Computational Finance
MATLAB® products for computational finance enable you to develop quantitative applications for investment and risk management, econometrics, pricing and valuation, insurance, and algorithmic trading. By writing only a few lines of code, you can:
Chart historical and live market data.
Analyze time series data and create predictive models.
Model interest rates and perform sensitivity analyses.
Optimize a portfolio and perform risk attribution.
Develop quantitative models to optimize performance and minimize risk.
Products for Computational Finance
Datafeed Toolbox
Access financial data from data service providers
Database Toolbox
Interact with relational and NoSQL databases
Econometrics Toolbox
Model and analyze financial and economic systems using statistical time series methods
Financial Toolbox
Analyze financial data and develop financial models
Financial Instruments Toolbox
Design, price, and hedge complex financial instruments
Risk Management Toolbox
Develop risk models and perform risk simulation
Spreadsheet Link
Use MATLAB from Microsoft Excel
Topics
Access Databases and Financial Data Exchanges
- Connect to Database (Database Toolbox)
After configuring a driver and data source, connect to your database. - Communicating with Data Service Providers (Datafeed Toolbox)
Find the connection function for each supported data service provider.
Analyze and Model Financial and Economic Time Series
- Analyze Time Series Data Using Econometric Modeler (Econometrics Toolbox)
Interactively visualize and analyze univariate or multivariate time series data. - Represent Time Series Models Using Econometrics Toolbox Objects (Econometrics Toolbox)
Learn how to represent time series models as model objects.
Optimize and Backtest Portfolios
- Portfolio Optimization Examples Using Financial Toolbox (Financial Toolbox)
Follow a sequence of examples that highlight features of thePortfolio(Financial Toolbox) object. - Backtest Investment Strategies Using Financial Toolbox (Financial Toolbox)
Perform backtesting of portfolio strategies using a backtesting framework. - Diversify ESG Portfolios (Financial Toolbox)
This example shows how to include qualitative factors for environmental, social, and corporate governance (ESG) in the portfolio selection process. - Create Hierarchical Risk Parity Portfolio (Financial Toolbox)
This example shows how to compute a hierarchical risk parity (HRP) portfolio.
Price Complex Financial Instruments
- Price American Basket Options Using Standard Monte Carlo and Quasi-Monte Carlo Simulation (Financial Toolbox)
Model the fat-tailed behavior of asset returns and assess the impact of alternative joint distributions on basket option prices. - Calibrate Shifted SABR Model Parameters for Swaption Instrument (Financial Instruments Toolbox)
Calibrate model parameters for aSwaption(Financial Instruments Toolbox) instrument when you use aSABRpricing method.
Quantify Risk and Validate Risk Models
- Risk Modeling with Risk Management Toolbox (Risk Management Toolbox)
Learn about the tools for modeling seven areas of risk assessment. - Bin Data to Create Credit Scorecards Using Binning Explorer (Risk Management Toolbox)
Create a credit scorecard using the Binning Explorer app. - Credit Scoring Using Logistic Regression and Decision Trees (Risk Management Toolbox)
This example shows how to create and compare two credit scoring models, which includes:
Deploy Applications to Production
- Deploy Relational Database Application with MATLAB Compiler (Database Toolbox)
Write a MATLAB script that connects to a relational database and deploy the script as a standalone database application to other machines.
Related Information
Featured Examples
Model the United States Economy
Use a vector error-correction model as a linear alternative to the Smets-Wouters DSGE macroeconomic model.
Hedge Options Using Reinforcement Learning Toolbox
Outperform the traditional BSM approach using an optimal option hedging policy.
Use Deep Learning to Approximate Barrier Option Prices with Heston Model
Use Deep Learning Toolbox™ to train a network and obtain predictions on barrier option prices with a Heston model.
Backtest Strategies Using Deep Learning
Construct trading strategies using a deep learning model and then backtest the strategies using the Financial Toolbox™ backtesting framework. The example uses Deep Learning Toolbox™ to train a predictive model from a set of time series and demonstrates the steps necessary to convert the model output into trading signals. It builds a variety of trading strategies that backtest the signal data over a 5-year period.
Interpret and Stress-Test Deep Learning Networks for Probability of Default
Train a credit risk for probability of default (PD) prediction using a deep neural network. The example also shows how to use the locally interpretable model-agnostic explanations (LIME) and Shapley values interpretability techniques to understand the predictions of the model. In addition, the example analyzes model predictions for out-of-sample values and performs a stress-testing analysis.
Incorporate Macroeconomic Scenario Projections in Loan Portfolio ECL Calculations
Generate macroeconomic scenarios and perform expected credit loss (ECL) calculations for a portfolio of loans. The workflow in this example shows important computational steps required to estimate provisions for credit losses following regulations such as IFRS 9 or CECL. In practice, the determination of loan provisions is a much more involved operational program requiring the collaboration of multiple departments in an institution. The goal of this example is to show useful computational tools that can support this process.
