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An overview of the estimation of large covariance and precision matrices

Citations

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  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Wu, Zeyu & Wang, Cheng, 2022. "Limiting spectral distribution of large dimensional Spearman’s rank correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
  8. Minseog Oh & Donggyu Kim, 2024. "Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups," Papers 2412.05664, arXiv.org.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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).
  18. 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.
  19. 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.
  20. 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.
  21. Aaron J Molstad & Adam J Rothman, 2018. "Shrinking characteristics of precision matrix estimators," Biometrika, Biometrika Trust, vol. 105(3), pages 563-574.
  22. 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.
  23. Farnè, Matteo & Montanari, Angela, 2020. "A large covariance matrix estimator under intermediate spikiness regimes," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
  24. 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).
  25. 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.
  26. 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.
  27. 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.
  28. Kashlak, Adam B., 2021. "Non-asymptotic error controlled sparse high dimensional precision matrix estimation," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
  29. McGillivray, Annaliza & Khalili, Abbas & Stephens, David A., 2020. "Estimating sparse networks with hubs," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
  30. 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.
  31. 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.
  32. Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
  33. 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.
  34. Ning Zhang & Jin Yang, 2023. "Sparse precision matrix estimation with missing observations," Computational Statistics, Springer, vol. 38(3), pages 1337-1355, September.
  35. 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).
  36. Anna Bykhovskaya & Vadim Gorin, 2023. "High-Dimensional Canonical Correlation Analysis," Papers 2306.16393, arXiv.org, revised Aug 2023.
  37. Lim Hao Shen Keith, 2024. "Covariance Matrix Analysis for Optimal Portfolio Selection," Papers 2407.08748, arXiv.org.
  38. 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).
  39. 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.
  40. 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.
  41. 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).
  42. 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.
  43. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
  44. 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).
  45. 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.
  46. 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.
  47. 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.
  48. 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).
  49. 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.
  50. 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.
  51. Lidan Tan & Khai X. Chiong & Hyungsik Roger Moon, 2018. "Estimation of High-Dimensional Seemingly Unrelated Regression Models," Papers 1811.05567, arXiv.org.
  52. Wang, Shaoxin, 2021. "An efficient numerical method for condition number constrained covariance matrix approximation," Applied Mathematics and Computation, Elsevier, vol. 397(C).
  53. Kolli, Praveen & Sarantsev, Andrey, 2019. "Large rank-based models with common noise," Statistics & Probability Letters, Elsevier, vol. 151(C), pages 29-35.
  54. 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.
  55. Brownlees, Christian & Mesters, Geert, 2021. "Detecting granular time series in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 544-561.
  56. Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
  57. 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.
  58. 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).
  59. 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.
  60. Jonathan Tuck & Shane Barratt & Stephen Boyd, 2021. "Portfolio Construction Using Stratified Models," Papers 2101.04113, arXiv.org, revised Feb 2021.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
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