To create a fully specified mean-variance portfolio optimization
problem, instantiate the
Portfolio object using
information on the workflow when using
objects, see Portfolio Object Workflow. For information about creating a Portfolio object, see Getting Started with Portfolio Optimization (13 min 31
|Create Portfolio object for mean-variance portfolio optimization and analysis|
- Creating the Portfolio Object
To create a fully specified mean-variance portfolio optimization problem, instantiate the Portfolio object using the Portfolio function.
- Common Operations on the Portfolio Object
Common operations for setting up a Portfolio object.
- Setting Up an Initial or Current Portfolio
The Portfolio object property
InitPortlets you identify an initial or current portfolio.
- Setting Up a Tracking Portfolio
The Portfolio object property
TrackingPortlets you identify a tracking portfolio.
- Asset Allocation Case Study
This example shows how to set up a basic asset allocation problem that uses mean-variance portfolio optimization with a
Portfolioobject to estimate efficient portfolios.
- Portfolio Optimization Examples Using Financial Toolbox™
Follow a sequence of examples that highlight features of the
- Portfolio Optimization Against a Benchmark
This example demonstrates optimizing a portfolio to maximize the information ratio relative to a market benchmark.
- Leverage in Portfolio Optimization with a Risk-Free Asset
This example shows how to use the
setBudgetfunction for the
Portfolioclass to define the limits on the
sum(AssetWeight_i)in risky assets.
- Portfolio Optimization with Semicontinuous and Cardinality Constraints
This example shows how to use a Portfolio object to directly handle semicontinuous and cardinality constraints.
- Black-Litterman Portfolio Optimization Using Financial Toolbox™
This example shows the workflow to implement the Black-Litterman model with the
Portfolioclass in Financial Toolbox™.
- Portfolio Optimization Using Factor Models
This example shows two approaches for using a factor model to optimize asset allocation under a mean-variance framework.
- Portfolio Optimization Using Social Performance Measure
Portfolioobject to minimize the variance, maximize return, and maximize the average percentage of women on a company's board.
- Risk Budgeting Portfolio
This example shows how to use
riskBudgetingPortfolioto create a risk budgeting portfolio and
portfolioRiskContributionto compute the risk contribution of the assets in the portfolio.
- Backtest Using Risk-Based Equity Indexation
This example shows how to use backtesting with a risk parity or equal risk contribution strategy rebalanced approximately every month as a risk-based indexation.
- Create Hierarchical Risk Parity Portfolio
This example shows how to compute a hierarchical risk parity (HRP) portfolio.
- Risk Parity or Budgeting with Constraints
This example shows how to solve risk parity or budgeting problems with constraints using
- Portfolio Optimization Theory
Portfolios are points from a feasible set of assets that constitute an asset universe.
- Portfolio Object
Using the Portfolio object and associated functions for portfolio optimization.
- Default Portfolio Problem
The default portfolio optimization problem has a risk and return proxy associated with a given problem, and a portfolio set that specifies portfolio weights to be nonnegative and to sum to
- When to Use Portfolio Objects Over Optimization Toolbox
The three cases for using Portfolio, PortfolioCVaR, PortfolioMAD object are: always use, preferred use, and use Optimization Toolbox.
- Role of Convexity in Portfolio Problems
Characteristics of convexity, concavity, and nonconvexity in portfolio problems.
- Getting Started with Portfolio Optimization (4 min 13 sec)
- Optimization in MATLAB for Financial Applications (63 min 00 sec)
- MATLAB for Portfolio Construction: Smart Beta (5 min 29 sec)
- Using MATLAB to Develop and Deploy Financial Applications (51 min 20
- Integrating MATLAB Based Financial Analytics into Databases, Web, and
Messaging Systems (29 min 32 sec)
- MATLAB Production Server for Financial Applications (38 min 28
- Commodities Trading with MATLAB (44 min 28 sec)
- Walk-Forward Analysis: Using MATLAB to Backtest Your Trading Strategy
(35 min 16 sec)
- Algorithmic Trading with MATLAB for Financial Applications (64 min 42
- Automated Trading System Development with MATLAB (70 min 21 sec)