Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T11:03:46.389Z Has data issue: false hasContentIssue false

LOCALIZED RADIAL BASIS FUNCTIONS FOR NO-ARBITRAGE PRICING OF OPTIONS UNDER STOCHASTIC ALPHA–BETA–RHO DYNAMICS

Published online by Cambridge University Press:  19 August 2021

N. THAKOOR*
Affiliation:
Department of Mathematics, University of Mauritius, Reduit80837, Mauritius

Abstract

Closed-form explicit formulas for implied Black–Scholes volatilities provide a rapid evaluation method for European options under the popular stochastic alpha–beta–rho (SABR) model. However, it is well known that computed prices using the implied volatilities are only accurate for short-term maturities, but, for longer maturities, a more accurate method is required. This work addresses this accuracy problem for long-term maturities by numerically solving the no-arbitrage partial differential equation with an absorbing boundary condition at zero. Localized radial basis functions in a finite-difference mode are employed for the development of a computational method for solving the resulting two-dimensional pricing equation. The proposed method can use either multiquadrics or inverse multiquadrics, which are shown to have comparable performances. Numerical results illustrate the accuracy of the proposed method and, more importantly, that the computed risk-neutral probability densities are nonnegative. These two key properties indicate that the method of solution using localized meshless methods is a viable and efficient means for price computations under SABR dynamics.

Type
Research Article
Copyright
© Australian Mathematical Society 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bayona, V., Moscoso, M., Carretero, M. and Kindelan, M., “RBF-FD formulas and convergence properties”, J. Comput. Phys. 229 (2010) 82818295; doi:10.1016/j.jcp.2010.07.008.CrossRefGoogle Scholar
Bayona, V., Moscoso, M. and Kindelan, M., “Optimal variable shape parameter for multiquadric based RBF-FD method”, J. Comput. Phys. 231 (2012) 24662481; doi:10.1016/j.jcp.2011.11.036.CrossRefGoogle Scholar
Black, F. and Scholes, M., “The pricing of options and other corporate liabilities”, J. Polit. Econ. 81 (1973) 637654; http://www.jstor.org/stable/1831029.CrossRefGoogle Scholar
Cai, N., Song, Y. and Chen, N., “Exact simulation of the SABR model”, Oper. Res. 65 (2017) 931951; doi:10.1287/opre.2017.1617.CrossRefGoogle Scholar
Charney, J. G., Fjørtoft, R. and Von Neumann, J., “Numerical integration of the barotropic vorticity equation”, Tellus 2 (1950) 237254; doi:10.3402/tellusa.v2i4.8607.CrossRefGoogle Scholar
Chen, N. and Yang, N., “The principle of not feeling the boundary for the SABR model”, Quant. Finance 19 (2019) 427436; doi:10.1080/14697688.2018.1486037.CrossRefGoogle ScholarPubMed
Chen, W., Hong, Y. and Lin, J., “The sample solution approach for the determination of the optimal shape parameter in the multiquadric function of the Kansa method”, Comput. Math. Appl. 75 (2018) 29422954; doi:10.1016/j.camwa.2018.01.023.CrossRefGoogle Scholar
Crank, J. and Nicolson, P., “A practical method for numerical evaluation of solutions of partial differential equations of the heat conduction type”, Proc. Cambridge Philos. Soc. 43 (1947) 5067; doi:10.1017/S0305004100023197.CrossRefGoogle Scholar
Cui, Z., Kirkby, J. L. and Nguyen, D., “A general valuation framework for stochastic alpha beta rho and stochastic volatility models”, SIAM. J. Financial Math. 9 (2018) 520563; doi:10.1137/1610.1017/S0305004100023197M1106572.CrossRefGoogle Scholar
Doust, P., “No-arbitrage SABR”, J. Comput. Finance 15 (2012) 331; doi:10.21314/JCF.2012.254.CrossRefGoogle Scholar
Dupire, B., “Pricing with a smile risk”, Risk 7 (1994) 1820; https://www.risk.net/derivatives/equity-derivatives/1500211/pricing-with-a-smile.Google Scholar
Düring, B. and Fournié, M., “High-order compact finite difference scheme for option pricing in stochastic volatility models”, J. Comput. Appl. Math. 236 (2012) 44624473; doi:10.1016/j.cam.2012.04.017.CrossRefGoogle Scholar
Düring, B. and Miles, J., “High-order ADI scheme for option pricing in stochastic volatility models”, J. Comput. Appl. Math. 316 (2017) 109121; doi:10.1016/j.cam.2016.09.040.CrossRefGoogle Scholar
Fasshauer, G. E. and Zhang, J. G., “On choosing optimal shape parameters for the RBF approximation”, Numer. Algorithms 45 (2007) 345368; doi:10.1007/s11075-007-9072-8.CrossRefGoogle Scholar
Fasshauer, G. F., Meshfree approximation methods with Matlab, Volume 6 (World Scientific, Singapore, 2007); doi:10.1142/6437.CrossRefGoogle Scholar
Hagan, P. S., Kumar, D., Lesniewski, A. S. and Woodward, D. E., “Managing smile risk”, Wilmott Mag. (2002) 84108; http://web.math.ku.dk/~rolf/SABR.pdf.Google Scholar
Hagan, P. S., Kumar, D., Lesniewski, A. S. and Woodward, D. E., “Arbitrage-free SABR”, Wilmott Mag. (2014) 6075; doi:10.1002/wilm.10290.CrossRefGoogle Scholar
in’t Hout, K. J. and Foulon, S., “ADI finite difference schemes for option pricing in the Heston model with correlation”, Int. J. Numer. Anal. Model. 7 (2010) 303320; http://global-sci.org/intro/article_detail/ijnam/721.html.Google Scholar
in’t Hout, K. J. and Welfert, B.D., “Unconditional stability of second-order ADI schemes applied to multidimensional diffusion equations with mixed derivatives”, Appl. Numer. Math. 59 (2009) 677692; doi:10.1016/j.apnum.2008.03.016.CrossRefGoogle Scholar
Kienitz, J., McWalter, T. and Sheppard, R., “PDE methods for SABR”, in: Novel methods in computational finance, Volume 25 of Math. Ind. (Springer, Cham, 2017), pp. 265291; doi:10.1007/978-3-319-61282-9_15.CrossRefGoogle Scholar
Le Floc’h, F. and Kennedy, G., “Finite difference techniques for arbitrage-free SABR”, J. Comput. Finance 20 (2017) 5179; doi:10.21314/JCF.2016.320.Google Scholar
Mishra, S. and Svard, M., “On the stability of numerical schemes via frozen coefficients and the magnetic induction equations”, BIT Numer. Math. 50 (2009) 85108; doi:10.1007/s10543-010-0249-5.CrossRefGoogle Scholar
Rambeerich, N., Tangman, D. Y., Lollchund, M. R. and Bhuruth, M., “High-order computational methods for option valuation under multifactor models”, European J. Oper. Res. 224 (2013) 219226; doi:10.1016/j.ejor.2012.07.023.CrossRefGoogle Scholar
Rippa, S., “An algorithm for selecting a good value for the parameter $c$ in radial basis function interpolation”, Adv. Comput. Math. 11 (1999) 193210; doi:10.1023/A:1018975909870.CrossRefGoogle Scholar
Schmelzer, T. and Trefethen, L. N., “Evaluating matrix functions for exponential integrators via Carathéodory–Fejér approximation and contour integrals”, Electron. Trans. Numer. Anal. 29 (2007) 118; https://core.ac.uk/download/pdf/1633681.pdf.Google Scholar
Soleymani, F., Barfeie, M. and Haghani, F. K., “Inverse multiquadric RBF for computing the weights of FD method: application to American options”, Commun. Nonlinear. Sci. Numer. Simulat. 64 (2018), 7488; doi:10.1016/j.cnsns.2018.04.011.CrossRefGoogle Scholar
Song, Y., “Essays on computational methods in financial engineering”, Ph. D. Thesis, Hong Kong University of Science and Technology, 2013; https://doi.org/10.14711/thesis-b1255611.CrossRefGoogle Scholar
Strikwerda, J. C., Finite difference schemes and partial differential equations, 2nd edn (SIAM, Philadelphia, 2004); https://epubs.siam.org/doi/pdf/10.1137/1.9780898717938.fm.Google Scholar
Thakoor, N., “A non-oscillatory scheme for the one-dimensional SABR model”, Pertanika J. Sci. Technol. 25 (2017) 12911306; http://www.myjurnal.my/filebank/published_article/58141/20.pdf.Google Scholar
Thakoor, N., Tangman, D. Y. and Bhuruth, M., “RBF-FD schemes for option valuation under models with price-dependent and stochastic volatility”, Eng. Anal. Bound. Elem. 92 (2018), 207217; doi:10.1016/j.enganabound.2017.11.003.CrossRefGoogle Scholar
Thakoor, N., Tangman, D. Y. and Bhuruth, M., “A spectral approach to pricing of arbitrage-free SABR discrete barrier options”, Comput. Econ. 54 (2019) 10851111; doi:10.1007/s10614-018-9868-8.CrossRefGoogle Scholar
Wang, F., Chen, W., Zhang, C. and Hua, Q., “Kansa method based on the Hausdorff fractal distance for Hausdorff derivative Poisson equations”, Fractals 26 (2018) 1850084; doi:10.1142/S0218348X18500846.CrossRefGoogle Scholar
Yang, N., Chen, N., Liu, Y. and Wan, X., “Approximate arbitrage-free option pricing under the SABR model”, J. Econom. Dynam. Control 83 (2017) 198214; doi:10.1016/j.jedc.2017.08.004.CrossRefGoogle Scholar
Yang, N., Liu, Y. and Cui, Z., “Pricing continuously monitored barrier options under the SABR model: a closed-form approximation”, J. Manag. Sci. Eng. 2 (2017) 116131; doi:10.3724/SP.J.1383.202006.CrossRefGoogle Scholar
Yang, N. and Wan, X., “The survival probability of the SABR model: asymptotics and application”, Quant. Finance 18 (2018) 17671779; doi:10.1080/14697688.2017.1422083.CrossRefGoogle Scholar
Zhu, S. P. and Chen, W. T., “A predictor–corrector scheme based on ADI method for pricing American puts with stochastic volatility”, Comput. Math. Appl. 62 (2011)126; doi:10.1016/j.camwa.2011.03.101.CrossRefGoogle Scholar