Mathematical Methods for Quantitative Finance
Massachusetts Institute of Technology
Learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R.
About this Course
Modern finance is the science of decision making in an uncertain world, and its language is mathematics. As part of the MicroMasters® Program in Finance, this course develops the tools needed to describe financial markets, make predictions in the face of uncertainty, and find optimal solutions to business and investment decisions. This course will help anyone seeking to confidently model risky or uncertain outcomes. Its topics are essential knowledge for applying the theory of modern finance to real-world settings. Quants, traders, risk managers, investment managers, investment advisors, developers, and engineers will all be able to apply these tools and techniques.
What You'll Learn
- Probability distributions in finance
- Time-series models: random walks, ARMA, and GARCH
- Continuous-time stochastic processes
- Optimization
- Linear algebra of asset pricing
- Statistical and econometric analysis
- Monte Carlo simulation
- Applied computational techniques
Prerequisites
- Calculus
- Probability and statistics
- Linear algebra
- Basic programming skills.
Instructors
Paul F. Mende
Senior Lecturer in the Finance Group at the MIT Sloan School of Management
Egor Matveyev
Executive Director of MicroMasters Program in Finance