Optimization Methods in Asset Management
Master financial optimization techniques, from Mean-Variance Analysis to CAPM, with hands-on portfolio construction and risk management applications.
Master financial optimization techniques, from Mean-Variance Analysis to CAPM, with hands-on portfolio construction and risk management applications.
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
Learn to construct optimal portfolios using Mean-Variance Analysis
Master the Capital Asset Pricing Model (CAPM) and its applications
Implement Value at Risk (VaR) and Conditional Value at Risk (CVaR)
Understand ETF trading strategies and market microstructures
Apply transaction cost modeling in portfolio management
Develop practical skills in portfolio optimization using Excel
Skills you'll gain
This course includes:
6.2 Hours PreRecorded video
18 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course focuses on advanced optimization methods in portfolio construction and risk management. Beginning with Mean-Variance Analysis and CAPM in arbitrage-free settings, it progresses through practical implementation challenges, risk measurements like VaR and CVaR, and ETF trading strategies. The curriculum covers statistical biases, market microstructures, and transaction cost modeling, providing students with both theoretical knowledge and practical skills for real-world asset management.
Course Overview
Module 1 · 48 Minutes to complete
Mean-Variance Analysis and CAPM
Module 2 · 2 Hours to complete
Assignment Week
Module 3 · 2 Hours to complete
Practical Issues in Implementing Mean Variance
Module 4 · 3 Hours to complete
Assignment Week
Module 5 · 3 Hours to complete
Other Applications of Financial Engineering
Module 6 · 1 Hours to complete
Fee Structure
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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