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Identifier to cite or link to this item: https://hdl.handle.net/10086/85066

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Title
Expected Shortfall Regression for High-Dimensional Additive Models
Creator
Creator Name
HONDA, Toshio HONDA, Toshio
本田, 敏雄 本田, 敏雄
Affiliation
Affiliation Name
Hitotsubashi University
Creator Name
PENG, Po-Hsiang PENG, Po-Hsiang
Affiliation
Affiliation Name
National Tsing Hua University
Key Word
expected shortfall
quantile regression
group Lasso
group SCAD
B-spline basis
additive models
Abstract (distribution restricted)
The expected shortfall (ES) regression can be a powerful and useful tool to analyze the relation between the response variable and the covariates through the conditional mean. As is well-known, there is no single loss function for expected shortfall estimation and there is a suitable loss function for joint estimation of quantile and expected shortfall. In addition to them, recently a very useful two-step procedure for ES regression was proposed : carry out quantile regression and then estimate the ES regression model by applying the least squares method. This procedure is successful due to the Neyman orthogonality. Then high dimensional linear regression models was considered based on the the findings. By exploiting those results, we assume additive models for both quantile and expected shortfall in the high-dimensional setting and consider the group Lasso and SCAD estimators. We establish the oracle inequality and the oracle property for them. Our theoretical results also imply that quantile estimation does not affect ES estimation asymptotically. We also present numerical results that demonstrate satisfactory performance in model selection, estimation accuracy, and prediction error for a moderate sample size together with an empirical study.
Publisher
Graduate School of Economics, Hitotsubashi University
Issued Date
2025-02
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Discussion papers ; No. 2025-01
Funding Reference
Funder Name
日本学術振興会 日本学術振興会
Japan Society for the Promotion of Science Japan Society for the Promotion of Science
Award Number
24K14850
awardURI
https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-24K14850/
Award Title
構造を持つノンパラメトリック回帰モデルによる超高次元データ解析に関する研究
Number of Pages
37
Sponsorship
This research is financially supported by JSPS KAKENHI Grant Number JP 24K14850 (HONDA) and Taiwan NSTC Grant Number 112-2122-M-007-001-MY3 (PENG).
Appears in Collections
Discussion papers / Graduate School of Economics, Hitotsubashi University
URL
https://hdl.handle.net/10086/85066