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Equity Valuation: Models from Leading Investment Banks by Armin Varmaz, Thorsten Poddig, Jan Viebig

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6

Monte Carlo FCFF Models

6.1 MONTE CARLO SIMULATION: THE IDEA

Economic variables are rarely constant over time. Financial analysts usually have to estimate future realizations of model inputs under uncertainty. As future realizations of economic variables – for example, future growth rates and operating margins – are uncertain, economists are well advised to simulate possible realizations of the input variables of their models to gain an understanding how the uncertain future could possibly look. Monte Carlo simulation is named after the city in Monaco which is known for its roulette tables. Despite its name, the Monte Carlo method was developed in Los Alamos where scientists like John von Neumann worked in the mid-1940s on the atomic bomb and first applied the Monte Carlo method to formulate solutions for the neutron diffusion problem and other questions of mathematical physics.1 The originator of the Monte Carlo method was Stanislaw Ulam who worked with John von Neumann at the time:

… The idea for what was later called the Monte Carlo method occurred to me when I was playing solitaire during my illness. I noticed that it may be much more practical to get an idea of the probability of the successful outcome of a solitaire game (like Canfield or some other where the skill of the player is not important) by laying down the cards, or experimenting with the process and merely noticing what proportion comes out successfully, rather than to try to compute all the combinatorial possibilities ...

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