Pregled bibliografske jedinice broj: 1258992
Estimating the Block-Diagonal Idiosyncratic Covariance in High- Dimensional Factor Models
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