Year: 2025
Type: article
Abstract: As an emerging production factor, data assets are gaining strategic prominence, yet their application in collateralized financing faces persistent challenges, including illiquidity and risk evaluation complexities. This study introduces an innovative Pmax model to enhance the Collateral Value of data assets through insurance mechanisms, systematically demonstrating the feasibility conditions under which risk transfer optimizes asset valuation and delineating implementation pathways to integrate data insurance with asset-backed financing. Building on the theoretical framework of Value-at-Risk (VaR), this study develops a dynamic valuation model to assess the value of the collateral before and after insurance. Our analysis shows that insurance coverage for potential losses significantly enhances financing viability when premiums satisfy Pmax. Empirical analysis employing Monte Carlo simulations reveals a nonlinear positive correlation between pledgees’ risk tolerance thresholds and the maximum acceptable premium Pmax. This study bridges theoretical gaps in understanding insurance-value relationships for data assets while providing conceptual foundations and operational blueprints to standardize data markets and foster financial innovation.
Source: Mathematics
DOI: 10.3390/math13223596
Authors: Nan Zhang, Chunjuan Qiu, Xianyi Wu, Yongchao Zhao
Institution: East China Normal University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416092725