by Stefan Weber, Sören Bettels
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Suggested foundations for monte carlo and numerical simulation and financial machine learning. Specialist prerequisites vary by paper.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Turn a discounted payoff into an estimator.
Study dependence and alternatives to independent sampling.
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.