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Gianluca Fabio De Nard

Personal Details

First Name:Gianluca
Middle Name:Fabio
Last Name:De Nard
Suffix:
RePEc Short-ID:pde1427
[This author has chosen not to make the email address public]

Affiliation

(50%) Institut für Volkswirtschaftslehre
Wirtschaftswissenschaftliche Fakutält
Universität Zürich

Zürich, Switzerland
http://www.econ.uzh.ch/
RePEc:edi:seizhch (more details at EDIRC)

(50%) Institut für Finanzdienstleistungen
Universität Liechtenstein

Vaduz, Liechtenstein
http://www.uni.li/University/Institute/Finanzdienstleistungen/tabid/151/Default.aspx
RePEc:edi:ifhlili (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.
  2. Antonello Cirulli & Gianluca De Nard & Joshua Traut & Patrick Walker, 2025. "Low risk, high variability: practical guide for portfolio construction," ECON - Working Papers 463, Department of Economics - University of Zurich, revised Jul 2025.
  3. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.
  4. Elliot Beck & Gianluca De Nard & Michael Wolf, 2023. "Improved inference in financial factor models," ECON - Working Papers 430, Department of Economics - University of Zurich.
  5. Gianluca De Nard & Robert F. Engle & Olivier Ledoit & Michael Wolf, 2020. "Large dynamic covariance matrices: enhancements based on intraday data," ECON - Working Papers 356, Department of Economics - University of Zurich, revised Jan 2022.
  6. 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.

Articles

  1. 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.
  2. Beck, Elliot & De Nard, Gianluca & Wolf, Michael, 2023. "Improved inference in financial factor models," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 364-379.
  3. 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.
  4. Gianluca De Nard, 2022. "Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage [Eigenvalue Ratio Test for the Number of Factors]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 569-611.
  5. 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.
  6. 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).
  7. 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.
  8. Gianluca De Nard & Olivier Ledoit & Michael Wolf, 2021. "Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly [Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 236-257.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.

    Cited by:

    1. Horn, Matthias & Oehler, Andreas, 2024. "Constructing stock portfolios by sorting on ESG ratings: Does the rating provider matter?," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    2. Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.
    3. Lichao Lin & Adrian (Wai Kong) Cheung, 2024. "Pricing cloud stocks: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 811-832, March.
    4. Mingyu Shu & Jieli Wang & Menglong Chen & Hanru Wang, 2025. "Multi-scale Dynamic Correlation Between Climate Shock and China's Stock Market: Evidence Based on High Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 2265-2304, September.

  2. Elliot Beck & Gianluca De Nard & Michael Wolf, 2023. "Improved inference in financial factor models," ECON - Working Papers 430, Department of Economics - University of Zurich.

    Cited by:

    1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.

  3. Gianluca De Nard & Robert F. Engle & Olivier Ledoit & Michael Wolf, 2020. "Large dynamic covariance matrices: enhancements based on intraday data," ECON - Working Papers 356, Department of Economics - University of Zurich, revised Jan 2022.

    Cited by:

    1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.
    2. Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.
    3. Jean-David Fermanian & Benjamin Poignard & Panos Xidonas, 2025. "Model-based vs. agnostic methods for the prediction of time-varying covariance matrices," Annals of Operations Research, Springer, vol. 346(1), pages 511-548, March.
    4. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Attention to oil prices and its impact on the oil, gold and stock markets and their covariance," Energy Economics, Elsevier, vol. 120(C).
    5. 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.
    6. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    7. 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).
    8. Richard Luger, 2024. "Regularizing stock return covariance matrices via multiple testing of correlations," Papers 2407.09696, arXiv.org.
    9. 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.
    10. Christian Bongiorno & Damien Challet, 2023. "The Oracle estimator is suboptimal for global minimum variance portfolio optimisation," Post-Print hal-03491913, HAL.
    11. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    12. Wenyang Huang & Huiwen Wang & Shanshan Wang, 2021. "Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA," Papers 2103.16908, arXiv.org.
    13. 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.
    14. 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.

  4. 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.

    Cited by:

    1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.
    2. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
    3. Molero-González, L. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A. & García-Medina, A., 2023. "Market Beta is not dead: An approach from Random Matrix Theory," Finance Research Letters, Elsevier, vol. 55(PA).
    4. Hafner, Christian M. & Wang, Linqi, 2024. "Dynamic portfolio selection with sector-specific regularization," Econometrics and Statistics, Elsevier, vol. 32(C), pages 17-33.
    5. 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.
    6. Gianluca De Nard & Robert F. Engle & Olivier Ledoit & Michael Wolf, 2020. "Large dynamic covariance matrices: enhancements based on intraday data," ECON - Working Papers 356, Department of Economics - University of Zurich, revised Jan 2022.
    7. 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).
    8. Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.
    9. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    10. Wu, Yunlin & Huang, Lei & Jiang, Hui, 2023. "Optimization of large portfolio allocation for new-energy stocks: Evidence from China," Energy, Elsevier, vol. 285(C).
    11. Emilija Dzuverovic & Matteo Barigozzi, 2023. "Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices," Papers 2305.08488, arXiv.org, revised Jul 2024.
    12. Llorens-Terrazas, Jordi & Brownlees, Christian, 2023. "Projected Dynamic Conditional Correlations," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1761-1776.
    13. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
    14. Jean-David Fermanian & Benjamin Poignard & Panos Xidonas, 2025. "Model-based vs. agnostic methods for the prediction of time-varying covariance matrices," Annals of Operations Research, Springer, vol. 346(1), pages 511-548, March.
    15. Beck, Elliot & De Nard, Gianluca & Wolf, Michael, 2023. "Improved inference in financial factor models," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 364-379.
    16. Christian Bongiorno & Damien Challet, 2024. "Covariance matrix filtering and portfolio optimisation: the average oracle vs non-linear shrinkage and all the variants of DCC-NLS," Quantitative Finance, Taylor & Francis Journals, vol. 24(9), pages 1227-1234, September.
    17. Bernardo K. Pagnoncelli & Domingo Ramírez & Hamed Rahimian & Arturo Cifuentes, 2023. "A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 187-204, June.
    18. 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.
    19. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio Selection: A Target-Distribution Approach," LIDAM Discussion Papers LFIN 2021005, Université catholique de Louvain, Louvain Finance (LFIN).
    20. Jianqing Fan & Donggyu Kim & Minseok Shin & Yazhen Wang, 2024. "Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data," Working Papers 202415, University of California at Riverside, Department of Economics.
    21. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    22. 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.
    23. Jin Yuan & Xianghui Yuan, 2023. "A Best Linear Empirical Bayes Method for High-Dimensional Covariance Matrix Estimation," SAGE Open, , vol. 13(2), pages 21582440231, June.
    24. 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).
    25. Francesco Sica & Francesco Tajani & Pierluigi Morano, 2025. "A Model for Sustainable Development in Territorial Production Systems," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(3), pages 4511-4528, June.
    26. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).
    27. Antonio Garcia-Amate & Laura Molero-González & Miguel Angel Sánchez-Granero & Juan Evangelista Trinidad-Segovia & Andres García-Medina, 2024. "Testing the significance of pricing factors of oil and gas companies," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-18, December.
    28. Ameer Tamoor Khan & Xinwei Cao & Shuai Li, 2023. "Using Quadratic Interpolated Beetle Antennae Search for Higher Dimensional Portfolio Selection Under Cardinality Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1413-1435, December.
    29. Raymond Kan & Xiaolu Wang, 2024. "Optimal Portfolio Choice with Unknown Benchmark Efficiency," Management Science, INFORMS, vol. 70(9), pages 6117-6138, September.
    30. Laura Molero-González & Juan E. Trinidad-Segovia & Miguel A. Sánchez-Granero & Andrés García-Medina, 2025. "Factors relevance in asset pricing: new evidences in emerging markets from random matrix theory," Economics and Business Letters, Oviedo University Press, vol. 14(2), pages 75-87.
    31. 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.
    32. Yujia Hu, 2023. "A Heuristic Approach to Forecasting and Selection of a Portfolio with Extra High Dimensions," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    33. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    34. Conlon, Thomas & Cotter, John & Kynigakis, Iason, 2025. "Asset allocation with factor-based covariance matrices," European Journal of Operational Research, Elsevier, vol. 325(1), pages 189-203.
    35. 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.
    36. 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).
    37. 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.
    38. Malick Fall, 2025. "Portfolio optimization in deformed time," Journal of Asset Management, Palgrave Macmillan, vol. 26(2), pages 176-185, March.
    39. Bongiorno, Christian & Lamrani, Lamia, 2025. "Quantifying the information lost in optimal covariance matrix cleaning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 657(C).

Articles

  1. 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.
    See citations under working paper version above.
  2. Beck, Elliot & De Nard, Gianluca & Wolf, Michael, 2023. "Improved inference in financial factor models," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 364-379.
    See citations under working paper version above.
  3. 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.

    Cited by:

    1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.
    2. Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.

  4. Gianluca De Nard, 2022. "Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage [Eigenvalue Ratio Test for the Number of Factors]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 569-611.

    Cited by:

    1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich, revised Mar 2024.
    2. Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.
    3. Lu, Cheng & Ndiaye, Papa Momar & Simaan, Majeed, 2024. "Improved estimation of the correlation matrix using reinforcement learning and text-based networks," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    4. 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.

  5. 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).
    See citations under working paper version above.
  6. 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.

    Cited by:

    1. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).

  7. Gianluca De Nard & Olivier Ledoit & Michael Wolf, 2021. "Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly [Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 236-257.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (4) 2018-07-09 2020-08-17 2023-04-10 2025-07-28. Author is listed
  2. NEP-ETS: Econometric Time Series (3) 2018-07-09 2020-08-17 2023-04-10. Author is listed
  3. NEP-RMG: Risk Management (3) 2023-03-27 2025-02-03 2025-07-28. Author is listed
  4. NEP-AIN: Artificial Intelligence (1) 2025-07-28
  5. NEP-BIG: Big Data (1) 2025-07-28
  6. NEP-CMP: Computational Economics (1) 2025-07-28
  7. NEP-ENV: Environmental Economics (1) 2023-03-27
  8. NEP-FMK: Financial Markets (1) 2025-02-03
  9. NEP-KNM: Knowledge Management and Knowledge Economy (1) 2018-07-09
  10. NEP-ORE: Operations Research (1) 2018-07-09

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