Overview of the MATLAB Financial Toolbox
Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed 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)
- Familiarity with linear algebra (i.e., matrix operations)
- Familiarity with basic statistics
- Understanding of financial principles
- Understanding of MATLAB fundamentals
- If you wish to take this course, but lack experience in MATLAB (or need a refresher), this course can be combined with a beginner's course and provided as: MATLAB Fundamentals + MATLAB for Finance.
- If you wish to adjust the topics covered in this course (e.g., remove, shorten, or lengthen coverage of certain features), please contact us to arrange.
Les exercices pratiques et la disponibilité du formateur pour répondre aux questions.
Sebastien Botte - SDECCI
Course - MATLAB Programming
Disponibilité et adaptabilité, réponses aux questions
Jean-Michel MEOT - CIRAD
Course - Introduction au Machine Learning avec MATLAB
J'ai beaucoup apprécié la capacité du formateur à s'adapter à notre besoin spécifique et à nous proposer un support de formation adapté.
Jeremy Herviou - Michelin
Course - Basic MATLAB Programming
The many examples and the building of the code from start to finish.