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High dimensional covariance matrices under dynamic volatility models: asymptotics and shrinkage estimation
DING YI1; ZHENG, Xinghua2
2024-08
Source PublicationThe Annals of Statistics
Volume52Pages:1027–1049
AbstractWe study the estimation of high-dimensional covariance matrices and their empirical spectral distributions under dynamic volatility models. Data under such models have nonlinear dependency both cross-sectionally and temporally. We establish the condition under which the limiting spectral dis- tribution (LSD) of the sample covariance matrix under scalar BEKK mod- els is different from the i.i.d. case. We then propose a time-variation ad- justed (TV-adj) sample covariance matrix and prove that its LSD follows the Marcˇenko–Pastur law. Based on the asymptotics of the TV-adj sample co- variance matrix, we develop a consistent population spectrum estimator and an asymptotically optimal nonlinear shrinkage estimator of the unconditional covariance matrix.
KeywordHigh-dimension Dynamic Volatility Model Sample Covariance Matrix Spectral Distribution Nonlinear Shrinkage
DOIhttps://doi.org/10.1214/24-AOS2381
Language英語English
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Document TypeJournal article
CollectionFaculty of Business Administration
DEPARTMENT OF FINANCE AND BUSINESS ECONOMICS
Corresponding AuthorZHENG, Xinghua
Affiliation1.University of Macau
2.HKUST
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
DING YI,ZHENG, Xinghua. High dimensional covariance matrices under dynamic volatility models: asymptotics and shrinkage estimation[J]. The Annals of Statistics, 2024, 52, 1027–1049.
APA DING YI., & ZHENG, Xinghua (2024). High dimensional covariance matrices under dynamic volatility models: asymptotics and shrinkage estimation. The Annals of Statistics, 52, 1027–1049.
MLA DING YI,et al."High dimensional covariance matrices under dynamic volatility models: asymptotics and shrinkage estimation".The Annals of Statistics 52(2024):1027–1049.
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