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Markus Pelger

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. Kasper Johansson & Mehmet Giray Ogut & Markus Pelger & Thomas Schmelzer & Stephen Boyd, 2023. "A Simple Method for Predicting Covariance Matrices of Financial Returns," Papers 2305.19484, arXiv.org, revised Nov 2023.

    Cited by:

    1. Shanyi Lin & Qian-Zhen Zheng & Laixu Shang & Ping-Feng Xu & Man-Lai Tang, 2025. "Fitting Penalized Estimator for Sparse Covariance Matrix with Left-Censored Data by the EM Algorithm," Mathematics, MDPI, vol. 13(3), pages 1-17, January.
    2. Kasper Johansson & Thomas Schmelzer & Stephen Boyd, 2025. "A Markowitz approach to managing a dynamic basket of moving-band statistical arbitrages," Journal of Asset Management, Palgrave Macmillan, vol. 26(4), pages 377-385, July.
    3. Gianluca Cubadda, 2024. "VAR models with an index structure: A survey with new results," Papers 2412.11278, arXiv.org, revised Sep 2025.
    4. Stephen Boyd & Kasper Johansson & Ronald Kahn & Philipp Schiele & Thomas Schmelzer, 2024. "Markowitz Portfolio Construction at Seventy," Papers 2401.05080, arXiv.org.
    5. G. Cubadda & S. Grassi & B. Guardabascio, 2022. "The Time-Varying Multivariate Autoregressive Index Model," Papers 2201.07069, arXiv.org.
    6. Andrew Lesniewski & Giulio Trigila, 2024. "Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics," Papers 2412.00036, arXiv.org, revised Feb 2025.

  2. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-Learning the Skill of Mutual Fund Managers," CEPR Discussion Papers 18129, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cong Wang, 2024. "Stock return prediction with multiple measures using neural network models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    2. Alexandre Momparler & Pedro Carmona & Francisco Climent, 2025. "Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1617-1642, March.
    3. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Dynamic Asset Allocation with Asset-Specific Regime Forecasts," Papers 2406.09578, arXiv.org, revised Aug 2024.
    4. Li, Zhiyong & Rao, Xiao, 2023. "Exploring the zoo of predictors for mutual fund performance in China," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    5. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    6. Ma, Tian & Wang, Wanwan & Jiang, Fuwei, 2025. "Machine learning the performance of hedge fund," Journal of International Money and Finance, Elsevier, vol. 155(C).
    7. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    8. Guilherme V. Moura & Andr'e P. Santos & Hudson S. Torrent, 2025. "Variable selection for minimum-variance portfolios," Papers 2508.14986, arXiv.org.
    9. Fragkiskos, Apollon & Krasotkina, Olga & Spilker, Harold D. & Wermers, Russ, 2025. "Private Equity Fund Performance: A Time-Series Approach," Journal of Banking & Finance, Elsevier, vol. 177(C).
    10. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    11. Ha, Yeonjeong & Oh, Haejune, 2024. "Choice for smart investment in mutual funds: Single- or multi-period performance ranks," Finance Research Letters, Elsevier, vol. 59(C).
    12. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    13. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    14. Maarten P. Scholl & Mahmoud Mahfouz & Anisoara Calinescu & J. Doyne Farmer, 2025. "Learning to Manage Investment Portfolios beyond Simple Utility Functions," Papers 2510.26165, arXiv.org.
    15. Jozef Barunik & Martin Hronec & Ondrej Tobek, 2024. "Forecasting stock return distributions around the globe with quantile neural networks," Papers 2408.07497, arXiv.org, revised Aug 2025.
    16. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org, revised Jan 2025.
    17. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2025. "Dynamic asset allocation with asset-specific regime forecasts," Annals of Operations Research, Springer, vol. 346(1), pages 285-318, March.
    18. Amit Pandey & Anil Kumar Sharma, 2023. "Indian institutional investor's portfolio concentration decision: skill and performance," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 21(1), pages 66-95, December.
    19. Inigo Martin-Melero & Raul Gomez-Martinez & Maria Luisa Medrano-Garcia & Felipe Hernandez-Perlines, 2025. "Comparison of sectorial and financial data for ESG scoring of mutual funds with machine learning," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-31, December.

  3. Damir Filipović & Markus Pelger & Ye Ye, 2022. "Stripping the Discount Curve - a Robust Machine Learning Approach," Swiss Finance Institute Research Paper Series 22-24, Swiss Finance Institute.

    Cited by:

    1. Darrell Duffie & Michael J. Fleming & Frank M. Keane & Claire Nelson & Or Shachar & Peter Van Tassel, 2023. "Dealer Capacity and U.S. Treasury Market Functionality," Staff Reports 1070, Federal Reserve Bank of New York.
    2. Eric Luxenberg & Philipp Schiele & Stephen Boyd, 2024. "Robust Bond Portfolio Construction via Convex–Concave Saddle Point Optimization," Journal of Optimization Theory and Applications, Springer, vol. 201(3), pages 1089-1115, June.
    3. Yahyaei, Hamid & Singh, Abhay & Smith, Tom, 2025. "Ex ante bond returns and time-varying monotonicity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 99(C).
    4. Stefan Nagel & Zhengyang Xu, 2024. "Movements in Yields, not the Equity Premium: Bernanke-Kuttner Redux," NBER Working Papers 32884, National Bureau of Economic Research, Inc.
    5. Eric Luxenberg & Philipp Schiele & Stephen Boyd, 2022. "Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization," Papers 2212.02570, arXiv.org, revised Jan 2024.
    6. Dennis Schroers, 2024. "Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions," Papers 2401.16286, arXiv.org, revised Sep 2025.

  4. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.

    Cited by:

    1. Elliot L. Epstein & Apaar Sadhwani & Kay Giesecke, 2025. "A Set-Sequence Model for Time Series," Papers 2505.11243, arXiv.org, revised Oct 2025.
    2. Yoonsik Hong & Diego Klabjan, 2025. "Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions," Papers 2508.14762, arXiv.org, revised Aug 2025.

  5. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.

    Cited by:

    1. Adam Bouland & Wim van Dam & Hamed Joorati & Iordanis Kerenidis & Anupam Prakash, 2020. "Prospects and challenges of quantum finance," Papers 2011.06492, arXiv.org.
    2. Hasan Fallahgoul, 2025. "High-Dimensional Learning in Finance," Papers 2506.03780, arXiv.org, revised Jul 2025.
    3. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    4. Kang, Hyoung-Goo & Ryu, Doojin, 2025. "A complementary valuation model and exit multiples," Finance Research Letters, Elsevier, vol. 79(C).
    5. Witter, Johannes, 2025. "Predicting stock returns with machine learning: Global versus sector models," Junior Management Science (JUMS), Junior Management Science e. V., vol. 10(3), pages 561-581.
    6. Wang, Jianqiu & Wang, Zhuo & Wu, Ke, 2025. "Forecasting stock market return with anomalies: Evidence from China," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1278-1295.
    7. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
    8. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Nov 2024.
    9. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    10. Avramov, D. & Ge, S. & Li, S. & Linton, O. B., 2025. "Dual Industry Effects and Cross-Stock Predictability," Janeway Institute Working Papers 2506, Faculty of Economics, University of Cambridge.
    11. Shunyao Wang & Ming Cheng & Christina Dan Wang, 2025. "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks," Papers 2505.06864, arXiv.org.
    12. Elysee Nsengiyumva & Joseph K. Mung’atu & Charles Ruranga, 2025. "Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk," FinTech, MDPI, vol. 4(2), pages 1-17, June.
    13. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    14. Jiajun Gu & Zichen Yang & Xintong Lin & Sixun Chen & YuTing Lu, 2024. "AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics," Papers 2412.12438, arXiv.org.
    15. Shanyan Lai, 2025. "Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models," Papers 2508.19006, arXiv.org.
    16. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
    17. Yanlong Wang & Jian Xu & Shao-Lun Huang & Danny Dongning Sun & Xiao-Ping Zhang, 2025. "Assessing Uncertainty in Stock Returns: A Gaussian Mixture Distribution-Based Method," Papers 2503.06929, arXiv.org.
    18. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    19. Elliot L. Epstein & Apaar Sadhwani & Kay Giesecke, 2025. "A Set-Sequence Model for Time Series," Papers 2505.11243, arXiv.org, revised Oct 2025.
    20. So-Yoon Cho & Jin-Young Kim & Kayoung Ban & Hyeng Keun Koo & Hyun-Gyoon Kim, 2025. "Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction," Papers 2511.07014, arXiv.org.
    21. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
    22. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    23. Shanyan Lai, 2025. "Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks," Papers 2505.01921, arXiv.org, revised May 2025.
    24. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
    25. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    26. Tian Guo & Emmanuel Hauptmann, 2025. "Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction," Papers 2510.15691, arXiv.org, revised Nov 2025.
    27. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
    28. Alessi, Lucia & Ossola, Elisa & Panzica, Roberto, 2023. "When do investors go green? Evidence from a time-varying asset-pricing model," International Review of Financial Analysis, Elsevier, vol. 90(C).
    29. Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025. "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, vol. 98(C).
    30. Mohamed Ben Alaya & Ahmed Kebaier & Djibril Sarr, 2021. "Deep Calibration of Interest Rates Model," Papers 2110.15133, arXiv.org, revised Sep 2024.
    31. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    32. Ma, Tian & Wang, Wanwan & Chen, Yu, 2023. "Attention is all you need: An interpretable transformer-based asset allocation approach," International Review of Financial Analysis, Elsevier, vol. 90(C).
    33. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
    34. Avramov, D. & Ge, S. & Li, S. & Linton, O. B., 2025. "Dual Industry Effects and Cross-Stock Predictability," Cambridge Working Papers in Economics 2512, Faculty of Economics, University of Cambridge.
    35. Qihui Chen & Nikolai Roussanov & Xiaoliang Wang, 2021. "Semiparametric Conditional Factor Models in Asset Pricing," Papers 2112.07121, arXiv.org, revised Apr 2025.
    36. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
    37. Shanyan Lai, 2025. "Asset Pricing in Pre-trained Transformer," Papers 2505.01575, arXiv.org, revised May 2025.
    38. Yuxiao Jiao & Guofu Zhou & Wu Zhu & Yingzi Zhu, 2025. "Interpretable Factors of Firm Characteristics," Papers 2508.02253, arXiv.org.
    39. Eghbal Rahimikia & Hao Ni & Weiguan Wang, 2025. "Re(Visiting) Time Series Foundation Models in Finance," Papers 2511.18578, arXiv.org.
    40. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    41. Anastasis Kratsios & Cody Hyndman, 2020. "Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization," Risks, MDPI, vol. 8(2), pages 1-30, April.
    42. Grammig, Joachim & Hanenberg, Constantin & Schlag, Christian & Sönksen, Jantje, 2020. "Diverging roads: Theory-based vs. machine learning-implied stock risk premia," University of Tübingen Working Papers in Business and Economics 130, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    43. Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
    44. 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 Jul 2025.
    45. Jian'an Zhang, 2025. "FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management," Papers 2510.02986, arXiv.org.

  6. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.

    Cited by:

    1. Cahan, Ercument & Bai, Jushan & Ng, Serena, 2023. "Factor-based imputation of missing values and covariances in panel data of large dimensions," Journal of Econometrics, Elsevier, vol. 233(1), pages 113-131.
    2. Zhou, Ruichao & Wu, Jianhong, 2023. "Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion," Economics Letters, Elsevier, vol. 232(C).
    3. Liu, Wei & Luo, Lan & Zhou, Ling, 2023. "Online missing value imputation for high-dimensional mixed-type data via generalized factor models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    4. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    5. Jin, Sainan & Lu, Xun & Su, Liangjun, 2025. "Three-dimensional heterogeneous panel data models with multi-level interactive fixed effects," Journal of Econometrics, Elsevier, vol. 249(PB).
    6. Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2024. "Asymmetric Sovereign Risk: Implications for Climate Change Preparation," IREA Working Papers 202401, University of Barcelona, Research Institute of Applied Economics, revised Jan 2024.
    7. Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
    8. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    9. Vivek F. Farias & Andrew A. Li & Tianyi Peng, 2021. "Learning Treatment Effects in Panels with General Intervention Patterns," Papers 2106.02780, arXiv.org, revised Mar 2023.
    10. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam & Cakici, Nusret, 2025. "A factor model for the cross-section of country equity risk premia," Journal of Banking & Finance, Elsevier, vol. 171(C).
    11. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
    12. Cen, Zetai & Lam, Clifford, 2025. "Tensor time series imputation through tensor factor modelling," LSE Research Online Documents on Economics 127231, London School of Economics and Political Science, LSE Library.
    13. Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP52/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Duan, Junting & Pelger, Markus & Xiong, Ruoxuan, 2024. "Target PCA: Transfer learning large dimensional panel data," Journal of Econometrics, Elsevier, vol. 244(2).
    15. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    16. Choi, Jungjun & Kwon, Hyukjun & Liao, Yuan, 2024. "Inference for low-rank completion without sample splitting with application to treatment effect estimation," Journal of Econometrics, Elsevier, vol. 240(1).
    17. Cen, Zetai & Lam, Clifford, 2025. "Tensor time series imputation through tensor factor modelling," Journal of Econometrics, Elsevier, vol. 249(PB).
    18. Matteo Barigozzi & Luca Trapin, 2025. "Estimation of large approximate dynamic matrix factor models based on the EM algorithm and Kalman filtering," Papers 2502.04112, arXiv.org, revised May 2025.
    19. Iv'an Fern'andez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-Rank Approximations of Nonseparable Panel Models," Papers 2010.12439, arXiv.org, revised Mar 2021.
    20. Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024. "High frequency monitoring of credit creation: A new tool for central banks in emerging market economies," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    21. Guido Imbens & Nathan Kallus & Xiaojie Mao, 2021. "Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models," Papers 2108.03849, arXiv.org.
    22. Luis Costa & Vivek F. Farias & Patricio Foncea & Jingyuan (Donna) Gan & Ayush Garg & Ivo Rosa Montenegro & Kumarjit Pathak & Tianyi Peng & Dusan Popovic, 2023. "Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure," Interfaces, INFORMS, vol. 53(5), pages 336-349, September.
    23. Guanhao Zhou & Yuefeng Han & Xiufan Yu, 2025. "Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models," Papers 2505.20536, arXiv.org.
    24. Jungjun Choi & Ming Yuan, 2023. "Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models," Papers 2308.02364, arXiv.org.
    25. Su, Liangjun & Wang, Fa, 2025. "Inference for large dimensional factor models under general missing data patterns," Journal of Econometrics, Elsevier, vol. 250(C).

  7. Markus Pelger & Ruoxuan Xiong, 2018. "State-Varying Factor Models of Large Dimensions," Papers 1807.02248, arXiv.org, revised Oct 2020.

    Cited by:

    1. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    2. Stephen Boyd & Kasper Johansson & Ronald Kahn & Philipp Schiele & Thomas Schmelzer, 2024. "Markowitz Portfolio Construction at Seventy," Papers 2401.05080, arXiv.org.
    3. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    4. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    5. Yuying Sun & Shaoxin Hong & Zongwu Cai, 2025. "State-Varying Model Averaging Prediction," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202507, University of Kansas, Department of Economics.
    6. Pedro Isaac Chavez-Lopez & Tae-Hwy Lee, 2025. "Quantile-Covariance Three-Pass Regression Filter," Working Papers 202501, University of California at Riverside, Department of Economics.
    7. Yufeng Mao & Bin Peng & Mervyn J Silvapulle & Param Silvapulle & Yanrong Yang, 2021. "Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model," Monash Econometrics and Business Statistics Working Papers 7/21, Monash University, Department of Econometrics and Business Statistics.
    8. Xialu Liu & John Guerard & Rong Chen & Ruey Tsay, 2024. "Improving Estimation of Portfolio Risk Using New Statistical Factors," Papers 2409.17182, arXiv.org.
    9. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).
    10. 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.
    11. Cheng, T. & Dong, C. & Gao, J. & Linton, O., 2022. "GMM Estimation for High-Dimensional Panel Data Models," Cambridge Working Papers in Economics 2245, Faculty of Economics, University of Cambridge.
    12. Barigozzi, Matteo & Massacci, Daniele, 2025. "Modelling large dimensional datasets with Markov switching factor models," Journal of Econometrics, Elsevier, vol. 247(C).
    13. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    14. Yufeng Mao & Bin Peng & Mervyn Silvapulle & Param Silvapulle & Yanrong Yang, 2021. "Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model," Papers 2101.06805, arXiv.org.
    15. Jiti Gao & Fei Liu & Bin peng, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 44/20, Monash University, Department of Econometrics and Business Statistics.
    16. Xialu Liu & John Guerard & Rong Chen & Ruey Tsay, 2025. "Improving estimation of portfolio risk using new statistical factors," Annals of Operations Research, Springer, vol. 346(1), pages 245-261, March.
    17. Kasper Johansson & Mehmet Giray Ogut & Markus Pelger & Thomas Schmelzer & Stephen Boyd, 2023. "A Simple Method for Predicting Covariance Matrices of Financial Returns," Papers 2305.19484, arXiv.org, revised Nov 2023.
    18. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
    19. Su, Liangjun & Wang, Fa, 2025. "Inference for large dimensional factor models under general missing data patterns," Journal of Econometrics, Elsevier, vol. 250(C).

  8. Lin Fan & Junting Duan & Peter W. Glynn & Markus Pelger, 2018. "Change-Point Testing for Risk Measures in Time Series," Papers 1809.02303, arXiv.org, revised Oct 2025.

    Cited by:

    1. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.

  9. Lettau, Martin & Pelger, Markus, 2018. "Estimating Latent Asset-Pricing Factors," CEPR Discussion Papers 12926, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    2. Elizaveta V. Anufrieva, 2019. "Influence of Macroeconomic Factors on the Return of Russian Stock Exchange Indices," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 75-87, August.
    3. Anna Bykhovskaya & Vadim Gorin & Sasha Sodin, 2025. "How weak are weak factors? Uniform inference for signal strength in signal plus noise models," Papers 2507.18554, arXiv.org.
    4. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
    5. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
    6. Martin Lettau & Markus Pelger, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," NBER Working Papers 24858, National Bureau of Economic Research, Inc.
    7. Stephen Boyd & Kasper Johansson & Ronald Kahn & Philipp Schiele & Thomas Schmelzer, 2024. "Markowitz Portfolio Construction at Seventy," Papers 2401.05080, arXiv.org.
    8. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    9. Poncela, Pilar & Ruiz Ortega, Esther & Miranda Gualdrón, Karen Alejandra, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Andreou, E. & Gagliardini, P. & Ghysels, E. & Rubin, M., 2025. "Spanning latent and observable factors," Journal of Econometrics, Elsevier, vol. 248(C).
    11. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    12. Kristoffer Pons Bertelsen, 2022. "The Prior Adaptive Group Lasso and the Factor Zoo," CREATES Research Papers 2022-05, Department of Economics and Business Economics, Aarhus University.
    13. Mao, Jie & Shao, Jingjing & Wang, Weiguan, 2025. "Risk premium principal components for the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 89(C).
    14. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2020. "Measurement of Factor Strength: Theory and Practice," Monash Econometrics and Business Statistics Working Papers 7/20, Monash University, Department of Econometrics and Business Statistics.
    15. Ding, Xiucai & Yang, Fan, 2022. "Edge statistics of large dimensional deformed rectangular matrices," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    16. Jushan Bai & Serena Ng, 2017. "Principal Components and Regularized Estimation of Factor Models," Papers 1708.08137, arXiv.org, revised Nov 2017.
    17. Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2021. "Bilinear Input Normalization for Neural Networks in Financial Forecasting," Papers 2109.00983, arXiv.org.
    18. Jushan Bai & Serena Ng, 2021. "Approximate Factor Models with Weaker Loadings," Papers 2109.03773, arXiv.org, revised Mar 2023.
    19. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    20. Duan, Junting & Pelger, Markus & Xiong, Ruoxuan, 2024. "Target PCA: Transfer learning large dimensional panel data," Journal of Econometrics, Elsevier, vol. 244(2).
    21. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    22. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
    23. Ayush Jha & Abootaleb Shirvani & Ali Jaffri & Svetlozar T. Rachev & Frank J. Fabozzi, 2025. "Winners vs. Losers: Momentum-based Strategies with Intertemporal Choice for ESG Portfolios," Papers 2505.24250, arXiv.org.
    24. Solène Collot & Tobias Hemauer, 2021. "A literature review of new methods in empirical asset pricing: omitted-variable and errors-in-variable bias," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 77-100, March.
    25. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    26. P. S. Morawakage & G. Earl & B. Liu & E. Roca & A. Omura, 2023. "Housing Risk and Returns in Submarkets with Spatial Dependence and Heterogeneity," The Journal of Real Estate Finance and Economics, Springer, vol. 67(4), pages 695-734, November.
    27. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    28. Jorge Guijarro-Ordonez, 2019. "High-dimensional statistical arbitrage with factor models and stochastic control," Papers 1901.09309, arXiv.org, revised Jun 2021.
    29. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    30. Wan, Runzhe & Li, Yingying & Lu, Wenbin & Song, Rui, 2024. "Mining the factor zoo: Estimation of latent factor models with sufficient proxies," Journal of Econometrics, Elsevier, vol. 239(2).
    31. Zhu, Zhoufan & Zhang, Ningning & Zhu, Ke, 2024. "Big portfolio selection by graph-based conditional moments method," Journal of Empirical Finance, Elsevier, vol. 78(C).
    32. Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023. "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, vol. 237(2).
    33. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," ISER Discussion Paper 1053r, Institute of Social and Economic Research, The University of Osaka, revised Mar 2020.
    34. Hyuksoo Kim & Saejoon Kim, 2024. "Estimating Asset Pricing Models in the Presence of Cross-Sectionally Correlated Pricing Errors," Mathematics, MDPI, vol. 12(21), pages 1-21, November.
    35. Kasper Johansson & Mehmet Giray Ogut & Markus Pelger & Thomas Schmelzer & Stephen Boyd, 2023. "A Simple Method for Predicting Covariance Matrices of Financial Returns," Papers 2305.19484, arXiv.org, revised Nov 2023.
    36. Mariano González-Sánchez & M. Encina Morales de Vega, 2021. "Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
    37. Songnian Chen & Junlong Feng, 2025. "Universal Factor Models," Papers 2501.15761, arXiv.org, revised Jul 2025.
    38. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.
    39. Sun, Yang & Zhang, Xuan & Zhang, Zhekai, 2022. "The reduced-rank beta in linear stochastic discount factor models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    40. Wang, Chuyu & Zhang, Guanglong, 2025. "In the shadows of opacity: Firm information quality and latent factor model performance," International Review of Financial Analysis, Elsevier, vol. 100(C).
    41. M. Hashem Pesaran & Ron P. Smith, 2021. "Factor Strengths, Pricing Errors, and Estimation of Risk Premia," CESifo Working Paper Series 8947, CESifo.

  10. Lettau, Martin & Pelger, Markus, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," CEPR Discussion Papers 13049, C.E.P.R. Discussion Papers.

    Cited by:

    1. Bandi, Federico M. & Chaudhuri, Shomesh E. & Lo, Andrew W. & Tamoni, Andrea, 2021. "Spectral factor models," Journal of Financial Economics, Elsevier, vol. 142(1), pages 214-238.
    2. Bryzgalova, Svetlana & Huang, Jiantao & Julliard, Christian, 2023. "Bayesian solutions for the factor zoo: we just ran two quadrillion models," LSE Research Online Documents on Economics 126151, London School of Economics and Political Science, LSE Library.
    3. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    5. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    6. Zhang, Ailian & Pan, Mengmeng & Zhang, Xuan, 2025. "The pricing ability of factor model based on machine learning: Evidence from high-frequency data in China," International Review of Economics & Finance, Elsevier, vol. 101(C).
    7. Lee, King Fuei, 2021. "An Anomaly within an Anomaly: The Halloween Effect in the Long-term Reversal Anomaly," MPRA Paper 110859, University Library of Munich, Germany.
    8. Gordon Dash & Nina Kajiji & Bruno G. Kamdem, 2024. "Asset Returns: Reimagining Generative ESG Indexes and Market Interconnectedness," JRFM, MDPI, vol. 17(10), pages 1-21, October.
    9. José Afonso Faias & Juan Arismendi Zambrano, 2022. "Equity Risk Premium Predictability from Cross-Sectoral Downturns [International asset allocation with regime shifts]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(3), pages 808-842.
    10. Liu, Yunting & Zhu, Yandi, 2025. "Good idiosyncratic volatility, bad idiosyncratic volatility, and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 170(C).
    11. Andrew Y. Chen, 2019. "The Limits of p-Hacking : A Thought Experiment," Finance and Economics Discussion Series 2019-016, Board of Governors of the Federal Reserve System (U.S.).
    12. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    13. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
    14. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
    15. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    16. Stephen Boyd & Kasper Johansson & Ronald Kahn & Philipp Schiele & Thomas Schmelzer, 2024. "Markowitz Portfolio Construction at Seventy," Papers 2401.05080, arXiv.org.
    17. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    18. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    19. Lin William Cong & Guanhao Feng & Jingyu He & Xin He, 2022. "Growing the Efficient Frontier on Panel Trees," NBER Working Papers 30805, National Bureau of Economic Research, Inc.
    20. Bang, Jeongseok & Kang, Yeonchan & Ryu, Doojin, 2024. "Potential pricing factors in the Korean market," Finance Research Letters, Elsevier, vol. 67(PB).
    21. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    22. Andreou, E. & Gagliardini, P. & Ghysels, E. & Rubin, M., 2025. "Spanning latent and observable factors," Journal of Econometrics, Elsevier, vol. 248(C).
    23. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    24. Baba-Yara, Fahiz & Boons, Martijn & Tamoni, Andrea, 2024. "Persistent and transitory components of firm characteristics: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 154(C).
    25. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    26. Martin Lettau & Markus Pelger, 2018. "Estimating Latent Asset-Pricing Factors," NBER Working Papers 24618, National Bureau of Economic Research, Inc.
    27. Eunchong Kim & Taehee Cho & Bonha Koo & Hyoung-Goo Kang, 2023. "Conditional autoencoder asset pricing models for the Korean stock market," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-30, July.
    28. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    29. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
    30. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
    31. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    32. Wu, Hongxu & Wang, Qiao & Li, Jianping & Deng, Zhibin, 2025. "Enhancing stock return prediction in the Chinese market: A GAN-based approach," Research in International Business and Finance, Elsevier, vol. 75(C).
    33. Yilie Huang & Yanwei Jia & Xun Yu Zhou, 2024. "Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study," Papers 2412.16175, arXiv.org, revised Aug 2025.
    34. Bagnara, Matteo & Goodarzi, Milad, 2023. "Clustering-based sector investing," SAFE Working Paper Series 397, Leibniz Institute for Financial Research SAFE.
    35. Käfer, Niclas & Mörke, Mathis & Weigert, Florian & Wiest, Tobias, 2025. "A Bayesian stochastic discount factor for the cross-section of individual equity options," CFR Working Papers 25-01, University of Cologne, Centre for Financial Research (CFR).
    36. Bryzgalova, Svetlana & Huang, Jiantao & Julliard, Christian, 2020. "Bayesian solutions for the factor zoo: we just ran two quadrillion models," LSE Research Online Documents on Economics 118924, London School of Economics and Political Science, LSE Library.
    37. Mao, Jie & Shao, Jingjing & Wang, Weiguan, 2025. "Risk premium principal components for the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 89(C).
    38. Mikhail Chernov & Magnus Dahlquist & Lars Lochstoer, 2023. "Pricing Currency Risks," Journal of Finance, American Finance Association, vol. 78(2), pages 693-730, April.
    39. Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    40. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
    41. Asano, Takao & Cai, Xiaojing & Sakemoto, Ryuta, 2024. "Currency portfolios and global foreign exchange ambiguity," Finance Research Letters, Elsevier, vol. 65(C).
    42. Breitung, Christian, 2023. "Automated stock picking using random forests," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 532-556.
    43. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    44. Wang, Jinzhe & Zhu, Yifeng, 2024. "A comparison of factor models in China," Journal of Empirical Finance, Elsevier, vol. 79(C).
    45. Vincent Tan & Stefan Zohren, 2020. "Estimation of Large Financial Covariances: A Cross-Validation Approach," Papers 2012.05757, arXiv.org, revised Jan 2023.
    46. Dohyun Chun & Jongho Kang & Jihun Kim, 2024. "Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
    47. Son, Bumho & Lee, Jaewook, 2022. "Graph-based multi-factor asset pricing model," Finance Research Letters, Elsevier, vol. 44(C).
    48. Wei Liu & James W. Kolari, 2022. "Multifactor Market Indexes," JRFM, MDPI, vol. 15(4), pages 1-26, March.
    49. Solène Collot & Tobias Hemauer, 2021. "A literature review of new methods in empirical asset pricing: omitted-variable and errors-in-variable bias," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 77-100, March.
    50. Andrew Detzel & Robert Novy‐Marx & Mihail Velikov, 2023. "Model Comparison with Transaction Costs," Journal of Finance, American Finance Association, vol. 78(3), pages 1743-1775, June.
    51. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    52. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    53. Rafael Branco & Alexandre Rubesam & Mauricio Zevallos, 2024. "Forecasting realized volatility: Does anything beat linear models?," Post-Print hal-04835657, HAL.
    54. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    55. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    56. Yu, Long & He, Yong & Kong, Xinbing & Zhang, Xinsheng, 2022. "Projected estimation for large-dimensional matrix factor models," Journal of Econometrics, Elsevier, vol. 229(1), pages 201-217.
    57. Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023. "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, vol. 237(2).
    58. Jozef Barunik & Matej Nevrla, 2022. "Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks," Papers 2208.14267, arXiv.org, revised Aug 2025.
    59. Bagnara, Matteo, 2024. "The economic value of cross-predictability: A performance-based measure," SAFE Working Paper Series 424, Leibniz Institute for Financial Research SAFE.
    60. Liyun Wu & Muneeb Ahmad & Salman Ali Qureshi & Kashif Raza & Yousaf Ali Khan, 2022. "An analysis of machine learning risk factors and risk parity portfolio optimization," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-19, September.
    61. Kasper Johansson & Mehmet Giray Ogut & Markus Pelger & Thomas Schmelzer & Stephen Boyd, 2023. "A Simple Method for Predicting Covariance Matrices of Financial Returns," Papers 2305.19484, arXiv.org, revised Nov 2023.
    62. James W. Kolari & Jianhua Huang & Wei Liu & Huiling Liao, 2025. "A Quantum Leap in Asset Pricing: Explaining Anomalous Returns," JRFM, MDPI, vol. 18(7), pages 1-28, July.
    63. van Binsbergen, Jules H. & Boons, Martijn & Opp, Christian C. & Tamoni, Andrea, 2023. "Dynamic asset (mis)pricing: Build-up versus resolution anomalies," Journal of Financial Economics, Elsevier, vol. 147(2), pages 406-431.
    64. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
    65. Molero González, Laura & Cerqueti, Roy & Mattera, Raffaele & Sánchez Granero, Miguel Ángel & Trinidad Segovia, Juan Evangelista, 2025. "Analyzing clustered factors in the cryptocurrency market with Random Matrix Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 665(C).
    66. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
    67. Wang, Chuyu & Zhang, Guanglong, 2025. "In the shadows of opacity: Firm information quality and latent factor model performance," International Review of Financial Analysis, Elsevier, vol. 100(C).

  11. Claudio Fontana & Markus Pelger & Eckhard Platen, 2017. "On the existence of sure profits via flash strategies," Papers 1708.03099, arXiv.org, revised Jul 2019.

    Cited by:

    1. Claudio Fontana & Zorana Grbac & Thorsten Schmidt, 2022. "Term structure modelling with overnight rates beyond stochastic continuity," Working Papers hal-03898872, HAL.
    2. Claudio Fontana & Zorana Grbac & Thorsten Schmidt, 2022. "Term structure modelling with overnight rates beyond stochastic continuity," Papers 2202.00929, arXiv.org, revised Aug 2023.
    3. Claudio Fontana & Zorana Grbac & Sandrine Gümbel & Thorsten Schmidt, 2020. "Term structure modelling for multiple curves with stochastic discontinuities," Finance and Stochastics, Springer, vol. 24(2), pages 465-511, April.
    4. Claudio Fontana & Zorana Grbac & Sandrine Gumbel & Thorsten Schmidt, 2018. "Term structure modeling for multiple curves with stochastic discontinuities," Papers 1810.09882, arXiv.org, revised Dec 2019.
    5. Alessio Calvelli, 2022. "No-Arbitrage Pricing, Dynamics and Forward Prices of Collateralized Derivatives," Papers 2208.08746, arXiv.org, revised Jun 2024.

  12. Claudio Fontana & Markus Pelger & Eckhard Platen, 2017. "Sure Profits via Flash Strategies and the Impossibility of Predictable Jumps," Research Paper Series 385, Quantitative Finance Research Centre, University of Technology, Sydney.

    Cited by:

    1. Fontana, Claudio & Schmidt, Thorsten, 2018. "General dynamic term structures under default risk," Stochastic Processes and their Applications, Elsevier, vol. 128(10), pages 3353-3386.

  13. Paul Glasserman & Chulmin Kang & Wanmo Kang, 2013. "Stress Scenario Selection by Empirical Likelihood," Working Papers 13-04, Office of Financial Research, US Department of the Treasury.

    Cited by:

    1. van Wijnbergen, Sweder & Chan, Stephanie, 2016. "CoCo Design, Risk Shifting and Financial Fragility," CEPR Discussion Papers 11099, C.E.P.R. Discussion Papers.
    2. Delphine Boursicot & Geneviève Gauthier & Farhad Pourkalbassi, 2019. "Contingent Convertible Debt: The Impact on Equity Holders," Risks, MDPI, vol. 7(2), pages 1-35, April.
    3. Mark D. Flood & George G. Korenko, 2015. "Systematic scenario selection: stress testing and the nature of uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 15(1), pages 43-59, January.
    4. Levy-Carciente, Sary & Kenett, Dror Y. & Avakian, Adam & Stanley, H. Eugene & Havlin, Shlomo, 2015. "Dynamical macroprudential stress testing using network theory," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 164-181.
    5. Mike Derksen & Peter Spreij & Sweder van Wijnbergen, 2018. "Accounting Noise and the Pricing of Cocos," Tinbergen Institute Discussion Papers 18-037/VI, Tinbergen Institute.
    6. Guglielmo Maria Caporale & Woo-Young Kang, 2019. "On the preferences of CoCo bond buyers and sellers," CESifo Working Paper Series 7551, CESifo.
    7. Farmer, J. Doyne & Kleinnijenhuis, Alissa & Goodhart, Charles, 2021. "Systemic implications of the bail-in design," INET Oxford Working Papers 2021-21, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    8. Bookstaber, Rick & Cetina, Jill & Feldberg, Greg & Flood, Mark & Glasserman, Paul, 2013. "Stress tests to promote financial stability: Assessing progress and looking to the future," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 7(1), pages 16-25, December.
    9. Stephanie Chan & Sweder van Wijnbergen, 2016. "Coco Design, Risk Shifting Incentives and Capital Regulation," Tinbergen Institute Discussion Papers 16-007/VI, Tinbergen Institute, revised 13 Nov 2017.
    10. Mahmoud Fatouh & Ayowande McMunn, 2019. "Shareholder risk-taking incentives in the presence of contingent capital," Bank of England working papers 775, Bank of England.

Articles

  1. Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
    See citations under working paper version above.
  2. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    See citations under working paper version above.
  3. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    See citations under working paper version above.
  4. Markus Pelger & Ruoxuan Xiong, 2022. "State-Varying Factor Models of Large Dimensions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1315-1333, June.
    See citations under working paper version above.
  5. Markus Pelger & Ruoxuan Xiong, 2022. "Interpretable Sparse Proximate Factors for Large Dimensions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1642-1664, October.

    Cited by:

    1. Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org, revised Nov 2024.
    2. Stephen Boyd & Kasper Johansson & Ronald Kahn & Philipp Schiele & Thomas Schmelzer, 2024. "Markowitz Portfolio Construction at Seventy," Papers 2401.05080, arXiv.org.
    3. Xiang, XueTing & Lim, Kian-Ping & Ong, Sheue-Li, 2025. "The dynamics and drivers of global market integration: Regional and cultural factors matter," Research in International Business and Finance, Elsevier, vol. 78(C).
    4. Kasper Johansson & Mehmet Giray Ogut & Markus Pelger & Thomas Schmelzer & Stephen Boyd, 2023. "A Simple Method for Predicting Covariance Matrices of Financial Returns," Papers 2305.19484, arXiv.org, revised Nov 2023.

  6. Lettau, Martin & Pelger, Markus, 2020. "Estimating latent asset-pricing factors," Journal of Econometrics, Elsevier, vol. 218(1), pages 1-31.
    See citations under working paper version above.
  7. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.

    Cited by:

    1. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Hai-Chuan Xu & Fredj Jawadi & Jie Zhou & Wei-Xing Zhou, 2023. "Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework," Empirical Economics, Springer, vol. 65(1), pages 93-110, July.
    3. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    4. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    5. Zhang, Ailian & Pan, Mengmeng & Zhang, Xuan, 2025. "The pricing ability of factor model based on machine learning: Evidence from high-frequency data in China," International Review of Economics & Finance, Elsevier, vol. 101(C).
    6. Ilze Kalnina & Kokouvi Tewou, 2024. "Cross-sectional Dependence in Idiosyncratic Volatility," Papers 2408.13437, arXiv.org, revised May 2025.
    7. Wang, Peiwan & Zong, Lu, 2023. "Does machine learning help private sectors to alarm crises? Evidence from China’s currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    8. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
    9. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
    10. Sweder van Wijnbergen & Daniël Dimitrov, 2023. "Macroprudential Regulation: A Risk Management Approach," Tinbergen Institute Discussion Papers 23-002/IV, Tinbergen Institute.
    11. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    12. Plante, Michael, 2025. "Investing in the batteries and vehicles of the future: A view through the stock market," Energy Economics, Elsevier, vol. 143(C).
    13. Markus Bibinger & Christopher J. Neely & Lars Winkelmann, 2017. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Working Papers 2017-12, Federal Reserve Bank of St. Louis.
    14. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2020. "Estimation of large dimensional conditional factor models in finance," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 219-282, Elsevier.
    15. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    16. Xinbing Kong & Cheng Liu & Bin Wu, 2025. "Data Synchronization at High Frequencies," Papers 2507.12220, arXiv.org.
    17. Torben G. Andersen & Yi Ding & Viktor Todorov & Seunghyeon Yu, 2025. "The Factor Structure of Jump Risk," Working Papers 202531, University of Macau, Faculty of Business Administration.
    18. Zhou, Dong-hai & Liu, Xiao-xing, 2023. "Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    19. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    20. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    21. Deniz Erdemlioglu & Christopher J. Neely & Xiye Yang, 2023. "Testing for Multi-Asset Systemic Tail Risk," Working Papers 2023-016, Federal Reserve Bank of St. Louis, revised 09 Sep 2025.
    22. Dinesh Gajurel & Mardi Dungey & Wenying Yao & Nagaratnam Jeyasreedharan, 2020. "Jump Risk in the US Financial Sector," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 331-349, September.
    23. Aleksy Leeuwenkamp & Wentao Hu, 2023. "New general dependence measures: construction, estimation and application to high-frequency stock returns," Papers 2309.00025, arXiv.org.
    24. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.

  8. Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
    See citations under working paper version above.
  9. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.

    Cited by:

    1. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Dovonon, Prosper & Taamouti, Abderrahim & Williams, Julian, 2022. "Testing the eigenvalue structure of spot and integrated covariance," Journal of Econometrics, Elsevier, vol. 229(2), pages 363-395.
    3. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    4. Cai, T. Tony & Hu, Jianchang & Li, Yingying & Zheng, Xinghua, 2020. "High-dimensional minimum variance portfolio estimation based on high-frequency data," Journal of Econometrics, Elsevier, vol. 214(2), pages 482-494.
    5. Zhang, Ailian & Pan, Mengmeng & Zhang, Xuan, 2025. "The pricing ability of factor model based on machine learning: Evidence from high-frequency data in China," International Review of Economics & Finance, Elsevier, vol. 101(C).
    6. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.
    7. Ilze Kalnina & Kokouvi Tewou, 2024. "Cross-sectional Dependence in Idiosyncratic Volatility," Papers 2408.13437, arXiv.org, revised May 2025.
    8. Chen, Dachuan, 2024. "High frequency principal component analysis based on correlation matrix that is robust to jumps, microstructure noise and asynchronous observation times," Journal of Econometrics, Elsevier, vol. 240(1).
    9. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas," Departmental Working Papers 201711, Rutgers University, Department of Economics.
    10. Plante, Michael, 2025. "Investing in the batteries and vehicles of the future: A view through the stock market," Energy Economics, Elsevier, vol. 143(C).
    11. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    12. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    13. Andreou, E. & Gagliardini, P. & Ghysels, E. & Rubin, M., 2025. "Spanning latent and observable factors," Journal of Econometrics, Elsevier, vol. 248(C).
    14. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2020. "Estimation of large dimensional conditional factor models in finance," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 219-282, Elsevier.
    15. Martin Lettau & Markus Pelger, 2018. "Estimating Latent Asset-Pricing Factors," NBER Working Papers 24618, National Bureau of Economic Research, Inc.
    16. 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).
    17. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    18. Xinbing Kong & Cheng Liu & Bin Wu, 2025. "Data Synchronization at High Frequencies," Papers 2507.12220, arXiv.org.
    19. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
    20. Torben G. Andersen & Yi Ding & Viktor Todorov & Seunghyeon Yu, 2025. "The Factor Structure of Jump Risk," Working Papers 202531, University of Macau, Faculty of Business Administration.
    21. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.
    22. Cen, Zetai & Lam, Clifford, 2025. "Tensor time series imputation through tensor factor modelling," LSE Research Online Documents on Economics 127231, London School of Economics and Political Science, LSE Library.
    23. Joongyeub Yeo & George Papanicolaou, 2016. "Random matrix approach to estimation of high-dimensional factor models," Papers 1611.05571, arXiv.org, revised Nov 2017.
    24. Duan, Junting & Pelger, Markus & Xiong, Ruoxuan, 2024. "Target PCA: Transfer learning large dimensional panel data," Journal of Econometrics, Elsevier, vol. 244(2).
    25. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
    26. Xin-Bing Kong & Yong-Xin Liu & Long Yu & Peng Zhao, 2022. "Matrix Quantile Factor Model," Papers 2208.08693, arXiv.org, revised Aug 2024.
    27. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models," Papers 1711.04392, arXiv.org, revised Dec 2018.
    28. Cen, Zetai & Lam, Clifford, 2025. "Tensor time series imputation through tensor factor modelling," Journal of Econometrics, Elsevier, vol. 249(PB).
    29. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    30. Markus Pelger & Jiacheng Zou, 2022. "Inference for Large Panel Data with Many Covariates," Papers 2301.00292, arXiv.org, revised Mar 2023.
    31. Noureddine Kouaissah & Amin Hocine, 2021. "Forecasting systemic risk in portfolio selection: The role of technical trading rules," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 708-729, July.
    32. Esparcia, Carlos & Escribano, Ana & Jareño, Francisco, 2024. "Assessing the crypto market stability after the FTX collapse: A study of high frequency volatility and connectedness," International Review of Financial Analysis, Elsevier, vol. 94(C).
    33. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    34. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org, revised Aug 2025.
    35. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    36. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2023. "High-dimensional estimation of quadratic variation based on penalized realized variance," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 331-359, July.
    37. Gribisch, Bastian & Hartkopf, Jan Patrick & Liesenfeld, Roman, 2020. "Factor state–space models for high-dimensional realized covariance matrices of asset returns," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 1-20.
    38. Esparcia, Carlos & López, Raquel, 2024. "Performance of crypto-Forex portfolios based on intraday data," Research in International Business and Finance, Elsevier, vol. 69(C).
    39. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.
    40. Donggyu Kim & Minseog Oh, 2024. "Dynamic Realized Minimum Variance Portfolio Models," Working Papers 202421, University of California at Riverside, Department of Economics.
    41. Esparcia, Carlos & Escribano, Ana & Jareño, Francisco, 2023. "Did cryptomarket chaos unleash Silvergate's bankruptcy? investigating the high-frequency volatility and connectedness behind the collapse," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).

  10. Nan Chen & Paul Glasserman & Behzad Nouri & Markus Pelger, 2017. "Contingent Capital, Tail Risk, and Debt-Induced Collapse," The Review of Financial Studies, Society for Financial Studies, vol. 30(11), pages 3921-3969.

    Cited by:

    1. Niedrig, Tobias & Gründl, Helmut, 2015. "The effects of Contingent Convertible (CoCo) bonds on insurers' capital requirements under solvency II," SAFE Working Paper Series 98, Leibniz Institute for Financial Research SAFE.
    2. Olivier Courtois & Xiaoshan Su, 2020. "Structural Pricing of CoCos and Deposit Insurance with Regime Switching and Jumps," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(4), pages 477-520, December.
    3. Ye Li & Simon Mayer & Simon Mayer, 2021. "Money Creation in Decentralized Finance: A Dynamic Model of Stablecoin and Crypto Shadow Banking," CESifo Working Paper Series 9260, CESifo.
    4. Bolton, Patrick & Avdjiev, Stefan & Bogdanova, Bilyana & Jiang, Wei & Kartasheva, Anastasia, 2017. "CoCo Issuance and Bank Fragility," CEPR Discussion Papers 12418, C.E.P.R. Discussion Papers.
    5. Delphine Boursicot & Geneviève Gauthier & Farhad Pourkalbassi, 2019. "Contingent Convertible Debt: The Impact on Equity Holders," Risks, MDPI, vol. 7(2), pages 1-35, April.
    6. Roman Goncharenko & Steven Ongena & Asad Rauf, 2019. "The Agency of CoCos: Why Contingent Convertible Bonds Aren't for Everyone," Swiss Finance Institute Research Paper Series 19-43, Swiss Finance Institute.
    7. Li, Ping & Guo, Yanhong & Meng, Hui, 2022. "The default contagion of contingent convertible bonds in financial network," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    8. Xia, Xin & Gan, Liu, 2020. "SME financing with new credit guarantee contracts over the business cycle," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 515-538.
    9. Deng, Kaihua & Fu, Qilong & Huang, Dongxia, 2025. "Soft going-concern capital buffer? CoCo non-calls and revealed bank distress," Journal of Corporate Finance, Elsevier, vol. 93(C).
    10. Hüser, Anne-Caroline & Hałaj, Grzegorz & Kok, Christoffer & Perales, Cristian & van der Kraaij, Anton, 2018. "The systemic implications of bail-in: A multi-layered network approach," Journal of Financial Stability, Elsevier, vol. 38(C), pages 81-97.
    11. Chia-Chien Chang & Min-Teh Yu, 2018. "Bank Contingent Capital: Valuation and the Role of Market Discipline," Journal of Financial Services Research, Springer;Western Finance Association, vol. 54(1), pages 49-80, August.
    12. Philippe Oster, 2020. "Contingent Convertible bond literature review: making everything and nothing possible?," Journal of Banking Regulation, Palgrave Macmillan, vol. 21(4), pages 343-381, December.
    13. Naeem, Muhammad Abubakr & Shahzad, Mohammad Rahim & Karim, Sitara & Assaf, Rima, 2023. "Tail risk transmission in technology-driven markets," Global Finance Journal, Elsevier, vol. 57(C).
    14. Allen N. Berger & Charles P. Himmelberg & Raluca A. Roman & Sergey Tsyplakov, 2022. "Bank bailouts, bail‐ins, or no regulatory intervention? A dynamic model and empirical tests of optimal regulation and implications for future crises," Financial Management, Financial Management Association International, vol. 51(4), pages 1031-1090, December.
    15. Anne G. Balter & Nikolaus Schweizer & Juan C. Vera, 2020. "Contingent Capital with Stock Price Triggers in Interbank Networks," Papers 2011.06474, arXiv.org.
    16. Natalya Martynova & Enrico Perotti, 2012. "Convertible Bonds and Bank Risk-Taking," Tinbergen Institute Discussion Papers 12-106/IV/DSF41, Tinbergen Institute, revised 10 Oct 2016.
    17. Borys Grochulski & Russell Wong, 2018. "Contingent Debt and Performance Pricing in an Optimal Capital Structure Model with Financial Distress and Reorganization," Working Paper 18-17, Federal Reserve Bank of Richmond.
    18. Mendes, Layla dos Santos & Leite, Rodrigo de Oliveira & Fajardo, José, 2022. "Do contingent convertible bonds reduce systemic risk?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    19. Jang, Hyun Jin & Na, Young Hoon & Zheng, Harry, 2018. "Contingent convertible bonds with the default risk premium," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 77-93.
    20. Giovanni Calice & Carlo Sala & Daniele Tantari, 2020. "Contingent Convertible Bonds in Financial Networks," Papers 2009.00062, arXiv.org, revised Dec 2023.
    21. Franklin Allen & Xian Gu, 2018. "The Interplay between Regulations and Financial Stability," Journal of Financial Services Research, Springer;Western Finance Association, vol. 53(2), pages 233-248, June.
    22. Zhang, Sijia & Xia, Xin & Gan, Liu, 2025. "Optimal conversion ratio of contingent capital under issuance constraints," International Review of Financial Analysis, Elsevier, vol. 100(C).
    23. Himmelberg, Charles P. & Tsyplakov, Sergey, 2020. "Optimal terms of contingent capital, incentive effects, and capital structure dynamics," Journal of Corporate Finance, Elsevier, vol. 64(C).
    24. Gupta, Aparna & Wang, Runzu & Lu, Yueliang, 2021. "Addressing systemic risk using contingent convertible debt – A network analysis," European Journal of Operational Research, Elsevier, vol. 290(1), pages 263-277.
    25. Felix Fie{ss}inger & Mitja Stadje, 2025. "Optimal Capital Structure for Life Insurance Companies Offering Surplus Participation," Papers 2504.12851, arXiv.org, revised Apr 2025.
    26. Chan, Stephanie & Wijnbergen, Sweder, 2017. "CoCo Design, Risk Shifting Incentives and Financial Fragility," ECMI Papers 12166, Centre for European Policy Studies.
    27. Zhao, Zhiming & Li, Shasha & Tang, Huiling, 2021. "Write-down bonds, credit risk and imperfect information," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    28. Chen, Lu & Li, Bingqing & Zheng, Wenyuan, 2024. "Pure risk, agency conflict, and hedging," Journal of Banking & Finance, Elsevier, vol. 168(C).

  11. An Chen & Markus Pelger & Klaus Sandmann, 2013. "New performance-vested stock option schemes," Applied Financial Economics, Taylor & Francis Journals, vol. 23(8), pages 709-727, April.

    Cited by:

    1. Yangyang Zhuang & Pan Tang, 2023. "Pricing of American Parisian option as executive option based on the least‐squares Monte Carlo approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1469-1496, October.

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