**Self-Paced Course****Location: Globally online****This course can be included as part of the Annual Subscription Service.****This course be taken In House**

**Outline:**

The course gives an overview of the Matlab system with a view towards financial engineering. Since this is a beginners course we start by giving an introduction to the basic functionality like plotting, handling of matrices, using m-files and running scripts. All examples are based on financial problems. Thus, we aim to implement the Black-Scholes pricing formula, calculate Greeks. Furthermore, we consider writing programs. To this end we develop basic programming skills and show how to transform algorithms to working Matlab code and how to arrange the code. We wish to use the Binomial model as an example. Finally, we cover useful functionality for everyday life such as interpolation, integration or special functions.

The last topic is on Monte Carlo simulation. We wish to outline the development of a Monte Carlo simulation application for option pricing. To this end we cover random number generation, calculating the Monte Carlo estimator as well as the Standard error and presenting the outcome as a convergence table or a convergence plot.

After the course you know the basic functionality of the Matlab system and you have a solid background for tackling financial problems with Matlab. Furthermore, you are able to explore further techniques such as object oriented programming and larger projects with the skills you acquired during this course.

**Lecture 1. Introduction to Matlab **

- The Matlab Workspace
- Working with Matlab (Importing Data, Vectors, Matrices, …)
- The Help Functionality
- Matlab for Financial Engineering – A Perspective

**Lecture 2. Basic Functionality **

- Plotting and Visualizing
- 2D Plots and Subplots
- Interpolation
- 3D Plots
- Further Issues with Plotting

**Lecture 3. Programming in Matlab **

- m-files
- Script m-files
- Introduction to Programming
- Standard techniques
- Special Matlab topics
- Summary of Basic Programming tasks

- Example: Black Scholes Merton Formula, Greeks, Binomial Trees

**Lecture 4. Data Types **

- Logic Arrays, n-dim Arrays, Sparse Arrays, CellArrays, …
- Function Handles
- Example: Optimization

**Lecture 5. Useful Functionality **

- Special Functions
- Integration and Transforms
- Example: Implementing Option Pricing Methods

**Lecture 6. Monte Carlo**

- Random Number Generation
- Path Generation
- Example: MC Application (Path-Dependent Options)

**About the Presenter:**

Jörg Kienitz is Partner at Quaternion Risk Management. Prior to this he was Director, Assurance FSI at Deloitte Germany and Head of Quantitative Analytics at Deutsche Postbank AG. He is currently running projects on model validation as well as the implementation of mathematical methods for risk management and exposure calculation. He also holds an adjunct professorship at the University of Cape Town (UCT) and lectures on Computational Finance at the University of Wuppertal (BUW). Jörg is a speaker at a number of major quant finance conferences including Global Derivatives and WBS Fixed Income. His books "Interest Rate Derivatives Explained I + II" appear with Palgrave McMillan and "Financial Modelling" with Wiley.

**CPD Certification:**

You will be able to receive up to **27 CPD points (9 hours of structured CPD and 18 hours of self-directed CPD)** for completing this course.

The CPD Certification Service was established in 1996 as the independent CPD accreditation institution operating across industry sectors to complement the CPD policies of professional and academic bodies. The CPD Certification Service provides recognised independent CPD accreditation compatible with global CPD principles.

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