CFRM 543 Portfolio Optimization and Asset Management

The content coverage will blend theory and analytics with computational methods implementation in R code.  The latter supports hands-on portfolio construction and analysis exercise portions of homework assignments.

  • Constrained long-only and long-short mean-variance portfolio optimization
  • Back-testing, portfolio performance analysis and estimation error
  • Expected tail loss/conditional value-at-risk portfolio optimization theory and computing
  • Expected utility and coherent risk measure portfolio optimization
  • Active/benchmark relative portfolio optimization theory and computation 
  • Information  ratios and coefficients and fundamental law of active management
  • Covariance estimation: classical, EWMA, robust, shrinkage, unequal histories methods
  • Fundamental factor models: risk analysis and alpha forecasts
  • Hedge funds: types, special problems, time series factor models, replication
  • Statistical factor models and PCA for risk analysis and forecasting
  • Marginal contributions to risk and implied returns guided asset allocation
  • Differential evolution optimization for “hard” problems.  Random portfolios.
  • Risk-parity portfolios, equal weight portfolios, volatility pumping and Kelly betting
  • Leverage: Types of leverage, return versus risk considerations
  • Liquidity and market impact:  Sadka liquidity risk beta (extra lectures by Sadka) 
  • Introduction to Bayes methods in finance
  • Bayes-Stein alpha estimates and Bayes shrinkage covariance estimates
  • Basic Black-Litterman model and its calibration. Meucci extensions
  • Additional Bayes topics TBD


R. Douglas Martin
Chincarini and Kim (2006). Quantitative Equity Portfolio Management, McGraw-Hill.; Martin, Yollin and Scherer (2013). Modern Portfolio Optimization, selected 2nd edition draft chapters to be provided by instructor.
R and R packages including PerformanceAnalytics and PortfolioAnalytics
CFRM 541 Investment Science and CFRM 542 Financial Data Modeling and Analysis in R, or equivalents, or by permission of instructor