Applied Computational Finance

Course Content

9 courselets

	ACF Lecture 1 - Primer on stochastic calculus (pdf)	3.86 M

	ACF Lecture 2 - Black-Scholes Model (pdf)	3.06 M

	Applied Computational Finance Lecture 3 - Local volatility (pdf)	898.45 K

	Applied Computational Finance Lecture 4 - Stochastic volatility (pdf)	2 M

	ACF Lecture 5 - Calibration (pdf)	1.64 M

	ACF Lecture 6 - Monte Carlo Methods (pdf)	1.29 M

	ACF Lecture 7 - Variance reduction methods (pdf)	971.57 K

	ACF Lecture 8 - Finite difference methods (pdf)	871.62 K

	ACF Lecture 9 - Optimisation (pdf)	969.02 K

Requirements

Applied Computational Finance

General Overview

Description

The main aim of this course is to introduce students to core techniques in computational finance, such as simulation of asset prices, pricing options using stochastic models, Monte Carlo methods as applied to complex derivatives, solving Black-Scholes type equations numerically, solving dynamic optimization problems numerically, and how to implement these techniques using modelling software packages and programming languages.

This course consists of nine lectures and is designed to span approximately ten teaching weeks in a Master’s program in Financial Engineering.

The recommended texts are:

Wilmott, P. (2006). Paul Wilmott on Quantitative Finance, Wiley. (PW) This book provides intuitive and heuristic explanations for various concepts in mathematical finance.
Bergomi, L. (2016). Stochastic Volatility Modeling. Chapman and Hall. (LB) This book will serve a core text for the topic of local and stochastic volatility.
Kloeden, P. and Platen, E. (1999). Numerical Solution of Stochastic Differential Equations. Springer, Third Edition. (KP) This book will be used to study stochastic differential equations.
Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer. (PG) This book will be used to study the basics of Monte Carlo simulation.