Assessment of dependent risk using extreme value theory in a time-varying framework

Bükre Yıldırım Külekci; Uğur Karabey; Sevtap Selcuk-kestel

Makale detayları

Assessment of dependent risk using extreme value theory in a time-varying framework

Yazarlar:

Bükre Yıldırım Külekci | Uğur Karabey | Sevtap Selcuk-kestel

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 1, pp.248-267

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001284000

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 Several extreme events in history have shown that the low probability and high impact extreme values may result in catastrophic losses. In this paper, we propose the use of extreme value theory with a time-varying framework to model the bivariate dependent insurance occurrences and provide more reliable risk measures, such as value at risk and expected shortfall. In this paper three models are considered; time series for the underlying volatility of the data, extreme value theory for the tail estimation, and copula to model the dependence structure are combined. The performance of the proposed generalized Pareto-GARCH-Copula model is tested using the violation numbers and backtesting methods. We then aim to assess the combined model in terms of its effectiveness in reducing the ruin probability. Results show that, compared to well-known traditional methods, which may underestimate the extreme risks, the dynamic generalized Pareto-GARCH-Copula model captures better the real-life datas behavior and results in lower ruin probabilities for heavy-tailed and non-conventional dependent insurance data.

 Several extreme events in history have shown that the low probability and high impact extreme values may result in catastrophic losses. In this paper, we propose the use of extreme value theory with a time-varying framework to model the bivariate dependent insurance occurrences and provide more reliable risk measures, such as value at risk and expected shortfall. In this paper three models are considered; time series for the underlying volatility of the data, extreme value theory for the tail estimation, and copula to model the dependence structure are combined. The performance of the proposed generalized Pareto-GARCH-Copula model is tested using the violation numbers and backtesting methods. We then aim to assess the combined model in terms of its effectiveness in reducing the ruin probability. Results show that, compared to well-known traditional methods, which may underestimate the extreme risks, the dynamic generalized Pareto-GARCH-Copula model captures better the real-life datas behavior and results in lower ruin probabilities for heavy-tailed and non-conventional dependent insurance data.

 Several extreme events in history have shown that the low probability and high impact extreme values may result in catastrophic losses. In this paper, we propose the use of extreme value theory with a time-varying framework to model the bivariate dependent insurance occurrences and provide more reliable risk measures, such as value at risk and expected shortfall. In this paper three models are considered; time series for the underlying volatility of the data, extreme value theory for the tail estimation, and copula to model the dependence structure are combined. The performance of the proposed generalized Pareto-GARCH-Copula model is tested using the violation numbers and backtesting methods. We then aim to assess the combined model in terms of its effectiveness in reducing the ruin probability. Results show that, compared to well-known traditional methods, which may underestimate the extreme risks, the dynamic generalized Pareto-GARCH-Copula model captures better the real-life datas behavior and results in lower ruin probabilities for heavy-tailed and non-conventional dependent insurance data.

 Several extreme events in history have shown that the low probability and high impact extreme values may result in catastrophic losses. In this paper, we propose the use of extreme value theory with a time-varying framework to model the bivariate dependent insurance occurrences and provide more reliable risk measures, such as value at risk and expected shortfall. In this paper three models are considered; time series for the underlying volatility of the data, extreme value theory for the tail estimation, and copula to model the dependence structure are combined. The performance of the proposed generalized Pareto-GARCH-Copula model is tested using the violation numbers and backtesting methods. We then aim to assess the combined model in terms of its effectiveness in reducing the ruin probability. Results show that, compared to well-known traditional methods, which may underestimate the extreme risks, the dynamic generalized Pareto-GARCH-Copula model captures better the real-life datas behavior and results in lower ruin probabilities for heavy-tailed and non-conventional dependent insurance data.

Makale detayları

Assessment of dependent risk using extreme value theory in a time-varying framework

Yazarlar:

Bükre Yıldırım Külekci | Uğur Karabey | Sevtap Selcuk-kestel

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 1, pp.248-267

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001284000

pISSN:

2651-477X

Extreme value theory

risk measures

copula

backtesting

ruin probability

Extreme value theory

risk measures

copula

backtesting

ruin probability

Sayı

Makale detayları

Yazarlar: Bükre Yıldırım Külekci | Uğur Karabey | Sevtap Selcuk-kestel

Yayıncı: Hacettepe University

Ana Başlıklar: Statistics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 1, pp.248-267

Belge Tipi: Makale

Yayın Tarihi: 2/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001284000

pISSN: 2651-477X

Sayı

Yazarlar:

Bükre Yıldırım Külekci | Uğur Karabey | Sevtap Selcuk-kestel

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 1, pp.248-267

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001284000

pISSN:

2651-477X