Paper: arXiv 2405.19075
Authors: Baishuai Zuo, Chuancun Yin
Abstract
In this paper, we discuss the worst-case of distortion riskmetrics for general distributions when only partial information (mean and variance) is known. This result is applicable to general class of distortion risk measures and variability measures. Furthermore, we also consider worst-case of weighted entropy for general distributions when only partial information is available. Specifically, we provide some applications for entropies, weighted entropies and risk measures. The commonly used entropies include Gini functional, cumulative residual entropy, tail-Gini functional, cumulative Tsallis past entropy, extended Gini coefficient and so on. The risk measures contain some premium principles and shortfalls based on entropy. The shortfalls include the Gini shortfall, extended Gini shortfall, shortfall of cumulative residual entropy and shortfall of cumulative residual Tsallis entropy with order $α$.
Complexity vs Empirical Score
- Math Complexity: 8.0/10
- Empirical Rigor: 2.0/10
- Quadrant: Lab Rats — theoretically deep, empirically untested
Why this score: The paper provides rigorous mathematical proofs establishing worst-case bounds for distortion riskmetrics and weighted entropy under partial moment information. However, it is entirely theoretical with minimal empirical validation or backtesting on real-world financial data.
Research Flowchart
flowchart TD
A[Research Goal: Worst-cases of Distortion Riskmetrics & Weighted Entropy under Partial Info] --> B[Inputs: Mean and Variance Information Only]
B --> C[Methodology: Optimization under Partial Information Constraints]
C --> D[Compute Worst-Case Bounds for Distortion Riskmetrics & Weighted Entropy]
D --> E[Apply to Specific Entropies: Gini, Cumulative Residual, & Tsallis]
D --> F[Apply to Risk Measures: Premium Principles & Entropy-based Shortfalls]
E --> G[Outcomes: Unified Framework for Tail Risk Evaluation & Robust Risk Management]
F --> G
Cited References (43)
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and 35 more
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1 of 43 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.