Improved Confidence Intervals for Expectiles
This paper develops higher-order Edgeworth expansions for kernel expectile estimators, yielding more accurate confidence intervals in financial risk management.
What it examines
Authors address shortcomings in quantile‐based risk measures by developing enhanced confidence intervals for expectiles through higher‐order Edgeworth expansions on kernel‐based estimators. Motivated by financial risk management and post‐crisis needs, the study introduces improved asymptotic methods suitable for small-to-moderate sample sizes and provides numerical illustrations.
What it concludes
The study concludes that Edgeworth expansions improve confidence intervals for expectiles in small-to-moderate samples, enhancing risk measurement accuracy. The nonparametric approach applies to financial risk management, expectile regression, and control chart design, suggesting promising directions for further research and practical applications.
Evidence objects
Researchers develop breakthrough methods for constructing improved confidence intervals for expectiles, integral to financial risk management, using higher-order asymptotic results with $$Edgeworth$$ expansions for standardized and studentized kernel-based estimators, notably.
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The study pioneers refined $$Cornish\\!-\\!Fisher$$ approximations and innovative kernel smoothing techniques while leveraging powerful nonparametric statistics and large deviation theory for U-statistics to yield superior interval coverage versus traditional approximations.
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Extensive simulations with standard exponential data validate the novel methods, significantly reducing tail bias despite complex derivations and challenging bandwidth selection, thus greatly advancing expectile estimation in financial risk management.
key_findings bullet 3 · key_findings · validation V0
This paper applies higher-order asymptotic methods with $$\text{Edgeworth expansion}$$ to construct improved confidence intervals for expectiles, refining quantitative risk management. Its originality lies in advancing beyond first-order approaches and addressing limitations in financial risk measurement. The work ingeniously integrates established techniques, yielding compelling incremental innovation and enhanced precision for practitioners.
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Raw abstract and provenance
- … gained renewed interest due to their relevance in financial risk management. In particular, the 2007–2009 global financial crisis highlighted the need for more robust risk …
Source row: 1075 · abstract type: snippet