Advanced Topics in Derivative Pricing
Improve financial engineering skills via derivative pricing models, portfolio optimization, and exploring applications such as real options, energy derivatives, and algorithmic trading.
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Course Description
- Develop expertise in financial engineering through advanced study, focusing on derivatives pricing models, asset allocation, and portfolio optimization.
- Formulate modeled returns and risks for significant asset classes and create optimal portfolios using a systematic, data-driven approach.
- Value complex financial derivatives using stochastic models, gaining the ability to apply this knowledge to real-world applications like algorithmic trading, commodity and energy derivatives, and credit derivatives.
- Implement trading models and signals in an active, live trading environment, enhancing practical skills in the field of financial engineering.
Course Prerequisites
- Intermediate to advanced undergraduate courses in probability, statistics, linear algebra, calculus, and optimization
- Excel and Python for programming assignments
What You Will Learn
By the end of this course, learners will be able to:
Value complex financial derivatives using stochastic models, gaining expertise in their pricing methodologies like the Black-Scholes model.
Formulate modeled returns and risks for significant asset classes and create optimal portfolios through a systematic, data-driven approach.
Backtest and implement trading models and signals in an active, live trading environment, enhancing practical skills for real-world scenarios.
Apply knowledge acquired from six comprehensive modules, covering topics such as risk management of derivatives portfolios, implied volatility, credit derivatives, structured products, and option pricing methodologies for natural gas and electricity-related options.
Course Outline
Module 1: Course overview
Module 2: Equity derivatives in practice: Part I
Module 3: Equity derivatives in practice: Part II
Module 4: Review and assignment for equity derivatives
Module 5: Credit derivatives and structured products
Module 6: Other applications of financial engineering
Instructors
Dr. Garud Iyengar is the Tang Family Professor of Industrial Engineering and Operations Research and Senior Vice Dean of Research and Academic Programs. His research is dedicated to understanding uncertain systems and exploiting available information using data-driven control and optimization algorithms. Iyengar's research group has explored diverse fields such as machine learning, systemic risk, asset management, operations management, sports analytics, and biology. They are currently working on several projects, including thermodynamics of sensing and memory in cells, automatic defensive assignment and event detection in NBA games, and attribution schemes for allocating payment in multi-channel advertising. Additionally, they are working on systemic risk associated with extreme weather, a deep neural-network-based framework for interpretable robust decision making, and an NLP-based model for predicting stock performance using news reports. Iyengar received his B Tech in electrical engineering from the Indian Institute of Technology in 1993 and his PhD in electrical engineering from Stanford University in 1998. He is also a member of Columbia's Data Science Institute.
Professor Ali Hirsa joined IEOR as a faculty member in July 2017, having previously served as an Adjunct Professor at Columbia University since 2000. In addition to his academic work, he is also Managing Partner at Sauma Capital, LLC, a New York Hedge Fund. Prior to this, he held a range of quantitative positions at firms such as Morgan Stanley, Banc of America Securities, and Prudential Securities. His research focuses on machine learning applications in finance, asset management, signal extraction from data, and other areas of computational and quantitative finance. Ali is the author of several publications, including “Computational Methods in Finance” and “An Introduction to Mathematics of Financial Derivatives”. He is also the Co-Editor-in-Chief of Journal of Investment Strategies and has spoken at various academic and practitioner conferences. Ali is a co-inventor of the "Methods for Post Trade Allocation" patent, which aims to create a fair and unbiased method for allocating filled orders to multiple managed accounts. Ali received his PhD in Applied Mathematics from the University of Maryland at College Park.
Martin Haugh is an Associate Professor at Imperial College Business School, having previously spent over a decade in the Department of IE & OR at Columbia University and four years in the hedge fund industry in New York and London. He holds a PhD in Operations Research from MIT and MS degrees in Mathematics and Applied Statistics from Cork and Oxford, respectively. Before joining Imperial College, he served as an Associate Professor of Practice at Columbia University. Martin's research focuses on computational finance and risk management, Markov Decision Processes, sub-optimal control, and machine learning/business analytics/"big data". He has recently begun working on research projects in the machine learning and business analytics space.
Please note that there are no instructors or course assistants actively monitoring this course.