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Stochastic Finance 2023-24 Module 3 (Spring 2024)

Announcements

  • The WeChat group will be created by TA. (No 1-to-1 chat please.)
  • Class mailing list will be created as [email protected] soon.

Resources

Lectures

No Date Contents
01 2.19 Mon Course overview, Probability Statistics Review (Slides)
02 2.22 Thur Probability Statistics Review (Slides)
03 2.26 Mon SCFA Ch. 1, World Series Derivative
04 2.29 Thur SCFA Ch. 2
05 3.04 Mon SCFA Ch. 3
06 3.07 Thur SCFA Ch. 4
07 3.11 Mon Bachelier (Normal) Model (Slides)
08 3.14 Thur SCFA Ch. 6, Questions Review (optional)
09 3.18 Mon Midterm Exam
10 3.20 Wed SCFA Ch. 5 (Knock-out option under Bachelier model), Ch. 6
11 3.21 Thur Ch. 7-8
12 3.25 Mon Midterm exam review. Ch. 9
13 3.28 Thur Ch. 9
14 4.01 Mon Ch. 10 (Black-Scholes)
15 4.08 Mon Ch. 12 (Martingale representation theorem), Ch. 13 (Girsanov Theorem)
16 4.11 Thur Ch. 13 (Girsanov Theorem)
17 4.15 Mon Ch. 14 (Equivalent Martingale Measure)
18 4.18 Thur Ch. 14
Exam 4.21 Sun Final Exam 11 AM - 1 PM

Homeworks:


Syllabus

Classes:

  • Lectures: Monday & Thursday 10:30 AM – 12:20 PM
  • Venue: PHBS Building, Room 313

Instructor: Jaehyuk Choi

  • Office: PHBS Building, Room 755
  • Phone: 86-755-2603-0568
  • Email: [email protected]
  • Office Hour: Monday 7 - 9 PM (Tentative)

Teaching Assistance: 周恩泽 (Zhou Enze)

Textbooks and Reading Materials

Assessment / Grading Details

  • Attendance 20%, Assignments 15%, Mid-term Exam 30%, Final Exam 35%
  • Attendance: Randomly checked. The score is calculated as 20 – 2x(#of absence). Leave requests should be made 24 hours before with supporting documents, except for emergencies. Job interview/internship cannot be a valid reason for leave
  • Mid-term Exam: 3.21 Tue, Final Exam: 4.29 Sat
    • Closed-book exam. No computer/phone/calculator.
    • Some problems will be similar to those in the past exams.
  • Grade in letters (e.g., A+, A-, ... ,D+, D, F). A- or above < 30% and B- or below > 10%.
  • The level of background knowledge may vary among students, but it will be ignored in grading. Grading will be strictly based on outcome, not on effort or progress.

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