Estimation of the Adjusted Standard-deviatile for Extreme Risks

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Hauptverfasser: Chen, Haoyu, Mao, Tiantian, Yang, Fan
Format: Preprint
Veröffentlicht: 2024
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author Chen, Haoyu
Mao, Tiantian
Yang, Fan
author_facet Chen, Haoyu
Mao, Tiantian
Yang, Fan
contents In this paper, we modify the Bayes risk for the expectile, the so-called variantile risk measure, to better capture extreme risks. The modified risk measure is called the adjusted standard-deviatile. First, we derive the asymptotic expansions of the adjusted standard-deviatile. Next, based on the first-order asymptotic expansion, we propose two efficient estimation methods for the adjusted standard-deviatile at intermediate and extreme levels. By using techniques from extreme value theory, the asymptotic normality is proved for both estimators. Simulations and real data applications are conducted to examine the performance of the proposed estimators.
format Preprint
id arxiv_https___arxiv_org_abs_2411_07203
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Estimation of the Adjusted Standard-deviatile for Extreme Risks
Chen, Haoyu
Mao, Tiantian
Yang, Fan
Statistics Theory
Risk Management
In this paper, we modify the Bayes risk for the expectile, the so-called variantile risk measure, to better capture extreme risks. The modified risk measure is called the adjusted standard-deviatile. First, we derive the asymptotic expansions of the adjusted standard-deviatile. Next, based on the first-order asymptotic expansion, we propose two efficient estimation methods for the adjusted standard-deviatile at intermediate and extreme levels. By using techniques from extreme value theory, the asymptotic normality is proved for both estimators. Simulations and real data applications are conducted to examine the performance of the proposed estimators.
title Estimation of the Adjusted Standard-deviatile for Extreme Risks
topic Statistics Theory
Risk Management
url https://arxiv.org/abs/2411.07203