by Alex Ng, Chia-Yi Chien, Dora Xu, George Lin, Hanxi Xiao, Irene Aldridge, Jolie An, Kexin Deng, Michael Cao, Olivia Guo, Percy Lyu, Qi Wang, Riley Burke, Ruipeng Deng, Sheng Zhang, Shunran He, Weihang Lin, Yichen Gao, Yuanyuan Xue, Zheng Li
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Suggested foundations for risk measurement and stress and financial machine learning. Specialist prerequisites vary by paper.
Represent losses as a distribution rather than one forecast.
Distinguish a loss quantile from the average loss in the tail.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Compare risk estimators and their assumptions.
Check forecasts against realised losses.
Account for dependence and overlapping observations.
Connect diversification, common risk factors, investor views, and the properties of a risk measure.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.