Upon completion of the course, students will be able to apply the fundamental mathematical and statistical concepts needed to estimate and analyze statistical models for asset returns and to apply these models to portfolio theory and risk analysis.ĬFRM 405, CFRM 410, or permission of instructor.
Probability and statistics applied to asset returns, including: univariate and multivariate distributions, covariance, descriptive statistics, time series concepts, estimation, hypothesis testing, Monte Carlo simulation, bootstrap standard errors.The course uses the material contained in CFRM 405 and CFRM 410 to build and analyze statistical models for asset returns. This course is an introduction to computational finance and financial econometrics. Upon completion of the course students will know the basic probability and statistics tools needed to effectively study quantitative finance areas such as fixed income, options and derivatives, portfolio optimization, and quantitative risk management. Parameter estimation theory: variance, bias and mean-squared error, maximum likelihood estimation of mean and standard deviation for normal distributions and location and scale for non-normal distributions Limit theorems: random variable convergence types, law of large numbers, central limit theorem.Univariate and multivariate random variables: distribution and density functions, moments, normal and fat-tailed skewed distributions, linear and non-linear transformations, conditional expectations.Probability theory: set theory, probability spaces, joint probability, conditional probability, Bayes theorem.
The main areas of focus are probability theory, random variables and their distributions, transformations of random variables, limit theorems, parameter estimation theory. This course reviews the basic statistical methods needed in quantitative and computational finance.