Which option pricing model is noted for being computationally slower but more accurate for longer-dated options?

The Binomial Option Pricing Model is recognized for its computational accuracy, particularly for longer-dated options. This model uses a discrete time framework to create a price tree that models the potential future movements of the underlying asset's price. For options with longer maturities, the binomial approach demonstrates its strengths, as it can incorporate changes in volatility and other factors that affect option pricing more comprehensively than simpler models.

The inherent flexibility of the binomial model allows it to more accurately reflect the complexities of the option's payoff structure, considering various paths the underlying asset could take. This results in better approximation for options that are sensitive to the conditions of the underlying market over a longer time horizon.

In contrast, while models like Black-Scholes are faster and more elegant in computation, they are based on certain assumptions (e.g., constant volatility and log-normal distribution of returns) that may not hold over longer time frames, leading to inaccuracies. Similarly, while Monte Carlo Simulation provides great flexibility and can model complex scenarios, it can often require significantly more computation time, thus not being ideal in scenarios where quick approximations are necessary. The Exponential Option Pricing Model is less commonly referenced in the context of standard option pricing techniques and may not provide the comparative depth required for assessing