Course Contents
This course covers advanced computational techniques crucial for modeling, analyzing, and solving complex financial problems such as derivatives pricing, risk analysis, and portfolio management. Students will explore stochastic modeling, volatility modeling, model calibration, Monte Carlo methods, and PDE-based methods, focusing on practical implementation and critical analysis of numerical solutions.
Aim and Objectives
The primary aim of this course is to provide a comprehensive understanding of mathematical and computational methods used in financial modeling. Specifically, students will:
- Understand foundational theories in financial pricing, risk modeling, and stochastic processes.
- Implement numerical methods (Monte Carlo simulations, PDE solvers) using Python.
- Model and calibrate financial instruments.
- Critically evaluate simulation results and effectively communicate technical findings.
- Develop practical computational skills through collaborative projects.
Prerequisites
Recommended prior knowledge includes basic programming skills and foundational mathematics (calculus and probability theory).
Literature and Materials
Lecture notes (provided).
Teaching Methods
- Lectures
- Computer lab sessions (practical training)
- Practitioner sessions by industry experts
Assessment Details
- Assessment Components:
- Written Exam (40%, individual, minimum grade ≥ 5 required)
- Lab Assignment 1 (20%, group)
- Lab Assignment 2 (20%, group)
- Lab Assignment 3 (20%, group)
Fraud and Plagiarism
This course adheres to the "Regulations governing fraud and plagiarism for UvA students." Suspected cases will be reported. For details, visit: student.uva.nl.