Provides AI agents with quantitative risk tools such as VaR, expected shortfall, GARCH volatility, backtesting, stress testing, tail risk analysis, and credit scoring using synthetic or user-supplied data.
Enables AI agents to run probabilistic portfolio analysis workflows, including input validation, instrument verification, simulation preparation, and approved interactive reporting.
Enables AI agents to forecast asset price paths using Monte Carlo simulation with EGARCH volatility and skewed-t shocks, providing risk metrics and percentiles.
Enables time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.
Enables AI agents to perform Black-Litterman portfolio optimization with investor views, backtesting, and asset analysis, generating dashboards for visualization.
Enables AI agents to assess credit default risk, run what-if scenarios, and evaluate portfolios through natural language, backed by an explainable XGBoost model.