Skewness and Kurtosis Trades

  • Oliver J. Blaskowitz
  • Wolfgang K. Härdle
  • Peter Schmidt


In this paper we investigate the profitability of’ skewness trades’ and ‘kurtosis trades’ based on comparisons of implied state price densities versus historical densities. In particular, we examine the ability of SPD comparisons to detect structural breaks in the options market behaviour. While the implied state price density is estimated by means of the Barle and Cakici Implied Binomial Tree algorithm using a cross section of DAX option prices, the historical density is inferred by a combination of a non-parametric estimation from a historical time series of the DAX index and a forward Monte Carlo simulation.


Option Price Trade Performance Call Option European Option Option Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Ait-Sahalia, Y, Wang, Y, Yared, F. 2001. Do Option Markets correctly Price the Probabilities of Movement of the Underlying Asset?, Journal of Econometrics 102: 67–110.Google Scholar
  2. [2]
    Barle, S., Cakici, N., 1998. How to Grow a Smiling Tree, The Journal of Financial Engineering 7: 127–146.Google Scholar
  3. [3]
    Black, F., Scholes, M., 1973. The Pricing of Options and Corporate Liabilities, Journal of Political Economy 81: 637–659.Google Scholar
  4. [4]
    Blaskowitz, O., Schmidt, P. 2002. Trading on Deviations of Implied and Historical Density, in: Applied Quantitative Finance, W. Härdle, T. Kleinow, G. Stahl, eds., Springer-Verlag, Heidelberg.Google Scholar
  5. [5]
    Cont, R. 1998. Beyond Implied Volatility: Extracting Information from Options Prices, in: Econophyiscs, Kluwer, Dodrecht.Google Scholar
  6. [6]
    Derman, E., Kani, I. 1994. The Volatility Smile and Its Implied Tree,
  7. [7]
    Dupire, B. 1994. Pricing with a Smile, Risk 7: 18–20.Google Scholar
  8. [8]
    Florens-Zmirou, D. 1993. On Estimating the Diffusion Coefficient from Discrete Observations, Journal of Applied Probability 30: 790–804.Google Scholar
  9. [9]
    Franke, J., Härdle, W., Hafner, C. 2001. Einführung in die Statistik der Finanzmärkte, Springer-Verlag, Heidelberg.Google Scholar
  10. [10]
    Härdle, W., Simar, L. 2003. Applied Multivariate Statistical Analysis, Springer-Verlag, Heidelberg.Google Scholar
  11. [11]
    Härdle, W., Tsybakov, A., 1997. Local Polynomial Estimators of the Volatility Function in Nonparametric Autoregression, Journal of Econometrics, 81: 223–242.Google Scholar
  12. [12]
    Härdle, W., Zheng, J. 2002. How Precise Are Price Distributions Predicted by Implied Binomial Trees?, in: Applied Quantitative Finance, W. Härdle, T. Kleinow, G. Stahl, eds., Springer-Verlag, HeidelbergGoogle Scholar
  13. [13]
    Jackwerth, J.C. 1999. Option Implied Risk Neutral Distributions and Implied Binomial Trees: A Literature Review, The Journal of Derivatives Winter: 66–82.Google Scholar
  14. [14]
    Kloeden, P., Platen, E., Schurz, H. 1994. Numerical Solution of SDE Through Computer Experiments, Springer-Verlag, Heidelberg.Google Scholar
  15. [15]
    Rubinstein, M. 1994. Implied Binomial Trees, Journal of Finance 49: 771–818.Google Scholar
  16. [16]
    Willmot, P. 2002. Paul Willmot Introduces Quantitative Finance, Wiley.Google Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Oliver J. Blaskowitz
    • 1
  • Wolfgang K. Härdle
    • 1
  • Peter Schmidt
    • 2
  1. 1.Center for Applied Statistics and Economics (CASE)Humboldt-Universität zu Berlin Wirtschaftswissenschaftliche FakultätBerlinGermany
  2. 2.Quantitative Analyst EquitiesAsset Management Research Bankgesellschaft Berlin AGBerlinGermany

Personalised recommendations