MATLAB for Financial Applications Training Course

matfin

Продължителност

21 hours (usually 3 days including breaks)

Изисквания

A-level maths or economics, or relevant experience in the workplace, is advisable for this material

Общ преглед

MATLAB is a numerical computing environment and programming language developed by MathWorks.

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:
1. Dealing with Missing data
2. Cleaning data, Unusual observations
3. Data Transformations
• Statistical functions

Part II – Financial Applications

Overview of Matlab toolboxes relevant to Financial Analysis

• Financial Toolbox
• Financial Instruments 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

• 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

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