@Article{CMR-36-4,
author = {Wenhao, Li and Wang, Bolong and Tianxiang, Shen 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 = {<p style="text-align: justify;">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.</p>},
issn = {2707-8523},
doi = {https://doi.org/10.4208/cmr.2020-0053},
url = {https://global-sci.com/article/81539/research-on-ruin-probability-of-risk-model-based-on-ar1-time-series}
}