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Cleaning large correlation matrices: tools from random matrix theory

Citations

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Cited by:

  1. Chun-Xiao Nie, 2021. "Studying the correlation structure based on market geometry," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(2), pages 411-441, April.
  2. K. B. Gubbels & J. Y. Ypma & C. W. Oosterlee, 2023. "Principal Component Copulas for Capital Modelling," Papers 2312.13195, arXiv.org.
  3. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2023. "Synchronization patterns in the European Union," Applied Economics, Taylor & Francis Journals, vol. 55(18), pages 2038-2059, April.
  4. Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2021. "Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination," Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 905-918, November.
  5. Andr'es Garc'ia-Medina & Benito Rodrigu'ez-Camejo, 2023. "Random matrix theory and nested clustered portfolios on Mexican markets," Papers 2306.05667, arXiv.org.
  6. repec:hal:spmain:info:hdl:2441/5q8fnecj1u87ka099dc571bhi2 is not listed on IDEAS
  7. Lei Wang & Yan Yan & Xiaoteng Li & Xiaosong Chen, 2018. "General Component Analysis (GCA): A new approach to identify Chinese corporate bond market structures," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-18, July.
  8. Sebastien Valeyre, 2022. "Optimal trend following portfolios," Papers 2201.06635, arXiv.org.
  9. Vincent Tan & Stefan Zohren, 2020. "Estimation of Large Financial Covariances: A Cross-Validation Approach," Papers 2012.05757, arXiv.org, revised Jan 2023.
  10. Christian Bongiorno & Damien Challet, 2020. "Nonparametric sign prediction of high-dimensional correlation matrix coefficients," Papers 2001.11214, arXiv.org.
  11. Anshul Verma & Orazio Angelini & Tiziana Di Matteo, 2019. "A new set of cluster driven composite development indicators," Papers 1911.11226, arXiv.org, revised Mar 2020.
  12. Adil Rengim Cetingoz & Jean-David Fermanian & Olivier Gu'eant, 2022. "Risk Budgeting Portfolios: Existence and Computation," Papers 2211.07212, arXiv.org, revised Sep 2023.
  13. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
  14. 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.
  15. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe de Peretti & Christophe Chorro, 2019. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Post-Print halshs-02354596, HAL.
  16. repec:spo:wpmain:info:hdl:2441/5q8fnecj1u87ka099dc571bhi2 is not listed on IDEAS
  17. Gautier Marti, 2019. "CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks," Papers 1910.09504, arXiv.org, revised Dec 2019.
  18. Armine Karami & Raphael Benichou & Michael Benzaquen & Jean-Philippe Bouchaud, 2020. "Conditional Correlations And Principal Regression Analysis For Futures," Working Papers hal-02567501, HAL.
  19. Armine Karami & Raphael Benichou & Michael Benzaquen & Jean-Philippe Bouchaud, 2019. "Conditional Correlations and Principal Regression Analysis for Futures," Papers 1912.12354, arXiv.org, revised Jan 2020.
  20. Firoozye, Nikan & Tan, Vincent & Zohren, Stefan, 2023. "Canonical portfolios: Optimal asset and signal combination," Journal of Banking & Finance, Elsevier, vol. 154(C).
  21. Sumanjay Dutta & Shashi Jain, 2023. "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation," Papers 2305.11298, arXiv.org.
  22. Jean-Philippe Bouchaud & Iacopo Mastromatteo & Marc Potters & Konstantin Tikhonov, 2022. "Excess Out-of-Sample Risk and Fleeting Modes," Papers 2205.01012, arXiv.org.
  23. 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.
  24. Jovanovic, Franck & Mantegna, Rosario N. & Schinckus, Christophe, 2019. "When financial economics influences physics: The role of Econophysics," International Review of Financial Analysis, Elsevier, vol. 65(C).
  25. George Barnes & Sanjaye Ramgoolam & Michael Stephanou, 2023. "Permutation invariant Gaussian matrix models for financial correlation matrices," Papers 2306.04569, arXiv.org.
  26. Christian Bongiorno & Damien Challet, 2023. "Covariance matrix filtering and portfolio optimisation: the Average Oracle vs Non-Linear Shrinkage and all the variants of DCC-NLS," Working Papers hal-04323624, HAL.
  27. Rubio-García, Álvaro & Fernández-Lorenzo, Samuel & García-Ripoll, Juan José & Porras, Diego, 2024. "Accurate solution of the Index Tracking problem with a hybrid simulated annealing algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
  28. Feng, Wenjun & Zhang, Zhengjun, 2023. "Risk-weighted cryptocurrency indices," Finance Research Letters, Elsevier, vol. 51(C).
  29. László PáL, 2022. "Asset Allocation Strategies Using Covariance Matrix Estimators," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 133-144, September.
  30. Anshul Verma & Riccardo Junior Buonocore & Tiziana di Matteo, 2017. "A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering," Papers 1712.02138, arXiv.org, revised May 2018.
  31. Tacchella, Andrea & Zaccaria, Andrea & Miccheli, Marco & Pietronero, Luciano, 2023. "Relatedness in the era of machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
  32. Christian Bongiorno & Damien Challet, 2022. "Reactive global minimum variance portfolios with k-BAHC covariance cleaning," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1344-1360, October.
  33. Nie, Chun-Xiao, 2021. "Analyzing financial correlation matrix based on the eigenvector–eigenvalue identity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
  34. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe de Peretti & Christophe Chorro, 2019. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02354596, HAL.
  35. Dimitar Kitanovski & Igor Mishkovski & Viktor Stojkoski & Miroslav Mirchev, 2024. "Network-based diversification of stock and cryptocurrency portfolios," Papers 2408.11739, arXiv.org.
  36. Andrea Tacchella & Andrea Zaccaria & Marco Miccheli & Luciano Pietronero, 2021. "Relatedness in the Era of Machine Learning," Papers 2103.06017, arXiv.org.
  37. Naoki Masuda & Kazuyuki Aihara & Neil G. MacLaren, 2024. "Anticipating regime shifts by mixing early warning signals from different nodes," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  38. Jerome Garnier-Brun & Michael Benzaquen & Stefano Ciliberti & Jean-Philippe Bouchaud, 2021. "A new spin on optimal portfolios and ecological equilibria," Post-Print hal-03378915, HAL.
  39. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe De Peretti & Christophe Chorro, 2019. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Documents de travail du Centre d'Economie de la Sorbonne 19022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  40. Baskerville, Nicholas P. & Granziol, Diego & Keating, Jonathan P., 2022. "Appearance of Random Matrix Theory in deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
  41. 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.
  42. Hideto Nakashima & Piotr Graczyk, 2022. "Wigner and Wishart ensembles for sparse Vinberg models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 399-433, June.
  43. Paolo Giudici & Gloria Polinesi & Alessandro Spelta, 2022. "Network models to improve robot advisory portfolios," Annals of Operations Research, Springer, vol. 313(2), pages 965-989, June.
  44. G. L. Zitelli, 2022. "Amalgamated Free Lévy Processes as Limits of Sample Covariance Matrices," Journal of Theoretical Probability, Springer, vol. 35(4), pages 2176-2193, December.
  45. Jerome Garnier-Brun & Michael Benzaquen & Stefano Ciliberti & Jean-Philippe Bouchaud, 2021. "A new spin on optimal portfolios and ecological equilibria," Papers 2104.00668, arXiv.org, revised Oct 2021.
  46. Andrew Butler & Roy H. Kwon, 2021. "Data-driven integration of norm-penalized mean-variance portfolios," Papers 2112.07016, arXiv.org, revised Nov 2022.
  47. Ahmad W. Bitar & Nathan de Carvalho & Valentin Gatignol, 2023. "Covariance matrix estimation for robust portfolio allocation," Working Papers hal-04046454, HAL.
  48. Bongiorno, Christian & Challet, Damien, 2023. "Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimization," Finance Research Letters, Elsevier, vol. 52(C).
  49. Ding, Xiucai & Ji, Hong Chang, 2023. "Spiked multiplicative random matrices and principal components," Stochastic Processes and their Applications, Elsevier, vol. 163(C), pages 25-60.
  50. Antti J Tanskanen & Jani Lukkarinen & Kari Vatanen, 2018. "Random selection of factors preserves the correlation structure in a linear factor model to a high degree," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-22, December.
  51. Armine Karami & Raphael Benichou & Michael Benzaquen & Jean-Philippe Bouchaud, 2021. "Conditional Correlations and Principal Regression Analysis for Futures," Post-Print hal-02567501, HAL.
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