by John Ery, Loris Michel
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Suggested foundations for options and derivatives pricing and financial machine learning. Specialist prerequisites vary by paper.
Build replication and risk-neutral probabilities in a tractable model.
Distinguish pricing probabilities from physical forecasts.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Connect the pricing equation, closed-form value and implied volatility.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
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.