IDEAS home Printed from https://ideas.repec.org/p/gnv/wpgsem/unige84999.html
   My bibliography  Save this paper

Comments on : Nonparametric Tail Risk, Stock Returns and the Macroeconomy

Author

Listed:
  • Scaillet, Olivier
  • Trojani, Fabio
  • Camponovo, Lorenzo

Abstract

This paper contains comments on Nonparametric Tail Risk, Stock Returns and the Macroeconomy.

Suggested Citation

  • Scaillet, Olivier & Trojani, Fabio & Camponovo, Lorenzo, 2016. "Comments on : Nonparametric Tail Risk, Stock Returns and the Macroeconomy," Working Papers unige:84999, University of Geneva, Geneva School of Economics and Management.
  • Handle: RePEc:gnv:wpgsem:unige:84999
    as

    Download full text from publisher

    File URL: https://luniarchidoc4.unige.ch/archive-ouverte/unige:84999/ATTACHMENT01
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. O. Scaillet, 2004. "Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall," Mathematical Finance, Wiley Blackwell, vol. 14(1), pages 115-129, January.
    2. Bollerslev, Tim & Todorov, Viktor & Xu, Lai, 2015. "Tail risk premia and return predictability," Journal of Financial Economics, Elsevier, vol. 118(1), pages 113-134.
    3. Yuichi Kitamura & Taisuke Otsu & Kirill Evdokimov, 2013. "Robustness, Infinitesimal Neighborhoods, and Moment Restrictions," Econometrica, Econometric Society, vol. 81(3), pages 1185-1201, May.
    4. Andrea Buraschi & Fabio Trojani & Andrea Vedolin, 2014. "When Uncertainty Blows in the Orchard: Comovement and Equilibrium Volatility Risk Premia," Journal of Finance, American Finance Association, vol. 69(1), pages 101-137, February.
    5. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    6. Goncalves, Silvia & White, Halbert, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," University of California at San Diego, Economics Working Paper Series qt8hx21540, Department of Economics, UC San Diego.
    7. Caio Almeida & Kym Ardison & René Garcia & Jose Vicente, 2017. "Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 333-376.
    8. Paul Schneider & Fabio Trojani, 2015. "Divergence and the Price of Uncertainty," Swiss Finance Institute Research Paper Series 15-60, Swiss Finance Institute.
    9. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    10. Gagliardini, Patrick & Trojani, Fabio & Urga, Giovanni, 2005. "Robust GMM tests for structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 139-182.
    11. Amit Goyal & Ivo Welch, 2003. "Predicting the Equity Premium with Dividend Ratios," Management Science, INFORMS, vol. 49(5), pages 639-654, May.
    12. Hong, H. & Scaillet, O., 2006. "A fast subsampling method for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 133(2), pages 557-578, August.
    13. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    14. Fermanian, Jean-David & Scaillet, Olivier, 2005. "Sensitivity analysis of VaR and Expected Shortfall for portfolios under netting agreements," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 927-958, April.
    15. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Philippe Bernard & Najat El Mekkaoui De Freitas & Bertrand B. Maillet, 2022. "A financial fraud detection indicator for investors: an IDeA," Annals of Operations Research, Springer, vol. 313(2), pages 809-832, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lorenzo Camponovo & O. Scaillet & Fabio Trojani, 2013. "Predictability Hidden by Anomalous Observations," Swiss Finance Institute Research Paper Series 13-05, Swiss Finance Institute.
    2. Jondeau, Eric & Zhang, Qunzi & Zhu, Xiaoneng, 2019. "Average skewness matters," Journal of Financial Economics, Elsevier, vol. 134(1), pages 29-47.
    3. Schneider, Paul, 2019. "An anatomy of the market return," Journal of Financial Economics, Elsevier, vol. 132(2), pages 325-350.
    4. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    6. 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.
    7. Andreou, Panayiotis C. & Kagkadis, Anastasios & Philip, Dennis & Taamouti, Abderrahim, 2019. "The information content of forward moments," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 527-541.
    8. Hollstein, Fabian & Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "International tail risk and World Fear," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 244-259.
    9. Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2011. "Empirical likelihood block bootstrapping," Journal of Econometrics, Elsevier, vol. 161(2), pages 110-121, April.
    10. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    11. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2014. "International R&D Spillovers, Absorptive Capacity and Relative Backwardness: A Panel Smooth Transition Regression Model," International Economic Journal, Taylor & Francis Journals, vol. 28(1), pages 137-160, March.
    12. Todorov, Viktor, 2022. "Nonparametric jump variation measures from options," Journal of Econometrics, Elsevier, vol. 230(2), pages 255-280.
    13. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "The risk premium of gold," Journal of International Money and Finance, Elsevier, vol. 94(C), pages 140-159.
    14. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
    15. Caio Almeida & Kym Ardison & René Garcia & Jose Vicente, 2017. "Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 333-376.
    16. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    17. Bevilacqua, Mattia & Tunaru, Radu, 2021. "The SKEW index: Extracting what has been left," Journal of Financial Stability, Elsevier, vol. 53(C).
    18. Liu, Li & Bu, Ruijun & Pan, Zhiyuan & Xu, Yuhua, 2019. "Are financial returns really predictable out-of-sample?: Evidence from a new bootstrap test," Economic Modelling, Elsevier, vol. 81(C), pages 124-135.
    19. Bing Han & Gang Li, 2021. "Information Content of Aggregate Implied Volatility Spread," Management Science, INFORMS, vol. 67(2), pages 1249-1269, February.
    20. Qian, Lihua & Zeng, Qing & Lu, Xinjie & Ma, Feng, 2022. "Global tail risk and oil return predictability," Finance Research Letters, Elsevier, vol. 47(PB).

    More about this item

    Keywords

    Tail Risk; Risk Factor; Risk-Neutral Probability; Prediction of Market Returns; Economic Predictability.;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gnv:wpgsem:unige:84999. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jean-Blaise Claivaz (email available below). General contact details of provider: https://edirc.repec.org/data/depgech.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.