Publications and working papers

Asset Securitization, Cross Holdings, and Systemic Risk in Banking

Published in Journal of Financial Stability (ABS 3*), 2023

Abstract: We present a theoretical framework for studying how the cross holdings of asset securitization products may affect systemic risk in banking. We demonstrate that cross holdings can be understood from the perspective of profit seeking and credit creation; these motives drive up banks’ leverage. We also show that the capital adequacy ratio regulatory constraint may become invalid with cross holdings, which adversely impacts the monitoring of the stability of a system. We demonstrate that, generally, the impact of asset securitization on systemic risk is nonmonotonic and critically hinges on the banking asset structure, cross-holding degree among banks, and asset securitization characteristics including its state of risk retention. We empirically examine theoretical predictions using a comprehensive set of data from 27 countries/regions spanning the past 15 years.

Recommended citation: Shuhua Xiao, Shushang Zhu, Ying Wu. (2023). " Asset Securitization, Cross Holdings, and Systemic Risk in Banking." Journal of Financial Stability. 67(101140). https://doi.org/10.1016/j.jfs.2023.101140

(Working paper)Systemic Risk Bailout: A PGO Approach Based on Neural Network

Published in arxiv, 2022

Abstract: The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ‘Prediction-Gradient-Optimization’ (PGO) framework to solve it, where the ‘Prediction’ means that the objective function without a closed-form is approximated and predicted by a neural network, the ‘Gradient’ is calculated based on the former approximation, and the ‘Optimization’ procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management.

Recommended citation: Shuhua Xiao, Jiali Ma, Li Xia, Shushang Zhu. (2022). " Systemic Risk Bailout: A PGO Approach Based on Neural Network." arxiv. 2212.05235. http://arxiv.org/abs/2212.05235