Financial Toolbox™ provides functions for the mathematical modeling and statistical analysis of financial data. You can analyze, backtest, and optimize investment portfolios taking into account turnover, transaction costs, semi-continuous constraints, and minimum or maximum number of assets. The toolbox enables you to estimate risk, model credit scorecards, analyze yield curves, price fixed-income instruments and European options, and measure investment performance.
The yield curve shows the relationship between the interest rate and the time to maturity for a given borrower in a given currency. The Financial Instruments Toolbox™ provides additional functionality to fit yield curves to market data using parametric fitting models and bootstrapping, estimate parameters and analyze different type of interest-rate curves. Explore major updates and new features across all MATLAB® and Simulink® products. This includes new Stateflow® charts, so you can graphically program, debug. The Individual license should be used by students, faculty, and staff to download an individual stand-alone copy of the software for each of the machines on which they are the sole MATLAB user (includes university-owned and personal machines).The MATLAB Portal is where end users can download MATLAB, get free training, contact support,. Financial Instruments Toolbox. Use objects to model and price financial instruments. Choose Instruments, Models, and Pricers. Der diesem MATLAB-Befehl entspricht. Learn about the system requirements for Financial Instruments Toolbox. Product Requirements & Platform Availability for Financial Instruments Toolbox - MATLAB 토글 주요 네비게이션.
Stochastic differential equation (SDE) tools let you model and simulate a variety of stochastic processes. Time series analysis functions let you perform transformations or regressions with missing data and convert between different trading calendars and day-count conventions.
Get Started
Learn the basics of Financial Toolbox
Data Preprocessing
Financial market data for dates and currencies
Timetables in Finance
Timetables, date transformations and merges, chart technical indicators
Financial Data Analytics
Cash flows and performance metrics, regression analysis, financial data charting
Portfolio Optimization and Asset Allocation
Create portfolios, evaluate composition of assets, perform mean-variance, CVaR, or mean absolute-deviation portfolio optimization, backtest investment strategies
Credit Risk
Credit risk, transition probabilities for credit ratings, credit quality thresholds, credit scorecards
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Price and Analyze Financial Instruments
Yield curves, valuation for fixed-income securities, equity derivatives pricing
Stochastic Differential Equation (SDE) Models
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Parametric models, such as Geometric Brownian Motion (GBM) and Heston Volatility
Calculate the expected credit loss for a financial portfolio
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Expected credit loss is a probability-weighted estimate of credit losses during the expected life of a financial instrument. The estimation method requires point-in-time (PIT) projections of probability of default (PD), loss given default (LGD), and exposures at default (EAD).
Most credit instruments have a quantifiable risk of default. Accounting for credit risk in the entire portfolio of instruments must consider the likelihood of future impairment and is commonly measured through expected loss and lifetime expected credit loss. To comply with IFRS 9 or CECL, risk managers need to calculate the expected credit loss on the portfolio of financial instruments over the lifetime of the portfolio. Credit and regulatory risk teams quantify the expected loss using:
- Asset classification, through statistical and machine learning methods
- Macroeconomic modeling
- Scenario generation and scenario analysis
- Instrument pricing and risk sensitivity
- Stochastic modeling of default and recovery
- Automated reporting, reflecting point-in-time model and data selection
For more information, see Statistics and Machine Learning Toolbox™, Financial Toolbox™, Financial Instruments Toolbox™, Risk Management Toolbox™, and MATLAB Report Generator™.
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Examples and How To
- Modeling Probabilities of Default with Cox Proportional Hazards - Example
- Stress Testing of Consumer Credit Default Probabilities - Example
- Using MATLAB for Macroeconomic Stress Testing (26:29) - Video
- Financial Risk Management Application and Code Examples - Example
- Fitting Data with Generalized Linear Models - Example
- Forecasting Corporate Default Rates with MATLAB (54:36) - Video
- Modeling Correlated Defaults with Copulas - Example
Software Reference
- Credit Simulation Using Copulas - Documentation
creditscorecard: Createcreditscorecardobject - Function- Binning Explorer: Bin data and export into a
creditscorecardobject - Documentation - Generalized Linear Models - Documentation
See also: Monte Carlo simulation, risk management solutions, IFRS 9, CECL