Course Outline
Overview of the MATLAB Financial Toolbox
Objective: Learn to leverage the various features of the MATLAB Financial Toolbox to perform quantitative analysis for the financial sector. 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: Execute capital allocation, asset allocation, and risk assessment.
- Estimating asset return and total return moments from price or return data
- Calculating portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR)
- Conducting constrained mean-variance portfolio optimization and analysis
- Examining the evolution of efficient portfolio allocations over time
- Performing capital allocation
- Incorporating turnover and transaction costs into portfolio optimization problems
Risk Analysis and Investment Performance
Objective: Define and resolve portfolio optimization problems.
- Specifying a portfolio name, the number of assets in the universe, and asset identifiers.
- Defining an initial portfolio allocation.
Fixed-Income Analysis and Option Pricing
Objective: Conduct fixed-income analysis and option pricing.
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- Analyzing cash flows
- Performing SEC-compliant fixed-income security analysis
- Conducting basic Black-Scholes, Black, and binomial option pricing
Financial Time Series Analysis
Objective: Analyze time series data within financial markets.
- Performing mathematical operations on data
- 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 (e.g., matrix operations)
- Knowledge of basic statistics
- Understanding of financial principles
- Knowledge of MATLAB fundamentals
Course Options
- If you are interested in this course but lack experience with MATLAB (or need a refresher), it can be combined with a beginner's course, offered as: MATLAB Fundamentals + MATLAB for Finance.
- If you wish to customize the topics covered (e.g., adding, removing, or adjusting the depth of coverage for specific features), please contact us to arrange a tailored schedule.
Testimonials (2)
That I knew topics that I didn't know
Ernesto Alonso Ocana Valenzuela - Instituto Tecnologico Superior de Comalcalco
Course - Introduction to Image Processing using Matlab
Machine Translated
Many useful exercises, well explained
