> ## Content Index > Fetch the complete content index at: https://fsc.stevens.edu/llms.txt > Use this file to discover other available public pages before exploring further. # FE621 Computational Methods in Finance - URL: https://fsc.stevens.edu/fe621-computational-methods-in-finance/ - Published: 2020-06-03T18:44:22.000Z - Updated: 2026-05-16T22:34:48.000Z - Author: Sakshi More - Tags: Courses, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14 Course Catalog Description # Introduction This course provides computational tools used in industry by the modern financial analyst. The current financial models and algorithms are further studied and numerically analyzed using regression and time series analysis, decision methods, and simulation techniques. The results are applied to forecasting involving asset pricing, hedging, portfolio and risk assessment, some portfolio and risk management models, investment strategies, and other relevant financial problems. Emphasis will be placed on using modern software. Students must have a strong mathematical background (FE543/FE610), and be familiar with derivatives terminology and concepts at the level of Hull’s textbook (FE620). The course is split in modules and each module will cover theory and test the student’s knowledge on developing and implementing algorithms to solve real problems. | Campus | Fall | Spring | Summer | | ---------- | ---- | ------ | ------ | | On Campus | X | X | | | Web Campus | X | X | X | Instructors | Professor | Email | Office | | --------------- | -------------------- | ------ | | Sveinn Olafsson | solafsso@stevens.edu | | Course Resources # Course Materials Class notes will be posted on Canvas. The topics covered can be found in a variety of textbooks and other online sources, such as 1. L. Clewlow and C. Strickland. Implementing derivative models, Wiley, 1998 2. P. Glasserman. Monte Carlo methods in financial engineering, Springer, 2003. 3. A. Hirsa. Computational methods in finance, Chapman & Hall, 2012. 4. M. Mariani and I. Florescu. Quantitative Finance, John Wiley & Sons, 2019 5. J. Hull. Options, futures, and other derivatives, Prentice Hall, 2014 Grading # Grading Policies Grades – Please submit ALL work using pdf format The final grade will be determined upon the student’s performance in the course. We will have multiple assignments and quizzes throughout the course. Most of the grade will be coming from the in class midterm as well as from the final. The work tends to be programming intensive so an early start is necessary especially if there are gaps in your programming skills. Only use the .pdf format for submitting assignment files. You should be able to transform any document into a pdf file. You can use Adobe acrobat - should be free to Stevens students as far as I know (please call the students help desk), or a simple alternative: use a pdf printer driver. I write all my documents in LATEXand that typesetting program produces pdf files. Generally the grade distribution follows the following percentages. - Assignments 85% - Class Participation 15% **Assignment submission guidelines** There will be at least five individual homework assigments. You have 5 "Late Days" over the semester, but only 3 late days can be used fora single assignment. Beyond that, late homework will not be accepted. 1. Present your solutions in a clear and readable manner. Please write complete sentences, highlight final answers, explain your reasoning etc. Submitting code without any interpretation of results will be severely penalized. 2. Make sure the TA does not have to search through multiple pages of code in order to find your results 3. You can discuss problems with other students but you must solve, write, and submit your own solution. Please see the Graduate Student Code of Academic Integrity. 4. You are not allowed to use solutions of former students, or plagiate online sources, including the output of generative AI technologies. Lecture Outline | | Topic | Reading | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------- | | Week 1 and Week 2 | Intro., Quadrature methods, MLE, Put/Call parity, Hedging Black Scholes, Heston and SABR models Implied vol, local vol, Bisection methods | MF-1,2 CS-1 AB-1, R-1,2 MF-3, CS-1 F-15.1, R-11, F-6 | | Week 3 and Week 4 | **Tree approximating methods,** Binomial Tree Model, Trinomial Tree Model and extensions | MF-6, CS-2 MF-6, CS-3 AB-2, 4 | | Week 5 to Week 7 | **PDE approximation methods** Finite difference methods Finite difference method for Heston Approximating American options | MF 4.2,7.1-7.5, CS 3,5 MF 7.6-7.7, R-11 MF 7.8-7.9 R-8,10 | | Week 8 | **PDE Transformation methods** Laplace, Fourier methods, Heston model | MF 4.4-4.6, 5 FR-7 | | Week 9 | **MIDTERM** | | | Week 10 | Optimization and parameter calibration | MF-1.10, 9.6 | | Week 11 Week 12 | **Path approximation methods** Random number generation Univariate Monte Carlo methods Cholesky decomposition Multivariate Monte Carlo, Variance reduction Markov Chain Monte Carlo (MCMC) | MF 8.1-8.4, CS-4 FR-1,2, R-7 MF 8.5-8.10 FR-13,14, R-7 | | Week 13 Week 14 | **Multivariate Stochastic processes** PCA and Factor models TBD and review | MF-16 TBD |