Body of Knowledge

Data Science for Finance

About the ARPM Lab
About quantitative finance: P and Q
Notation
The "Checklist": executive summary
Elliptical distributions
Scenario-probability distributions
Exponential family distributions
Mixture distributions
Univariate results
Definition and properties of copulas
Special classes of copulas
Implementation
Expectation and variance
Expectation and covariance
L2 geometry
Quest for invariance
Simple tests
Efficiency: random walk
Mean-reversion (discrete state)
Long memory: fractional integration
Volatility clustering
Multivariate quest
Covariance stationary processes
Order-one autoregression
VARMA processes
Linear state space models
Cointegration
Estimation
Setting the flexible probabilities
Historical
Maximum likelihood principle
Maximum likelihood
Missing data
Robustness
(Dynamic) copula-marginal
Bayesian statistics
Bayesian estimation
Step 3. Estimation - Historical
Step 3. Estimation - Monte Carlo
Factor models and learning
Overview
Regression LFM's
Principal component LFM's
Factor-analysis LFM's
Cross-sectional LFM's
Overview
Point vs. probabilistic statements
Inference and learning
Least squares regression
Functional bias reduction
Linear basis expansion
Quasi-linear adaptive basis
Adaptive networks
Gradient boosting
Classification
Least squares autoencoders
Discriminant regression
Discriminant classification
Probabilistic graphical models
Overview
Least squares dynamic models
Wiener-Kolmogorov filtering
Dynamic principal component
Probabilistic state space models
Foundations of decision theory
Background
Estimation risk assessment
Regularization
Shrinkage
Sparse principal component
Ensemble learning
Time series models
Maximum likelihood
Bayesian
Mixed approach
Background
Fit and assessment
Logistic regression
Interactions
Encoding
Regularization
Trees
Gradient boosting
Cross-validation
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