MATLAB Financial Toolbox provides functions for mathematical modeling and statistical analysis of financial data. You can optimize portfolios of financial instruments, optionally taking into
account turnover and transaction costs. The toolbox enables you to estimate risk, analyze interest rate levels, price equity and interest rate derivatives, and measure investment performance.
Time series analysis capabilities let you perform transformations or regressions with missing data and convert between different trading calendars and day-count conventions.The major themes
developed in this book are:CVar Portfolio Optimization TollsPortfolio Optimization TheoryPortfolio Optimization ProblemsPortfolio Problem SpecificationReturn ProxyRisk ProxyPortfolio Set for
Portfolio OptimizationDefault Portfolio ProblemPortfolioCVaR ObjectPortfolioCVaR Object Properties and MethodsWorking with PortfolioCVaR ObjectsSetting and Getting PropertiesDisplaying
PortfolioCVaR ObjectsSaving and Loading PortfolioCVaR Objects Estimating Efficient Portfolios and Frontiers Arrays of PortfolioCVaR ObjectsSubclassing PortfolioCVaR ObjectsConventions for
Representation of Data Constructing the PortfolioCVaR ObjectSyntaxPortfolioCVaR Problem SufficiencyConstructor ExamplesCommon Operations on the PortfolioCVaR Object Naming a PortfolioCVaR
ObjectConfiguring the Number of Assets in the Asset UniverseSetting Up a List of Asset IdentifiersTruncating and Padding Asset ListsSetting Up an Initial or Current PortfolioAsset Returns and
ScenariosHow Stochastic Optimization WorksWhat are Scenarios?Setting Scenarios Using the PortfolioCVaR ConstructorSetting Scenarios Using the setScenarios MethodEstimating the Mean and
Covariance of ScenariosSimulating Normal ScenariosSimulating Normal Scenarios from Returns or PricesSimulating Normal Scenarios from Returns or Prices withMissing DataSimulating Normal
Scenarios from Time Series DataSimulating Normal Scenarios with Mean and Covariance ofAsset ReturnsWorking with a Riskless AssetWorking with Transaction CostsWorking with CVaR Portfolio
ConstraintsSetting Default Constraints for Portfolio Weights Working with Bound ConstraintsWorking with Budget ConstraintsWorking with Group ConstraintsWorking with Group Ratio
ConstraintsWorking with Linear Equality ConstraintsWorking with Linear Inequality Constraints Working with Average Turnover Constraints Working with One-Way Turnover Constraints Validate the
CVaR Portfolio ProblemValidating a CVaR Portfolio SetValidating CVaR PortfoliosEstimate Efficient PortfoliosObtaining Portfolios Along the Entire Efficient FrontierObtaining Endpoints of the
Efficient FrontierObtaining Efficient Portfolios for Target Returns Obtaining Efficient Portfolios for Target Risks Choosing and Controlling the SolverEstimate Efficient FrontiersObtaining CVaR
Portfolio Risks and ReturnsObtaining Portfolio Standard Deviation andValue-at-RiskPlotting the Efficient FrontierPostprocessing ResultsSetting Up Tradable PortfoliosWorking with Other Portfolio
ObjectsTroubleshooting CVaR Portfolio Optimization Results
