IDEAS home Printed from https://ideas.repec.org/p/cem/doctra/659.html

Measuring and trading volatility on the US stock market: A regime switching approach

Author

Listed:
  • José P. Dapena
  • Juan A. Serur
  • Julián R. Siri

Abstract

The volatility premium is a well-documented phenomenon, which can be approximated by the difference between the previous month level of the VIX Index and the rolling 30-day close-to-close volatility. Along with the literature, we show evidence that VIX is generally above the 30-day rolling volatility giving rise to the volatility premium, so selling volatility can become a profitable trading strategy as long as proper risk management is under place. As a contribution, we introduced the implementation of a Hidden Markov Model (HMM), identifying two states of the nature and showing that the volatility premium undergoes temporal breaks in its behavior. Based on this, we formulate a trading strategy by selling volatility and switching to medium-term U.S. Treasury Bills when appropriated. We test the performance of the strategy using the conventional Carhart four-factor model showing a positive and statistically significant alpha.

Suggested Citation

  • José P. Dapena & Juan A. Serur & Julián R. Siri, 2018. "Measuring and trading volatility on the US stock market: A regime switching approach," CEMA Working Papers: Serie Documentos de Trabajo. 659, Universidad del CEMA.
    • Handle: RePEc:cem:doctra:659
    as

    Download full text from publisher

    File URL: https://ucema.edu.ar/publicaciones/download/documentos/659.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. José P. Dapena & Julian R. Siri, 2015. "Index options realized returns distributions from passive investment strategies," CEMA Working Papers: Serie Documentos de Trabajo. 580, Universidad del CEMA.
    2. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    3. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.
    4. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. "Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, vol. 51(5), pages 1611-1632, December.
    5. Fama, Eugene F. & French, Kenneth R., 2012. "Size, value, and momentum in international stock returns," Journal of Financial Economics, Elsevier, vol. 105(3), pages 457-472.
    6. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    7. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
    8. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
    9. Roman Kozhan & Anthony Neuberger & Paul Schneider, 2013. "The Skew Risk Premium in the Equity Index Market," The Review of Financial Studies, Society for Financial Studies, vol. 26(9), pages 2174-2203.
    10. Andersen, Torben G. & Fusari, Nicola & Todorov, Viktor, 2015. "The risk premia embedded in index options," Journal of Financial Economics, Elsevier, vol. 117(3), pages 558-584.
    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. José P. Dapena & Juan A. Serur & Julián R. Siri, 2019. "Risk on-Risk off: A regime switching model for active portfolio management," CEMA Working Papers: Serie Documentos de Trabajo. 706, Universidad del CEMA.

    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. Paul Schneider & Christian Wagner & Josef Zechner, 2020. "Low‐Risk Anomalies?," Journal of Finance, American Finance Association, vol. 75(5), pages 2673-2718, October.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, March.
    3. Scott Condie & Lars Stentoft & Marie-Louise Vierø, 2023. "Unawareness Premia," Economics Working Papers 2023-09, Department of Economics and Business Economics, Aarhus University.
    4. 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).
    5. Victoria Dobrynskaya, 2017. "Dynamic Momentum and Contrarian Trading," HSE Working papers WP BRP 61/FE/2017, National Research University Higher School of Economics.
    6. Christoffersen, Peter & Fournier, Mathieu & Jacobs, Kris & Karoui, Mehdi, 2021. "Option-Based Estimation of the Price of Coskewness and Cokurtosis Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(1), pages 65-91, February.
    7. Bressan, Silvia & Weissensteiner, Alex, 2021. "The financial conglomerate discount: Insights from stock return skewness," International Review of Financial Analysis, Elsevier, vol. 74(C).
    8. Chen, Steven Shu-Hsiu, 2024. "International crash risk premium," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 94(C).
    9. Chabi-Yo, Fousseni & Ruenzi, Stefan & Weigert, Florian, 2018. "Crash Sensitivity and the Cross Section of Expected Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1059-1100, June.
    10. Dobrynskaya, Victoria, 2019. "Avoiding momentum crashes: Dynamic momentum and contrarian trading," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    11. François-Éric Racicot & Raymond Théoret, 2009. "Integrating volatility factors in the analysis of the hedge fund alpha puzzle," Journal of Asset Management, Palgrave Macmillan, vol. 10(1), pages 37-62, April.
    12. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    13. Andrew Ang & Joseph Chen & Yuhang Xing, 2001. "Downside Risk and the Momentum Effect," NBER Working Papers 8643, National Bureau of Economic Research, Inc.
    14. Langlois, Hugues, 2020. "Measuring skewness premia," Journal of Financial Economics, Elsevier, vol. 135(2), pages 399-424.
    15. Mohammadreza Tavakoli Baghdadabad & Girijasankar Mallik & Sriram Shankar, 2026. "Market-News Co-Moments and the Cross Section of Stock Returns," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 33(1), pages 413-504, March.
    16. Lin, Chaonan & Chen, Hong-Yi & Ko, Kuan-Cheng & Yang, Nien-Tzu, 2021. "Time-dependent lottery preference and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 272-294.
    17. Sean A. Anthonisz & Tālis J. Putniņš, 2017. "Asset Pricing with Downside Liquidity Risks," Management Science, INFORMS, vol. 63(8), pages 2549-2572, August.
    18. Şahin, Baki Cem & Danışoğlu, Seza, 2022. "Ambiguity and asset pricing: An empirical investigation for an emerging market," International Review of Financial Analysis, Elsevier, vol. 84(C).
    19. Chang, Bo Young & Christoffersen, Peter & Jacobs, Kris, 2013. "Market skewness risk and the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 107(1), pages 46-68.
    20. Dheeraj Misra & Sushma Vishnani & Ankit Mehrotra, 2019. "Four-moment CAPM Model: Evidence from the Indian Stock Market," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(1_suppl), pages 137-166, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • N2 - Economic History - - Financial Markets and Institutions
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cem:doctra:659. 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: Lucila Solla (email available below). General contact details of provider: https://edirc.repec.org/data/cemaaar.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.