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TOTC1119 — STOCHASTIC SIMULATION WITH APPLICATIONS IN FINANCE | Chương trình đào tạo NEU

TOTC1119 — STOCHASTIC SIMULATION WITH APPLICATIONS IN FINANCE

Credits
3
Self-study hours
90
Showing intake
K68-2026

Course description

This course introduces Monte Carlo stochastic simulation with an emphasis on applications in finance and insurance. Students will learn to simulate univariate and multivariate random variables, as well as stochastic processes such as random walks, point processes, geometric Brownian motion, and Poisson processes. The simulations will be applied to numerically estimate key quantities of interest, including probabilities, Value at Risk, option prices, expected values, and integrals. The course also covers techniques to improve simulation efficiency (variance reduction methods) and approaches to statistical output analysis (such as confidence intervals). While the primary focus is on financial applications, additional examples from other fields will be presented. R programming will be employed throughout the course.

Course requirements

Requirements for Final Examination

Attendance score must be at least 5 (according to University regulations).

See Appendix 2, Rubric 1 for attendance evaluation.

Requirements for Class Attendance

Students are responsible for attending all scheduled classes.

In case of absence due to force majeure, students must actively acquire the knowledge through the materials provided by the lecturer and other assigned readings.

Requirements for In-class Behaviour

The course is conducted on the principle of mutual respect between lecturers and students.

Students are required to take full notes and participate in answering questions.

Students must read assigned materials in advance.

Homework assignments must be completed as required.

Students must not make noise and disturb others.

Laptops, tablets, and mobile phones may only be used for note-taking, calculations, or learning purposes, and must not be used for any other activities.

Lecturers

  • Vuong Van Yen
  • Nguyễn Quang Huy

Teaching plan

Week 1

Lecture 1. Overview - Review of basic probability and statistics - Intro to R programming
References
- Syllabus - [1] Chapter 1, 2 - [2] Chapter 1, 2
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check

Week 2

Lecture 1. Overview (cont.) + Vector, Matrix, Array in R + Control flow + Loops + Functions
References
- [1] Chapter 1, 2 - [2] Chapter 1, 2
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 3

Lecture 2. Introduction to simulation - Discrete event simulation - Monte Carlo simulation
References
[1] Chapter 1,2 [2] Chapter 5
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 4

Lecture 2. Introduction to simulation (cont.) - Conduct Monte Carlo method to solve probability problems
References
[1] Chapter 1,2 [2] Chapter 5
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 5

Lecture 3. Random number - Pseudorandom number generation - Numerical integration
References
[1] Chapter 3
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 6

Lecture 4. Generation of univariate random variable - The inverse method - The acceptance-rejection method
References
[1] Chapter 4, 5
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 7

Lecture 4. Generation of univariate random variable (cont.) - Continuous random variables. - Discrete random variables. - Mixed random variables. Midterm 1
References
[1] Chapter 4, 5
Assessment
Criteria: Calculation problems Tools: Rubric 1, 2 Method: Written Exam

Week 8

Lecture 5. Generation of multivariate random variable - Conditional distribution computation - Inverse method and conditional distribution
References
[1] Chapter 6 [2] Chapter 2
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 9

Lecture 5. Generation of multivariate random variable (cont.) - Multivariate normal distribution - Introduction to copula
References
[1] Chapter 6 [2] Chapter 2
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 10

Lecture 6. Generation of stochastic process - Poisson process: homogeneous, nonhomogeneous and compound
References
[2] Chapter 7
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 11

Lecture 6. Generation of stochastic process (cont.) - Brownian motion - Geometric Brownian motion - Ornstein–Uhlenbeck process
References
[2] Chapter 7
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 12

Lecture 7. Application of Monte Carlo in finance and insurance - Option pricing - One-factor short rate models and the term structure of interest rate
References
[2] Chapter 9, 10, 11, 12
Assessment
Criteria: Calculation problems Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 13

Lecture 7. Application of Monte Carlo in finance and insurance (cont.) - Collective model and ruin theory
References
[2] Chapter 9, 10, 11, 12
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 14

Lecture 7. Application of Monte Carlo in finance and insurance (cont.) - Portfolio credit risk analysis Midterm 2
References
[2] Chapter 9, 10, 11, 12
Assessment
Criteria: Calculation problems Tools: Rubric 1, 2 Method: Written Exam

Week 15

Summary and Revision
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Learning resources

  • 1. Course Books
  • . Monte Carlo Simulation with Applications to Finance — Hui Wang, Chapman and Hall/CRC Financial Mathematics, 2012
  • 2. References
  • . Stochastic Simulation and Applications in Finance — Huu Tue Huynh, Van Son Lai, Issouf Soumare, 2th Edition, 2008
  • R programing