by Matthew Lorig
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Suggested foundations for optimisation and numerical methods and stochastic processes and pricing. 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 what it means to condition on current information.
Follow Brownian dynamics and the second-order term in Itô calculus.
Understand identifiability and fitted model parameters.
Separate model error from unstable numerical computation.
Connect stochastic dynamics to no-arbitrage prices.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.