Course Code: bspmatfi
Duration: 14 hours
Prerequisites:
  • An understanding of ___.
  • Experience with ___.
  • ___ programming experience.

Audience

  • ___
  • ___
  • ___
Course Outline:
Asset Allocation and Portfolio Optimization
Objective: perform capital allocation, asset allocation, and risk assessment.
  • Estimating asset return and total return moments from price or return data
  • Computing portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR)
  • Performing constrained mean-variance portfolio optimization and analysis
  • Examining the time evolution of efficient portfolio allocations + illustrate tangent line to efficient frontier
  • Performing capital allocation
  • Accounting for turnover and transaction costs in portfolio optimization problems
+
  • I would like the portfolio to be rebalanced every quarterly (i.e. the weights of the assets) reset every three months.
  • I would like the underlying data of the efficient frontier to be exported into Excel as numbers
  • Portfolio Optimization against a benchmark
  • Portfolio Optimization against a target risk (standard deviation) level
  • Portfolio Optimization based on VAR
  • Turnover and Tracking error constraint
Risk Analysis and Investment Performance
Objective: Define and solve portfolio optimization problems.
  • Specifying a portfolio name, the number of assets in an asset universe, and asset identifiers.
  • Defining an initial portfolio allocation.
Financial Time Series Analysis
Objective: analyze time series data in financial markets.
  • Performing data math
  • Transforming and analyzing data
  • Technical analysis
  • Charting and graphics
Objective: Practice using performance metrics and specialized plots.
  • Moving averages
  • Oscillators, stochastics, indexes, and indicators
  • Maximum drawdown and expected maximum drawdown
  • Charts, including Bollinger bands, candlestick plots, and moving averages
Monte Carlo Simulation of SDE Models
Objective: Create simulations and apply SDE models
  • Brownian Motion (BM)
  • Geometric Brownian Motion (GBM)