Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to utilise the various features of the MATLAB Financial Toolbox to conduct quantitative analysis for the financial sector. Gain the knowledge and practical skills required to efficiently develop real-world applications involving financial data.
- Asset Allocation and Portfolio Optimisation
- 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 Optimisation
Objective: Execute capital allocation, asset allocation, and risk assessment.
- Estimating asset return and total return moments from price or return data
- Computing portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR)
- Executing constrained mean-variance portfolio optimisation and analysis
- Examining the time evolution of efficient portfolio allocations
- Executing capital allocation
- Accounting for turnover and transaction costs in portfolio optimisation problems
Risk Analysis and Investment Performance
Objective: Define and solve portfolio optimisation 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
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: Execute multivariate normal regression with or without missing data.
- Performing common regressions
- Estimating 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
- Familiarity with linear algebra (i.e., matrix operations)
- Familiarity with basic statistics
- Understanding of financial principles
- Understanding of MATLAB fundamentals
Course options
- If you wish to undertake this course but lack MATLAB experience (or require a refresher), this course can be combined with a beginner's course and delivered as: MATLAB Fundamentals + MATLAB for Finance.
- If you wish to modify the topics covered in this course (e.g., by removing, shortening, or extending coverage of specific features), please contact us to arrange this.
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
