Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to utilize the diverse features of the MATLAB Financial Toolbox to conduct quantitative analysis for the finance industry. Participants will gain the knowledge and practical experience required to efficiently develop real-world applications using 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 returns and total returns 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
- Reviewing the evolution of efficient portfolio allocations over time
- Executing capital allocation strategies
- Accounting for turnover and transaction costs in portfolio optimization models
Risk Analysis and Investment Performance
Objective: Define and resolve portfolio optimization challenges.
- Specifying portfolio names, the count of assets in an asset 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 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 manipulation
- 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 regression analyses
- Estimating the log-likelihood function and standard errors for hypothesis testing
- Completing calculations when data points are missing
Technical Indicators and Financial Charts
Objective: Gain proficiency in 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 stochastic differential equation (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 (specifically matrix operations)
- Familiarity with basic statistics
- Understanding of core financial principles
- Foundational knowledge of MATLAB
Course Options
- If you wish to enroll but lack sufficient experience with MATLAB (or require a refresher), this course can be paired with a beginner-level module, offered as: MATLAB Fundamentals + MATLAB for Finance.
- If you need to customize the topics covered (e.g., by adding, removing, or extending the depth of certain features), please contact us to discuss 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
