Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to utilise the various features within the MATLAB Financial Toolbox to conduct quantitative analysis for the financial sector. Gain the knowledge and practical skills necessary 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 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
- Conducting SIA-Compliant fixed-income security analysis
- Performing basic Black-Scholes, Black, and binomial option-pricing
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
- Familiarity with linear algebra (specifically matrix operations)
- Knowledge of basic statistics
- Understanding of core financial principles
- Understanding of MATLAB fundamentals
Course Options
- If you wish to attend this course but lack MATLAB experience or require a refresher, this course can be combined with a beginner module, offered as: MATLAB Fundamentals + MATLAB for Finance.
- If you require adjustments to the topics covered in this course (e.g., removing, shortening, or extending the coverage of certain features), please contact us to arrange customisation.
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
