An alternative tree method for calibration of the local volatility

Wenxiu Gong; Zuoliang Xu

doi:10.3934/jimo.2020146

Article Contents

2022, Volume 18, Issue 1: 137-156. Doi: 10.3934/jimo.2020146

This issue Previous Article Dynamic-programming-based heuristic for multi-objective operating theater planning Next Article Modified spectral PRP conjugate gradient method for solving tensor eigenvalue complementarity problems

An alternative tree method for calibration of the local volatility

Wenxiu Gong^1, and
Zuoliang Xu^2, ,

1.
College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China
2.
School of Mathematics, Renmin University of China, Beijing, 100872, China

^* Corresponding author: Zuoliang Xu
^* Corresponding author: Zuoliang Xu

Received: February 28, 2019

Revised: June 30, 2020

Early access: September 2020

Published: January 2022

This work is supported by National Natural Science Foundation of China(11571365) and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China(18XNH107)

Abstract Full Text(HTML) Figure(8) / Table(1) Related Papers Cited by

Abstract

In this paper, we combine the traditional binomial tree and trinomial tree to construct a new alternative tree pricing model, where the local volatility is a deterministic function of time. We then prove the convergence rates of the alternative tree method. The proposed model can price a wide range of derivatives efficiently and accurately. In addition, we research the optimization approach for the calibration of local volatility. The calibration problem can be transformed into a nonlinear unconstrained optimization problem by exterior penalty method. For the optimization problem, we use the quasi-Newton algorithm. Finally, we test our model by numerical examples and options data on the S & P 500 index. Numerical results confirm the excellent performance of the alternative tree pricing model.

Keywords:

Mathematics Subject Classification: Primary: 91G20; Secondary: 91G60.

Citation:

FullText(HTML)

Figure 1. The left figure presents CRR method and steps while the right figure presents TTM and steps. The blue line denotes the BS price. The red line denotes the CRR and TTM price. The green line denotes CRR price with odd steps while the black line denotes CRR price with even steps

Download: Full-size image PowerPoint slide

Figure 2. The alternative tree

Download: Full-size image PowerPoint slide

Figure 3. CRR and TTM price with different time steps

Download: Full-size image PowerPoint slide

Figure 4. Volatility function $ \sigma_{ex}(t) $ and volatility estimation for $ n = 7 $

Download: Full-size image PowerPoint slide

Figure 5. Stability analysis of the algorithm

Download: Full-size image PowerPoint slide

Figure 6. Comparison of the exact value and the optimal with alternative tree, TTM and CRR tree

Download: Full-size image PowerPoint slide

Figure 7. Volatility calibrated by linear and quadratic penalty method

Download: Full-size image PowerPoint slide

Figure 8. Local volatility and calibrated volatility with $ \frac{K}{S_0} = 100\%, 110\% $

Download: Full-size image PowerPoint slide

Table 1. Some tree methods for calibration of the local volatility

Auther	Tree method	volatility function
Derman(1996), Barle(1999)	Recombining TTM	$ \sigma=\sigma(S, t) $
Li(2001)	Recombining BTM	$ \sigma=\sigma(S, t) $
Crépey (2003)	TTM with regularization	$ \sigma=\sigma(S, t) $
Charalambous et al. (2007)	Nonrecombining BTM	$ \sigma=\sigma(t) $
Lok and Lyuu (2017)	Recombining waterline tree	$ \sigma=\sigma(S)\sigma(t) $
Gong and Xu (2019)	Nonrecombining TTM	$ \sigma=\sigma(t) $

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	J. Ahn and M. Song, Convergence of the trinomial tree method for pricing European/American options, Appl. Math. Comput., 189 (2007), 575-582. doi: 10.1016/j.amc.2006.11.132.
[2]	K. Amin, On the computation of continuous time option prices using discrete approximations, Journal of Financial and Quantitative Analysis, 26 (1991), 477-495. doi: 10.2307/2331407.
[3]	L. Andersen and J. Andreasen, Jump-Diffusion processes: Volatility smile fitting and numerical methods for option pricing, Rev. Derivatives Res., 4 (2000), 231-262. doi: 10.2139/ssrn.171438.
[4]	K. Atkinson, An Introduction to Numerical Analysis, 2$^{nd}$ edition, John Wiley & Sons, New York, 1989.
[5]	S. Barle and N. Cakici, How to grow a smiling tree, J. Financ. Eng., 7 (1999), 127-146.
[6]	F. Black and M. Scholes, The pricing of options and corporate liabilities, J. Polit. Econ., 81 (1973), 637-654. doi: 10.1086/260062.
[7]	P. P. Boyle, Option valuation using a three-jump process, Int. Options J., 3 (1986), 7-12.
[8]	D. M. Chance, A synthesis of binomial option pricing models for lognormally distributed asset, J. Appl. Finance, 18 (2008), 38-56. doi: 10.2139/ssrn.969834.
[9]	L. B. Chang and K. Palmer, Smooth convergence in the binomial model, Finance and Stochastics, 11 (2007), 91-105. doi: 10.1007/s00780-006-0020-6.
[10]	C. Charalambous, N. Christofides, E. Constantinide and S. Martzoukos, Implied non-recombining trees and calibration for the volatility smile, Quant. Finance, 7 (2007), 459-472. doi: 10.1080/14697680701488692.
[11]	J. C. Cox, S. A. Ross and M. Rubinstein, Option pricing: A simplified approach, J. Financ. Econ., 7 (1979), 229-263. doi: 10.1016/0304-405X(79)90015-1.
[12]	S. Crépey, Calibration of the local volatility in a trinomial tree using Tikhonov regularization, Inverse Problems, 19 (2003), 91-127. doi: 10.1088/0266-5611/19/1/306.
[13]	T. S. Dai and Y. D. Lyuu, The Bino-Trinomial tree: A simple model for efficient and accurate option pricing, J. Deriv., (2010), 7–24.
[14]	E. Derman, I. Kani and N. Chriss, Implied trinomial trees of the volatility smile, J. Deriv., 3 (1996), 7-22.
[15]	F. Diener and M. Diener, Asymptotics of the price oscillations of a European call option in a tree model, Math. Finance, 14 (2004), 271-293. doi: 10.1111/j.0960-1627.2004.00192.x.
[16]	B. Dupire, Pricing with a smile, Risk, 7 (1994), 18-20.
[17]	W. X. Gong and Z. L. Xu, Non-recombining trinomial tree pricing model and calibration for the volatility smile, J. Inverse Ill-Posed Probl., 27 (2019), 353-366. doi: 10.1515/jiip-2018-0005.
[18]	D. P. J. Leisen and M. Reimer, Binomial models for option value-examining and improving convergence, Appl. Math. Finance, 3 (1996), 319-346.
[19]	Y. Li, A new algorithm for constructing implied binomial trees: Does the implied model fit any volatility smile?, J. Comput. Finance, 4 (2001), 69-98.
[20]	U. H. Lok and Y. D. Lyuu, The waterline tree for separable local-volatility models, Comput. Math. Appl., 73 (2017), 537-559. doi: 10.1016/j.camwa.2016.12.008.
[21]	J. T. Ma and T. F. Zhu, Convergence rates of trinomial tree methods for option pricing under regime-switching models, Appl. Math. Lett., 39 (2015), 13-18. doi: 10.1016/j.aml.2014.07.020.
[22]	J. Rendleman, J. Richard and B. J. Bartter, Two-state option pricing, J. Finance, 34 (1979), 1093-1110. doi: 10.1111/j.1540-6261.1979.tb00058.x.
[23]	K. Talias, Implied Binomial Trees and Genetic Algorithms, Ph.D thesis, Imperial College, 2005.
[24]	J. B. Walsh, The rate of convergence of the binomial tree scheme, Finance Stoch., 7 (2003), 337-361. doi: 10.1007/s007800200094.