Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to apply various features of the MATLAB Financial Toolbox to conduct quantitative analysis for the financial industry. Gain the knowledge and practical skills 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: Perform capital allocation, asset allocation, and risk assessment.
- Estimating asset return and total return moments from price or return data.
- Computing portfolio-level statistics, including mean, variance, value at risk (VaR), and conditional value at risk (CVaR).
- Performing 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: Perform fixed-income analysis and option pricing.
- Analyzing cash flows.
- Performing 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 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.
- Performing common regressions.
- Estimating log-likelihood functions 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 concepts, particularly matrix operations.
- Familiarity with basic statistical principles.
- Understanding of fundamental financial principles.
- Knowledge of MATLAB fundamentals.
Course Options
- For those who wish to take this course but lack MATLAB experience or require a refresher, this course can be combined with a beginner-level module as: MATLAB Fundamentals + MATLAB for Finance.
- If you wish to customize the topics covered in this course (such as removing, shortening, or extending the coverage of specific features), please contact us to make the necessary 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
