ARPM Bootcamp

Title:
ARPM Bootcamp
Organizer:
ARPM, Sapienza Università di Roma
Start date - End date:
September 30, 2019 - December 22, 2019
Instructors:
Rita D’Ecclesia, Luca Passalacqua, Gilberto Castellani
Language:
English
Weekly commitment:
~12 hours per week
Support:
Q&A forum follow up, live classes
Reference material:
Calendar:

What you will learn

You will gain a broad overview of 4 learning modules: 

  • Data Science for Finance

  • Financial Engineering for Investment

  • Quantitative Risk Management

  • Quantitative Portfolio Management


You will grasp the intuition behind all the quantitative techniques covered by the course, and master selected topics within each learning module. Refer to the syllabus for more details.


Upon successful completion of the course, you will be able to:

  • correctly map all the techniques adopted in quantitative finance onto a unified theoretical framework, appreciating the interconnections, and gaining a fresh perspective on the known techniques;

  • avoid the most common pitfalls in risk management and portfolio management applications;

  • interact with your classmates (and with the ARPM community) using a common language and notation;

  • navigate the ARPM Lab to find detailed reference material to deepen your knowledge of the topics covered by the course, and more.


Syllabus

Introduction
About the ARPM Lab
About quantitative finance: P and Q
Notation
The "Checklist": executive summary
The Checklist: Step 1 - Risk drivers identification
Risk drivers identification
Equities
Currencies
Fixed-income
Derivatives
Credit
Strategies
The Checklist: Step 2 - Quest for invariance (univariate)
Quest for invariance
Simple tests
Efficiency: random walk
Mean-reversion (continuous state): ARMA
Mean-reversion (discrete state)
Volatility clustering
Distributions
Representations of a distribution
Normal distribution
Notable multivariate distributions
Elliptical distributions
Scenario-probability distributions
Exponential family distributions
Mixture distributions
Location and dispersion
Expectation and variance
Expectation and covariance
L2 geometry
Generalized location-dispersion: affine equivariance
Generalized location-dispersion: variational principles
Copulas
Univariate results
Definition and properties of copulas
Special classes of copulas
Implementation
The Checklist: Step 2 - Quest for invariance (multivariate)
Multivariate quest
Order-one autoregression
Cointegration
The Checklist: Step 3 - Estimation
Estimation
Setting the flexible probabilities
Historical
Maximum likelihood
Robustness
(Dynamic) copula-marginal
Missing data
Shrinkage
Linear factor models
Factor models and learning
Overview
Regression LFM's
Principal component LFM's
Systematic-idiosyncratic LFM's
Cross-sectional LFM's
Application: principal component analysis of the yield curve
Capital asset pricing model framework
Machine learning: foundations and prediction
Overview
Point vs. probabilistic statements
Inference and learning
Least squares regression
Quantile and non-least-squares regression
Classification
Least squares autoencoders
Probabilistic graphical models
Machine learning: out of sample enhancements
Estimation risk assessment
Regularization and features selection
Bayesian estimation
Ensemble learning
Application: credit default classification
Dynamic models
Overview
Linear state space models
Cramer representation
Probabilistic state space models
The Checklist: Step 4 - Projection
Projection
One-step historical projection
Efficiency: Lévy processes
Mean-reversion (discrete state)
Multivariate analytical projection
Monte Carlo
Application to credit risk
Historical
The Checklist: Step 5 - Pricing at the horizon
Pricing at the horizon
Exact repricing
Taylor approximations
The Checklist: Step 6 - Aggregation
Aggregation
Stock variables
Portfolio P&L
Returns
Static market/credit risk
Dynamic market/credit risk
Stress-testing
Enterprise risk management
The Checklist: Step 7 - Ex-ante evaluation
Ex-ante evaluation
Stochastic dominance
Satisfaction/risk measures
Mean-variance trade-off
Expected utility and certainty-equivalent
Quantile (value at risk)
Spectral satisfaction measures/Distortion expectations
Coherent satisfaction measures
Non-dimensional ratios
The Checklist: Step 8a - Ex-ante attribution: performance
Ex-ante attribution: performance
Bottom-up exposures
Top-down exposures: factors on demand
Application: hedging
The Checklist: Step 8b - Ex-ante attribution: risk
Ex-ante attribution: risk
Risk budgeting: general criteria
Homogenous measures and Euler decomposition
The Checklist: Step 9a - Construction: portfolio optimization
Construction: portfolio optimization - Overview
Mean-variance principles
Analytical solutions of the mean-variance problem
Benchmark allocation
Convex programming
Integer N-choose-K heuristics
Mean-variance pitfalls
The Checklist: Step 9b - Construction: estimation and model risk
Estimation risk measurement
Robust allocation
Diversification management
Black-Litterman
Equilibrium prior
Active views
Posterior
Limit cases and generalizations
Views processing
General views processing
Minimum relative entropy
Analytical implementation: views on correlations, means, volatilities
Signals
Signals
Value signals
Technical signals
Signal processing (*)
The Checklist: Step 9c - Construction: cross-sectional strategies
Overview
Simplistic portfolio construction
Advanced portfolio construction
Relationship with FLAM and APT
Multiple portfolios
Fundamental law of active management
The Checklist: Step 9d - Construction: times series strategies
Construction: time series strategies
The market
Expected utility maximization
Option based portfolio insurance
Rolling horizon convex/concave strategies
Signal induced strategy
The Checklist: Step 10 - Execution
Overview
High frequency risk drivers
Market impact modeling
Order scheduling
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