Computational Methods in Pricing and Model Calibration
This course is part of Financial Engineering and Risk Management Specialization.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Financial Engineering and Risk Management Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Implement numerical methods for option pricing
Master model calibration techniques
Analyze interest rate instruments and curves
Develop Python programs for financial modeling
Apply optimization algorithms
Understand fixed income pricing methods
Skills you'll gain
This course includes:
7.5 Hours PreRecorded video
9 assignments
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FullTime access
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There are 5 modules in this course
This comprehensive course covers advanced computational methods in financial engineering. Beginning with option pricing and numerical approaches, it progresses through model calibration techniques and interest rate instruments. Students learn to implement various pricing models including Black-Scholes, Heston, and Variance Gamma using Python. The course includes practical applications of Fourier transforms, optimization algorithms, and regression techniques for model calibration.
Course Overview
Module 1 · 30 Minutes to complete
Option Pricing and Numerical Approach
Module 2 · 5 Hours to complete
Model Calibration
Module 3 · 6 Hours to complete
Interest Rates and Interest Rate Instruments Part I
Module 4 · 5 Hours to complete
Interest Rates and Interest Rate Instruments Part II
Module 5 · 6 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Financial Engineering and Risk Management Specialization
Instructors
Expert in Optimization and Professor at Columbia University
Dr. Garud N. Iyengar is the Tang Family Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he has been a faculty member since 1998. He also serves as the Avanessians Director of the Data Science Institute (DSI) at Columbia, leading initiatives in education and research for data science. Dr. Iyengar's research interests encompass a variety of topics, including convex optimization, robust optimization, queuing networks, combinatorial optimization, and mathematical finance.He received his Ph.D. and M.S. in Electrical Engineering from Stanford University and his B.Tech from the Indian Institute of Technology (IIT). Dr. Iyengar has published extensively in his field, with over 90 publications and two patents to his name. He has held several leadership roles, including Chair of the Department of Industrial Engineering and Operations Research from 2013 to 2019 and Senior Vice Dean for Research and Academic Programs at Columbia Engineering.Dr. Iyengar teaches several courses on Coursera, such as "Introduction to Financial Engineering and Risk Management" and "Optimization Methods in Asset Management," aimed at equipping students with essential skills in finance and operations research.
Expert in Financial Engineering and Professor at Columbia University
Dr. Ali Hirsa is a Professor of Professional Practice in the Department of Industrial Engineering and Operations Research at Columbia University, where he has been a faculty member since 2017, having previously served as an Adjunct Professor since 2000. He is also the Managing Partner at Sauma Capital, LLC, a New York hedge fund. Dr. Hirsa's expertise lies in financial engineering, with a focus on asset pricing, derivatives, and optimization methods in asset management.He holds a Ph.D. in Applied Mathematics from the University of Maryland at College Park and an MBA with an emphasis on Finance. Dr. Hirsa has extensive industry experience, having held significant positions at firms such as Morgan Stanley and Banc of America Securities, where he developed quantitative trading strategies.Dr. Hirsa teaches several courses on Coursera, including "Introduction to Financial Engineering and Risk Management" and "Advanced Topics in Derivative Pricing." His research interests include algorithmic trading, machine learning, and computational finance, and he has authored multiple publications in these areas.
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