MATLAB for Financial Applications Training Course

Part I – Computational Foundations in MATLAB

Core MATLAB Operations

• MATLAB interface navigation and environment configuration
• Variable declaration and assignment protocols
• Foundational data structures: vectors, matrices, and tables
• Essential data manipulation techniques
• Character encoding and string handling
• Evaluation of relational expressions
• Utilization of built-in numerical functions
• Data import and export procedures
• Data visualization: graphics options, annotations, and customization

MATLAB Scripting and Programming

• Automation of tasks through script execution
• Logical operations and flow control structures (if, if-else, switch, nested conditions)
• Iteration via loop statements and vectorized code implementation
• Function development and modular coding practices

Processing Financial Data Sets

• Data structures: cell arrays, structures, tables, and time series objects
• Management of date and time formats
• Interconversion of data types and associated operations
• Table modification and manipulation routines
• Data filtering, indexing, logical indexing, and categorical analysis
• Data preparation workflows:
  1. Handling missing values
  2. Data cleaning and identification of outliers
  3. Data transformation methods
• Application of statistical functions

Part II – Financial Analysis Applications

Overview of Relevant MATLAB Toolboxes for Government and Public Sector Financial Analysis

• Financial Toolbox
• Financial Instruments Toolbox
• Trading Toolbox
• Risk Management Toolbox
• Econometrics Toolbox
• Optimization Toolbox
• Statistics and Machine Learning Toolbox

Fundamentals of Financial Modeling

• Random variables, probability distributions, and stochastic processes
• Distribution fitting techniques
• Linear regression analysis
• Simulation modeling: Monte Carlo methods
• Optimization modeling frameworks
• Optimization under uncertainty conditions

Regression Analysis and Volatility Modeling

• Linear regression principles
• Identification and mitigation of spurious regression
• Analysis of nonstationarity
• Cointegration techniques
• Conditional volatility models: ARCH and GARCH frameworks

Portfolio Theory and Asset Allocation Strategies

• Dividend discount model applications
• Modern portfolio theory implementation

Asset Pricing Models

• Capital Asset Pricing Model (CAPM)

Market Risk Management Techniques

• Value at Risk (VaR) via historical simulation
• Value at Risk (VaR) via Monte Carlo simulation
• Integration of VaR with Principal Component Analysis (PCA)

Advanced Optimization Methods

• Convex optimization techniques
• Linear programming applications
• Dynamic programming strategies
• Non-convex optimization approaches