by Christoph Reisinger, Cornelis S. L. de Graaf, Drona Kandhai
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Suggested foundations for optimisation and numerical methods and monte carlo and numerical simulation. Specialist prerequisites vary by paper.
Read the linear algebra used to define a numerical problem.
Identify the objective, constraints and conditions for an optimum.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Understand identifiability and fitted model parameters.
Separate model error from unstable numerical computation.
Turn a discounted payoff into an estimator.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.