Institutional research on orbital credit risk.
Analysis bridging space systems engineering, Bayesian inference, and structured finance. Produced for lenders, insurers, and institutional investors deploying capital into orbital infrastructure.
The Filing Is Out: Marking Our SpaceX Model to the S-1
SpaceX filed its S-1 on 20 May 2026. We marked our pre-filing model to the audited segment data. The revised base equity value is $295 billion, an 85% discount to the reported $2 trillion target.
Debris, Debt, and the Deal: The Hidden Risks in the SpaceX IPO
A sum-of-parts analysis values SpaceX at $361.5 billion base case, an 82% discount to the reported $1.75–2.0 trillion IPO target. Three risk categories explain the gap.
Sovereign Dependency Is the Hidden Credit Risk in Space Finance
The space economy is routinely described as a commercial growth story. The revenue base underpinning much of that activity is not as commercial as the headline figures suggest.
Space Operators Are Paying an Equity Premium for Debt-Compatible Assets
In 2023, space insurers collected $557 million in premiums and paid out $995 million in claims. Those numbers contain a credit signal that the lending market has not yet read.
The Space Sector Is Financeable. Lenders Cannot Prove It Yet.
Private investment peaked at $18 billion in 2021, fell to $5.9 billion in 2024, and rebounded to $12.4 billion in 2025. The problem is analytical, not structural.
Concentration Risk in Orbital Portfolios
Why diversification across operators fails under debris stress, and how altitude-correlated default modelling exposes hidden portfolio concentration.
From Orbital Mechanics to Default Probability
Why engineering risk metrics fail to translate into credit decisions, and the standardised pathway from conjunction density to loss given default.
Three-Layer Risk Architecture for Orbital Assets
Unified data architecture, multidimensional risk quantification, and portfolio-level analytics as the framework for institutional space underwriting.
A Proposed Orbital Credit Framework for Institutional Lenders
A structured framework for integrating orbital risk metrics into standard credit underwriting. Addresses Basel III risk weight assignment, Solvency II SCR calculation, and IFRS 9 expected credit loss modelling.
Bayesian Uncertainty in Orbital Risk
Prior calibration and posterior inference for satellite default forecasting.
Ensemble Methods for Non-Linear Default Detection in Mega-Constellation Operators
Gradient-boosted and mixture-model approaches to credit risk in high-asset-count LEO portfolios.
SPV Structures in Space Finance: How Programmatic Vehicles Create Institutional Demand
Programmatic SPVs resolve three structural barriers simultaneously: repossession, valuation, and regulatory capital treatment.
Berlin Space Protocol: Why Zero Ratifications After 14 Years Matters to Lenders
The absence of a ratified space debris liability framework is the single largest source of unpriced tail risk in satellite-backed lending.
The Musk Premium: SpaceX Pre-IPO Valuation Across Three Frameworks
DCF, comparable transactions, and sum-of-the-parts applied to SpaceX's reported FY2025 financials. The key driver is not Starlink revenue.
Covenant Design for Satellite-Secured Lending: DSCR Triggers, ICR Floors, and Orbital Risk Maintenance Tests
How lenders can structure maintenance covenants for orbital assets, translating SIGMA scores and PD trajectories into enforceable financial controls.
Gradient Boosting Challenger Model: AUC Analysis on 991 Operator-Quarter Samples
Validation of the gradient-boosting ML challenger component on 991 operator-quarter samples from institutional financial databases (22 publicly traded space operators, 2010–2025). The AUC reflects discriminatory power on operators with complete financial data. System-level validation is reported separately via the 41-case SR 11-7 operational backtest.
Bayesian Prior Calibration Using Moody's Annual Default Study
How Moody's infrastructure default series is used to construct Beta-Binomial priors for orbital assets with zero or sparse default histories. Includes hyperparameter selection methodology and sensitivity analysis.