Computational Finance

6 EC

Semester 2, period 5

5284COFI6Y

Owner Master Computational Science (joint degree)
Coordinator dr. Sven Karbach
Part of Master Computational Science (Joint Degree), Master Stochastics and Financial Mathematics, specialization Data Analysis and Statistics, year 1Master Stochastics and Financial Mathematics, specialization Financial Mathematics, year 1Master Stochastics and Financial Mathematics, specialisation Probability and Decision Making, year 1
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Course manual 2024/2025

Course content

Many models used in finance end up in formulation of highly mathematical problems. Solving these equations exactly in closed form is impossible as the experience in other fields suggests. Therefore, we have to look for efficient numerical algorithms in solving complex problems such as option pricing, risk analysis, portfolio management, etc.

The course "Computational Finance" offers an in-depth exploration of the mathematical and computational techniques essential for modeling and computations used in finance. The course is structured to bridge theoretical foundations with practical applications, preparing students for challenges in both academic research and the finance industry. Key topics include stochastic modeling, derivatives pricing, volatility modeling, model calibration, Monte Carlo methods, and PDE techniques. Students will learn to implement advanced techniques to model complex financial systems, price derivatives, and evaluate risks effectively.

Study materials

Other

  • Lecture Notes

     

Objectives

  • Understand foundational theories in financial pricing, risk modeling, and stochastic processes.
  • Implement numerical methods (e.g., Monte Carlo simulations, PDE solvers) using Python.
  • Model and calibrate financial instruments
  • Develop computational solutions for pricing derivatives and managing risk
  • Critically evaluate simulation results and effectively communicate technical findings.
  • Collaborate on practical projects and independently solve complex financial problems.

Teaching methods

  • Lecture
  • Computer lab session/practical training

The course emphasizes knowledge transfer and practical guidance, with lectures focusing on core principles and computational methods. Practitioner sessions, led by industry experts, complement the theoretical content by addressing real-world problems in computational finance.

Through hands-on coding exercises and group projects, students will develop robust problem-solving skills and technical proficiency. The course also introduces industry-standard tools in Python, fostering practical expertise applicable to various domains in quantitative finance.

Learning activities

Activity

Number of hours

Computerpracticum

30

Hoorcollege

30

Zelfstudie

108

Attendance

This programme does not have requirements concerning attendance (Ter part B).

Assessment

Item and weight Details

Final grade

2 (40%)

Tentamen

Must be ≥ 5

1 (20%)

Lab Assignment 1

1 (20%)

Lab Assignment 2

1 (20%)

Lab Assignment 3

Assignments

Project 1

  • First Lab Exercise and Homeowk (group, feedback on assignment, graded)

Project 2

  • Second Lab Exercise and Homeowk (group, feedback on assignment, graded)

Project 3

  • Third Lab Exercise and Homeowk (group, feedback on assignment, graded)

Exam

  • Written Exam (individual, feedback on exam, graded)

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1
2
3
4
5
6
7
8

Additional information

Recommended prior knowledge: Basic programming skills and mathematics (calculus and probability theory).

Contact information

Coordinator

  • dr. Sven Karbach