About the content
Learn mathematical and statistical tools and techniques used in quantitative and computational finance. Use the open source R statistical programming language to analyze financial data, estimate statistical models, and construct optimized portfolios. Analyze real world data and solve real world problems.
- Computing asset returns
- Univariate random variables and distributions
- Characteristics of distributions, the normal distribution, linear function of random variables, quantiles of a distribution, Value-at-Risk
- Bivariate distributions
- Covariance, correlation, autocorrelation, linear combinations of random variables
- Time Series concepts
- Covariance stationarity, autocorrelations, MA(1) and AR(1) models
- Matrix algebra
- Descriptive statistics
- histograms, sample means, variances, covariances and autocorrelations
- The constant expected return model
- Monte Carlo simulation, standard errors of estimates, confidence intervals, bootstrapping standard errors and confidence intervals, hypothesis testing , Maximum likelihood estimation, review of unconstrained optimization methods
- Introduction to portfolio theory
- Portfolio theory with matrix algebra
- Review of constrained optimization methods, Markowitz algorithm, Markowitz Algorithm using the solver and matrix algebra
- Statistical Analysis of Efficient Portfolios
- Risk budgeting
- Euler’s theorem, asset contributions to volatility, beta as a measure of portfolio risk
- The Single Index Model
- Estimation using simple linear regression
- Eric Zivot - Economics Department
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