CFRM 461 Probability and Statistics for Computational Finance

This MS-CFRM pre-program course reviews the basic statistical methods needed in quantitative and computational finance. The main areas of focus are probability theory, random variables and their distributions, transformations of random variables, limit theorems, parameter estimation theory.

Topics covered include the following:

  • Probability theory: set theory, probability spaces, joint probability, conditional probability, Bayes theorem
  • Univariate and multivariate random variables: distribution and density functions, moments, normal and fat-tailed skewed distributions, linear and non-linear transformations, conditional expectations
  • Limit theorems: random variable convergence types, law of large numbers, central limit theorem
  • 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

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.
Students can get a head-start by studying the materials for weeks 2-5 of Professor Zivot’s ECON 424 course.


Course Dates: Summer Quarter 2014.

Credits:  Undergraduate level credit does not count toward the MS-CFRM degree or the Computational Finance Certificate requirements. Credit will count towards the Quantitative Fundamentals Certificate in Computational Finance.


Single Course Enrollment Applicants:

·         To apply for single course enrollment please fill out the application here

·         Questions? Contact

Kjell Konis
CFRM 460 or equivalent or by permission.