IDEAS home Printed from https://ideas.repec.org/a/ibf/ijbfre/v9y2015i3p59-69.html
   My bibliography  Save this article

Hedging Strategy Comparisons Of Volatility Index Options Using Diffusion Models

Author

Listed:
  • Jun-Biao Lin

Abstract

With the innovation of derivatives, the Standard and Poor’s (S&P) 500 index -- as an underlying asset of the volatility index (VIX) introduced by the Chicago Board Options Exchange (CBOE) -- was adopted as the research subject in this study. Since the financial crisis of 2008, the degree of market volatility has increased substantially. In addition, a random process has been found jumping about in the VIX data. In this study we compare VIX options based on different diffusion models. In this study, when a jump component is considered in the VIX process, the expectation maximization (EM) method is used to estimate parameters; this is a different perspective of evaluation from other studies. This paper further analyzes different hedging strategies based on different diffusion models

Suggested Citation

  • Jun-Biao Lin, 2015. "Hedging Strategy Comparisons Of Volatility Index Options Using Diffusion Models," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 9(3), pages 59-69.
    • Handle: RePEc:ibf:ijbfre:v:9:y:2015:i:3:p:59-69
    as

    Download full text from publisher

    File URL: http://www.theibfr2.com/RePEc/ibf/ijbfre/ijbfr-v9n3-2015/IJBFR-V9N3-2015-5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tim Bollerslev & George Tauchen & Hao Zhou, 2009. "Expected Stock Returns and Variance Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4463-4492, November.
    2. Jinji Hao & Jin E. Zhang, 2013. "GARCH Option Pricing Models, the CBOE VIX, and Variance Risk Premium," Journal of Financial Econometrics, Oxford University Press, vol. 11(3), pages 556-580, June.
    3. Jérôme Detemple & Carlton Osakwe, 2000. "The Valuation of Volatility Options," Review of Finance, European Finance Association, vol. 4(1), pages 21-50.
    4. Wagner, Niklas & Szimayer, Alexander, 2004. "Local and spillover shocks in implied market volatility: evidence for the U.S. and Germany," Research in International Business and Finance, Elsevier, vol. 18(3), pages 237-251, September.
    5. Wong, Woon K. & Tu, Anthony H., 2009. "Market imperfections and the information content of implied and realized volatility," Pacific-Basin Finance Journal, Elsevier, vol. 17(1), pages 58-79, January.
    6. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    7. Kaeck, Andreas & Alexander, Carol, 2012. "Volatility dynamics for the S&P 500: Further evidence from non-affine, multi-factor jump diffusions," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3110-3121.
    8. Zhiguang Wang & Robert T. Daigler, 2011. "The performance of VIX option pricing models: Empirical evidence beyond simulation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(3), pages 251-281, March.
    9. Dotsis, George & Psychoyios, Dimitris & Skiadopoulos, George, 2007. "An empirical comparison of continuous-time models of implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3584-3603, December.
    10. Jin‐Chuan Duan, 1994. "Maximum Likelihood Estimation Using Price Data Of The Derivative Contract," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 155-167, April.
    11. Zhou, Chunsheng, 2001. "The term structure of credit spreads with jump risk," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 2015-2040, November.
    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. Yoo, Eun Gyu & Yoon, Sun-Joong, 2020. "CBOE VIX and Jump-GARCH option pricing models," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 839-859.
    2. Kozarski, R., 2013. "Pricing and hedging in the VIX derivative market," Other publications TiSEM 221fefe0-241e-4914-b6bd-c, Tilburg University, School of Economics and Management.
    3. Gonzalez-Perez, Maria T., 2015. "Model-free volatility indexes in the financial literature: A review," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 141-159.
    4. Simon Lalancette & Jean†Guy Simonato, 2017. "The Role of the Conditional Skewness and Kurtosis in VIX Index Valuation," European Financial Management, European Financial Management Association, vol. 23(2), pages 325-354, March.
    5. Huang, Hung-Hsi & Lin, Shin-Hung & Wang, Chiu-Ping, 2019. "Reasonable evaluation of VIX options for the Taiwan stock index," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 111-130.
    6. Venter, Pierre J & Maré, Eben, 2022. "Price discovery in the volatility index option market: A univariate GARCH approach," Finance Research Letters, Elsevier, vol. 44(C).
    7. Wugan Cai & Jiafeng Pan, 2017. "Stochastic Differential Equation Models for the Price of European CO 2 Emissions Allowances," Sustainability, MDPI, vol. 9(2), pages 1-12, February.
    8. Mencía, Javier & Sentana, Enrique, 2013. "Valuation of VIX derivatives," Journal of Financial Economics, Elsevier, vol. 108(2), pages 367-391.
    9. Turalay Kenc & Emrah Ismail Cevik, 2021. "Estimating volatility clustering and variance risk premium effects on bank default indicators," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1373-1392, November.
    10. Bao, Qunfang, 2013. "Mean-Reverting Logarithmic Modeling of VIX," MPRA Paper 46413, University Library of Munich, Germany.
    11. Dotsis, George & Psychoyios, Dimitris & Skiadopoulos, George, 2007. "An empirical comparison of continuous-time models of implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3584-3603, December.
    12. Bao, Qunfang & Li, Shenghong & Gong, Donggeng, 2012. "Pricing VXX option with default risk and positive volatility skew," European Journal of Operational Research, Elsevier, vol. 223(1), pages 246-255.
    13. Xiao, Weilin & Zhang, Xili, 2016. "Pricing equity warrants with a promised lowest price in Merton’s jump–diffusion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 219-238.
    14. Daskalakis, George & Psychoyios, Dimitris & Markellos, Raphael N., 2009. "Modeling CO2 emission allowance prices and derivatives: Evidence from the European trading scheme," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1230-1241, July.
    15. Zhou, Qing & Zhang, Xili, 2020. "Pricing equity warrants in Merton jump–diffusion model with credit risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    16. Wu, Xinyu & Zhao, An & Liu, Li, 2023. "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    17. Sang Byung Seo & Jessica A. Wachter, 2019. "Option Prices in a Model with Stochastic Disaster Risk," Management Science, INFORMS, vol. 65(8), pages 3449-3469, August.
    18. Chenxu Li, 2014. "Closed-Form Expansion, Conditional Expectation, and Option Valuation," Mathematics of Operations Research, INFORMS, vol. 39(2), pages 487-516, May.
    19. Bujar Huskaj & Marcus Nossman, 2013. "A Term Structure Model for VIX Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(5), pages 421-442, May.
    20. Nan Chen & S. G. Kou, 2009. "Credit Spreads, Optimal Capital Structure, And Implied Volatility With Endogenous Default And Jump Risk," Mathematical Finance, Wiley Blackwell, vol. 19(3), pages 343-378, July.

    More about this item

    Keywords

    VIX; Jump Process; MLE; EM Algorithm; Hedging Strategy;
    All these keywords.

    JEL classification:

    • 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:ibf:ijbfre:v:9:y:2015:i:3:p:59-69. 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: Mercedes Jalbert (email available below). General contact details of provider: .

    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.