Catastrophe Insurance for Historical Buildings Based on a Panel Quantile Regression Model

DOI

10.2991/978-94-6463-562-1_36How to use a DOI?

Keywords

Panel Quantile Regression Model; Catastrophe Insurance; Historical Building Repair Value Assessment Model

Abstract

In this paper, we investigate the issue of whether insurance companies should provide coverage for historical buildings under specific catastrophic scenarios, particularly as extreme weather events become increasingly frequent. Initially, we briefly analyze the repair data and budget area ratios for historical buildings across the country over recent years. Subsequently, we selected the economic development level (GDP), population density, and repair efficiency from 2018 to 2021 across 28 provincial-level administrative regions in China as conditioning variables. Using a panel quantile regression model, we estimated the repair costs of historical buildings, thereby establishing a historical building repair value assessment model. Based on fundamental insurance theories and in conjunction with the repair value assessment model, we then employed a risk index method—considering both the occurrence of extreme weather events and their impact on the insured regions—to design a catastrophe insurance scheme specifically for historical buildings (targeting extreme weather). This approach aims to ensure the long-term financial health and stability of insurance companies engaged in this business. Finally, we summarize the strengths and weaknesses of the catastrophe insurance model and offer prospects in light of the rapid advancements in artificial intelligence.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuanyu Li
PY  - 2024
DA  - 2024/11/13
TI  - Catastrophe Insurance for Historical Buildings Based on a Panel Quantile Regression Model
BT  - Proceedings of the 2024 5th International Conference on Big Data and Social Sciences (ICBDSS 2024)
PB  - Atlantis Press
SP  - 392
EP  - 412
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-562-1_36
DO  - 10.2991/978-94-6463-562-1_36
ID  - Li2024
ER  -