87281 - ADVANCED COMPUTATIONAL FINANCE

Learning outcomes

The objective of the course is to address frontier topics in computational finance and muster the computational skills needed to tackle them. At the end of the course the student will have a working experience on selecting the most appropriate model and the capability to devise, and implement the numerical tools needed for problem at hand. The student will be able to judge the quality of the results from data analysis, and she will have enough knowledge to set up benchmarks to test for the accuracy of the numerical results. The topics addressed will refer to state-of-the-art issues in pricing, risk management and numerical techniques and programming languages. The student will be exposed, and gain proficiency, in rapid prototyping languages like Python and languages better suited to achieve high computational performance, like C/C++

Course contents

Generating Random Variables

Binary Trees

• European Options
• American Options
  

Log Normal Processes

• Vanilla Options
• Cap and floor options
  

Jumps diffusion processes

• The Merton Model
• The Bernoulli model
• Kou double exponential model
• Variance Gamma model

Barrier Options

• The Feynman-Kac formula
• First passage time
• Knock-in and Knock-out options

Short Rate Models

• Vasicek model
• Hull-White model

Inflation Rate Models

• The Mercurio Model
• Jarrow-Yildirim Model
  

Readings/Bibliography

Lecture Notes from the Teacher

Seggested readings:

Monte Carlo method in financial engineering

by prof. Paul Glasserman

Teaching methods

Lecture from the teacher

Homeworks assignement

Assessment methods

At the end of the course students will have to complete a project consisting in a MC simulation to price options using one of the models introduced

Office hours

See the website of Pietro Rossi