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Learner Reviews & Feedback for Computational Methods in Pricing and Model Calibration by Columbia University

4.0
stars
13 ratings

About the Course

This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments....
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1 - 5 of 5 Reviews for Computational Methods in Pricing and Model Calibration

By daniel h

Oct 5, 2022

Very practical and useful course, congratulations to the professor, I really enjoyed it!! :)

pd: I recommend to have a good background in calculus and programing before applying

By Kostya T

Sep 19, 2021

Great course, one of the best in Financial Engineering and Risk Management specialization. Really enjoyed doing quizes and assignments, change from Excel to Python is fantastic. At the time of taking the course is a bit rough around the ages, especially closer to the end, e.g. with one part of a lecture missing and quite a few lectures duplicated. Would have been nice to have more references to literature/papers for further study. Also would be really cool to add DNN pricing from the 2019 paper by the professor.

All in all a very positive experience, many thanks professor Ali Hirsa!

By Murray S

Oct 19, 2021

I found that the course relied extensively on the use of Python in order to complete the assignments/quizzes. Not being a Python user, I spent a lot of time understanding notebooks in Python and Python coding conventions, which I would have rather spent on learning the concepts in the course. That said, the course content itself was fine.

By Angela T

Feb 26, 2023

The course is easy to follow, and I like the laboratory part, where we can apply what we learn using Python. The only four disadvantages of the course are 1.- You cannot download the lessons in pdf and, therefore, it takes more time than expected to complete them (because you need to copy everything by yourself) 2.-There are some .py files that are not available. Supposedly, Coursera is not allowed to share them, so, in the end, you end up with some Python files that you won't be able to reuse (Module 5) 3.- Some Python homework asks for unnecessary tasks (such as in Module 3, and the use of Brute-Force) where you end up stuck. 4.-The help desk is slow; Coursera should improve this.

By J

Feb 3, 2022

I purchased a subscription and started this course just to realize after some time that it is utterly incomplete. Slides of the presentation are missing (why?) and after several months staff confirmed that slides would never be provided, no reasons given (why??) - the situation is so incomprehensible that lead me to cancel my subcription.