/2018.M1.ASP

2018-19 Module 1 (Fall), Applied Stochastic Processes

Applied Stochastic Processes (FIN 514, 2018-19 Module 1)

Announcements

  • Email is the preferred method of communication. Class mailing list will be created as PHBS.ASP@allmail.net.

Lectures:

  • 18 (11.09 Tues): Course project presentation
  • ...
  • 13 (10.23 Tues): Midterm exam
  • ....
  • NO CLASS on 10.16 Tues
  • ...
  • 04 (09.12 Wed 10:30 AM instead of 10.16 Tues): Python crash course
  • 03 (09.11 Tues): TBA
  • 02 (09.07 Fri): Scientific computing, Monte Carlo method, Random number generation (Slides, Py demo).
  • 01 (09.04 Tues): Course overview (Syllabus), Probability Statistics Review (Slides)

Topics to Cover

Homeworks:

  • Set 1 [Due by 09.11 Tues]

    • Register on Github.com and send your ID to TA via email. Use your full name in your profile. Accept invitation to the PHBS organization from TA. Install Github Desktop (available on Machine Learning CMS).
    • Install Anaconda Python distribution (3.X version, 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 both softwares installed to TA. (Example: Github Desktop, Anaconda Spyder)

Course Project:

You are very welcome to do the project on your own original idea and you will get additional credit for that. Otherwise, pick one from my suggestions which are basically understanding and implementing literatures. The github repository for the project should be consist of

  • Core implementation (.py): python class and functions
    • Make sure to comment in detail.
    • Put them in a separate subfolder (e.g., option_models) Do not mix with testing/manual notebook files
  • Documentation/Manual (.ipynb): one Jupyter notebook file briefly describing the method (base theory, equations, SDE, strength/weakness, etc), the function prototype and arguments (manual style) and the usage examples
    • The best examples are from numpy documentation: example
  • Validation/Test (.ipynb): one Jupyter notebook file briefly test the code/model.
    • Be a bit creative here
    • BSM/Normal model: make sure to include the analytic-vs-numerical risk test.
    • SV (SABR/Heston): make sure that the price converge to BSM/Normal if alpha(vov parameter) goes to 0
    • Spread/Basket: make sure that the price is same as single asset BSM if the weigit is 1 for only one asset and zero otherwise.

Other guidelines for the course project:

  • The contribution will be individually graded. Make sure to show the contribution via github desktop commits (not online upload).
  • The presentation next week doesn't have to be complete. Show your plan and understanding so far, e.g. function prototypes & arguments, etc and the tests to put on.

Classes:

  • Lectures: Tues & Fri 1:30 – 3:20 PM
  • Venue: PHBS Building, Room 211

Instructor: Jaehyuk Choi

  • Office: PHBS Building, Room 755
  • Phone: 86-755-2603-0568
  • Email: jaehyuk@phbs.pku.edu.cn
  • Office Hour: Tues & Fri 10:30 – 11:30 AM or by appointment

Teaching Assistance: Yeda Du (都业达)

Course overview:

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.

Prerequisites:

Stochastic Finance (FIN 519), a year 1 required course for quantitative finance program, is also highly recommended as it provides theoretical background. Undergraduate-level knowledge in probability, statistics, linear algebra and programming skill (Python) are highly recommended. The students without these recommended prerequisites are expected to take extra efforts.

Textbooks and Reading Materials

Assessment/Grading Details

Attendance 20%, Mid-term Exam 30%, Assignments 20%, Course Project 30%

  • Midterm exam: 10.23 Tues. Open-book exam without computer/phone/calculator use. No final exam.
  • Course project: Presentation (11.09 Fri). Group up to 2 people.
  • Attendance: Randomly checked. The score is calculated as 20 – 2x(#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 C+ or below > 10%.