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Courses Portfolio Optimization

Course 7Coming soon

Portfolio Optimization

From estimating risk and return to Markowitz, risk parity, robust and factor portfolios, trading costs, index tracking and honest backtests.

  • 16 modules
  • Before you start: Foundations Levels 3, 4 and 5; Financial Econometrics

Coming soon

Explore the syllabus below, and start with the courses it builds on.

How each module works

  1. 1The market problemWhy practitioners need the model
  2. 2Model and codeThe model, then its implementation
  3. 3Practice setQuick questions with instant feedback
  4. 4Case studyAn industry problem, solved in code
  5. 5Limits and risksWhen the model fails, and what to do

Modules

1.1Data for portfoliosComing soon
You will learn:
Returns, Stylised facts, i.i.d. and time-series models, Heavy tails.
Case study:
Profile a 30-asset universe
1.2Estimating means and covariancesComing soon
You will learn:
Sample estimators, Ledoit–Wolf shrinkage, Factor models, Robust estimators, Estimation error.
Case study:
How noisy inputs move optimal weights
1.3Convex optimisationComing soon
You will learn:
Convex sets and functions, LP, QP, SOCP, SDP, Duality and KKT, Solvers and cvxpy.
Case study:
Solve one portfolio four ways
1.4MarkowitzComing soon
You will learn:
Mean-variance, Efficient frontier, Minimum variance, Maximum Sharpe, Utility.
Case study:
Frontier for a multi-asset fund
1.5Real-world constraintsComing soon
You will learn:
Long-only, Leverage, Turnover, Sector and factor limits, Cardinality.
Case study:
A constrained institutional mandate
1.6Risk beyond varianceComing soon
You will learn:
Downside risk, VaR and CVaR optimisation, Drawdown.
Case study:
CVaR portfolio in a crash year
1.7Risk parity and risk budgetingComing soon
You will learn:
Equal risk contribution, Risk budgets, Leverage.
Case study:
All-weather style allocation
1.8Graph-based and hierarchical portfoliosComing soon
You will learn:
Hierarchical risk parity, Clustering, Financial graphs.
Case study:
HRP against minimum variance out of sample
1.9Robust and Bayesian portfoliosComing soon
You will learn:
Uncertainty sets, Resampling, Black–Litterman.
Case study:
Blend analyst views with Black–Litterman
1.10Costs and rebalancingComing soon
You will learn:
Transaction cost models, Rebalancing rules, Multi-period optimisation.
Case study:
Rebalance a pension portfolio
1.11Index trackingComing soon
You will learn:
Tracking error, Sparse replication, Enhanced indexing.
Case study:
Track an index with 50 names
1.12Factor investing and active managementComing soon
You will learn:
Alpha, Information ratio, The fundamental law, Risk models.
Case study:
A factor-tilted portfolio
1.13Pairs and statistical arbitrage portfoliosComing soon
You will learn:
Cointegrated baskets as portfolios.
Case study:
A market-neutral basket
1.14Learning-based portfoliosComing soon
You will learn:
End-to-end and deep learning portfolios, Their limits.
Case study:
Compare with a classic baseline
1.15Backtesting portfoliosComing soon
You will learn:
Walk-forward tests, Multiple testing, Reporting.
Case study:
A one-page backtest report
1.16Capstone: a multi-asset allocationComing soon
You will learn:
Constraints, Costs, Risk report, Out-of-sample test.
Case study:
Graded with a checklist