Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models (CROSBI ID 733359)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Žignić, Lucija ; Begušić, Stjepan ; Kostanjčar, Zvonko
engleski
Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models
Factor models are often used to infer lower-dimensional correlation structures in data, especially when the number of variables grows close to or beyond the number of data points. The data covariance under a factor model structure is a combination of a low-rank component due to common factors and a diagonal or sparse idiosyncratic component. In this paper we consider the estimation of the idiosyncratic component under the assumption of grouped variables, which result in a block-diagonal matrix. We propose a shrinkage approach which ensures the positive definiteness of the estimated matrix, using either known group structures or clustering algorithms to determine them. The proposed methods are tested in a portfolio optimization scenario using simulations and historical data. The results show that the cluster based estimators yield improved performance in terms of out-of-sample portfolio variance, as well as remarkable stability in terms of resilience to the error in the estimated number of latent factors.
high-dimensional factor model, idiosyncratic component, thresholding, shrinkage, clustering
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Podaci o prilogu
1-6.
2022.
objavljeno
10.23919/softcom55329.2022.9911372
Podaci o matičnoj publikaciji
2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Institute of Electrical and Electronics Engineers (IEEE)
Podaci o skupu
2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
predavanje
22.09.2022-24.09.2022
Split, Hrvatska