Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Acquire the skills to utilize the various features of the MATLAB Financial Toolbox for quantitative analysis within the financial industry. Gain the knowledge and practical experience required to efficiently develop 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 moments of asset return and total return from price or return data
- Calculating portfolio-level statistics, such as 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
- Executing capital allocation
- Accounting for turnover and transaction costs in portfolio optimization scenarios
Risk Analysis and Investment Performance
Objective: Define and solve portfolio optimization problems.
- Specifying portfolio name, the number of assets in the universe, and asset identifiers
- Establishing an initial portfolio allocation
Fixed-Income Analysis and Option Pricing
Objective: Conduct fixed-income analysis and option pricing.
- Analyzing cash flows
- Performing IFRS-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 mathematics
- 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
- Understanding of linear algebra, particularly matrix operations
- Knowledge of basic statistics
- Familiarity with core financial principles
- Grasp of MATLAB fundamentals
Course Options
- For those interested in this course but lacking MATLAB experience or requiring a refresher, it can be combined with a beginner-level course as: MATLAB Fundamentals + MATLAB for Finance.
- To customize the topics covered (e.g., by adding, removing, or adjusting the duration of specific feature coverage), please contact us to make arrangements.
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
