This paper addresses the complex evolution of tiered returns and losses in structured credit assets under the combined pressure of multiple adverse factors—namely, interest rate volatility, rising default rates, and declining recovery rates. A framework model based on the cash flow waterfall model for scenario-specific quantitative analysis is proposed. Three typical tiering methods are employed: senior, mezzanine, and subordinate. Default rate, prepayment rate, loss on default, and discount rate are used as four key determining variables. Finally, the expected return levels, extreme loss tails, and credit enhancement losses under various stress scenarios (mild, moderate, and severe stress) are simulated through cash flow allocation during the rolling period, loss absorption pattern ranking, and duration-weighted yield calculation. The conclusions are as follows: Under mild stress, the senior tier offers good defensive capabilities but suffers a significant reduction in returns; under moderate stress, the mezzanine tier exhibits the most volatile return /risk ratio. Severe pressure leads to net asset value losses for the lower tranche. Research on cross-tier risk transmission after excess spreads are exhausted, mezzanine impairments are incurred, and additional support for senior tranches is damaged results in recommendations such as tier thickness adjustment, real-time activation mechanisms, and loan pool penetration checks, which provide quantitative references for pricing, risk control, and regulatory disclosure of structured credit products.

Cewen Chi. Quantitative Evaluation of Tranche-Level Returns and Loss Distributions of Structured Credit Assets under Stress Scenarios. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 2: 19-27. https://doi.org/10.38007/IJBMET.2026.070203.

[1] Lyu, N. (2026, April). Toward Robust AI Agents: A Closed-Loop Task Planning–Execution–Feedback Framework for Open Scenarios. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1182-1188). IEEE.

[2] Zhang, K. (2025, December). Research on Cross-Selling Precision Marketing Strategy Based On Xgboost Model and SHAP Explanatory Framework. In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM) (pp. 1-7). IEEE.

[3] Zhu, P. (2025, December). Construction of Multi-Scale Biostatistical Analysis Framework and its Application in Biomedical Signal Feature Recognition and Classification. In 2025 5th International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-7). IEEE.

[4] Gao, Y. (2026). Research on the Design of Governance Structure for Private Equity Funds and the balance of GP and LP Rights.

[5] Chang, C. W. (2026). Privacy Infrastructure Vulnerability Mining and Automated Framework Based on Multimodal AI. Procedia Computer Science, 279, 702-711.

[6] Chen, M. (2026). Real-Time Compliance Monitoring Engine Based on eBPF: Privacy-Enabled Data Tracking for Edge Computing. Procedia Computer Science, 281, 216-225.

[7] Wang, Y. (2026). Construction of Supply Chain Demand Forecasting Algorithm Based On Time Series Data Analysis and Improvement of Its Management Efficiency. Procedia Computer Science, 281, 484-493.

[8] Yu, X. (2026). Application of Time Series Data Analysis Algorithm Combined with Attention Mechanism in Growth Marketing User Lifetime Value Prediction. Procedia Computer Science, 281, 57-64.

[9] Wang, Z. (2026). Mineral Commodity Time-Series Cycle Modeling and Feature Learning Algorithm Based On Improved Transformer. Procedia Computer Science, 281, 1347-1356.

[10] Wang, Z. (2026). Relationship between Supply–Demand Structures of Base Metals and the Evolution of Corporate Value.

[11] Chen, J. (2026). Construction of a Cloud-Native High-Performance Service Engineering System for Real-Time Decision-Making Platforms.

[12] Chen, J. (2026). Elastic Scaling and Stability Assurance Mechanisms for Distributed Systems under High-Throughput Scenarios.

[13] Qian, X. (2026). Supply Chain Collaboration Mechanisms Driven by Demand Orchestration in Omni-Channel Retail Environments.

[14] Liang, Q. (2026). Reconstruction of Commercial Building Space Reuse Mode Driven by Composite Business Types. International Journal of Engineering Advances, 3(1), 107-113.

[15] Gao, Y. (2026). The Role and Practice of Delaware Law in Global Cross-border M&A Transactions. International Journal of Law, Policy & Society, 2(1), 38-46.

[16] Gao, Y. (2026). Application of Delaware Corporate Law in Cross-Border M&A: Business Structures, Contractual Risk, and Case-Based Lessons. Economics and Management Innovation, 3(2), 128-136.

[17] Wang, N. (2026). Research on Evaluation and Optimization Models of Enterprise Resource Allocation Efficiency from a Data-driven Perspective. Advances in Computer and Communication, 7(1).

[18] Liang, Q. (2026). How Architectural Design and Utility Infrastructure Impacts AI Supporting Campus and Drive Future Innovation, Operational Efficiency and Sustainable Advancement in Utility-Critical Environment. European Journal of Engineering and Technologies, 2(2), 71-77.

[19] Su, J. (2026). Research on Android Real-time Communication System Architecture and High-reliability Assurance Pathways Integrating AI-based Anomaly Detection Mechanisms. Engineering Advances, 6(2).

[20] Wang, Y. (2025). Application of Data Completion and Full Lifecycle Cost Optimization Integrating Artificial Intelligence in Supply Chain.

[21] Sun, J. (2025). Quantile Regression Study on the Impact of Investor Sentiment on Financial Credit from the Perspective of Behavioral Finance.

[22] Chen, M. (2025). Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing.

[23] Liang, Q. (2026). Research on the Renovation Design Path for Enhancing the Efficiency of Mixed-Use Office and Retail Spaces. European Journal of AI, Computing & Informatics, 2(2), 163-170.

[24] Wu, Y. (2025). Optimization of Generative AI Intelligent Interaction System Based on Adversarial Attack Defense and Content Controllable Generation.

[25] Wu, Y. (2025). Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization. Machine Learning Theory and Practice, 5(1), 64-75.

[26] Wu, Y. (2025). Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization. Machine Learning Theory and Practice, 5(1), 64-75.

[27] Liu, D., Shen, Q., & Liu, J. (2026). The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density. Computation, 14(1), 22.

[28] Yin, J. (2026, March). An Efficient Iterative Algorithm for Calibrating Agency MBS Estimation Parameters Based on Bayesian Optimization, Implemented in Python. In 2026 IEEE Madhya Pradesh Section Conference (MPCON) (pp. 1462-1467). IEEE.

[29] Huang, J. (2026). From Policy Authorization to Practical Execution: A Decision-Support Framework for Implementing Housing Supply Strategies in the United States. Strategic Management Insights, 3(1), 24-31.