Estimation of Large Volatility Matrices with Low-Rank Signal Plus Sparse Noise Structures

Runyu Dai and Yasumasa Matsuda

No 135, DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: In this paper, we propose a parsimonious model to estimate large volatility matrices by combining DCC-GARCH, sparsity-induced weak factors (sWFs) and POET framework in Fan et al. (2013). We call this method the DCC and sWFs extended POET (DCC-ePOET). Built on the mixed factor structures, we estimate volatility matrices through the univariate volatilities of observable factors and weak latent factors with a linear transformation. We further include a sparse noise covariance estimator obtained by an aptivethreshold method proposed in POET to dressthe singularity issue when the cross-sectional dimension N is larger than the sample size T, and capture the weak correlations in the factor models'idiosyncratic terms. Simulation studies show that our proposed method achieves good finite-sample performance. Empirical studies demonstrate that the developed method is superior to several candidates in the analysis of out-of-sample minimum variance portfolio allocations.

Pages: 23 pages
Date: 2023-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10097/00137340

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:toh:dssraa:135

Access Statistics for this paper

More papers in DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University Contact information at EDIRC.
Bibliographic data for series maintained by Tohoku University Library ().