16:643:628 Special Topics in Math Finance - Analysis of Large-Scale Industry Events in Quantitative Finance

Fall 2023: Class Meets Tuesday Nights from 7:00 – 10:00 PM in Hill 705
Instructor: Alvin Huang, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Office hours: MTh 3:00 – 4:30 pm and by appointment


16:643:621 Math Finance I , 16:643:622 Math Finance II , and Either 16:954:596 Regression & Time Series or 16:958:565 Financial Time Series Analysis or Equivalent Econ course 16:220:607 or Econ 16:220:608


  1. John Hull, Options, Futures and Other Derivatives, 10th Edition, 2018
  2. John Hull, Risk Management and Financial Institutions, 5th Edition, 2018
  3. Eryk Lewinson, Python for Finance, 2020


  1. R. Cont, R. Mondescuy, and Y. Yuz, Central Clearing of Interest Rate Swaps: a Comparison of Offerings, 2011

Course objective: This course will have students analyze large scale industry events (i.e. 2008 financial crisis, negative oil prices in 2020, etc.) and the impact and relation of those events to quantitative finance topics introduced in previous courses (i.e. implied volatility, option pricing, delta hedging, VaR, etc.). Students will work with financial time-series data with real-world considerations – handling incomplete data sets and outliers, working with data and calculations at scale, and leveraging cloud computing environments to do such calculations. Students will also gain exposure to big data tools including Python, Spark (PySpark), financial data APIs (Yahoo Finance and Tiingo), and financial packages used in industry such as QuantLib. Each class will be a mix of quantitative finance theory and hands-on programming and implementation.

Course outline:

  1. Course introduction and review of American Options
  2. Impact of 2008 financial crisis on options implied volatility
  3. How delta hedging of options exacerbated the meme stock (GME and AMC) phenomenon of 2021
  4. Impact of negative oil prices in 2020 on options pricing and risk management and CME’s shift to the Bachelier model
  5. Statistical arbitrage and Long-Term Capital Management’s (LTCM) collapse in 1998
  6. Factor models (3, 4, 5 factor models)
  7. Kelly Criterion
  8. Investment strategy back-testing
  9. How DRW used convexity bias in interest rate swaps as a trading strategy
  10. Midterm
  11. Gaussian copula and the 2008 financial crisis
  12. Implementing end-of-day risk management (value at risk) in practice and at scale
  13. Impact of 2020 pandemic and the industry’s shift to real-time risk management
  14. Final project presentations

Final project:
In weeks 5-9, through homework assignments, students will begin to create their own investment portfolios using one (or a combination) of the investment strategies introduced. In weeks 11-12, again through homework assignments, students will risk manage their portfolios. This approach hopes to drive a student’s ownership and motivation as this is their own portfolio. By the end of week 12, students will have a foundational investment portfolio with foundational risk management capabilities. For the final project, students will have the option to:

  • Expand on their foundational portfolios to be multi-asset (minimum 3 asset classes) with at least one derivative product
  • Extend their foundational risk management capability to real-time/on-demand risk (minimum 1,000 securities)
  • Create a custom factor model (i.e. using ESG as a factor)
  • Implement a trading strategy using machine learning and then back-test the strategy
  • Price and risk manage a complex derivative product, such as a range accrual or auto-callables

Grade breakdown:

Attendance: 10%
Homework: 20%
Midterm: 20%
Final project: 50%

 Academic Integrity: All students are expected to know, understand, and live up to the standards of academic integrity explained in Rutgers’ Policy on Academic Integrity, http://academicintegrity.rutgers.edu/academicintegrity-at-rutgers. The minimum penalty for any cheating in an exam is immediate failure of the course.