by Pablo Rodriguez Manzi
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Suggested foundations for financial machine learning and volatility models and surfaces. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Follow diffusion-based models and their assumptions.
Separate conditional volatility from observed returns.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Study a correlated stochastic-variance model.
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.