Risk Management System

Stress Testing

DolphinDB's high-performance time-series storage and distributed computing framework powers large-scale stress scenario simulation and portfolio revaluation at speed. Historical market data, risk factors, and scenario libraries are managed on a single platform — vectorized batch revaluation with parallel execution of hundreds to thousands of scenarios across distributed nodes. Yield curve shifts, volatility surface shocks, and cross-asset correlated stress scenarios are fully scriptable. Historical simulation, Monte Carlo, and sensitivity analysis methods generate complete risk reports covering VaR impact, maximum loss, and capital adequacy — across the full portfolio, in minutes.

Counterparty Risk

Continuous exposure tracking across derivatives, repo, and equity pledge financing — with automatic fair value revaluation triggered by market data, position, and collateral updates. Current Exposure (CE), Potential Future Exposure (PFE), and Credit Valuation Adjustment (CVA) are computed in real time within a unified framework supporting multiple valuation models and limit management rules. Vectorized and distributed execution ensures low-latency updates under high concurrency, with full support for netting agreements and collateral management — real-time risk computation, historical lookback, and default early warning in one engine.

Snowball Option Pricing

Built-in matrix operations, random number generation, and time-series window functions enable efficient pricing of path-dependent exotic structures including autocallables and snowballs. Knock-in/knock-out conditions, barrier monitoring, coupon accrual, and early redemption logic run natively in-engine, with distributed Monte Carlo across hundreds of thousands to millions of simulation paths. Python and LibTorch integration supports local volatility, stochastic volatility, and proprietary pricing models — with volatility surface interpolation and Greeks computed entirely in-engine. Real-time pricing and batch revaluation modes both supported for live quoting and end-of-day risk.

Monte Carlo Simulation

Vectorized computation and high-performance math libraries support high-dimensional Monte Carlo simulation across multi-asset, multi-factor portfolios. Covariance matrix estimation, Cholesky decomposition, path generation, and stochastic process simulation execute natively in-engine, with distributed task scheduling for elastic scaling. Fully scriptable across GBM, Heston, Hull-White, and other stochastic process models, with variance reduction techniques — antithetic variates, control variates — available to improve convergence. Applicable to market risk VaR, exotic derivatives pricing, scenario stress testing, and portfolio optimization.