Monte Carlo Path Simulation

Monte Carlo path simulation is a computational technique that uses repeated random sampling to determine the probability of various outcomes in a financial system. By generating thousands or millions of possible price paths for a cryptocurrency asset, analysts can estimate the value of complex, path-dependent derivatives.

This method is highly flexible, allowing for the inclusion of non-linear constraints, such as liquidation triggers or changing interest rates, which are common in decentralized finance protocols. Because it does not rely on a closed-form formula, it can handle sophisticated risk scenarios that traditional models cannot solve.

It is a cornerstone of modern risk management, providing a distribution of potential portfolio values rather than a single point estimate. This enables traders to stress-test their positions against a wide array of simulated market conditions.