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Model extreme market scenarios. Assess portfolio resilience. Meet Basel III compliance. Mitigate tail risks.
2008, 2020, dot-com; analyze portfolio behavior in real crises
Regulatory requirements (CCAR, DFAST, PRA); custom scenarios
PCA + Autoencoders extract latent risk factors; generate synthetic crises
VaR, ES, Max Drawdown, Duration, Concentration, Greeks
Identify portfolio configurations that lead to unacceptable losses
Model counterparty networks & systemic shock propagation
AWS/Azure/GCP - scalable Monte Carlo, GPU-accelerated inference
Full data control; GPU/TPU clusters; air-gapped environments
Portfolio data on-prem, scenario generation & analytics in cloud
Audit portfolio, risk infrastructure, regulatory requirements
Develop models for pilot portfolio; backtest against history
Deploy across full portfolio; integrate with risk systems
Monitor models; update scenarios; expand asset classes
VaR, CVaR/ES, Maximum Drawdown, Duration, Convexity, Greeks (Delta, Gamma, Vega, Rho)
Equities, Fixed Income, FX, Commodities, Derivatives, Crypto
Historical crises (2008, 2020), Regulatory (CCAR/DFAST), Hypothetical, AI-generated
Interest rates, credit spreads, equity volatility, FX shocks, commodity prices, liquidity
Model extreme scenarios. Meet regulatory requirements. Protect against tail risks.
Schedule a Demo →Standard practice is 10-20 core scenarios (3-5 regulatory + 5-10 hypothetical + 5-10 AI-generated). We typically run 1,000-10,000 Monte Carlo paths per scenario for robust distributions. More scenarios increase rigor but also computational cost.
Regulatory stress tests (CCAR/DFAST) are annual. For risk management: quarterly updates recommended. For real-time monitoring: daily or continuous. We recommend monthly scenario refreshes as minimum to adapt to emerging risks.
Stress tests model portfolio sensitivity to scenarios but cannot perfectly predict actual losses (market structure breaks down, circuit breakers trigger, etc.). They provide bounds and order-of-magnitude estimates for capital planning and risk limits.
We use copula models and regime-switching models that explicitly capture dynamic correlations. During crises, correlations spike toward 1.0; our models adapt to these structural breaks.
We use Greeks (Delta, Gamma, Vega) for quick mapping of new positions to existing risk factors. For complex derivatives, we integrate with pricing libraries (QuantLib, Numerix) to model payoffs under stressed scenarios.