Matlab for Finance Training Course

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)
• Heston

Conclusion