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Course Outline
Part I – Matlab Fundamentals
Matlab Basics
- Matlab User interface
- Variables and Assignments Statements
- Basic data objects: Vector, Matrix, Table
- Basic data manipulation
- Character and Strings objects
- Relational expressions
- Built-in numerical functions
- Data Import/Export
- Visualizing data, Graphics options, Annotations, customizing graphics
Matlab Programming
- Automating commands with scripts
- Logic and flow control - if, if-else, switch, nested ifs
- Loop statements and vectorized code
- Writing functions
Working with Financial Data
- Data objects – Cell arrays, Structures, Tables, Time series
- Working with dates and times
- Conversion amongst different data types, data operations
- Modifying tables, table operations
- Data filtering, Indexing, Logical indexing, Categories
- Data preparation:
- Dealing with Missing data
- Cleaning data, Unusual observations
- Data Transformations
- Statistical functions
Part II – Financial Applications
Overview of Matlab toolboxes relevant to Financial Analysis
- Financial Toolbox
- Financial Instruments Toolbox
- Trading Toolbox
- Risk Management Toolbox
- Econometrics Toolbox
- Optimization Toolbox
- Statistics Toolbox
Financial modelling basics
- Random variables, probability distributions, random processes
- Distribution fitting
- Linear regression
- Simulation modelling – Monte Carlo Simulation
- Optimization modelling
- Optimization under uncertainty
Regression and volatility
- Linear regression
- Spurious regression
- Nonstationarity
- Cointegration
- Conditional volatility models ARCH, GARCH
Portfolio theory and asset allocation
- Dividend discount model
- Modern portfolio theory
Asset pricing models
- CAPM
Market risk management
- VAR by the historical simulation
- VAR by Monte Carlo simulation
- VAR and PCA
Optimization methods
- Convex optimization
- Linear Programming
- Dynamic Programming
- Non-convex optimization
Requirements
A-level maths or economics, or relevant experience in the workplace, is advisable for this material
21 Hours
Testimonials (3)
Concrete, hands-on exercises that were relevant to our core business. Having a trainer with a scientific background was a real asset because we could delve into deeper discussions, not just about programming but also about science and how to combine the two. The practical sessions in Jupyter Notebook format were interesting.
Victor - Vermon
Course - Python for Matlab Users
Machine Translated
The many examples and the building of the code from start to finish.
Toon - Draka Comteq Fibre B.V.
Course - Introduction to Image Processing using Matlab
The practical exercises and the trainer's availability to answer questions.
Sebastien Botte - SDECCI
Course - MATLAB Programming
Machine Translated
