Course description
This is the basic foundational class of the Mathematics in Finance program for the buy side of the quantitative finance industry. It covers the theory, basic models, and statistical methods that support modern investment decisions and strategies. We make heavy use of computing, which will be done in Matlab. The later part of the class is in the process of being revised in light of the recent meltdown and current investment environment. Specific topics include:
- Mean variance analysis, linear return vs. quadratic risk, the efficient frontier
- Economic theories of market risk and return, CAPM and APT
- Factor models and principal component analysis
- Statistical estimation using least squares, maximum likelihood, and moments
- Linear regression in the Gaussian model
- Non-Gaussian and non-parametric models of correlation, copulas
- Identifying stable invariants in market time series data
- Statistical estimation risk, decision theory, and utility
- Robust statistical estimation, influence functions, shrinkage, missing data
- Value at Risk, and other measures of risk
- Robust portfolio selection