Document Type : Research Article
Authors
1 Department of Electrical Engineering, National Taiwan Ocean University, Keelung, Taiwan
2 Department of Mathematics and Physics, University of Campania Luigi Vanvitelli, Caserta, Italy
3 Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
Abstract
This study explores the application of dynamic systems for modeling and valuing catastrophe bonds to establish a more intelligent and adaptive approach to determining their volatility parameter. These financial instruments hold significant importance for insurance companies in safeguarding against the risk of insolvency stemming from the escalating frequency and severity of natural disasters worldwide. Employing mathematical principles, this research formulated a pricing partial differential equation and introduced a dynamic system for its resolution. The damage model was assumed to follow a stochastic process, and a radial basis neural network was utilized to estimate the volatility parameter of this stochastic process by leveraging historical data. The study scrutinized the pricing framework of catastrophe bonds related to floods and storms in China, ultimately demonstrating that the proposed methodology proved effective and computationally efficient when contrasted with alternative approaches.
Keywords
Volatility Forecasting: An Artificial Differential Equation Neural Network, AIMS Mathematics, 8 (2023), No. 6, pp. 13907-13922.
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