Financial Toolbox provides a comprehensive suite of portfolio optimization and analysis tools for performing capital allocation, asset allocation, and risk assessment. With these tools, you can:
The portfolio optimization object provides a simplified interface for defining and solving portfolio optimization problems that include descriptive metadata. You can specify a portfolio name, the number of assets in an asset universe, and asset identifiers. Additionally, you can define an initial portfolio allocation.
The toolbox supports two approaches to portfolio optimization:
Supported constraints include: linear inequality, linear equality, bound, budget, group, group ratio, average turnover, and one-way turnover.
Additionally, you can work with transaction costs in the portfolio optimization problem definition. You apply transaction costs on either gross or net portfolio return optimization. Transaction costs can be proportional or fixed, and they are incorporated as units of total return.
The portfolio optimization object provides error checking during the portfolio construction phase. For complex problems defined with multiple constraints, validating your inputs to or outputs from the portfolio optimization can reduce error-checking time prior to solving the optimization problem. Methods to estimate bounds and check problem feasibility are available.
Depending on your goals, you can identify efficient portfolios or efficient frontiers. The portfolio optimization object provides methods for both. You can solve for efficient portfolios by providing one or more target risks or returns.
To obtain optimal portfolios on the efficient frontier, you can
Additionally, you can model long-short portfolios with or without turnover constraints.
After you identify a portfolio’s risk and return, you can use the portfolio optimization object methods to:
The portfolio object supports the generation of a trade record as a dataset array. You can use the dataset array to keep track of purchases and sales of assets and to capture trades to execute.
Financial Toolbox provides a comprehensive suite of tools for analyzing and assessing risk and investment performance.
Performance metrics include:
The toolbox provides a collection of tools for credit risk analysis that enable you to:
Financial Toolbox offers time-value-of-money functionality to:
The toolbox provides Securities Industry Association or SIA-compatible analytics are provided for pricing, yield curve modeling, and sensitivity analysis for government, corporate, and municipal fixed-income securities. Specific analytics include:
You can price stepped and zero coupon bonds with Financial Instruments Toolbox™.
With Financial Toolbox, you can:
With Financial Instruments Toolbox, you can price equity and fixed-income derivatives using a range of models and methods, including Heath-Jarrow-Morton and Cox-Ross-Rubinstein binomial models.
Financial Toolbox provides a collection of tools for analyzing time series data in the financial markets. The toolbox includes a financial time series object that supports:
Financial Toolbox provides tools for performing multivariate normal regression with or without missing data. You can:
Missing data estimation functionality helps you determine the effect of data quality on your models and simulations. For example, you can account for the effects of missing data on estimating coefficients of CAPM models or on calculating the efficient frontier of a portfolio of assets. Missing data effects can result in significantly different results.
Financial Toolbox provides numerous well-known technical indicators, performance metrics, and specialized plots, including:
Financial Toolbox offers a variety of well-known Stochastic Differential Equation (SDE) models. SDE models are used in many different ways, such as pricing financial derivatives, interest-rate modeling, risk analysis, and back-testing. Supported SDE models include: