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金融工学向け MATLAB® 製品を使用して、投資とリスクの管理、計量経済学、価格付けと評価、保険、およびアルゴリズム取引のための定量的アプリケーションを開発できます。わずか数行のコードを記述するだけで、以下を行うことができます。

  • 過去と現在の市場データをグラフで表示する。

  • 時系列データを解析し、予測モデルを作成する。

  • 金利をモデル化し、感度解析を実行する。

  • ポートフォリオを最適化し、リスク アトリビューションを実行する。

  • パフォーマンスを最適化し、リスクを最小化するための定量モデルを開発する。





  • Portfolio Optimization Examples Using Financial Toolbox (Financial Toolbox)
    Follow a sequence of examples that highlight features of the Portfolio (Financial Toolbox) object. Specifically, the examples use the Portfolio (Financial Toolbox) object to show how to set up mean-variance portfolio optimization problems that focus on the two-fund theorem, the impact of transaction costs and turnover constraints, how to obtain portfolios that maximize the Sharpe ratio, and how to set up two popular hedge-fund strategies — dollar-neutral and 130-30 portfolios.
  • Backtest Investment Strategies Using Financial Toolbox (Financial Toolbox)
    Perform backtesting of portfolio strategies using a backtesting framework. Backtesting is a useful tool to compare how investment strategies perform over historical or simulated market data. This example develops five different investment strategies and then compares their performance after running over a one-year period of historical stock data. The backtesting framework is implemented in two Financial Toolbox™ classes: backtestStrategy (Financial Toolbox) and backtestEngine (Financial Toolbox).
  • 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. The example extends the traditional mean-variance portfolio using a Portfolio (Financial Toolbox) object to include the ESG metric. First, the estimateFrontier (Financial Toolbox) function computes the mean-variance efficient frontier for different ESG levels. Then, the example illustrates how to combine the ESG performance measure with portfolio diversification techniques. Specifically, it introduces hybrid models that use the Herfindahl-Hirshman (HH) index and the most diversified portfolio (MDP) approach using the estimateCustomObjectivePortfolio (Financial Toolbox) function. Finally, the backtestEngine (Financial Toolbox) framework compares the returns and behavior of the different ESG strategies.
  • Create Hierarchical Risk Parity Portfolio (Financial Toolbox)
    This example shows how to compute a hierarchical risk parity (HRP) portfolio. You can use HRP as a technique for portfolio diversification where the assets are divided and weighted according to a hierarchical tree structure. The weights of the assets within a cluster and between clusters can be assigned in many ways. A few ideas of the ways to allocate the weights are:


  • 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. Using various implementations of a separable multivariate Geometric Brownian Motion (GBM) process, often referred to as a multi-dimensional market model, the example simulates risk-neutral sample paths of an equity index portfolio and prices basket put options using the technique of Longstaff & Schwartz.
  • Calibrate Shifted SABR Model Parameters for Swaption Instrument (Financial Instruments Toolbox)
    Calibrate model parameters for a Swaption (Financial Instruments Toolbox) instrument when you use a SABR pricing method.

リスクの定量化とリスク モデルの検証

  • 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. Use the Binning Explorer to bin the data, plot the binned data information, and export a creditscorecard object. Then use the creditscorecard object with functions from Financial Toolbox™ to fit a logistic regression model, determine a score for the data, determine the probabilities of default, and validate the credit scorecard model using three different metrics.
  • Credit Scoring Using Logistic Regression and Decision Trees (Risk Management Toolbox)
    Create and compare two credit scoring models, one based on logistic regression and the other based on decision trees.