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Estimation of high conditional tail risk based on expectile regression

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
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003  MAP
005  20210922173205.0
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040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20210031885‎$a‎Jie Hu
24510‎$a‎Estimation of high conditional tail risk based on expectile regression‎$c‎Jie Hu, Yu Chen, Keqi Tan
520  ‎$a‎Assessing conditional tail risk at very high or low levels is of great interest in numerous applications. Due to data sparsity in high tails, the widely used quantile regression method can suffer from high variability at the tails, especially for heavy-tailed distributions. As an alternative to quantile regression, expectile regression, which relies on the minimization of the asymmetric l2-norm and is more sensitive to the magnitudes of extreme losses than quantile regression, is considered. In this article, we develop a new estimation method for high conditional tail risk by first estimating the intermediate conditional expectiles in regression framework, and then estimating the underlying tail index via weighted combinations of the top order conditional expectiles. The resulting conditional tail index estimators are then used as the basis for extrapolating these intermediate conditional expectiles to high tails based on reasonable assumptions on tail behaviors. Finally, we use these high conditional tail expectiles to estimate alternative risk measures such as the Value at Risk (VaR) and Expected Shortfall (ES), both in high tails. The asymptotic properties of the proposed estimators are investigated. Simulation studies and real data analysis show that the proposed method outperforms alternative approaches.
650 4‎$0‎MAPA20210011108‎$a‎Riesgo
650 4‎$0‎MAPA20210010484‎$a‎Datos abiertos
7001 ‎$0‎MAPA20140009237‎$a‎Chen, Yu
7001 ‎$0‎MAPA20210031892‎$a‎Tan, Keqi
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎10/05/2021 Volumen 51 Número 2 - mayo 2021 , p. 539 - 570