How Does Monte Carlo Simulation Enhance Traditional Sensitivity Analysis?
Monte Carlo simulation enhances traditional sensitivity analysis by modeling thousands of possible outcomes based on a range of probability distributions for key uncertain variables (e.g. transaction volume, discount rate). Unlike traditional sensitivity analysis, which tests only a few discrete scenarios, Monte Carlo provides a full probability distribution of the potential intrinsic value, allowing investors to quantify the likelihood of different outcomes and the associated risk.
Glossar
Monte Carlo Simulation
Iteration ⎊ Within cryptocurrency derivatives and options trading, iteration forms the core of a Monte Carlo Simulation, representing the repeated sampling process used to approximate probability distributions and assess risk.
Sensitivity Analysis
Variance ⎊ Sensitivity analysis within cryptocurrency, options trading, and financial derivatives quantifies the impact of fluctuating inputs on model outputs, primarily focusing on price discovery and risk exposure.
Sensitivity
Delta ⎊ This fundamental measure quantifies the first-order sensitivity of a derivative's price to a marginal change in the underlying asset's price.