Estimation of Conditional Value-at-Risk in Linear Model

Authors: Jana Jurečková, J. Picek, Jan Kalina
Publication year: 2024
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence
Bibliographic citation: JUREČKOVÁ, Jana, PICEK, J., KALINA, Jan. Estimation of Conditional Value-at-Risk in Linear Model. In: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence. Cham: Springer, 2024, S. 200-207. Advances in Intelligent Systems and Computing. ISBN 978-3-031-65992-8. ISSN 2194-5357
Dostupné z: doi:10.1007/978-3-031-65993-5_24.
DOI: 10.1007/978-3-031-65993-5_24
ID: 598425
Online record: View online record

Abstract

The conditional value-at-risk (CVaR) represents a popular risk measure often exploited e.g. within portfolio optimization. The situation with a nuisance linear regression is considered here, in other words, we do not observe directly the loss Z of interest, but only Y=\beta _0+X\beta+Z, where the covariates are not under our control. We propose a novel estimator of CVaR(Z) based on the averaged two-step regression quantile combined with an R-estimate of regression parameters.