For about 20 years now, discrepancies between market option prices and Black and Scholes (BS) prices have widened. The observed market option price showed that the BS implied volatility, computed from the market option price by inverting the BS formula varies with strike price and time to expiration. These variations are known as “the volatility smile (skew)” and volatility term structure, respectively.
In this chapter, we describe the numerical construction of the IBT and compare the predicted implied price distributions. In Section 10.1, a detailed construction of the IBT algorithm for European options is presented. First, we introduce the Derman and Kani (1994) (DK) algorithm and show its possible drawbacks. Afterwards, we follow an alternative IBT algorithm by Barle and Cakici (1998) (BC), which modifies the DK method by a normalisation of the central nodes according to the forward price in order to increase its stability in the presence of high interest rates. In Section 10.2 we compare the SPD estimations with simulated conditional density from a diffusion process with a non-constant volatility. In the last section, we apply the IBT to a real data set containing underlying asset price, strike price, time to maturity, interest rate, and call/put option price from EUREX (Deutsche Börse Database). We compare the SPD estimated by real market data with those predicted by the IBT.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
Ait-Sahalia, Y. and Lo, A. 1998. Nonparametric Estimation of State-Price Densities Im-plicit in Financial Asset Prices, Journal of Finance, 53: 499-547.
Ait-Sahalia, Y. , Wang, Y. and Yared, F.2001. Do Option Markets Correctly Price the Probabilities of Movement of the Underlying Asset? Journal of Econometrics, 102: 67-110.
Barle, S. and Cakici, N. 1998. How to Grow a Smiling Tree The Journal of Financial Engineering, 7: 127-146.
Bingham, N.H. and Kiesel, R. 1998. Risk-neutral Valuation: Pricing and Hedging of Financial Derivatives, Springer Verlag, London.
Cox, J., Ross, S. and Rubinstein, M. 1979. Option Pricing: A simplified Approach, Jouranl of Financial Economics 7: 229-263.
Derman, E. and Kani, I.1994.The Volatility Smile and Its Implied Tree http://www.gs.com/qs/
Derman, E. and Kani, I. 1998. Stochastic Implied Trees: Arbitrage Pricing with Stochastic Term and Strike Structure of Volatility, International Journal of Theroetical and Applied Finance 1: 61-110.
Dupire, B. 1994. Pricing with a Smile, Risk 7: 18-20.
Fengler, M. R.2005. Semiparametric Modeling of Implied Volatility, Springer Verlag, Hei-delberg.
Fengler, M. R., Härdle, W. and Villa, Chr. 2003. The Dynamics of Implied Volatilities: A Common Principal Components Approach, Review of Derivative Research 6: 179-202.
Härdle, W., Hlávka, Z. and Klinke, S. 2000. XploRe Application Guide, Springer Verlag, Heidelberg.
Hui, E.C. 2006. An enhanced implied tree model for option pricing: A study on Hong Kong property stock options, International Review of Economics and Finance 15: 324-345.
Hull, J. and White, A. 1987. The Pricing of Options on Assets with Stochastic Volatility, Journal of Finance 42: 281-300.
Jackwerth, J. 1999. Optional-Implied Risk-Neutral Distributions and Implied Binomial Trees: A Literature Review, Journal of Finance 51: 1611-1631.
Jackwerth, J. and Rubinstein, M. 1996. Recovering Probability Distributions from Option Prices, Journal of Finance 51: 1611-1631.
Kim, I.J. and Park, G.Y. 2006. An empirical comparison of implied tree models for KOSPI 200 index options, International Review of Economics and Finance 15: 52-71.
Kloeden, P., Platen, E. and Schurz, H. 1994. Numerical Solution of SDE Through Computer Experiments, Springer Verlag, Heidelberg.
Merton, R. 1976. Option Pricing When Underlying Stock Returns are Discontinuous, Journal of Financial Economics January-March: 125-144.
Moriggia, V., Muzzioli,S. and Torricelli, C. 2007. On the no-arbitrage condition in option implied trees, European Journal of Operational Research forthcoming.
Muzzioli,S. and Torricelli, C. 2005. The pricing of options on an interval binomial tree. An application to the DAX-index option market, European Journal of Operational Research 163: 192-200.
Rubinstein, M. 1994. Implied Binomial Trees. Journal of Finance 49: 771-818.
Yatchew, A. and Härdle,W. 2006. Nonparametric state price density estimation using constrained least squares and the bootstrap, Journal of Econometrics 133: 579-599.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Härdle, W., Myšičková, A. (2009). Numerics of Implied Binomial Trees. In: Härdle, W.K., Hautsch, N., Overbeck, L. (eds) Applied Quantitative Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69179-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-69179-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69177-8
Online ISBN: 978-3-540-69179-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)