- Email is the preferred method of communication. Class mailing list will be created as [email protected].
- Course project is due on 11.15 (Friday) night.
- Prelims: Probability Statistics Review
- Past Exam: 2017-18 Midterm, 2018-19 Midterm
- Scientific computing, MC method, RN generation (Slides | Py demo)
- Black-Scholes model (Py demo, MC demo): Also see Ch. 10 of StoFin Course Notes
- Normal (Bachelier) model (Slides) from Stochastic Finance class
- Implied volatility (Slides | Py demo)
- Spread/Basket options (Slides)
- SABR model (Slides) | NSVh model (Slides)
- Copula (Slides, Py demo)
| No | Date | Contents |
|---|---|---|
| 01 | 9.03 Tue | Course overview, Scientific computing, MC method, RN generation (Slides | Py demo) |
| 02 | 9.06 Fri | Continued (Slides | Py demo) |
| 03 | 9.10 Tue | Black-Scholes-Merton and Normal option pricing with MC (Py Demo), Normal model (Slides), Black-Scholes implementation (Py Demo), Implied volatility (Slides, Py demo) |
| 04 | 9.11 Wed | Python crash course (Basic | Numpy). More cheatsheets also available in MLF CMS. HW1 |
| 05 | 9.17 Tue | Spread/Basket options (Slides), Correlated Normal RNs (Slides | Py Demo) |
| 06 | 9.20 Fri | Spread/Basket options continued, HW2: Spread/Basket option implementation |
| 07 | 9.24 Tue | SABR model (Slides: Volatility smile, Local volatility model) |
| 08 | 9.27 Fri | SABR model continued (Slides: Model intro, Euler/Milstein method). |
| x | x | No Class: National Day Week |
| 09 | 10.08 Tue | SABR model continued (Slides: Conditional MC method), Python Import (Py Demo), Suggested project topics. HW3: MC method for SABR |
| 10 | 10.11 Fri | SV Model Simulation for Project (Slides) |
| 11 | 10.15 Tue | Research Presentation: NSVh model and Normal SABR (Slides), Introduction to PyFE, Github Pull-request |
| 12 | 10.18 Fri | Review for midterm exam (Past Exams: 2017-18, 2018-19) |
| 13 | 10.22 Tue | Midterm Exam (Solution) |
| 14 | 10.25 Fri | Copula (Slides, Py demo) |
| 15 | 10.29 Tue | Copula (Slides, Py demo) |
| 16 | 11.01 Fri | Research Presentation: (Sum of BSM models) and HW3 review |
| 17 | 11.05 Tue | Course project presentation |
| 18 | 11.08 Fri | Course project presentation |
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- Register on Github.com and send your ID and student number to Prof. Choi via email ([email protected]). Use your full name in your profile. Accept invitation to the PHBS organization from TA. Install Github Desktop.
- Install Anaconda Python distribution (3.X version, not 2.X version). Anaconda distribution is core Python + useful scientific computation libraries (e.g., numpy, scipy, pandas) + package management system (pip or conda)
- Send the screenshot of Github desktop and Anaconda installed to TA. (Example: Github Desktop, Anaconda Spyder)
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Set 1 [Due by 9.19 Fri] Simple corporate (default) bond pricing by MC simulation. Starter Code
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Set 2 [Due by 9.27 Fri] Pricing basket and spread option using MC. Starter Code
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Set 3 [Due by 10.17 Fri] Simulating SABR model. Starter Code
Course Project: Project Description (Previous year: 2018)
- Lectures: Tues & Fri 1:30 – 3:20 PM
- Venue: PHBS Building, Room 209
Instructor: Jaehyuk Choi
- Office: PHBS Building, Room 755
- Phone: 86-755-2603-0568
- Email: [email protected]
- Office Hour: Tues 11:30AM-12:30PM & Fri 3:30-4:30 PM
- Email: [email protected]
- TA Office Hour: TBA (Room 213/214)
Applied Stochastic Processes (ASP) is intended for the students who are seeking advanced knowledge in stochastic calculus and are eventually interested in the jobs in financial engineering. As the name indicates, the course will emphasis on applications such as numerical calculation and programming. On completion of this course, the students will learn how financial observations (e.g. stock prices and FX rate) are modelled with stochastic processes and how they can be computed using analytics or computer simulations.
Stochastic Finance (FIN 519), a year 1 required course for quantitative finance program, is a prerequisite for the ASP since it provides theoretical background. Undergraduate-level knowledge in probability, statistics, linear algebra and programming skill (Python) are also highly recommended.
- Monte Carlo Methods in Finance by Peter Jaeckel
- Option Valuation Under Stochastic Volatility by Alan Lewis
- Stochastic Calculus and Financial Applications by J. Michael Steele (Stochastic finance course notes)
Attendance 20%, Mid-term Exam 30%, Assignments 20%, Course Project 30%
- Midterm exam: 10.22 Tues. Open-book exam without computer/phone/calculator use. No final exam.
- Course project: Presentation (Last week). Group up to X people.
- Attendance: Randomly checked. The score is calculated as 20 – 2
x(#of absence). Leave request should be made 24 hours before with supporting documents, except for emergency. Job interview/internship cannot be a valid reason for leave - Grade in letters (e.g., A+, A-, ... ,D+, D, F). A- or above < 30% and B- or below > 10%.