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Quant. Risk Management
Quant. Portf. Management
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Marathon
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Special topics:
Data Science for Finance
Fin. Eng. for Investment
Quant. Risk Mngt.
Quant. Portfolio Mngt.
Refresher - Mathematics
Refresher - Python
Refresher - MATLAB
Classroom »
Certificate
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Testing
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Forum
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About ARPM
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Attilio Meucci
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Overview
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» Classroom
Data Science for Finance
35-hours course
Mathematics
Python
MATLAB
Data Science for Finance
Quantitative Risk Management
Financial Engineering for Investment
Quantitative Portfolio Management
This course prepares for the
Data Science for Finance
module of the ARPM Certificate Body of Knowledge.
REGISTER NOW
1. Introduction
About ARPM (Lab, Courses, Certification)
ARPM Lab - how it works
ARPM Lab – contents
About Quantitative Finance: P vs Q
Notation
The “Checklist” executive summary
Data science for finance – overview
2. Distributions
Representations of a distribution
Marginalization
Conditioning
Elliptical distributions
Scenario-probability distributions
Exponential family distributions
Other special classes of distributions
Notable distributions
3. Location and dispersion
Univariate location and dispersion
Multivariate location and dispersion
Expectation and covariance
4. Copulas
Overview
Univariate results: grades as non-linear Z-scores
Definition and properties of copulas
Special classes of copulas: elliptical and Archimedean
Copula-marginal implementation
Summary
5. Quest for invariance (econometrics)
Quest for invariance – overview
Invariance tests
Efficiency: random walk
Mean reversion (continuous state): ARMA processes
Mean reversion (discrete state): Markov chains
Long memory: fractional integration
Volatility clustering: GARCH and stochastic volatility
Multivariate quest: VAR(1) and alternative models
Cointegration and statistical arbitrage
Relationships among processes
6. Estimation: invariants distribution
Estimation – overview
Setting the flexible probabilities
Non-parametric estimation: invariants
Exponential moving moments and statistics
Maximum likelihood principle
Maximum-likelihood estimation: invariants
Missing observations
Robustness
(Dynamic) copula-marginal
Bayesian statistics
Bayesian estimation: invariants (Normal-inverse Wishart model)
Shrinkage: location and dispersion
Factor analysis shrinkage
7. Estimation assessment
Overview
Fundations of decision theory
Estimation assessment
8. Linear factor models: theory
Executive summary
The r-squared
Linear factor models: theory
Regression LFMs: theory
Principal components LFMs: theory
Application: principal component analysis of the swap curve
Systematic-idiosyncratic LFMs: theory
Cross-sectional LFM's: theory
9. Linear factor models: estimation
Overview
Regression LFM’s
Principal component LFM’s
Systematic-idiosyncratic LFM’s
Cross sectional LFM’s
Truncation
10. Machine learning foundations
Overview
Key ideas from linear factor models
Key concepts for machine learning
Supervised point prediction: regression
Supervised point prediction: classification
Supervised probabilistic prediction
Unsupervised autoencoders
Probabilistic graphical models
11. Machine learning enhancements
Motivation: bias vs variance
Feature engineering
Gradient boosting
Regularization
Ensemble learning
12. Dynamic models
Overview
Linear state space models
Probabilistic linear state space models
Probabilistic state space models
The Fourier transform
Spectral analysis of time series
Wiener-Kolmogorov filtering
Dynamic principal component
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