Abstract
The value-at-risk (VaR) at a small level q, 0<q<1, is the size of the loss that occurs with a probability q. Semi-parametric partially reduced-bias (PRB) VaR-estimation procedures based on the mean-of-order-p of a set of k quotients of upper order statistics, with p any real number, are put forward. After the study of their asymptotic behavior, these PRB VaR-estimators are altogether compared with the classical ones for finite samples, through a large-scale Monte-Carlo simulation study. A brief application to financial log-returns is provided, as well as some final remarks.
| Original language | English |
|---|---|
| Pages (from-to) | 867-885 |
| Journal | Communications in Statistics: Simulation and Computation |
| Volume | 49 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2 Apr 2020 |
Keywords
- Heavy right tails
- Heuristic sample fraction selection; Monte-Carlo simulation; Semi-parametric estimation; Statistics of extremes; Value-at-risk estimation
- Primary 62G32; Secondary 65C05
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Cite this
Gomes, M. I., Caeiro, F., Figueiredo, F., Henriques-Rodrigues, L., & Pestana, D. (2020). Corrected-Hill versus partially reduced-bias value-at-risk estimation. Communications in Statistics: Simulation and Computation, 49(4), 867-885. https://doi.org/10.1080/03610918.2018.1489053