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