Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to utilize the various features of the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Develop the knowledge and practical skills needed to efficiently create real-world applications involving financial data.
- Asset Allocation and Portfolio Optimization
- Risk Analysis and Investment Performance
- Fixed-Income Analysis and Option Pricing
- Financial Time Series Analysis
- Regression and Estimation with Missing Data
- Technical Indicators and Financial Charts
- Monte Carlo Simulation of SDE Models
Asset Allocation and Portfolio Optimization
Objective: Execute capital allocation, asset allocation, and risk assessment.
- Estimating asset return and total return moments from price or return data
- Calculating portfolio-level statistics, including mean, variance, value at risk (VaR), and conditional value at risk (CVaR)
- Conducting constrained mean-variance portfolio optimization and analysis
- Examining the time evolution of efficient portfolio allocations
- Performing capital allocation
- Accounting for turnover and transaction costs in portfolio optimization problems
Risk Analysis and Investment Performance
Objective: Define and solve portfolio optimization problems.
- Specifying a portfolio name, the number of assets in an asset universe, and asset identifiers.
- Defining an initial portfolio allocation.
Fixed-Income Analysis and Option Pricing
Objective: Conduct fixed-income analysis and option pricing.
- Analyzing cash flow
- Performing SIA-Compliant fixed-income security analysis
- Executing basic Black-Scholes, Black, and binomial option-pricing models
Financial Time Series Analysis
Objective: Analyze time series data within financial markets.
- Performing data math
- Transforming and analyzing data
- Technical analysis
- Charting and graphics
Regression and Estimation with Missing Data
Objective: Perform multivariate normal regression with or without missing data.
- Conducting common regressions
- Estimating the log-likelihood function and standard errors for hypothesis testing
- Completing calculations when data is missing
Technical Indicators and Financial Charts
Objective: Practice using performance metrics and specialized plots.
- Moving averages
- Oscillators, stochastics, indexes, and indicators
- Maximum drawdown and expected maximum drawdown
- Charts, including Bollinger bands, candlestick plots, and moving averages
Monte Carlo Simulation of SDE Models
Objective: Create simulations and apply SDE models
- Brownian Motion (BM)
- Geometric Brownian Motion (GBM)
- Constant Elasticity of Variance (CEV)
- Cox-Ingersoll-Ross (CIR)
- Hull-White/Vasicek (HWV)
- Heston
Conclusion
Requirements
- Knowledge of linear algebra (e.g., matrix operations)
- Familiarity with basic statistics
- Understanding of fundamental financial principles
- Familiarity with MATLAB fundamentals
Course Options
- If you wish to take this course but lack MATLAB experience or need a refresher, this module can be combined with a beginner's course, offered as: MATLAB Fundamentals + MATLAB for Finance.
- For adjustments to the topics covered in this course (such as removing, shortening, or extending the coverage of specific features), please contact us to arrange a customized schedule.
Testimonials (2)
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
Many useful exercises, well explained
