COMPUTATIONAL TOOLS FOR FINANCE

The purpose of the course is understanding the main computational techniques to solve financial problems. In particular: studying the programming language VBA linked to Excel, using numerical applications for option pricing, understanding the Monte-Carlo methods, understandinghow to simulate stochastic models.

Basic of programming languages, option pricing, binomial trees, Black-Scholes model

Knowledge and understanding: The program aims to consolidate mathematical-statistical tools and advanced knowledge in the financial markets and portfolio theory. Applying knowledge and understanding: The students will be able to: • understand financial problems and build algorithms; • price and hedge basic derivatives; • use computational techniques as Monte-Carlo simulation. Making judgements: We expect students to be able to understand advanced financial problems and to define optimal numerical solutions to solve them. Throughout the whole course, students will be invited to critically analyse the algorithms in order to optimally define them. Communications Skills: Students will be encouraged to share ideas and doubts with the goal of shared solution. For this purpose, team project will be helpful. Learning skills: Through active student involvement and sharing of advanced algorithms. These learning outcomes will be verified through intermediate evaluations.

VBA and excel, the CRR model and option pricing, Monte-Carlo methods, Value at risk, Finite difference method, stochastic processes

Lecture notes. J.C. Hull, Option futures and other derivatives. P. Glasserman, Monte Carlo methods in financial engineering

Traditional lecture, exercises, team work

The exam is divided in two parts. 50% group project (solving a computational problem) and 50% the oral exam. Students may do the written midterm exam instead of the oral exam.

To be agreed.