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asp's Introduction

Applied Stochastic Processes (FIN 514, 2019-20 Module 1)

Announcements

  • 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.

Course Slides and Other Resources

Lectures

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

Homeworks:

  • Set 0: (Due by 9.6 Fri)

    • 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)
  • Set 1 [Due by 9.19 Fri] Simple corporate (default) bond pricing by MC simulation. Starter Code

  • Set 2 [Due by 9.27 Fri] Pricing basket and spread option using MC. Starter Code

  • Set 3 [Due by 10.17 Fri] Simulating SABR model. Starter Code

  • Set 4

Course Project: Project Description (Previous year: 2018)

Classes:

  • Lectures: Tues & Fri 1:30 โ€“ 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

Teaching Assistance: TBA

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 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.

Extra Reading Materials

Assessment/Grading Details

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 โ€“ 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 B- or below > 10%.

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