With the transformation of mortgage securitization into a dynamic process of penetration analysis, loan tranche information offers a finer backing on cash flow forecasts, credit loss projections, and tiered pricing. The paper develops a simulation model based on the core axis of the model i.e. loan performance - asset pool cash flows - tiered securities profit and loss, which includes default events, prepayment events, recovery rates, and time interval allocation rules. Basic scenarios, a housing price downturn scenario, and an interest rate shock scenario are used to compare the cash flow sensitivity of different tiers. The results show that the higher tranche has strong resilience to minor default shocks, but overreacts to the nonlinear combination of continuous prepayments and credit tail risks. The mezzanine tranche bears the greatest risk transfer amplification effect, while the lowest tranche, although able to absorb the first wave of risk, quickly loses its buffering effect in extreme environments. This paper argues that the combination of loan tranche modeling, tiered trigger mechanism design, and dynamic sizing is crucial for strengthening the stability of mortgage securitization.

Cewen Chi. Research on Mortgage Asset Cash Flow Simulation and Risk Transmission Mechanisms across Structural Tranches Based on Loan-Level Data. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 82-89. https://doi.org/10.38007/IJMC.2026.070110

[1] Furfine C, Zborowski B. Signaling through security design: Evidence from commercial mortgage-backed securities[J]. Available at SSRN, 2025.

[2] Bell I, Bennett M, Duponcheele G, et al. Strengthening Financial Stability through Insurance-based Credit Risk Transfer[J]. Report, May.(Available on PCS and IACPM websites), 2025.

[3] Yanchun Wang. (2025) Research on Enhancing ERP System Efficiency Through AI in Cross-border Supply Chain Environments. Advances in Computer and Communication, 6(5), 268-273.

[4] Wang, Z. (2026). Exploration of the Application of Resource Circular Economy in the Agricultural Industry Chain.

[5] 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.

[6] Zheng, H. (2026, April). Research on Cloud-Edge Collaborative Elastic Computing and Cost Optimization for High-Concurrency Scenarios. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1489-1496). IEEE.

[7] Liu, H. (2025, December). Mlops Model Deployment System for Multi-Cloud Environments and Improvement of Commercial AI Service Availability. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 1047-1052). IEEE.

[8] Hong, Y. (2025, December). Chain Demand Mutation Identification and Emergency Response Decision-Making Integrated With Social Media Sentiment Analysis. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 425-432). IEEE.

[9] Chen, M. (2026, March). Design of a Privacy-Oriented AI Compliance Hook System Based on Static Code Analysis. In 2026 IEEE Madhya Pradesh Section Conference (MPCON) (pp. 648-654). IEEE.

[10] 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.

[11] Wang, Y. (2025, December). Construction of an Intelligent Recommendation System for Personalized Training Plans for Sports Rehabilitation Patients Based on Graph Neural Networks. In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM) (pp. 1-6). IEEE.

[12] Wang, Y. (2025, July). Research on Federated Learning Algorithm Design and Privacy Protection Mechanism of Sports Rehabilitation Data Based on Multimodal Fusion. In International Conference on Frontier Computing (pp. 563-574). Singapore: Springer Nature Singapore.

[13] Zhang, Q. (2025, July). Research on Improving the Effectiveness of Programmatic Advertising Using Multi-armed Bandit Algorithms. In International Conference on Frontier Computing (pp. 543-552). Singapore: Springer Nature Singapore.

[14] 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.

[15] Wu, W. (2025, June). Construction and optimization of intelligent gateway software management platform based on jenkins cluster management under cloud edge integration architecture in industrial internet of things. In International Conference on 6G Communications Networking and Signal Processing (pp. 633-645). Singapore: Springer Nature Singapore.

[16] Wu, Y. (2025, October). Multi-Level Belief Rule Base Modeling Architecture and Intelligent Optimization Technology for Decision Support Systems. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-8). IEEE.

[17] Zhang, Q. (2025, October). Application of Reinforcement Learning in Dynamic Advertising Content Generation. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-5). IEEE.

[18] Liu, X., & Yang, D. (2025, March). LLM Data Strategy: Improving Data Availability and Efficiency. In Doctoral Symposium on Computational Intelligence (pp. 425-437). Singapore: Springer Nature Singapore.

[19] Zhang, C., Han, J., Zou, Y., Dong, K., Li, Y., Ding, J., & Han, X. (2024, April). Detecting the anomalies in LiDAR pointcloud. In WCX SAE World Congress Experience. SAE Technical Paper.

[20] Ding, J. (2025). Research On CODP Localization Decision Model Of Automotive Supply Chain Based On Delayed Manufacturing Strategy. arXiv preprint arXiv:2511.05899.