Sessional Lecturer - MMF2021H1F: Numerical Methods for Finance (Section LEC 0101)
About The Position
This course provides a rigorous introduction to numerical methods essential for modern quantitative finance. Students will master key techniques, including finite difference methods for solving partial differential equations arising in option pricing, Monte Carlo simulation and variance reduction techniques for pricing complex derivatives and risk measurement, numerical optimization algorithms for portfolio optimization and calibration of financial models. We will focus on practical applications to real-world problems in derivatives pricing, risk management, and algorithmic trading. Through theoretical lectures, coding assignments, and case studies, students will develop the ability to select, implement, and validate appropriate numerical methods for quantitative finance challenges. The delivery method for this course is in-person.
Requirements
- Advanced degree in Mathematical Finance
- Industry experience in partial differential equations, pricing options and complex derivatives
- Prior experience teaching this course (or a similar course) at the university level
- Ability and experience teaching large classes
Nice To Haves
- Industry experience in numerical optimization algorithms and portfolio optimization
Responsibilities
- Preparation and delivery of lectures in this course
- Preparation, supervision and grading of tests and examinations in accordance with university regulations
- Providing scheduled office hours for academic counseling of students
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Career Level
Mid Level
