Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to apply the various features of the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice 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, such as 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 flow.
- 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 in 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 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 (e.g., matrix operations).
- Familiarity with basic statistics.
- Understanding of financial principles.
- Understanding of MATLAB fundamentals.
Course Options
- If you wish to enroll but lack experience with MATLAB (or require a refresher), this course can be combined with a beginner's course, offered as: MATLAB Fundamentals + MATLAB for Finance.
- If you wish to customize the topics covered in this course (e.g., removing, shortening, or extending coverage of specific features), please contact us to arrange a tailored schedule.
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
