unified risk scoring

Relying solely on Total Value Locked (TVL) is insufficient for accurately assessing the true risk of tokenized real-world assets (RWAs), as it overlooks critical vulnerabilities such as illiquidity, holder concentration, and poor market quality. This work proposes the first multidimensional, interpretable risk assessment framework that transcends TVL by evaluating RWAs along three dimensions—liquidity (L), concentration (C), and market quality (M)—using on-chain public data. The framework constructs a composite risk score incorporating the Herfindahl-Hirschman Index to measure holder concentration, alongside metrics such as transaction frequency, active address count, and turnover ratio. Empirical analysis identifies several RWA tokens exhibiting high TVL yet elevated risk, effectively uncovering hazards obscured by TVL alone and establishing a transparent, comparable benchmark for evaluating tokenized assets.

This study addresses the challenge of effectively integrating heterogeneous risks across multiple scenarios in financial markets by proposing a Weighted Generalized Risk Measure (WGRM) and its associated Weighted Risk Quadrangle (WRQ), thereby extending the generalized risk measure and risk quadrangle framework to a weighted setting for the first time. Theoretically, the work establishes analytical characterizations of WGRM under both discrete and continuous settings, proving that its structural properties remain invariant and revealing intrinsic connections among risk, deviation, regret, and error under weighting. Computationally, it leverages convex analysis, stochastic optimization, and linear programming reformulation techniques to transform complex risk optimization problems into tractable linear programs. Empirical results demonstrate that portfolios constructed using WGRM significantly improve risk-adjusted returns, enhance downside resilience, and mitigate losses caused by misjudgments in individual scenarios on NASDAQ 100 and S&P 500 constituents.

Traditional measures of risk spillovers struggle to capture the local interaction structures that drive systemic risk. This study proposes a novel analytical framework based on directed triadic motifs, integrating quantile-based connectivity networks with asset sector labels to construct multiscale backbone networks. By introducing colored motifs and a diversity metric based on orbit positions, the approach uniquely bridges local topological features with tail systemic impact and portfolio construction. Empirical results demonstrate that motif-driven portfolios significantly outperform minimum-correlation and minimum-connectivity benchmarks in terms of risk-adjusted returns. Moreover, assets exhibiting high orbit diversity within tail-risk networks are more likely to act as net risk transmitters.

This paper addresses the quantification of systemic risk and optimal allocation of bailout capital in stochastic financial networks, with explicit modeling of bilateral debt obligations. Methodologically, it integrates the Eisenberg–Noe clearing mechanism into a graph neural network (GNN) framework, proposing XPENN—a novel architecture satisfying strict permutation equivariance—thereby achieving the first theoretically consistent generalization of systemic risk measures to graph-structured data. Combining stochastic optimization with numerical approximation, the approach computes the minimal total bailout capital and its optimal stochastic allocation policy. Experiments demonstrate that XPENN substantially outperforms baseline methods across key dimensions: reduced bailout cost, enhanced robustness to network perturbations, and improved interpretability. The framework establishes a new paradigm for financial network risk assessment, balancing theoretical rigor with computational tractability.

This paper addresses the absence of non-probabilistic, non-heuristic risk decision frameworks under extreme uncertainty—such as “unknown unknowns” and severe resource constraints. Methodologically, it introduces the RDOT classification paradigm, a structured taxonomy that categorizes cross-disciplinary risk strategies into six types: structural, responsive, formal, adversarial, multi-stage, and proactive. It systematically identifies over 110 domain-agnostic strategies, transcending the traditional dichotomy between probabilistic modeling and cognitive heuristics, and bridges theoretical gaps in robust design and emergency planning. The framework integrates multi-objective optimization, multi-attribute utility theory, and structured workflow modeling—requiring neither probability estimation nor predictive modeling. Empirically validated in engineering and public policy contexts, RDOT demonstrates robustness and embeddability, delivering the first lightweight, actionable, cross-domain risk decision toolkit. (149 words)

Current evaluations of financial models rely on a single ground-truth answer, overlooking the legitimate disagreements inherent among professional analysts and thereby risking misjudgment of AI systems. This work proposes GAUGE, a novel benchmark grounded in 1,001 real analyst workbooks and 196 distinct tasks, which introduces the first evaluation framework anchored in collective professional practice. GAUGE employs a three-tiered practice envelope, 56 auditable dimensions, eight validity gates, and a failure-aware scoring mechanism to enable multidimensional, structured assessment of valuation models. Experimental results show that under the φ₀ metric, senior analysts achieve an average score of 88.3, while the best AI agent scores 53.4—surpassing student-level performance yet significantly lagging behind humans on judgment-intensive tasks. This gap highlights a core limitation of current AI: proficiency in modeling but deficiency in value-based reasoning.

This study addresses the challenge of accurately attributing aggregate prediction bias to individual components within complex modeling frameworks. Within an expected loss framework, it formalizes traditional walk-through analysis, exposing its inherent sequential dependency limitations, and proposes two order-agnostic attribution methods: an extension of the Logarithmic Mean Divisia Index (LMDI) tailored to the expected loss structure, and a Shapley-value-based approach that averages marginal contributions. For the first time, both methods are systematically applied to a comprehensive suite of financial risk models incorporating probability of default (PD), loss given default (LGD), exposure at default (EAD), and survival model multiplier (SMM) components, along with Monte Carlo simulation layers. The authors derive efficient vectorized computation formulas, enabling empirical attribution on real-world portfolio scales in mere seconds of additional runtime, thereby substantially enhancing computational efficiency and result consistency while providing robust support for model validation and regulatory compliance.

This study investigates the transmission mechanisms of financial systemic risk through multiple channels and identifies key sources of risk propagation. To this end, the authors propose a multiplex network Hawkes model that explicitly disentangles three distinct contagion channels—asset similarity, solvency, and profitability—within a unified framework by incorporating excitation weights dependent on node and edge covariates, thereby overcoming the limitations of traditional single-layer homogeneous excitation assumptions. Employing a Bayesian inference approach based on Markov chain Monte Carlo (MCMC) and leveraging large-scale credit default swap (CDS) data, the empirical analysis covers 99 European and U.S. financial institutions from 2004 to 2022. The findings reveal that systemic risk is predominantly driven by a small subset of institutions, that industry similarity constitutes the most robust asset-related linkage, and that all three channels significantly contribute to risk contagion.