IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22318.html

Studying the Properties of the Correlation Trades

Author

Listed:
  • Cayetano, Gea

Abstract

This thesis tries to explore the profitability of the dispersion trading strategies. We begin examining the different methods proposed to price variance swaps. We have developed a model that explains why the dispersion trading arises and what the main drivers are. After a description of our model, we implement a dispersion trading in the EuroStoxx 50. We analyze the profile of a systematic short strategy of a variance swap on this index while being long the constituents. We show that there is sense in selling correlation on short-term. We also discuss the timing of the strategy and future developments and improvements.

Suggested Citation

  • Cayetano, Gea, 2007. "Studying the Properties of the Correlation Trades," MPRA Paper 22318, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22318
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22318/1/MPRA_paper_22318.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark Britten‐Jones & Anthony Neuberger, 2000. "Option Prices, Implied Price Processes, and Stochastic Volatility," Journal of Finance, American Finance Association, vol. 55(2), pages 839-866, April.
    2. Heynen, Ronald & Kemna, Angelien & Vorst, Ton, 1994. "Analysis of the Term Structure of Implied Volatilities," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 29(1), pages 31-56, March.
    3. Eric Ghysels & Christian Gouriéroux & Joann Jasiak, 1995. "Market Time and Asset Price Movements Theory and Estimation," CIRANO Working Papers 95s-32, CIRANO.
    4. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    5. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    6. Peter P. Carr & Robert A. Jarrow, 2008. "The Stop-Loss Start-Gain Paradox and Option Valuation: A new Decomposition into Intrinsic and Time Value," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 4, pages 61-84, World Scientific Publishing Co. Pte. Ltd..
    7. repec:hum:wpaper:sfb649dp2006-052 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    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. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    2. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    3. Jiang, George J. & Tian, Yisong S., 2010. "Misreaction or misspecification? A re-examination of volatility anomalies," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2358-2369, October.
    4. Eric Ghysels & Christian Gouriéroux & Joann Jasiak, 1995. "Trading Patterns, Time Deformation and Stochastic Volatility in Foreign Exchange Markets," CIRANO Working Papers 95s-42, CIRANO.
    5. Diep Duong & Norman R. Swanson, 2011. "Volatility in Discrete and Continuous-Time Models: A Survey with New Evidence on Large and Small Jumps," Advances in Econometrics, in: Missing Data Methods: Time-Series Methods and Applications, pages 179-233, Emerald Group Publishing Limited.
    6. Ghysels, E. & Jasiak, J., 1994. "Stochastic Volatility and time Deformation: an Application of trading Volume and Leverage Effects," Cahiers de recherche 9403, Universite de Montreal, Departement de sciences economiques.
    7. Peter Carr & Liuren Wu, 2014. "Static Hedging of Standard Options," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 3-46.
    8. Bollerslev, Tim & Gibson, Michael & Zhou, Hao, 2011. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Journal of Econometrics, Elsevier, vol. 160(1), pages 235-245, January.
    9. Ammann, Manuel & Buesser, Ralf, 2013. "Variance risk premiums in foreign exchange markets," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 16-32.
    10. Shao, Chengwu & Bhar, Ramaprasad & Colwell, David B. & Sheng, Ni & Wei, Xinyang, 2024. "Variance dynamics and term structure of the natural gas market," Energy Economics, Elsevier, vol. 137(C).
    11. Meddahi, N., 2001. "An Eigenfunction Approach for Volatility Modeling," Cahiers de recherche 2001-29, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    12. Jin Zhang & Yi Xiang, 2008. "The implied volatility smirk," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 263-284.
    13. Thaddeus Neururer & George Papadakis & Edward J. Riedl, 2016. "Tests of investor learning models using earnings innovations and implied volatilities," Review of Accounting Studies, Springer, vol. 21(2), pages 400-437, June.
    14. Mixon, Scott, 2007. "The implied volatility term structure of stock index options," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 333-354, June.
    15. Wei He & Yen-Sheng Lee & Peihwang Wei, 2010. "Do option traders on value and growth stocks react differently to new information?," Review of Quantitative Finance and Accounting, Springer, vol. 34(3), pages 371-381, April.
    16. Christian Keller & Michael C. Tseng, 2023. "Arrow-Debreu Meets Kyle: Price Discovery Across Derivatives," Papers 2302.13426, arXiv.org, revised Feb 2026.
    17. Vyacheslav Abramov & Fima Klebaner, 2007. "Estimation and Prediction of a Non-Constant Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(1), pages 1-23, March.
    18. Meddahi, Nour & Renault, Eric, 2004. "Temporal aggregation of volatility models," Journal of Econometrics, Elsevier, vol. 119(2), pages 355-379, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    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:pra:mprapa:22318. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.