IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v51y2023ics1544612322005694.html
   My bibliography  Save this article

S&P volatility, VIX, and asymptotic volatility estimates

Author

Listed:
  • Bonaparte, Yosef
  • Chatrath, Arjun
  • Christie-David, Rohan

Abstract

We examine the efficacy of the VIX as a predictor of 30-day forward S&P 500 volatility. We find that its accuracy hovers between 20% and 25%, depending on sampling period. An alternative framework, built on asymptotic distribution theory (AVE), predicts this volatility with an accuracy between 90% to 95%. The adjusted R-square adjudicates for accuracy. Other goodness-of-fit measures corroborate this evidence. We suggest that this outcome is underpinned by the fact that (a) options price behaviors do not adequately reflect stock market volatility patterns, and (b) our methodology accounts more comprehensively for idiosyncratic risk.

Suggested Citation

  • Bonaparte, Yosef & Chatrath, Arjun & Christie-David, Rohan, 2023. "S&P volatility, VIX, and asymptotic volatility estimates," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005694
    DOI: 10.1016/j.frl.2022.103392
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612322005694
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.103392?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. K. Victor Chow & Wanjun Jiang & Jingrui Li, 2020. "Does VIX Truly Measure Return Volatility?," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 40, pages 1533-1559, World Scientific Publishing Co. Pte. Ltd..
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. v{S}tefan Ly'ocsa & Tom'av{s} Pl'ihal, 2022. "Russia's Ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention," Papers 2205.09179, arXiv.org.
    4. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    5. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    6. Bonaparte, Yosef & Cooper, Russell & Zhu, Guozhong, 2012. "Consumption smoothing and portfolio rebalancing: The effects of adjustment costs," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 751-768.
    7. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    8. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    9. Tauchen, George, 1985. "Diagnostic testing and evaluation of maximum likelihood models," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 415-443.
    10. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
    11. Liu, Zhichao & Liu, Jing & Zeng, Qing & Wu, Lan, 2022. "VIX and stock market volatility predictability: A new approach," Finance Research Letters, Elsevier, vol. 48(C).
    12. repec:bla:jfinan:v:59:y:2004:i:2:p:711-753 is not listed on IDEAS
    13. Lyócsa, Štefan & Plíhal, Tomáš, 2022. "Russia’s ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention," Finance Research Letters, Elsevier, vol. 48(C).
    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. Ayoub, Mahmoud & Qadan, Mahmoud, 2024. "Ambiguity and risk in the oil market," Economic Modelling, Elsevier, vol. 132(C).
    2. Bonaparte, Yosef, 2023. "Introducing the Cryptocurrency VIX: CVIX✰," Finance Research Letters, Elsevier, vol. 54(C).

    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. Bonaparte, Yosef, 2023. "Introducing the Cryptocurrency VIX: CVIX✰," Finance Research Letters, Elsevier, vol. 54(C).
    2. Oikonomou, Ioannis & Stancu, Andrei & Symeonidis, Lazaros & Wese Simen, Chardin, 2019. "The information content of short-term options," Journal of Financial Markets, Elsevier, vol. 46(C).
    3. Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020. "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series 20-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    4. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    5. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    6. Chen, Cathy W.S. & Watanabe, Toshiaki & Lin, Edward M.H., 2023. "Bayesian estimation of realized GARCH-type models with application to financial tail risk management," Econometrics and Statistics, Elsevier, vol. 28(C), pages 30-46.
    7. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    8. Bams, Dennis & Blanchard, Gildas & Lehnert, Thorsten, 2017. "Volatility measures and Value-at-Risk," International Journal of Forecasting, Elsevier, vol. 33(4), pages 848-863.
    9. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    10. Peter Reinhard Hansen & Zhuo Huang & Chen Tong & Tianyi Wang, 2024. "Realized GARCH, CBOE VIX, and the Volatility Risk Premium," Journal of Financial Econometrics, Oxford University Press, vol. 22(1), pages 187-223.
    11. Byun, Suk Joon & Kim, Jun Sik, 2013. "The information content of risk-neutral skewness for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 142-161.
    12. Prokopczuk, Marcel & Symeonidis, Lazaros & Wese Simen, Chardin, 2017. "Variance risk in commodity markets," Journal of Banking & Finance, Elsevier, vol. 81(C), pages 136-149.
    13. Ahn, Jungkyu, 2024. "Options illiquidity in an over-the-counter market," International Review of Financial Analysis, Elsevier, vol. 94(C).
    14. Silvia Muzzioli, 2013. "The Information Content of Option-Based Forecasts of Volatility: Evidence from the Italian Stock Market," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 1-46.
    15. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
    16. González-Urteaga, Ana & Rubio, Gonzalo, 2016. "The cross-sectional variation of volatility risk premia," Journal of Financial Economics, Elsevier, vol. 119(2), pages 353-370.
    17. Baruník, Jozef & Hlínková, Michaela, 2016. "Revisiting the long memory dynamics of the implied–realized volatility relationship: New evidence from the wavelet regression," Economic Modelling, Elsevier, vol. 54(C), pages 503-514.
    18. Yaw‐Huei Wang & Kuang‐Chieh Yen, 2019. "The information content of the implied volatility term structure on future returns," European Financial Management, European Financial Management Association, vol. 25(2), pages 380-406, March.
    19. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    20. DeLisle, R. Jared & Diavatopoulos, Dean & Fodor, Andy & Kassa, Haimanot, 2022. "Variation in option implied volatility spread and future stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 83(C), pages 152-160.

    More about this item

    Keywords

    VIX; Realized volatility; Asymptotic distribution theory;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eee:finlet:v:51:y:2023:i:c:s1544612322005694. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.