Our approach is grounded in the disciplined analysis of macroeconomic structures, capital flows, and derivative market behavior, with particular attention to system-level constraints and the unintended consequences of policy actions. We do not assume equilibrium conditions or linear responses. Instead, we focus on regime-dependent dynamics, behavioral discontinuities, and latent stress points embedded within the financial system. Our interest lies in the forward mapping of institutional reactions—fiscal, monetary, regulatory—onto real economy outcomes and market prices, using both structural logic and empirical calibration. We employ a combined framework of balance sheet analytics, derivatives sensitivity mapping, and probabilistic inference. Machine learning modules are used not as black-box forecasts, but as tools to identify hidden structure—volatility clustering, conditional dependencies, and regime shifts. This hybrid method allows us to isolate macro asymmetries and construct views where optionality is mispriced, policy space is constrained, or narrative diverges from positioning. We do not seek consensus. We seek points where the system must respond.

The institute operates the Macro Simulation Engine for global economy, a proprietary platform developed to evaluate economic stress scenarios across global macro variables, derivatives markets, and capital structures. The system is engineered to integrate traditional economic logic with modern data-driven methodologies.

• Multi-Sector Architecture: Real economy, policy institutions, and financial intermediaries are modeled as interconnected balance sheets. Sectoral adjustments are dynamically computed under simulated shocks.
• Derivatives Sensitivity: Volatility surfaces, convexity effects, and cross-gamma exposures are modeled under shifting macro regimes. Stress testing includes path-dependency and liquidity overlays.
• Policy Transmission: Central bank functions are represented through structured reaction functions with non-linear triggers, threshold-based QE paths, and real-time expectations anchoring.
• Machine Learning Components: Regime classification, time-series clustering, and pattern recognition modules augment structural logic. Outputs are adjusted for latent state transitions, using supervised and unsupervised methods.
• Global Shock Propagation: Cross-border spillovers are modeled through trade flows, capital account reactions, and FX derivatives markets. Each shock is traced through second- and third-order effects on yield curves, credit spreads, and macro volatility.
• Stock-Flow Consistency: All outcomes adhere to accounting integrity across time and agents. No assumption is made of equilibrium—only adjustment.

The engine is used internally to identify mispricings, fragility points, and non-linear payoff asymmetries under volatile macroeconomic conditions. It is not available for external use.