Optimal Portfolio Compression for Priority-Proportional Clearing with Defaulting Costs

A team of computer scientists has published groundbreaking research on portfolio compression in financial networks, where banks are interconnected through bilateral liabilities and face default risks. The paper, titled 'Optimal Portfolio Compression for Priority-Proportional Clearing with Defaulting Costs,' examines how debt netting arrangements can be optimized to prevent financial contagion. The researchers developed a polynomial-time algorithm that computes maximal clearing outcomes under priority-proportional clearing models, where banks repay creditors according to predetermined priority classes. This represents a significant computational advance for understanding systemic risk.

On the positive side, the team showed it's possible to decide in polynomial time whether a compression exists that limits defaults to at most one bank. However, they also proved several optimization problems are computationally intractable - deciding whether compression can reduce defaults below a given threshold or save a specific bank from defaulting is NP-hard, even in restricted settings. To address this complexity, the researchers developed a mixed integer linear programming (MILP) formulation that computes compressions maximizing the number of non-defaulting banks.

The team validated their approach through simulations on both synthetic and real-world financial datasets, analyzing how portfolio compression affects clearing outcomes. Their 35-page paper provides the first comprehensive computational characterization of compression benefits and limitations, bridging game theory, financial mathematics, and algorithmic complexity. The work has implications for financial regulators and risk managers seeking to design more resilient banking systems through optimal debt restructuring mechanisms.