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The Factor Structure of Jump Risk

Author

Listed:
  • Torben G. Andersen

    (Department of Finance, Northwestern University)

  • Yi Ding

    (Faculty of Business Administration, University of Macau)

  • Viktor Todorov

    (Department of Finance, Northwestern University)

  • Seunghyeon Yu

    (Department of Finance, Northwestern University)

Abstract

We develop nonparametric estimates for tail risk in the cross-section of asset prices at high frequencies. We show that the tail behavior of the crosssectional return distribution depends on whether the time interval contains a systematic jump event. If so, the cross-sectional return tail is governed by the assets’ exposures to the systematic event while, otherwise, it is determined by the idiosyncratic jump tails of the stocks. We develop an estimator for the tail shape of the cross-sectional return distribution that display distinct properties with and without systematic jumps. Empirically, we provide evidence for symmetric cross-sectional return tails at high-frequency that exhibit nontrivial and persistent time series variation. A hypothesis of equal cross-sectional return tail shapes during periods with and without systematic jump events is strongly rejected by the data.

Suggested Citation

  • 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.
  • Handle: RePEc:boa:wpaper:202531
    as

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    File URL: https://fba.um.edu.mo/wp-content/uploads/RePEc/doc/202531.pdf
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    References listed on IDEAS

    as
    1. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
    2. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    3. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    4. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
    5. Qihui Chen & Zheng Fang, 2018. "Improved Inference on the Rank of a Matrix," Papers 1812.02337, arXiv.org, revised Mar 2019.
    6. Todorov, Viktor & Bollerslev, Tim, 2010. "Jumps and betas: A new framework for disentangling and estimating systematic risks," Journal of Econometrics, Elsevier, vol. 157(2), pages 220-235, August.
    7. Jia Li & Viktor Todorov & George Tauchen, 2019. "Jump factor models in large cross‐sections," Quantitative Economics, Econometric Society, vol. 10(2), pages 419-456, May.
    8. Jia Li & Viktor Todorov & George Tauchen & Huidi Lin, 2019. "Rank Tests at Jump Events," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 312-321, April.
    9. Yacine Aït-Sahalia & Dacheng Xiu, 2019. "Principal Component Analysis of High-Frequency Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 287-303, January.
    10. Yacine Aït-Sahalia & Jean Jacod & Dacheng Xiu, 2020. "Inference on Risk Premia in Continuous-Time Asset Pricing Models," NBER Working Papers 28140, National Bureau of Economic Research, Inc.
    11. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    12. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
    13. Qihui Chen & Zheng Fang, 2019. "Improved inference on the rank of a matrix," Quantitative Economics, Econometric Society, vol. 10(4), pages 1787-1824, November.
    14. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    15. Bollerslev, Tim & Li, Sophia Zhengzi & Todorov, Viktor, 2016. "Roughing up beta: Continuous versus discontinuous betas and the cross section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 120(3), pages 464-490.
    16. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Jumps; high-dimensional analysis; high-frequency data; infinitely divisible distribution; linear factor model;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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