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From Skews to a Skewed-t

Author

Listed:
  • de Jong, C.M.
  • Huisman, R.

Abstract

In this paper we present a new methodology to infer the implied risk-neutral distribution function from European-style options. We introduce a skewed version of the Student-t distribution, whose main advantage is that its shape depends on only four parameters, of which two directly control for the levels of skewness and kurtosis. We can thus easily vary parameters to compare different distributions and use the parameters as inputs to price other options. We explain the method, provide some empirical results and compare them with the results of alternative models. The results indicate that our model provides a better fit to market prices of options than the Shimko or implied tree models, and has a lower computation time than most other models. We conclude that the skewed Student-t method provides a good alternative for European-style options.

Suggested Citation

  • de Jong, C.M. & Huisman, R., 2000. "From Skews to a Skewed-t," ERIM Report Series Research in Management ERS-2000-12-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:21
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    File URL: https://repub.eur.nl/pub/21/erimrs20000518152023.pdf
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    Citations

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    Cited by:

    1. Markose, Sheri M & Alentorn, Amadeo, 2005. "The Generalized Extreme Value (GEV) Distribution, Implied Tail Index and Option Pricing," Economics Discussion Papers 3726, University of Essex, Department of Economics.
    2. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2009. "Pricing European Options with a Log Student's t-Distribution: a Gosset Formula," Papers 0906.4092, arXiv.org.
    3. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2010. "Student's t-Distribution Based Option Sensitivities: Greeks for the Gosset Formulae," Papers 1003.1344, arXiv.org, revised Jul 2010.
    4. Post, G.T., 2001. "LP Tests for MV Efficiency," ERIM Report Series Research in Management ERS-2001-66-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Wang, Xiao-Tian & Li, Zhe & Zhuang, Le, 2017. "European option pricing under the Student’s t noise with jumps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 848-858.
    6. repec:esx:essedp:713 is not listed on IDEAS
    7. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    8. José L. Vilar-Zanón & Olivia Peraita-Ezcurra, 2019. "A linear goal programming method to recover risk neutral probabilities from options prices by maximum entropy," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 259-276, June.
    9. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2013. "Log Student’s t -distribution-based option sensitivities: Greeks for the Gosset formulae," Quantitative Finance, Taylor & Francis Journals, vol. 13(8), pages 1289-1302, July.
    10. Markose, Sheri M & Peng, Yue & Alentorn, Amadeo, 2012. "Forecasting Extreme Volatility of FTSE-100 With Model Free VFTSE, Carr-Wu and Generalized Extreme Value (GEV) Option Implied Volatility Indices," Economics Discussion Papers 3713, University of Essex, Department of Economics.
    11. Shi-jie Jiang & Mujun Lei & Cheng-Huang Chung, 2018. "An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution," Sustainability, MDPI, vol. 10(6), pages 1-17, June.
    12. Marcos Massaki Abe & Eui Jung Chang & Benjamin Miranda Tabak, 2007. "Forecasting Exchange Rate Density Using Parametric Models: the Case of Brazil," Brazilian Review of Finance, Brazilian Society of Finance, vol. 5(1), pages 29-39.
    13. Sheri Markose & Amadeo Alentorn, 2005. "Option Pricing and the Implied Tail Index with the Generalized Extreme Value (GEV) Distribution," Computing in Economics and Finance 2005 397, Society for Computational Economics.
    14. Post, G.T., 2001. "Testing for Stochastic Dominance with Diversification Possibilities," ERIM Report Series Research in Management ERS-2001-38-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    15. Post, G.T., 2001. "Spanning and Intersection: a stochastic dominance approach," ERIM Report Series Research in Management ERS-2001-63-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    More about this item

    Keywords

    implied distribution; implied volatility; options; skewness; student-t;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

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