## Financial Toolbox |

- Mean-variance and CVaR-based object-oriented portfolio optimization
- Cash flow analysis, risk analysis, financial time-series modeling, date math, and calendar math
- Basic SIA-compliant fixed-income security analysis
- Basic Black-Scholes, Black, and binomial option pricing
- Regression and estimation with missing data
- Basic GARCH estimation, simulation, and forecasting
- Technical indicators and financial charts

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:

- Estimate asset return and total return moments from price or return data
- Compute portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR)
- Perform constrained mean-variance portfolio optimization and analysis
- Examine the time evolution of efficient portfolio allocations
- Perform capital allocation
- Account for turnover and transaction costs in portfolio optimization problems

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:

- Mean-variance portfolio optimization uses variance as a risk proxy. You define asset return moments either as arrays or as estimations from the return time series in a matrix or financial time series objects.
- CVaR portfolio optimization uses conditional value at risk (CVaR) as a risk proxy. You work with simulations of asset returns data.

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

- Specify the number of portfolios to find
- Solve for the optimal portfolios at the efficient frontier endpoints
- Extract the Sharpe ratio-maximizing portfolio

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:

- Troubleshoot questionable results
- Adjust the problem definition to move toward an efficient portfolio
- Set up an asset trading record

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:

- Sharpe ratio
- Information ratio
- Tracking error
- Risk-adjusted return
- Sample and expected lower partial moments
- Maximum drawdown and expected maximum drawdown

The toolbox provides a collection of tools for credit risk analysis that enable you to:

- Preprocess and estimate transition probabilities from credit ratings data
- Aggregate credit ratings data into categories
- Convert from transition probabilities to credit quality thresholds and vice versa

Financial Toolbox offers time-value-of-money functionality to:

- Calculate present and future values
- Determine nominal, effective, and modified internal rates of return
- Calculate amortization and depreciation
- Determine the periodic interest rate paid on a loan or annuity

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:

- Complete cash flow date, cash flow amounts, and time-to-cash-flow mapping for a bond
- Price and yield maturity
- Duration and convexity

You can price stepped and zero coupon bonds with Financial Instruments Toolbox™.

With Financial Toolbox, you can:

- Use a standard market model of equity pricing with Black and Black-Scholes formulas
- Compute the sensitivities of option greeks, such as lambda, theta, and delta

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:

- Date math, including business days and holidays
- Data transformation and analysis
- Technical analysis
- Charting and graphics

The Financial Time Series app provides a convenient interface for creating, managing, and manipulating financial time series objects, including transforming to or from MATLAB^{®} numeric arrays. You can also load data in the tool directly from a file, database (with Database Toolbox™), or financial datafeed provider (with Datafeed Toolbox™).

Financial Toolbox includes tools for working with univariate GARCH models. These tools help you:

- Estimate parameters of a univariate GARCH(p, q) model with Gaussian innovations
- Simulate univariate GARCH(p, q) processes
- Forecast conditional variances

Econometrics Toolbox™ includes tools for working with additional GARCH models and performing time series regression.

Financial Toolbox provides tools for performing multivariate normal regression with or without missing data. You can:

- Perform common regressions based on the underlying model, such as seemingly unrelated regression (SUR)
- Estimate log-likelihood function and standard errors for hypothesis testing
- Complete calculations in the presence of missing data

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:

- Moving averages
- Oscillators, stochastics, indexes, and indicators
- Maximum drawdown and expected maximum drawdown
- Charts, including Bollinger bands, candlestick plots, and moving averages