by Dimitrios Emmanoulopoulos, Justin Lyon, Namid R. Stillman, Ollie Olby
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Suggested foundations for stochastic processes and pricing and monte carlo and numerical simulation. Specialist prerequisites vary by paper.
Understand what it means to condition on current information.
Follow Brownian dynamics and the second-order term in Itô calculus.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Connect stochastic dynamics to no-arbitrage prices.
Translate continuous dynamics into a numerical experiment.
Turn a discounted payoff into an estimator.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.