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Jia Li

Personal Details

First Name:Jia
Middle Name:
Last Name:Li
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RePEc Short-ID:pli1690
[This author has chosen not to make the email address public]
https://sites.google.com/view/jiali/work

Affiliation

(50%) Singapore Management University

Singapore, Singapore
http://www.smu.edu.sg/
RePEc:edi:smunisg (more details at EDIRC)

(50%) Department of Economics
Duke University

Durham, North Carolina (United States)
http://www.econ.duke.edu/
RePEc:edi:dedukus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
  2. Jia Li & Zhipeng Liao & Mengsi Gao, 2021. "Uniform nonparametric inference in time series via Stata," Economics Virtual Symposium 2021 1, Stata Users Group.
  3. Federico A. Bugni & Jia Li & Qiyuan Li, 2020. "Permutation-based tests for discontinuities in event studies," Papers 2007.09837, arXiv.org, revised Jul 2022.
  4. Tim Bollerslev & Jia Li & Yuan Xue, 2016. "Volume, Volatility and Public News Announcements," CREATES Research Papers 2016-19, Department of Economics and Business Economics, Aarhus University.
  5. Jia Li & Andrew J. Patton, 2013. "Asymptotic Inference about Predictive Accuracy Using High Frequency Data," Working Papers 13-27, Duke University, Department of Economics.

Articles

  1. Tim Bollerslev & Jia Li & Yuexuan Ren, 2024. "Optimal Inference for Spot Regressions," American Economic Review, American Economic Association, vol. 114(3), pages 678-708, March.
  2. Jia Li, 2013. "Robust Estimation and Inference for Jumps in Noisy High Frequency Data: A Local‐to‐Continuity Theory for the Pre‐Averaging Method," Econometrica, Econometric Society, vol. 81(4), pages 1673-1693, July.

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.

    Mentioned in:

    1. Long memory and weak ID
      by Francis Diebold in No Hesitations on 2022-09-03 16:42:00

Working papers

  1. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Carsten H. Chong & Viktor Todorov, 2024. "A nonparametric test for rough volatility," Papers 2407.10659, arXiv.org.

  2. Tim Bollerslev & Jia Li & Yuan Xue, 2016. "Volume, Volatility and Public News Announcements," CREATES Research Papers 2016-19, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Schmeling, Maik & Schrimpf, Paul & Kroencke, Tim, 2019. "The FOMC Risk Shift," CEPR Discussion Papers 14037, C.E.P.R. Discussion Papers.
    2. Guo, Feng & Lin, Zhiyuan & Lyu, Xiaoliang & Shi, Qingling, 2023. "Does air pollution influence music sentiment? Measuring music sentiment by machine learning," Journal of Asian Economics, Elsevier, vol. 87(C).
    3. Matthias R. Fengler & Jeannine Polivka, 2024. "Proxy-identification of a structural MGARCH model for asset returns," Swiss Finance Institute Research Paper Series 24-55, Swiss Finance Institute.
    4. Bertelsen, Kristoffer Pons & Borup, Daniel & Jakobsen, Johan Stax, 2021. "Stock market volatility and public information flow: A non-linear perspective," Economics Letters, Elsevier, vol. 204(C).
    5. Ferreira Batista Martins, Igor & Virbickaitè, Audronè & Nguyen, Hoang & Freitas Lopes, Hedibert, 2025. "Volume-driven time-of-day effects in intraday volatility models," Working Papers 2025:14, Örebro University, School of Business.
    6. Crego, Julio & Gider, Jasmin, 2024. "The dynamic informativeness of scheduled news," Other publications TiSEM d4538ed2-3aeb-4259-b1f1-2, Tilburg University, School of Economics and Management.
    7. Akbari, Amir & Krystyniak, Karolina, 2021. "Government real estate interventions and the stock market," International Review of Financial Analysis, Elsevier, vol. 75(C).
    8. Sherif, Mohamed, 2020. "The impact of Coronavirus (COVID-19) outbreak on faith-based investments: An original analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    9. 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.
    10. Doojin Ryu & Doowon Ryu & Heejin Yang, 2021. "The impact of net buying pressure on index options prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 27-45, January.
    11. Bianchi, Francesco & Gómez-Cram, Roberto & Kind, Thilo & Kung, Howard, 2023. "Threats to central bank independence: High-frequency identification with twitter," Journal of Monetary Economics, Elsevier, vol. 135(C), pages 37-54.
    12. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    13. Sonya Zhu, 2023. "Volume dynamics around FOMC announcements," BIS Working Papers 1079, Bank for International Settlements.
    14. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    15. Jian, Zhihong & Wu, Shuai & Zhu, Zhican, 2018. "Asymmetric extreme risk spillovers between the Chinese stock market and index futures market: An MV-CAViaR based intraday CoVaR approach," Emerging Markets Review, Elsevier, vol. 37(C), pages 98-113.
    16. Alexander Koch & Toan Luu Duc Huynh & Mei Wang, 2024. "News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 5-34, January.
    17. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2022. "Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    18. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Mar 2025.
    19. Fengler, Matthias & Polivka, Jeanine, 2022. "Identifying Structural Shocks to Volatility through a Proxy-MGARCH Model," VfS Annual Conference 2022 (Basel): Big Data in Economics 264010, Verein für Socialpolitik / German Economic Association.
    20. Filippou, Ilias & Gozluklu, Arie E. & Nguyen, My T. & Viswanath-Natraj, Ganesh, 2025. "Signal in the noise: Trump tweets and the currency market," Journal of International Money and Finance, Elsevier, vol. 156(C).
    21. Ratliff, David J. & Philipps, Collin S., 2025. "Which corporate leaders matter to financial markets?," International Review of Financial Analysis, Elsevier, vol. 97(C).
    22. Crego, Julio A., 2020. "Why does public news augment information asymmetries?," Journal of Financial Economics, Elsevier, vol. 137(1), pages 72-89.
    23. Laurent Bouton & Aniol Llorente-Saguer & Antonin Macé & Adam Meirowitz & Shaoting Pi & Dimitrios Xefteris, 2024. "Public Information as a Source of Disagreement," Working Papers halshs-04075483, HAL.
    24. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    25. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    26. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    27. Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers 2023-013, Federal Reserve Bank of St. Louis, revised 28 Sep 2024.
    28. Federico A. Bugni & Jia Li & Qiyuan Li, 2020. "Permutation-based tests for discontinuities in event studies," Papers 2007.09837, arXiv.org, revised Jul 2022.
    29. Sophia Zhengzi Li & Zeyao Luan, 2025. "News-based investor disagreement and stock returns," Review of Accounting Studies, Springer, vol. 30(3), pages 2312-2375, September.
    30. Wang, Cindy S.H. & Chen, Yi-Chi & Lo, Hsin-Yu, 2021. "A fresh look at the risk-return tradeoff," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    31. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Janeway Institute Working Papers 2423, Faculty of Economics, University of Cambridge.
    32. Caporin, Massimiliano & Poli, Francesco, 2022. "News and intraday jumps: Evidence from regularization and class imbalance," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    33. Jameson K. M. Watts, 2020. "Language Consistency and Stock Market Trading Volume," SAGE Open, , vol. 10(2), pages 21582440209, May.
    34. Bodilsen, Simon & Eriksen, Jonas N. & Grønborg, Niels S., 2021. "Asset pricing and FOMC press conferences," Journal of Banking & Finance, Elsevier, vol. 128(C).
    35. Hasan Fallahgoul, 2020. "Inside the Mind of Investors During the COVID-19 Pandemic: Evidence from the StockTwits Data," Papers 2004.11686, arXiv.org, revised May 2020.
    36. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    37. Angelo Ranaldo & Paolo Santucci de Magistris, 2018. "Trading Volume, Illiquidity and Commonalities in FX Markets," Working Papers on Finance 1823, University of St. Gallen, School of Finance, revised Oct 2019.
    38. Tim Bollerslev & Jia Li & Zhipeng Liao, 2021. "Fixed‐k inference for volatility," Quantitative Economics, Econometric Society, vol. 12(4), pages 1053-1084, November.
    39. Tim Bollerslev & Jia Li & Leonardo Salim Saker Chaves, 2021. "Generalized Jump Regressions for Local Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1015-1025, October.
    40. Monaco, Eleonora & Murgia, Lucia Milena, 2023. "Retail attention and the FOMC equity premium," Finance Research Letters, Elsevier, vol. 53(C).
    41. Meirowitz, Adam & Pi, Shaoting, 2022. "Voting and trading: The shareholder’s dilemma," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1073-1096.
    42. Doureige J. Jurdi, 2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    43. Bollerslev, Tim & Li, Jia & Li, Qiyuan, 2024. "Optimal nonparametric range-based volatility estimation," Journal of Econometrics, Elsevier, vol. 238(1).
    44. Federico A. Bugni & Jia Li & Qiyuan Li, 2023. "Permutation‐based tests for discontinuities in event studies," Quantitative Economics, Econometric Society, vol. 14(1), pages 37-70, January.
    45. Voges, Michelle & Sibbertsen, Philipp, 2021. "Cyclical fractional cointegration," Econometrics and Statistics, Elsevier, vol. 19(C), pages 114-129.
    46. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2021. "How does the financial market update beliefs about the real economy? Evidence from the oil market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 938-961, November.
    47. Christensen, Kim & Kolokolov, Aleksey, 2024. "An unbounded intensity model for point processes," Journal of Econometrics, Elsevier, vol. 244(1).
    48. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    49. Marc Gronwald & Sania Wadud & Kingsley Dogah, 2024. "Oil Market Efficiency, Quantity of Information, and Oil Market Turbulence," CESifo Working Paper Series 10995, CESifo.
    50. Ai, Hengjie & Han, Leyla Jianyu & Pan, Xuhui Nick & Xu, Lai, 2022. "The cross section of the monetary policy announcement premium," Journal of Financial Economics, Elsevier, vol. 143(1), pages 247-276.
    51. Martina Halouskov'a & v{S}tefan Ly'ocsa, 2025. "Forecasting U.S. equity market volatility with attention and sentiment to the economy," Papers 2503.19767, arXiv.org.

  3. Jia Li & Andrew J. Patton, 2013. "Asymptotic Inference about Predictive Accuracy Using High Frequency Data," Working Papers 13-27, Duke University, Department of Economics.

    Cited by:

    1. Kalnina, Ilze & Tewou, Kokouvi, 2025. "Cross-sectional dependence in idiosyncratic volatility," Journal of Econometrics, Elsevier, vol. 249(PB).
    2. Lai, Yu-Sheng, 2022. "Improving hedging performance by using high–low range," Finance Research Letters, Elsevier, vol. 48(C).
    3. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    4. Buccheri, Giuseppe & Renò, Roberto & Vocalelli, Giorgio, 2025. "Taking advantage of biased proxies for forecast evaluation," Journal of Econometrics, Elsevier, vol. 251(C).
    5. Michael W. McCracken, 2020. "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers 2020-050, Federal Reserve Bank of St. Louis.
    6. Coroneo, Laura & Iacone, Fabrizio & Profumo, Fabio, 2024. "Survey density forecast comparison in small samples," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1486-1504.
    7. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    8. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    9. Yu‐Sheng Lai, 2023. "Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 677-701, May.
    10. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
    11. Lukas Bauer, 2025. "Evaluating financial tail risk forecasts: Testing Equal Predictive Ability," Papers 2505.23333, arXiv.org.
    12. Lai, Yu-Sheng, 2023. "Economic evaluation of dynamic hedging strategies using high-frequency data," Finance Research Letters, Elsevier, vol. 57(C).
    13. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.
    14. Yu‐Sheng Lai, 2022. "Use of high‐frequency data to evaluate the performance of dynamic hedging strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 104-124, January.
    15. Michael W. McCracken, 2019. "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers 2019-11, Federal Reserve Bank of St. Louis.

Articles

  1. Tim Bollerslev & Jia Li & Yuexuan Ren, 2024. "Optimal Inference for Spot Regressions," American Economic Review, American Economic Association, vol. 114(3), pages 678-708, March.

    Cited by:

    1. Yasin Simsek, 2025. "Beyond Returns: A Candlestick-Based Approach to Spot Covariance Estimation," Papers 2510.12911, arXiv.org.
    2. Xinbing Kong & Cheng Liu & Bin Wu, 2025. "Data Synchronization at High Frequencies," Papers 2507.12220, arXiv.org.
    3. Guillaume Coqueret & Martial Laguerre, 2025. "Overparametrized models with posterior drift," Papers 2506.23619, arXiv.org.

  2. Jia Li, 2013. "Robust Estimation and Inference for Jumps in Noisy High Frequency Data: A Local‐to‐Continuity Theory for the Pre‐Averaging Method," Econometrica, Econometric Society, vol. 81(4), pages 1673-1693, July.

    Cited by:

    1. Bibinger, Markus & Winkelmann, Lars, 2014. "Common price and volatility jumps in noisy high-frequency data," SFB 649 Discussion Papers 2014-037, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Peter Christoffersen & Bruno Feunou & Yoontae Jeon, 2014. "Option Valuation with Observable Volatility and Jump Dynamics," CREATES Research Papers 2015-07, Department of Economics and Business Economics, Aarhus University.
    3. Duong, Diep & Swanson, Norman R., 2015. "Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction," Journal of Econometrics, Elsevier, vol. 187(2), pages 606-621.
    4. Harin, Alexander, 2014. "Problems of utility and prospect theories. A discontinuity of Prelec’s function," MPRA Paper 61027, University Library of Munich, Germany.
    5. Ding, Yi & Li, Yingying & Liu, Guoli & Zheng, Xinghua, 2024. "Stock co-jump networks," Journal of Econometrics, Elsevier, vol. 239(2).
    6. Liu, Zhi & Kong, Xin-Bing & Jing, Bing-Yi, 2018. "Estimating the integrated volatility using high-frequency data with zero durations," Journal of Econometrics, Elsevier, vol. 204(1), pages 18-32.
    7. Jui-Chung Yang & Ke-Li Xu, 2013. "Estimation and Inference under Weak Identi cation and Persistence: An Application on Forecast-Based Monetary Policy Reaction Function," 2013 Papers pya307, Job Market Papers.
    8. Li, M. Z. & Linton, O., 2021. "Robust Estimation of Integrated and Spot Volatility," Cambridge Working Papers in Economics 2115, Faculty of Economics, University of Cambridge.
    9. Peter Reinhard Hansen, 2015. "A Martingale Decomposition of Discrete Markov Chains," CREATES Research Papers 2015-18, Department of Economics and Business Economics, Aarhus University.
    10. Z. Merrick Li & Oliver Linton, 2022. "A ReMeDI for Microstructure Noise," Econometrica, Econometric Society, vol. 90(1), pages 367-389, January.
    11. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    12. Zhang, Congshan & Li, Jia & Bollerslev, Tim, 2022. "Occupation density estimation for noisy high-frequency data," Journal of Econometrics, Elsevier, vol. 227(1), pages 189-211.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 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 (3) 2013-12-29 2020-08-31 2022-08-29
  2. NEP-ETS: Econometric Time Series (3) 2013-12-29 2020-08-31 2022-08-29
  3. NEP-MST: Market Microstructure (2) 2013-12-29 2016-07-16
  4. NEP-SEA: South East Asia (2) 2022-02-07 2022-08-29
  5. NEP-FOR: Forecasting (1) 2013-12-29
  6. NEP-GER: German Papers (1) 2016-07-16

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