Model-guided Nonparametric Option Pricing
Author | : Wentao Yan |
Publisher | : |
Total Pages | : 166 |
Release | : 2009 |
Genre | : |
ISBN | : |
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Author | : Wentao Yan |
Publisher | : |
Total Pages | : 166 |
Release | : 2009 |
Genre | : |
ISBN | : |
Author | : Jianqing Fan |
Publisher | : |
Total Pages | : 55 |
Release | : 2009 |
Genre | : |
ISBN | : |
Parametric option pricing models are largely used in Finance. These models capture several features of asset price dynamics. However, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that learn and correct empirically the pricing errors. In this paper, we propose a new nonparametric method for pricing derivatives assets. Our method relies on the state price distribution instead of the state price density because the former is easier to estimate nonparametrically than the latter. A parametric model is used as an initial estimate of the state price distribution. Then the pricing errors induced by the parametric model are fitted nonparametrically. This model-guided method estimates the state price distribution nonparametrically and is called Automatic Correction of Errors (ACE). The method is easy to implement and can be combined with any model-based pricing formula to correct the systematic biases of pricing errors. We also develop a nonparametric test based on the generalized likelihood ratio to document the efficacy of the ACE method. Empirical studies based on Samp;P 500 index options show that our method outperforms several competing pricing models in terms of predictive and hedging abilities.
Author | : Qiang Luo |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : |
ISBN | : |
In this paper, a closed-form analytical solution of option price under the Bi-Heston model is derived. Through empirical analysis, the advantages and disadvantages of the parametric pricing model are compared and analysed with those of the non-parametric model. The analysis shows that: (1) the parametric pricing model significantly outperforms the machine learning model in terms of in-sample pricing effects, while the Bi-Heston model slightly outperforms the Heston model. (2) In terms of out-of-sample pricing, the machine learning model is inferior to the parametric model for call options, while the Bi-Heston model is significantly better than the other two models for put options, and the other two models are similar. (3) In the robustness analysis of the three pricing models, the machine learning model shows strong instability, while the Bi-Heston model shows a more stable side.
Author | : Chengzhan Chi |
Publisher | : |
Total Pages | : 78 |
Release | : 2013 |
Genre | : Estimation theory |
ISBN | : |
There are a variety of parametric and nonparametric option pricing models commonly used in Finance. A combination of them can enhance the pricing performance significantly. Specifically, one proposes to fit the data with a parametric method and then correct the pricing errors empirically with a nonparametric learning approach. This thesis extends Fan and Mancini's (2009) model-guided nonparametric method to barrier option pricing using market traded European option data. Adopting automatic correction of errors (ACE) method to estimate the risk neutral conditional survivor function, by which the pricing error of the initial parametric estimates is captured nonparametrically, enables the nonparametric pricing procedure to value a barrier option as a sum of sequence of European options. As a byproduct from the valuation process, this thesis also provides a modified fractional fast Fourier transform technique compute the characteristic function of the running maximum log-price of the underlying asset nonparametrically through the calibrated survivor functions.
Author | : Manuel Expósito Langa |
Publisher | : |
Total Pages | : 37 |
Release | : 2002 |
Genre | : |
ISBN | : |
Author | : Ta-Peng Wu |
Publisher | : |
Total Pages | : 298 |
Release | : 2000 |
Genre | : Options (Finance) |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 24 |
Release | : 1997 |
Genre | : |
ISBN | : |
Author | : Eric Ghysels |
Publisher | : Montréal : CIRANO |
Total Pages | : 24 |
Release | : 1997 |
Genre | : |
ISBN | : |
Author | : Melanie Birke |
Publisher | : |
Total Pages | : 0 |
Release | : 2007 |
Genre | : |
ISBN | : |
Author | : Lishang Jiang |
Publisher | : World Scientific Publishing Company |
Total Pages | : 343 |
Release | : 2005-07-18 |
Genre | : Business & Economics |
ISBN | : 9813106557 |
From the unique perspective of partial differential equations (PDE), this self-contained book presents a systematic, advanced introduction to the Black-Scholes-Merton's option pricing theory.A unified approach is used to model various types of option pricing as PDE problems, to derive pricing formulas as their solutions, and to design efficient algorithms from the numerical calculation of PDEs. In particular, the qualitative and quantitative analysis of American option pricing is treated based on free boundary problems, and the implied volatility as an inverse problem is solved in the optimal control framework of parabolic equations.