Efficiently Weighted Estimation of Tail and Interquantile Expectations.

Tail expectations have recently attracted much attention in economics for their ability to capture risk. We develop a semiparametric estimator for the joint estimation of (nonlinear) models of tail expectations with some tail quantile as the left or right threshold, and interquantile expectations, p...

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Publicado en:Journal of Financial Econometrics Vol. 24; no. 2; pp. 1 - 28
Autor principal: Barendse, Sander
Formato: Artículo
Publicado: Oxford University Press / USA 2026
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2026
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      pub: Oxford University Press / USA
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        10.1093/jjfinec/nbag003
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        atl: Efficiently Weighted Estimation of Tail and Interquantile Expectations.
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        au: Barendse, Sander
        affil: Faculty of Economics and Business, University of Amsterdam, Roetersstraat 11, Amsterdam, 1018 WB, The Netherlands
      su:
        Heterogeneity
        Investment risk
        Conditional probability
        Finance software
        Portfolio management (Investments)
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        subj:
          Heterogeneity
          Portfolio Management
          Investment risk
          Conditional probability
          Finance software
          Portfolio management (Investments)
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        copyrightHolder:Oxford University Press
        copyrightYear:2026
        expected shortfall
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        interquantile expectation
        publisher:Oxford University Press
        quantile
        quantile regression
        risk management
        sameAs:https://dx.doi.org/10.1093/jjfinec/nbag003
        tail expectation
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        copyrightHolder:Oxford University Press
        copyrightYear:2026
        expected shortfall
        G32
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        interquantile expectation
        publisher:Oxford University Press
        quantile
        quantile regression
        risk management
        sameAs:https://dx.doi.org/10.1093/jjfinec/nbag003
        tail expectation
      ab: Tail expectations have recently attracted much attention in economics for their ability to capture risk. We develop a semiparametric estimator for the joint estimation of (nonlinear) models of tail expectations with some tail quantile as the left or right threshold, and interquantile expectations, partial expectations between two thresholding quantiles. The joint estimator of these quantities can be used to test for heterogeneity in the conditional distribution, with special attention to distinct tail behavior. We derive efficient weights and asymptotic properties of the estimator for time-series data. The estimator does not require the specification of the conditional distribution, and its computation relies on standard techniques. In an empirical application in finance, we test for a disproportionate contribution of tail events to the average abnormal return of portfolio strategies.
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      doctype: Article
      src: R
    language: English
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