MAT16 Computational Finance with Python

• Topics

  The course comprehensively introduces numerical simulations of financial derivatives using Python.

  Numerical simulations, a cornerstone in many fields, including finance, economics, and ecological and climate sciences, are not just tools. They are the foundation for analyzing complex systems, enabling researchers and decision-makers to explore scenarios, predict outcomes, and develop strategies. These simulations are essential for informed decision-making, policy development, and long-term planning in a rapidly changing world.

  This course introduces the computation of financial derivatives, focusing on both the theoretical aspects and practical implementation using Python. Students will learn about various types of derivatives. The course also covers pricing models, hedging strategies, and risk management techniques.

  Practical sessions using Python reinforce the concepts learned in lectures, allowing students to apply theoretical knowledge to real-world financial problems. With its versatility and powerful capabilities, Python has become one of the most popular programming languages in recent years, finding applications in both business and scientific research. Learning Python is increasingly important across various fields.

  After completion of the course, you will be able to perform numerical simulations in Python. We focus on the computation of financial derivatives. However, the skills developed by the course can be used to perform numerical simulations for other models. Such qualifications can be relevant for different projects and a master's thesis.

• Learning outcome

  After completion of the course, the students

  KNOWLEDGE 

  • Have obtained essential knowledge of financial derivatives and learned how to perform numerical computations in Python.

   

  SKILLS

  • Can describe the fundamental concepts and types of financial derivatives, including options, futures, swaps, and their role in financial markets.
  • Can utilize Python programming for financial analysis, including proficiency in using libraries such as NumPy and Matplotlib for basic financial computations and visualization.
  • Can develop and implement numerical models for pricing financial derivatives, including the Black-Scholes model, binomial tree models, and Monte Carlo simulations.
  • Understand the impact of volatility, interest rates, and time to maturity.
  • Can calculate and interpret key risk metrics, such as Delta, Gamma, Vega, Theta, and Rho (the "Greeks"), using Python to assess and manage the risks associated with derivative positions.
  • Can critically analyze the outcomes of numerical simulations, interpret the results in the context of financial theory, and make informed decisions based on these insights.
  • Can present simulation results and analysis clearly and effectively
  • Can use appropriate visualizations and reports to communicate complex financial concepts to technical and non-technical audiences.

   

  GENERAL COMPETENCE

  • Are familiar with advanced methods of numerical simulations and can apply them in Python to numerical computation of financial derivatives and other areas (economics, ecology, climate science, etc.).

• Teaching

  Plenary lectures 2 x (2x45) / week,

  possibly practical sessions for the use of Python.

• Recommended prerequisites

  An introductory programming experience (preferably in Python) will be helpful, though it is not required.

• Required prerequisites

  Basic knowledge of mathematics (standard concepts from analysis, linear algebra, probability, and statistic, which correspond to MET1 Mathematics for economists and MET2 Statistics for economists).

• Compulsory Activity

  One obligatory assignment is required for taking the final exam.

• Assessment

  4-hour home exam. The exam will have to be answered in English.

• Grading Scale

  Grading scale A - F

• Computer tools

  The course will use Python (other special programs can also be used), which is open-source. Details regarding the installation of different packages and additional tools will be provided.  

• Literature

  Course textbook:

  Elisa Alòs and Raúl Merino (2023) Introduction to Financial Derivatives with Python, CRC Press/Taylor & Francis Group.

  There will be additional notes in Canvas.

• Permitted Support Material

  Allowed materials: All written and digital.

  No use of Generative AI is allowed. All text, code, and analysis must be the student's own.