Volume 36, Issue 4
Research on Ruin Probability of Risk Model Based on AR(1) Time Series

Wenhao Li, Bolong Wang, Tianxiang Shen, Ronghua Zhu & Dehui Wang

Commun. Math. Res., 36 (2020), pp. 390-402.

Published online: 2020-11

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  • Abstract

The insurance industry typically exploits ruin theory on collected data to gain more profits. However, state-of-art approaches fail to consider the dependency of the intensity of claim numbers, resulting in the loss of accuracy. In this work, we establish a new risk model based on traditional AR(1) time series, and propose a fine-gained insurance model which has a dependent data structure. We leverage Newton iteration method to figure out the adjustment coefficient and evaluate the exponential upper bound of the ruin probability. We claim that our model significantly improves the precision of insurance model and explores an interesting direction for future research.

  • Keywords

Dependent structure, moment estimation, adjustment coefficient, ruin probability.

  • AMS Subject Headings

91G05

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CMR-36-390, author = {Li , Wenhao and Wang , Bolong and Shen , Tianxiang and Zhu , Ronghua and Wang , Dehui}, title = {Research on Ruin Probability of Risk Model Based on AR(1) Time Series}, journal = {Communications in Mathematical Research }, year = {2020}, volume = {36}, number = {4}, pages = {390--402}, abstract = {

The insurance industry typically exploits ruin theory on collected data to gain more profits. However, state-of-art approaches fail to consider the dependency of the intensity of claim numbers, resulting in the loss of accuracy. In this work, we establish a new risk model based on traditional AR(1) time series, and propose a fine-gained insurance model which has a dependent data structure. We leverage Newton iteration method to figure out the adjustment coefficient and evaluate the exponential upper bound of the ruin probability. We claim that our model significantly improves the precision of insurance model and explores an interesting direction for future research.

}, issn = {2707-8523}, doi = {https://doi.org/10.4208/cmr.2020-0053}, url = {http://global-sci.org/intro/article_detail/cmr/18359.html} }
TY - JOUR T1 - Research on Ruin Probability of Risk Model Based on AR(1) Time Series AU - Li , Wenhao AU - Wang , Bolong AU - Shen , Tianxiang AU - Zhu , Ronghua AU - Wang , Dehui JO - Communications in Mathematical Research VL - 4 SP - 390 EP - 402 PY - 2020 DA - 2020/11 SN - 36 DO - http://doi.org/10.4208/cmr.2020-0053 UR - https://global-sci.org/intro/article_detail/cmr/18359.html KW - Dependent structure, moment estimation, adjustment coefficient, ruin probability. AB -

The insurance industry typically exploits ruin theory on collected data to gain more profits. However, state-of-art approaches fail to consider the dependency of the intensity of claim numbers, resulting in the loss of accuracy. In this work, we establish a new risk model based on traditional AR(1) time series, and propose a fine-gained insurance model which has a dependent data structure. We leverage Newton iteration method to figure out the adjustment coefficient and evaluate the exponential upper bound of the ruin probability. We claim that our model significantly improves the precision of insurance model and explores an interesting direction for future research.

Wenhao Li, Bolong Wang, Tianxiang Shen, Ronghua Zhu & Dehui Wang. (2020). Research on Ruin Probability of Risk Model Based on AR(1) Time Series. Communications in Mathematical Research . 36 (4). 390-402. doi:10.4208/cmr.2020-0053
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