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Pregled bibliografske jedinice broj: 1258992

Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models


Žignić, Lucija; Begušić, Stjepan; Kostanjčar, Zvonko
Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models // 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Split, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 1-6 doi:10.23919/softcom55329.2022.9911372 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models

Autori
Žignić, Lucija ; Begušić, Stjepan ; Kostanjčar, Zvonko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) / - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 1-6

Skup
2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

Mjesto i datum
Split, Hrvatska, 22.09.2022. - 24.09.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
high-dimensional factor model, idiosyncratic component, thresholding, shrinkage, clustering

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
HRZZ-IP-2019-04-5241 - Algoritmi dubokog podržanog učenja za upravljanje rizicima (DREAM) (Kostanjčar, Zvonko, HRZZ ) ( CroRIS)
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi

Citiraj ovu publikaciju:

Žignić, Lucija; Begušić, Stjepan; Kostanjčar, Zvonko
Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models // 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Split, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 1-6 doi:10.23919/softcom55329.2022.9911372 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Žignić, L., Begušić, S. & Kostanjčar, Z. (2022) Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models. U: 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) doi:10.23919/softcom55329.2022.9911372.
@article{article, author = {\v{Z}igni\'{c}, Lucija and Begu\v{s}i\'{c}, Stjepan and Kostanj\v{c}ar, Zvonko}, year = {2022}, pages = {1-6}, DOI = {10.23919/softcom55329.2022.9911372}, keywords = {high-dimensional factor model, idiosyncratic component, thresholding, shrinkage, clustering}, doi = {10.23919/softcom55329.2022.9911372}, title = {Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models}, keyword = {high-dimensional factor model, idiosyncratic component, thresholding, shrinkage, clustering}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Split, Hrvatska} }
@article{article, author = {\v{Z}igni\'{c}, Lucija and Begu\v{s}i\'{c}, Stjepan and Kostanj\v{c}ar, Zvonko}, year = {2022}, pages = {1-6}, DOI = {10.23919/softcom55329.2022.9911372}, keywords = {high-dimensional factor model, idiosyncratic component, thresholding, shrinkage, clustering}, doi = {10.23919/softcom55329.2022.9911372}, title = {Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models}, keyword = {high-dimensional factor model, idiosyncratic component, thresholding, shrinkage, clustering}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Split, Hrvatska} }

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