by Daniel Oeltz, Fabienne Schmid
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Suggested foundations for financial machine learning and options and derivatives pricing. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Build replication and risk-neutral probabilities in a tractable model.
Distinguish pricing probabilities from physical forecasts.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Connect the pricing equation, closed-form value and implied volatility.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.