by Jun Maeda
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Suggested foundations for stochastic processes and pricing and optimisation and numerical methods. 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.
Read the linear algebra used to define a numerical problem.
Identify the objective, constraints and conditions for an optimum.
Connect stochastic dynamics to no-arbitrage prices.
Translate continuous dynamics into a numerical experiment.
Understand identifiability and fitted model parameters.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.