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Dependence Uncertainty Bounds for the Expectile of a Portfolio

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Reducing Model Risk via Positive and Negative Dependence Assumptions

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Model uncertainty and VaR aggregation

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Assessing value at risk with CARE, the Conditional Autoregressive Expectile models

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Quantiles, expectiles and splines

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The complete mixability and convex minimization problems with monotone marginal densities

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Isotonicity properties of generalized quantiles

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Sharp bounds on the expected shortfall for a sum of dependent random variables

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The safest dependence structure among risks

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Nonparametric risk management and implied risk aversion

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The Variance Gamma Process and Option Pricing

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General convex order on risk aggregation

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Computation of Sharp Bounds on the Expected Value of a Supermodular Function of Risks with Given Marginals

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The Generalized Hyperbolic Skew Student's t-Distribution

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Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling

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Coherent Measures of Risk

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The t Copula and Related Copulas

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Value‐at‐Risk Bounds With Variance Constraints

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COHERENCE AND ELICITABILITY

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Expected shortfall or median shortfall

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Making and Evaluating Point Forecasts

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Inequalities for Distributions with Given Marginals

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Joint Mixability

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Quantile of a Mixture with Application to Model Risk Assessment

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Computing VAR and AVaR in Infinitely Divisible Distributions

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Generalized Quantiles as Risk Measures

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Measuring Market Risk Under the Basel Accords: VaR, Stressed VaR, and Expected Shortfall

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General Extremal Dependence Concepts

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Asymmetric Least Squares Estimation and Testing

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Hyperbolic Distributions in Finance

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An Academic Response to Basel 3.5

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Scenario Aggregation Method for Portfolio Expectile Optimization

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Optimal strategies under Omega ratio

inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115724openalex-snapshot

An empirical analysis of the cardinality constrained expectile-based VaR portfolio optimization problem

inferred← citeshttps://doi.org/10.1016/j.jbankfin.2018.07.002openalex-snapshot

A Clustering Approach and a Rule of Thumb for Risk Aggregation

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Worst-Case Range Value-at-Risk with Partial Information

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Multivariate extensions of expectiles risk measures

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Bernstein’s inequality with a proper cover and upper bound of portfolio VaR and TVaR

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Scenario aggregation method for portfolio expectile optimization

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Expectile Order and Asset Allocation

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Empirical Analysis of Value at Risk (VaR) of Stock Portfolio Based on Python

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