金融工学

金融データの取り込みと解析、リスクと経済システムのモデルの開発、投資管理、および複雑な商品の価格付け

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。

金融工学 向け製品

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

トピック

データベースへのアクセスと金融データ交換

金融および経済の時系列の解析とモデル化

ポートフォリオの最適化とバックテスト

複雑な金融商品の価格付け

リスクの定量化とリスク モデルの検証

アプリケーションの本番環境への展開

関連情報

注目の例

Model the United States Economy

Model the United States Economy

Use a vector error-correction model as a linear alternative to the Smets-Wouters DSGE macroeconomic model.

(Econometrics Toolbox)

Hedge Options Using Reinforcement Learning Toolbox

Hedge Options Using Reinforcement Learning Toolbox

Outperform the traditional BSM approach using an optimal option hedging policy.

(Financial Toolbox)

Use Deep Learning to Approximate Barrier Option Prices with Heston Model

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.

(Deep Learning Toolbox)

Backtest Strategies Using Deep Learning

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.

(Deep Learning Toolbox)

Interpret and Stress-Test Deep Learning Networks for Probability of Default

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.

(Deep Learning Toolbox)

Incorporate Macroeconomic Scenario Projections in Loan Portfolio ECL Calculations

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.

(Risk Management Toolbox)