Big O notation defines the upper bound of an algorithm’s performance, helping compare efficiency and scalability as input size grows, crucial for optimization.

A detailed analysis of option pricing, including the roles of put and call options, the BlackScholes model and the factors influencing the fair value of options

Explore how the Black-Scholes model's hedge errors can be mitigated using finite difference techniques and Monte Carlo simulations.

This study reveals that finite difference methods improve option pricing accuracy, combining theory and practice for enhanced risk management strategies.

Analyze option pricing methods, comparing FDM and Monte Carlo simulations, while exploring variance reduction techniques in the Black-Scholes model.

Discover theorems for reducing hedge error variance in option pricing. Explore strategies for optimal portfolio selection and enhanced risk management.

Explore innovative methods to enhance hedge error approximations in the Black-Scholes model using finite difference techniques and multivariate securities.

This research presents innovative methods to reduce hedge errors in the Black-Scholes model, enhancing the accuracy and efficiency of option pricing simulations

Finite difference methods approximate derivatives to simulate market dynamics, offering flexibility and accuracy for option pricing and hedging strategies.

This literature review examines classical and modern methods for hedge error approximation in the Black-Scholes model.

Explore how variance reduction techniques like importance sampling, antithetic variates, and control variates improve accuracy and efficiency in simulations.

Learn how the Black-Scholes model calculates European option prices using factors like volatility, strike price, and risk-free rates,.