计算金融学
您可以使用 MATLAB® 提供的计算金融产品来为投资与风险管理、计量经济学、定价与估值、保险以及算法交易开发定量应用程序。只需编写几行代码，您就可以：
绘制历史和实时市场数据图。
分析时间序列数据并创建预测模型。
对利率建模并执行敏感度分析。
优化投资组合并执行风险归因。
开发量化模型，以优化业绩和最小化风险。
适用产品: 计算金融学
Datafeed Toolbox
通过数据服务提供商访问金融数据
Database Toolbox
Interact with relational and NoSQL databases
Econometrics Toolbox
Model and analyze financial and economic systems using statistical time series methods
Financial Toolbox
分析金融数据并开发金融模型
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
主题
访问数据库和金融数据交换
- 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.
对金融和经济时间序列进行分析和建模
- 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.
优化和回测投资组合
- 使用 Financial Toolbox 的投资组合优化示例 (Financial Toolbox)
通过一组示例来了解Portfolio(Financial Toolbox) 对象的主要功能。具体来说，这些示例使用Portfolio(Financial Toolbox) 对象来展示如何设置重点关注两基金分离定理、交易成本和换手率约束影响的均值-方差投资组合优化问题，如何获取最大化夏普比率的投资组合，以及如何建立两种流行的对冲基金策略（美元中性和 130/30 投资组合）。 - 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.
复杂金融工具定价
- 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.
量化风险和验证风险模型
- 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:
将应用程序部署到生产中
- 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.
精选示例
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 (Risk Management Toolbox) or CECL (Risk Management Toolbox). 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.
