On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data
This research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attribut...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124005466 |
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| author | Chrisandi R. Lande Nur Iriawan Dedy Dwi Prastyo |
| author_facet | Chrisandi R. Lande Nur Iriawan Dedy Dwi Prastyo |
| author_sort | Chrisandi R. Lande |
| collection | DOAJ |
| description | This research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attributes that standard models, including previous method such as the Gaussian Mixture Autoregressive (GMAR) model and other autoregressive methodologies, find problematic to manage effectively. The GEVMAR model integrates the Generalized Extreme Value (GEV) distribution alongside Bayesian estimation techniques, augmented by a modified Signal-to-Noise Ratio (SNR) metric to improve predictive accuracy. Compared to preceding studies that adopted Gaussian-based or more elementary autoregressive models, the GEVMAR model displays a significantly elevated capacity to interpret complex data dynamics. The effectiveness of this methodological advancement has been rigorously assessed through its implementation to claim reserves data from insurance companies in Indonesia covering the period from 2015 to 2023, demonstrating that the GEVMAR model (GEV type I) consistently attains an improved adjusted SNR metric (1.3894 × 10⁶) coupled with a reduced Mean Absolute Percentage Error (MAPE) (0.0189) when compared to the GMAR model (MAPE 7.5812). Furthermore, the Bayesian methodology employed within the GEVMAR framework affords substantial versatility in incorporating prior distributions, thereby conferring a pivotal advantage in analyzing heavy-tailed datasets characterized by extreme variability. This study emphasizes the limitations of existing models, such as their reduced accuracy in capturing multimodal patterns and inability to address extreme volatility effectively. Some highlights of the proposed method are: • Development of a new model for the generalized extreme value mixture autoregressive. • Adjustment of SNR type 2 for the generalized extreme value mixture autoregressive model. • Application of the Bayesian GEVMAR (GEV type I) model to non-standard claim reserves data. |
| format | Article |
| id | doaj-art-f4ac6a26c43244a398e852418ca84d73 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-f4ac6a26c43244a398e852418ca84d732024-12-16T05:35:56ZengElsevierMethodsX2215-01612025-06-0114103095On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial dataChrisandi R. Lande0Nur Iriawan1Dedy Dwi Prastyo2Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia; Politeknik Ilmu Pelayaran Makassar, Makassar 90165, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia; Corresponding author.Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 IndonesiaThis research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attributes that standard models, including previous method such as the Gaussian Mixture Autoregressive (GMAR) model and other autoregressive methodologies, find problematic to manage effectively. The GEVMAR model integrates the Generalized Extreme Value (GEV) distribution alongside Bayesian estimation techniques, augmented by a modified Signal-to-Noise Ratio (SNR) metric to improve predictive accuracy. Compared to preceding studies that adopted Gaussian-based or more elementary autoregressive models, the GEVMAR model displays a significantly elevated capacity to interpret complex data dynamics. The effectiveness of this methodological advancement has been rigorously assessed through its implementation to claim reserves data from insurance companies in Indonesia covering the period from 2015 to 2023, demonstrating that the GEVMAR model (GEV type I) consistently attains an improved adjusted SNR metric (1.3894 × 10⁶) coupled with a reduced Mean Absolute Percentage Error (MAPE) (0.0189) when compared to the GMAR model (MAPE 7.5812). Furthermore, the Bayesian methodology employed within the GEVMAR framework affords substantial versatility in incorporating prior distributions, thereby conferring a pivotal advantage in analyzing heavy-tailed datasets characterized by extreme variability. This study emphasizes the limitations of existing models, such as their reduced accuracy in capturing multimodal patterns and inability to address extreme volatility effectively. Some highlights of the proposed method are: • Development of a new model for the generalized extreme value mixture autoregressive. • Adjustment of SNR type 2 for the generalized extreme value mixture autoregressive model. • Application of the Bayesian GEVMAR (GEV type I) model to non-standard claim reserves data.http://www.sciencedirect.com/science/article/pii/S2215016124005466Bayesian Generalized Extreme Value Mixture Autoregressive |
| spellingShingle | Chrisandi R. Lande Nur Iriawan Dedy Dwi Prastyo On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data MethodsX Bayesian Generalized Extreme Value Mixture Autoregressive |
| title | On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data |
| title_full | On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data |
| title_fullStr | On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data |
| title_full_unstemmed | On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data |
| title_short | On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data |
| title_sort | on the bayesian generalized extreme value mixture autoregressive model with adjusted snr in non standard actuarial data |
| topic | Bayesian Generalized Extreme Value Mixture Autoregressive |
| url | http://www.sciencedirect.com/science/article/pii/S2215016124005466 |
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