A multiquadric quasi-interpolations method for CEV option pricing model

Abstract The pricing of option contracts when the underlying process follows the constant elasticity of variance (CEV) model is considered. For CEV European options, the closed-form solutions involve the non-central chi-square distribution, whose computations by the current literatures are rather unstable and extremely expensive. Based on multiquadric quasi-interpolation methods, this study suggests a stable and fast numerical algorithm for CEV option pricing model. The method is confirmed to be a multinomial tree, in which the underlying variable moves from its initial value to an infinity of possible values of the next time step. The probabilities in the associated tree are ensured to be positive, which is a sufficient condition for stability and convergence. The method is flexible, since it is simple to implement with the nonuniform knots. Moreover, the method is easy to value the Greek letters which are important parameters in financial engineering, as the multiquadric function is infinitely continuously differentiable. Besides, the method does not require solving a resultant full matrix, the ill-conditioning problem arising when using the radial basis functions as a global interpolant can be avoided. Numerical experiments imply that the method is highly effective to calculate the stock options and its Greeks under the CEV model.

[1]  Vadim Linetsky,et al.  Pricing and Hedging Path-Dependent Options Under the CEV Process , 2001, Manag. Sci..

[2]  D. Nicholls,et al.  A Discontinuous Galerkin Method for Pricing American Options Under the Constant Elasticity of Variance Model , 2015 .

[3]  Nira Dyn,et al.  Multiquadric B-splines , 1996 .

[4]  Jing Zhao,et al.  An Artificial Boundary Method for American Option Pricing under the CEV Model , 2008, SIAM J. Numer. Anal..

[5]  Jun Zhang,et al.  Multilayer Ensemble Pruning via Novel Multi-sub-swarm Particle Swarm Optimization , 2009, J. Univers. Comput. Sci..

[6]  R. Franke Scattered data interpolation: tests of some methods , 1982 .

[7]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[8]  Kwok-wing Chau,et al.  Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition , 2015, Water Resources Management.

[9]  Eduardo S. Schwartz,et al.  Finite Difference Methods and Jump Processes Arising in the Pricing of Contingent Claims: A Synthesis , 1977 .

[10]  Richard Lu,et al.  Valuation of Standard Options under the Constant Elasticity of Variance Model , 2005 .

[11]  E. Kansa,et al.  Exponential convergence and H‐c multiquadric collocation method for partial differential equations , 2003 .

[12]  Desmond J. Higham,et al.  An Introduction to Financial Option Valuation , 2004 .

[13]  Alan G. White,et al.  Valuing Derivative Securities Using the Explicit Finite Difference Method , 1990, Journal of Financial and Quantitative Analysis.

[14]  Mark Schroder Computing the Constant Elasticity of Variance Option Pricing Formula , 1989 .

[15]  David C. Emanuel,et al.  Further Results on the Constant Elasticity of Variance Call Option Pricing Model , 1982, Journal of Financial and Quantitative Analysis.

[16]  K. Chau,et al.  A hybrid model coupled with singular spectrum analysis for daily rainfall prediction , 2010 .

[17]  Jian Ping Liu,et al.  Generalized Strang–Fix condition for scattered data quasi-interpolation , 2005, Adv. Comput. Math..

[18]  Zongmin Wu,et al.  Dynamically knots setting in meshless method for solving time dependent propagations equation , 2004 .

[19]  R. L. Hardy Multiquadric equations of topography and other irregular surfaces , 1971 .

[20]  Muddun Bhuruth,et al.  A new fourth-order numerical scheme for option pricing under the CEV model , 2013, Appl. Math. Lett..

[21]  Shengliang Zhang,et al.  Conservative multiquadric quasi-interpolation method for Hamiltonian wave equations , 2013 .

[22]  Kwok-wing Chau,et al.  Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines , 2015 .

[23]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[24]  Shmuel Rippa,et al.  An algorithm for selecting a good value for the parameter c in radial basis function interpolation , 1999, Adv. Comput. Math..

[25]  E. Kansa Multiquadrics—A scattered data approximation scheme with applications to computational fluid-dynamics—I surface approximations and partial derivative estimates , 1990 .

[26]  T. Belytschko,et al.  Element‐free Galerkin methods , 1994 .

[27]  Zongmin Wu,et al.  Shape preserving properties and convergence of univariate multiquadric quasi-interpolation , 1994 .

[28]  Shanwen Zhang,et al.  Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification , 2009, ICIC.

[29]  Limin Ma,et al.  Approximation to the k-th derivatives by multiquadric quasi-interpolation method , 2009, J. Comput. Appl. Math..

[30]  M. Giles,et al.  Convergence analysis of Crank-Nicolson and Rannacher time-marching , 2006 .