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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
We will discuss both the Partial Differential Equations approach, and the probabilistic, martingale approach. We will also cover an introduction to modeling of interest rates and fixed income derivatives.
I teach the same class at Caltech, as an advanced undergraduate class. This means that the class may be challenging, and demand serious effort. On the other hand, successful completion of the class will provide you with a full understanding of the standard option pricing models, and will enable you to study the subject further on your own, or otherwise.
Prerequisites
A basic knowledge of calculus based probability/statistics. Some exposure to stochastic processes and partial differential equations is helpful, but not mandatory. It is strongly recommended you take the prerequisites test available in Unit 0, to see if your mathematical background is strong enough for successfully completing the course. If you get less than 70% on the test, it may be more useful to work further on your math skills before taking this course. Or you can just do a part of the course.
Syllabus
WEEK 1: Unit 0: Pre-course: Since this is a quantitative course, a certain level of mathematical background is necessary for a student to master the course material. In this unit, I would like to invite you to take the prerequisites assessment.
WEEK 2: Unit 1. Stocks, Bonds, Derivatives
WEEK 3: Unit 2. Interest Rates, Forward Rates, Bond Yields
WEEK 4: Unit 3. No-Arbitrage Pricing Relations
WEEK 5: Unit 4: Pricing in Discrete Time Models
WEEK 6: Unit 5. Brownian Motion and Ito Calculus
WEEK 7: Unit 6. Pricing in Black-Scholes-Merton model
WEEK 8: Unit 7. Extensions of Black-Scholes-Merton
WEEK 9: Unit 8. Hedging
WEEK 10: Unit 9. Beyond Black-Scholes-Merton
WEEK 11: Unit 10. Pricing in Fixed Income Markets
WEEK 12: Final Exam
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.