by Ziyao Wang
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Suggested foundations for stochastic processes and pricing and financial machine learning. 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.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Connect stochastic dynamics to no-arbitrage prices.
Translate continuous dynamics into a numerical experiment.
Account for dependence and overlapping observations.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.