A computational method that uses random sampling to model complex financial scenarios and estimate outcomes by running thousands or millions of possible scenarios. It's particularly useful for pricing exotic derivatives and assessing portfolio risks under uncertainty.
Named after the Monte Carlo casino in Monaco, referencing the random nature of gambling. The method was developed during World War II for nuclear weapons research and adapted to finance in the 1970s as computing power increased, allowing complex probabilistic modeling.
Monte Carlo simulation is like having a time machine that lets you live through thousands of possible futures in seconds - each run creates a different random path for market variables, and the average result gives you the answer! Investment banks use these simulations to stress-test portfolios against scenarios like 'what if we had another 2008 crisis but with today's interest rates?'
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