ST463 Half Unit
Stochastic Simulation, Training, and Calibration
This information is for the 2025/26 session.
Course Convenor
Giulia Livieri
Availability
This course is compulsory on the MSc in Quantitative Methods for Risk Management. This course is available on the MSc in Econometrics and Mathematical Economics, MSc in Financial Mathematics, MSc in Mathematics and Computation, MSc in Operations Research & Analytics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process.
How to apply: Priority will be given to students on the MSc in Quantitative Methods for Risk Management.
Any student who has not taken ST409 should provide a statement explaining the extent to which they have studied topics from ST409 before.
If students can demonstrate a good understanding of relevant statistical and machine learning models, this may compensate for ST409, provided the student is ready to acquire the necessary background in probability and stochastic processes through self-study
Deadline for application: Due to the nature of the method of application, interested students should apply as soon as possible after the opening selection and no later than 10.00am on Friday 26 September 2025.
Course lecturers will aim to make initial offers to students on LSE For You by Friday 26 September.
For queries contact: Stats-Msc@lse.ac.uk
From 2025/26, this course will be compulsory for MSc in Quantitative Methods for Risk Management students.
This course has a limited number of places (it is controlled access). Priority is given to students on the MSc Quantitative Methods for Risk Management.
Requisites
Additional requisites:
Any student who has not taken ST409 would need to obtain permission from the lecturer. They need to provide a statement explaining the extent to which they have studied topics from ST409 before. If students can demonstrate a good understanding of relevant statistical and machine learning models, this may compensate for ST409, provided the student is ready to acquire the necessary background in probability and stochastic processes through self-study.
Course content
The course will equip students with the skills to competently apply modern statistical and machine-learning methods to critical computational problems within the nexus of quantitative finance, risk management, and insurance. The course will start by covering key aspects of Monte Carlo methods, simulation of stochastic processes, and generative adversarial networks with applications to risk management. Next, the course will discuss generalized linear models, building a bridge to deep neural networks and looking at novel applications in insurance. From there, the course proceeds to discuss the key challenges for effective calibration of statistical and machine learning models in general. Finally, the course concludes with a treatment of reinforcement learning and applications to hedging in commodity and energy markets through swing option pricing.
Teaching
10 hours of seminars and 20 hours of lectures in the Winter Term.
Formative assessment
The formative assessment of the course will be based on weekly problem sets given in the seminars/computer classes; the examples in the computer classes will be based both on synthetic (i.e., simulated) and real data examples.
Indicative reading
- P. Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2003
- I. Goodfellow et al., Generative Adversarial Networks, Communications of the ACM, 2020.
- Dobson, A.J.; Barnett, A.G., Introduction to Generalized Linear Models (3rd ed.). 2008.
- A. Bella et. al, Calibration of a Machine Learning model, 2012.
- Sutton, R. and Barto, A., Reinforcement Learning: An Introduction, 1998.
Assessment
Project (40%) in Winter Term Week 6
Project (30%) in Spring Term Week 1
Project (30%) in Spring Term Week 1
Key facts
Department: Statistics
Course Study Period: Winter Term
Unit value: Half unit
FHEQ Level: Level 7
CEFR Level: Null
Total students 2024/25: 33
Average class size 2024/25: 17
Controlled access 2024/25: NoCourse selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Self-management
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Commercial awareness
- Specialist skills