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An overview of the estimation of large covariance and precision matrices
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Cited by:
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2018.
"Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices,"
Working Paper Series of the Department of Economics, University of Konstanz
2018-07, Department of Economics, University of Konstanz.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2020. "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Working Paper series 20-03, Rimini Centre for Economic Analysis.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2019. "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Papers 1906.05545, arXiv.org.
- Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
- Yunxi Zhang & Soeun Kim, 2024. "Gaussian Graphical Model Estimation and Selection for High-Dimensional Incomplete Data Using Multiple Imputation and Horseshoe Estimators," Mathematics, MDPI, vol. 12(12), pages 1-15, June.
- Benoît Carmichael & Gilles Boevi Koumou & Kevin Moran, 2023.
"Unifying Portfolio Diversification Measures Using Rao’s Quadratic Entropy,"
Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(4), pages 769-802, December.
- Benoît Carmichael & Gilles Boevi Koumou & Kevin Moran, 2015. "Unifying Portfolio Diversification Measures Using Rao's Quadratic Entropy," Cahiers de recherche 1508, CIRPEE.
- Gilles Boevi Koumou & Kevin Moran, 2015. "Unifying Portfolio Diversification Measures Using Rao's Quadratic Entropy," Cahiers de recherche 1502, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
- Kevin Moran & Benoît Carmichael & Gilles Boevi Koumou, 2015. "Unifying Portfolio Diversification Measures Using Rao's Quadratic Entropy," CIRANO Working Papers 2015s-16, CIRANO.
- Minseog Oh & Donggyu Kim, 2024. "Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups," Papers 2412.05664, arXiv.org.
- Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021.
"Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
- Anne Opschoor & André Lucas & Istvan Barra & Dick van Dijk, 2019. "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings," Tinbergen Institute Discussion Papers 19-013/IV, Tinbergen Institute, revised 23 Oct 2019.
- Mei Choi Chiu & Chi Seng Pun & Hoi Ying Wong, 2017. "Big Data Challenges of High‐Dimensional Continuous‐Time Mean‐Variance Portfolio Selection and a Remedy," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1532-1549, August.
- Anders Bredahl Kock & David Preinerstorfer, 2024. "Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $\infty$-norm," Papers 2407.17888, arXiv.org, revised Oct 2024.
- Mörstedt, Torsten & Lutz, Bernhard & Neumann, Dirk, 2024. "Cross validation based transfer learning for cross-sectional non-linear shrinkage: A data-driven approach in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(2), pages 670-685.
- Evan L. Reynolds & Brian C. Callaghan & Michael Gaies & Mousumi Banerjee, 2023. "Regression Trees and Ensemble for Multivariate Outcomes," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 77-109, May.
- Jianqing Fan & Donggyu Kim & Minseok Shin, 2024. "Adaptive Robust Large Volatility Matrix Estimation Based on High-Frequency Financial Data," Working Papers 202419, University of California at Riverside, Department of Economics.
- Kashlak, Adam B., 2021. "Non-asymptotic error controlled sparse high dimensional precision matrix estimation," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
- Huangdi Yi & Qingzhao Zhang & Cunjie Lin & Shuangge Ma, 2022. "Information‐incorporated Gaussian graphical model for gene expression data," Biometrics, The International Biometric Society, vol. 78(2), pages 512-523, June.
- Zhou Tang & Zhangsheng Yu & Cheng Wang, 2020. "A fast iterative algorithm for high-dimensional differential network," Computational Statistics, Springer, vol. 35(1), pages 95-109, March.
- Donggyu Kim & Minseog Oh, 2024. "Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups," Working Papers 202420, University of California at Riverside, Department of Economics.
- Shin, Minseok & Kim, Donggyu & Fan, Jianqing, 2023. "Adaptive robust large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 237(1).
- Kim, Seungkyu & Park, Seongoh & Lim, Johan & Lee, Sang Han, 2023. "Robust tests for scatter separability beyond Gaussianity," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
- Yan Zhang & Jiyuan Tao & Zhixiang Yin & Guoqiang Wang, 2022. "Improved Large Covariance Matrix Estimation Based on Efficient Convex Combination and Its Application in Portfolio Optimization," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
- Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
- Lidan Tan & Khai X. Chiong & Hyungsik Roger Moon, 2018. "Estimation of High-Dimensional Seemingly Unrelated Regression Models," Papers 1811.05567, arXiv.org.
- Kolli, Praveen & Sarantsev, Andrey, 2019. "Large rank-based models with common noise," Statistics & Probability Letters, Elsevier, vol. 151(C), pages 29-35.
- Rasoul Lotfi & Davood Shahsavani & Mohammad Arashi, 2022. "Classification in High Dimension Using the Ledoit–Wolf Shrinkage Method," Mathematics, MDPI, vol. 10(21), pages 1-13, November.
- Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
- Monika Bours & Ansgar Steland, 2021. "Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 610-654, June.
- Dong, Yingjie & Tse, Yiu-Kuen, 2020. "Forecasting large covariance matrix with high-frequency data using factor approach for the correlation matrix," Economics Letters, Elsevier, vol. 195(C).
- Zeyu Wu & Cheng Wang & Weidong Liu, 2023. "A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 619-648, August.
- Jonathan Tuck & Shane Barratt & Stephen Boyd, 2021. "Portfolio Construction Using Stratified Models," Papers 2101.04113, arXiv.org, revised Feb 2021.
- Peter C. B. Phillips & Liang Jiang, 2025. "Cross Section Curve Data Autoregression," Cowles Foundation Discussion Papers 2439, Cowles Foundation for Research in Economics, Yale University.
- Wang, Hanchao & Peng, Bin & Li, Degui & Leng, Chenlei, 2021. "Nonparametric estimation of large covariance matrices with conditional sparsity," Journal of Econometrics, Elsevier, vol. 223(1), pages 53-72.
- Evangelos E. Ioannidis, 2022. "A new non‐parametric cross‐spectrum estimator," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 808-827, September.
- Linton, Oliver B. & Tang, Haihan, 2022.
"Estimation Of The Kronecker Covariance Model By Quadratic Form,"
Econometric Theory, Cambridge University Press, vol. 38(5), pages 1014-1067, October.
- Linton, O. & Tang, H., 2020. "Estimation of the Kronecker Covariance Model by Quadratic Form," Cambridge Working Papers in Economics 2050, Faculty of Economics, University of Cambridge.
- Shi Yafeng & Ai Chunrong & Yanlong Shi & Ying Tingting & Xu Qunfang, 2023. "Large covariance estimation using a factor model with common and group‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2217-2248, December.
- Andrew Shephard & Xu Cheng & Alejándro Sanchez-Becerra, 2023. "How to weight in moments matchings: A new approach and applications to earnings dynamics," CeMMAP working papers 13/23, Institute for Fiscal Studies.
- Shaoxin Wang & Hu Yang & Chaoli Yao, 2019. "On the penalized maximum likelihood estimation of high-dimensional approximate factor model," Computational Statistics, Springer, vol. 34(2), pages 819-846, June.
- Wu, Zeyu & Wang, Cheng, 2022. "Limiting spectral distribution of large dimensional Spearman’s rank correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
- Denis Belomestny & Mathias Trabs & Alexandre Tsybakov, 2017. "Sparse covariance matrix estimation in high-dimensional deconvolution," Working Papers 2017-25, Center for Research in Economics and Statistics.
- Paolo Vidoni, 2021. "Boosting multiplicative model combination," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 761-789, September.
- Marilena Mitrouli & Athanasios Polychronou & Paraskevi Roupa & Ondřej Turek, 2021. "Estimating the Quadratic Form x T A −m x for Symmetric Matrices: Further Progress and Numerical Computations," Mathematics, MDPI, vol. 9(12), pages 1-13, June.
- Vahe Avagyan, 2022. "Precision matrix estimation using penalized Generalized Sylvester matrix equation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 950-967, December.
- Enrico Bernardi & Matteo Farnè, 2022. "A Log-Det Heuristics for Covariance Matrix Estimation: The Analytic Setup," Stats, MDPI, vol. 5(3), pages 1-11, July.
- Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation," Papers 2107.05201, arXiv.org, revised Oct 2021.
- Xu, Hao & Gardoni, Paolo, 2020. "Conditional formulation for the calibration of multi-level random fields with incomplete data," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
- Lars Heinrich & Antoniya Shivarova & Martin Zurek, 2021. "Factor investing: alpha concentration versus diversification," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 464-487, October.
- Wei Dong & Hongzhen Liu, 2024. "Distributed Sparse Precision Matrix Estimation via Alternating Block-Based Gradient Descent," Mathematics, MDPI, vol. 12(5), pages 1-15, February.
- Ata Kabán & Efstratios Palias, 2024. "A Bhattacharyya-type Conditional Error Bound for Quadratic Discriminant Analysis," Methodology and Computing in Applied Probability, Springer, vol. 26(4), pages 1-17, December.
- Aaron J Molstad & Adam J Rothman, 2018. "Shrinking characteristics of precision matrix estimators," Biometrika, Biometrika Trust, vol. 105(3), pages 563-574.
- Farnè, Matteo & Montanari, Angela, 2020. "A large covariance matrix estimator under intermediate spikiness regimes," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
- Morrison, Rebecca & Baptista, Ricardo & Basor, Estelle, 2022. "Diagonal nonlinear transformations preserve structure in covariance and precision matrices," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
- Zou, Tao & Lan, Wei & Li, Runze & Tsai, Chih-Ling, 2022. "Inference on covariance-mean regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 318-338.
- McGillivray, Annaliza & Khalili, Abbas & Stephens, David A., 2020. "Estimating sparse networks with hubs," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
- Yutong Lu & Gesine Reinert & Mihai Cucuringu, 2023. "Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets," Papers 2302.09382, arXiv.org, revised May 2024.
- Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
- Ning Zhang & Jin Yang, 2023. "Sparse precision matrix estimation with missing observations," Computational Statistics, Springer, vol. 38(3), pages 1337-1355, September.
- Chen, Dachuan & Mykland, Per A. & Zhang, Lan, 2024. "Realized regression with asynchronous and noisy high frequency and high dimensional data," Journal of Econometrics, Elsevier, vol. 239(2).
- Anna Bykhovskaya & Vadim Gorin, 2023. "High-Dimensional Canonical Correlation Analysis," Papers 2306.16393, arXiv.org, revised Jan 2025.
- Lim Hao Shen Keith, 2024. "Covariance Matrix Analysis for Optimal Portfolio Selection," Papers 2407.08748, arXiv.org.
- Khai X. Chiong & Hyungsik Roger Moon, 2017. "Estimation of Graphical Models using the $L_{1,2}$ Norm," Papers 1709.10038, arXiv.org, revised Oct 2017.
- Min Dai & Hanqing Jin & Steven Kou & Yuhong Xu, 2021. "A Dynamic Mean-Variance Analysis for Log Returns," Management Science, INFORMS, vol. 67(2), pages 1093-1108, February.
- David Neděla & Sergio Ortobelli Lozza & Tomáš Tichý, 2025. "Dynamic Return Scenario Generation Approach for Large-Scale Portfolio Optimisation Framework," Computational Economics, Springer;Society for Computational Economics, vol. 65(2), pages 819-843, February.
- Zhang, Qingzhao & Ma, Shuangge & Huang, Yuan, 2021. "Promote sign consistency in the joint estimation of precision matrices," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
- M. Perrot‐Dockès & C. Lévy‐Leduc & L. Rajjou, 2022. "Estimation of large block structured covariance matrices: Application to ‘multi‐omic’ approaches to study seed quality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 119-147, January.
- Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
- Krivobokova, Tatyana & Serra, Paulo & Rosales, Francisco & Klockmann, Karolina, 2022. "Joint non-parametric estimation of mean and auto-covariances for Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
- Gautam Sabnis & Debdeep Pati & Anirban Bhattacharya, 2019. "Compressed Covariance Estimation with Automated Dimension Learning," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 466-481, December.
- Lucija v{Z}igni'c & Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar, 2024. "Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series," Papers 2407.03781, arXiv.org.
- Wang, Shaoxin, 2021. "An efficient numerical method for condition number constrained covariance matrix approximation," Applied Mathematics and Computation, Elsevier, vol. 397(C).
- Brownlees, Christian & Mesters, Geert, 2021.
"Detecting granular time series in large panels,"
Journal of Econometrics, Elsevier, vol. 220(2), pages 544-561.
- Geert Mesters & Christian Brownlees, 2017. "Detecting Granular Time Series in Large Panels," Working Papers 991, Barcelona School of Economics.
- Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
- De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
- Minshuo Chen & Renyuan Xu & Yumin Xu & Ruixun Zhang, 2025. "Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure," Papers 2504.06566, arXiv.org, revised May 2025.