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Large Dynamic Covariance Matrices

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Cited by:

  1. Hautsch, Nikolaus & Voigt, Stefan, 2019. "Large-scale portfolio allocation under transaction costs and model uncertainty," Journal of Econometrics, Elsevier, vol. 212(1), pages 221-240.
  2. Zhao Zhao & Olivier Ledoit & Hui Jiang, 2019. "Risk reduction and efficiency increase in large portfolios: leverage and shrinkage," ECON - Working Papers 328, Department of Economics - University of Zurich, revised Jan 2020.
  3. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2024. "Factor-Mimicking Portfolios for Climate Risk," Financial Analysts Journal, Taylor & Francis Journals, vol. 80(3), pages 37-58, July.
  4. Chen Tong & Peter Reinhard Hansen & Ilya Archakov, 2024. "Cluster GARCH," Papers 2406.06860, arXiv.org.
  5. Olivier Ledoit & Michael Wolf, 2019. "The power of (non-)linear shrinking: a review and guide to covariance matrix estimation," ECON - Working Papers 323, Department of Economics - University of Zurich, revised Feb 2020.
  6. Kawakatsu Hiroyuki, 2021. "Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 33-52, January.
  7. 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.
  8. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
  9. Elliot Beck & Damian Kozbur & Michael Wolf, 2023. "Hedging Forecast Combinations With an Application to the Random Forest," Papers 2308.15384, arXiv.org, revised Aug 2023.
  10. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
  11. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
  12. Vincent Tan & Stefan Zohren, 2020. "Estimation of Large Financial Covariances: A Cross-Validation Approach," Papers 2012.05757, arXiv.org, revised Jan 2023.
  13. Andries C. van Vlodrop & Andre (A.) Lucas, 2018. "Estimation Risk and Shrinkage in Vast-Dimensional Fundamental Factor Models," Tinbergen Institute Discussion Papers 18-099/III, Tinbergen Institute.
  14. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
  15. He, Zhongfang, 2018. "A Class of Generalized Dynamic Correlation Models," MPRA Paper 84820, University Library of Munich, Germany.
  16. Morana, Claudio, 2019. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Econometrics and Statistics, Elsevier, vol. 12(C), pages 42-65.
  17. Gao, Jiti & Liu, Fei & Peng, Bin & Yan, Yayi, 2023. "Binary response models for heterogeneous panel data with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 1654-1679.
  18. 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.
  19. Llorens-Terrazas, Jordi & Brownlees, Christian, 2023. "Projected Dynamic Conditional Correlations," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1761-1776.
  20. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
  21. Plachel, Lukas, 2019. "A unified model for regularized and robust portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
  22. Kei Nakagawa & Yusuke Uchiyama, 2020. "GO-GJRSK Model with Application to Higher Order Risk-Based Portfolio," Mathematics, MDPI, vol. 8(11), pages 1-12, November.
  23. Gianluca De Nard & Olivier Ledoit & Michael Wolf, 2018. "Factor models for portfolio selection in large dimensions: the good, the better and the ugly," ECON - Working Papers 290, Department of Economics - University of Zurich, revised Dec 2018.
  24. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2022. "Sparsity and stability for minimum-variance portfolios," Risk Management, Palgrave Macmillan, vol. 24(3), pages 214-235, September.
  25. Francq, Christian & Zakoïan, Jean-Michel, 2020. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Journal of Econometrics, Elsevier, vol. 217(2), pages 356-380.
  26. Chazi, Abdelaziz & Samet, Anis & Azad, A.S.M. Sohel, 2023. "Volatility and correlation of Islamic and conventional indices during crises," Global Finance Journal, Elsevier, vol. 55(C).
  27. Yusuke Uchiyama & Kei Nakagawa, 2020. "TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model," Papers 2002.06243, arXiv.org.
  28. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
  29. Ledoit, Olivier & Wolf, Michael, 2017. "Numerical implementation of the QuEST function," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 199-223.
  30. Stadtmüller, Immo & Auer, Benjamin R. & Schuhmacher, Frank, 2022. "On the benefits of active stock selection strategies for diversified investors," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 342-354.
  31. Liu, Cheng & Wang, Moming & Xia, Ningning, 2022. "Design-free estimation of integrated covariance matrices for high-frequency data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  32. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.
  33. Raddant, Matthias & Kenett, Dror Y., 2021. "Interconnectedness in the global financial market," Journal of International Money and Finance, Elsevier, vol. 110(C).
  34. Ledoit, Olivier & Wolf, Michael, 2021. "Shrinkage estimation of large covariance matrices: Keep it simple, statistician?," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
  35. Olivier Ledoit & Michael Wolf, 2019. "Shrinkage estimation of large covariance matrices: keep it simple, statistician?," ECON - Working Papers 327, Department of Economics - University of Zurich, revised Jun 2021.
  36. Firoozye, Nikan & Tan, Vincent & Zohren, Stefan, 2023. "Canonical portfolios: Optimal asset and signal combination," Journal of Banking & Finance, Elsevier, vol. 154(C).
  37. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
  38. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
  39. Qifa Xu & Junqing Zuo & Cuixia Jiang & Yaoyao He, 2021. "A large constrained time‐varying portfolio selection model with DCC‐MIDAS: Evidence from Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3417-3435, July.
  40. Bian, Zhicun & Liao, Yin & O’Neill, Michael & Shi, Jing & Zhang, Xueyong, 2020. "Large-scale minimum variance portfolio allocation using double regularization," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
  41. Ge, S. & Li, S. & Linton, O. B. & Liu, W. & Su, W., 2024. "Should We Augment Large Covariance Matrix Estimation with Auxiliary Network Information?," Cambridge Working Papers in Economics 2427, Faculty of Economics, University of Cambridge.
  42. 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.
  43. Moura, Guilherme V. & Santos, André A. P., 2019. "Comparing Forecasts of Extremely Large Conditional Covariance Matrices," DES - Working Papers. Statistics and Econometrics. WS 29291, Universidad Carlos III de Madrid. Departamento de Estadística.
  44. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
  45. Olivier Ledoit & Michael Wolf, 2017. "Analytical nonlinear shrinkage of large-dimensional covariance matrices," ECON - Working Papers 264, Department of Economics - University of Zurich, revised Nov 2018.
  46. Kei Nakagawa & Mitsuyoshi Imamura & Kenichi Yoshida, 2018. "Risk-Based Portfolios with Large Dynamic Covariance Matrices," IJFS, MDPI, vol. 6(2), pages 1-14, May.
  47. 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.
  48. Ding, Yi & Li, Yingying & Zheng, Xinghua, 2021. "High dimensional minimum variance portfolio estimation under statistical factor models," Journal of Econometrics, Elsevier, vol. 222(1), pages 502-515.
  49. Catherine D'Hondt & Rudy De Winne & Eric Ghysels & Steve Raymond, 2019. "Artificial Intelligence Alter Egos: Who benefits from Robo-investing?," Papers 1907.03370, arXiv.org.
  50. Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
  51. Yfanti, Stavroula & Karanasos, Menelaos & Zopounidis, Constantin & Christopoulos, Apostolos, 2023. "Corporate credit risk counter-cyclical interdependence: A systematic analysis of cross-border and cross-sector correlation dynamics," European Journal of Operational Research, Elsevier, vol. 304(2), pages 813-831.
  52. Hediger, Simon & Näf, Jeffrey, 2024. "Combining the MGHyp distribution with nonlinear shrinkage in modeling financial asset returns," Journal of Empirical Finance, Elsevier, vol. 77(C).
  53. Yusuke Uchiyama & Kei Nakagawa, 2020. "TPLVM: Portfolio Construction by Student’s t -Process Latent Variable Model," Mathematics, MDPI, vol. 8(3), pages 1-10, March.
  54. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
  55. Karim M Abadir, 2023. "Explicit minimal representation of variance matrices, and its implication for dynamic volatility models," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 88-104.
  56. Benjamin Poignard & Manabu Asai, 2023. "High‐dimensional sparse multivariate stochastic volatility models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 4-22, January.
  57. László PáL, 2022. "Asset Allocation Strategies Using Covariance Matrix Estimators," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 133-144, September.
  58. Carlos Trucíos & Mauricio Zevallos & Luiz K. Hotta & André A. P. Santos, 2019. "Covariance Prediction in Large Portfolio Allocation," Econometrics, MDPI, vol. 7(2), pages 1-24, May.
  59. Jian Zhang & Jie Li, 2022. "Factorized estimation of high‐dimensional nonparametric covariance models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 542-567, June.
  60. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
  61. D’Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 278-299.
  62. Xiaoning Kang & Xinwei Deng & Kam‐Wah Tsui & Mohsen Pourahmadi, 2020. "On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices," International Statistical Review, International Statistical Institute, vol. 88(3), pages 616-641, December.
  63. Sama Haddad, 2023. "Global Financial Market Integration: A Literature Survey," JRFM, MDPI, vol. 16(12), pages 1-27, November.
  64. Olivier Ledoit & Michael Wolf, 2019. "Quadratic shrinkage for large covariance matrices," ECON - Working Papers 335, Department of Economics - University of Zurich, revised Dec 2020.
  65. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
  66. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Sparsity and Stability for Minimum-Variance Portfolios," Papers 1910.11840, arXiv.org.
  67. 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.
  68. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
  69. Stanislav Anatolyev & Vladimir Pyrlik, 2021. "Shrinkage for Gaussian and t Copulas in Ultra-High Dimensions," CERGE-EI Working Papers wp699, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  70. De Nard, Gianluca & Engle, Robert F. & Ledoit, Olivier & Wolf, Michael, 2022. "Large dynamic covariance matrices: Enhancements based on intraday data," Journal of Banking & Finance, Elsevier, vol. 138(C).
  71. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
  72. Emilija Dzuverovic & Matteo Barigozzi, 2023. "Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices," Papers 2305.08488, arXiv.org, revised Jul 2024.
  73. van Zundert, Jeroen, 2018. "Empirical studies on the cross-section of corporate bond and stock markets," Other publications TiSEM 338205fc-a031-4e06-a636-9, Tilburg University, School of Economics and Management.
  74. Masaya Abe & Kei Nakagawa, 2020. "Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management," Papers 2002.06975, arXiv.org.
  75. Cai, Zhanrui & Li, Changcheng & Wen, Jiawei & Yang, Songshan, 2024. "Asset splitting algorithm for ultrahigh dimensional portfolio selection and its theoretical property," Journal of Econometrics, Elsevier, vol. 239(2).
  76. Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.
  77. Rafael Alves & Diego S. de Brito & Marcelo C. Medeiros & Ruy M. Ribeiro, 2023. "Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage," Papers 2303.16151, arXiv.org.
  78. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
  79. M. Raddant & T. Di Matteo, 2023. "A look at financial dependencies by means of econophysics and financial economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(4), pages 701-734, October.
  80. Khashanah, Khaldoun & Simaan, Majeed & Simaan, Yusif, 2022. "Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process," International Review of Financial Analysis, Elsevier, vol. 81(C).
  81. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
  82. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
  83. Damir Filipovic & Paul Schneider, 2024. "Fundamental properties of linear factor models," Papers 2409.02521, arXiv.org, revised Oct 2024.
  84. Jan Patrick Hartkopf, 2023. "Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models," Empirical Economics, Springer, vol. 64(1), pages 393-436, January.
  85. Laura Liu & Christian Matthes & Katerina Petrova, 2022. "Monetary Policy Across Space and Time," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 37-64, Emerald Group Publishing Limited.
  86. Cheng Yu & Dong Li & Feiyu Jiang & Ke Zhu, 2023. "Matrix GARCH Model: Inference and Application," Papers 2306.05169, arXiv.org.
  87. 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.
  88. Anja Vinzelberg & Benjamin R. Auer, 2022. "Unprofitability of food market investments," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 2887-2910, October.
  89. Qiao, W. & Bu, D. & Gibberd, A. & Liao, Y. & Wen, T. & Li, E., 2023. "When “time varying” volatility meets “transaction cost” in portfolio selection," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 220-237.
  90. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).
  91. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
  92. Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
  93. 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).
  94. 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.
  95. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
  96. 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).
  97. Richard Luger, 2024. "Regularizing stock return covariance matrices via multiple testing of correlations," Papers 2407.09696, arXiv.org.
  98. Ge, S. & Li, S. & Linton, O. B. & Liu, W. & Su, W., 2024. "Should We Augment Large Covariance Matrix Estimation with Auxiliary Network Information?," Janeway Institute Working Papers 2416, Faculty of Economics, University of Cambridge.
  99. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318, arXiv.org, revised Jan 2024.
  100. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
  101. 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.
  102. 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.
  103. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
  104. Sun, Chuting & Wu, Qi & Yan, Xing, 2024. "Dynamic CVaR portfolio construction with attention-powered generative factor learning," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
  105. Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro, 2021. "A dynamic conditional approach to forecasting portfolio weights," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1111-1126.
  106. Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020. "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 118(C).
  107. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).
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