convert the...

This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning. We adopted a data-driven approach to find a market-appropriate generator of the backward stochastic differential equation, which is achieved by...

The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies. These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing...

Blockchain, Adoption, Trust, TOE framework, Accounting, Multimethod researchThe Black-Scholes equation is one of the most important partial differential equations governing the value of financial derivatives in financial markets. The Black-Scholes model for pricing stock options has been applied...

This work explores the potential of different machine learning (ML) algorithms in the valuation of American options (Aos), contrasting them with the Longstaff-Schwartz (L-S) model. To carry out this research, the algorithms K-Nearest Neighbors (KNN), Random Forest (RF), Multi-Layer Perceptron...