Research
Methodologies and Frameworks for Orbital Capital
We develop the analytical standards required to move space infrastructure from speculative venture to a bankable asset class. Our research focuses on the intersection of orbital mechanics, credit risk, and global banking regulation.
Proprietary Risk & Credit Architectures
Methodology: Seven-Factor Multiplicative Risk. A standardized methodology for quantifying orbital risk across seven critical dimensions, including orbital decay, spectrum allocation, and conjunction frequency.
Coverage: LEO, MEO, GEO, and Cislunar regimes.
Output: High-fidelity technical risk scores calibrated for financial modeling.
Methodology: Three-Model Credit Architecture. Translates technical mission risk into institutional credit metrics via a hybrid architecture evaluating project finance viability (DSCR-based), growth-stage liquidity (Cash Runway), and sovereign-backed credit profiles.
Focus: Asset-level Probability of Default (PD) and Loss Given Default (LGD).
Backtest Results: Predictive Accuracy & Model Integrity. A comprehensive study validating the SFIS® engine against 41 historical satellite operator outcomes. This paper details our 1.00 Recall and 0.93 F1 Score, providing the “Effective Challenge” data required by bank risk committees.
Annual Sector Report. An institutional-grade overview of the orbital economy. This report analyzes the risk distribution across 2,500 scored assets and 81 global operators, providing credit band analysis and sector-level default probability assessments.
Why institutional capital remains locked out of orbital infrastructure—and the credit intelligence gap that must be closed before lenders can underwrite the space economy.
Why engineering risk metrics fail to translate into credit decisions—and the standardized pathway from conjunction density to loss given default.
Unified data architecture, multidimensional risk quantification, and portfolio-level analytics as the framework for institutional space underwriting.
Why diversification across operators fails under debris stress—and how altitude-correlated default modeling exposes hidden portfolio concentration.
Prior calibration and posterior inference for satellite default forecasting.
Gradient-boosted and mixture-model approaches to credit risk in high-asset-count LEO portfolios.
Available to qualified institutional investors, lenders, and insurers.
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