We introduce a previously unused numerical framework for estimating the Black-Scholes partial differential equation. The approach, known as the Power Series Method (PSM), offers several advantages over traditional finite difference methods. Our objective is to highlight the advantages of the PSM over traditionally used numerical approximation approaches. To meet this we deploy a numerical approximation scheme to illustrate the PSM. The PSM is more stable than explicit methods and thus computationally more efficient. It is as accurate as hybrid approaches like Crank Nicolson and faster to compute. It is more accurate over a far wider spectrum of time steps. Finally, and importantly, it can be expressed analytically thus offering the capability of performing comparative statics in a far more stable and accurate environment. For a more complex application this last advantage may have wide implications in producing hedge ratios for synthetic replication purposes.
KeywordsBlack-Scholes-Merton Options PricingPartial Differential EquationsFinite Difference MethodsCrank-Nicolson MethodPower Series Methods
Parker, G. and Sochacki, J. (1996) Implementing the Picard Iteration. Neural, Parallel, and Scientific Computation, 4, 97-112.
Parker, G. and Sochacki, J. (2000) A Picard-McLaurin Theorem for Initial Value PDE’s. Abstract Analysis and Its Applications, 5, 47-63. https://doi.org/10.1155/S1085337500000063
Gofen, A.M. (2009) The Ordinary Differential Equations and Automatic Differentiation Unified. Complex Variables and Elliptic Equations, 54, 825-854. https://doi.org/10.1080/17476930902998852
Neidinger, R.D. (2010) Introduction to Automatic Differentiation and Matlab Object-Oriented Programming. SIAM Review, 52, 545-563. https://doi.org/10.1137/080743627
Mirzaee, F. (2011) Differential Transform Method for Solving Linear and Nonlinear Systems of Ordinary Differential Equations. Applied Mathematical Sciences, 5, 3465-3472.
Stewart, R.D. and Bair, W. (2009) Spiking Neural Network Simulation: Numerical Integration with the Parker-Sochacki Method. Journal of Computational Neuroscience, 27, 115-133. https://doi.org/10.1007/s10827-008-0131-5
Rudmin, J.W. (1998) Application of the Parker-Sochacki Method to Celestial Mechanics. Technical Report, James Madison University, Harrisonburg.
Anwar, M. and Andallah, L. (2018) A Study on Numerical Solution of Black-Scholes Model. Journal of Mathematical Finance, 8, 372-381. https://doi.org/10.4236/jmf.2018.82024
Cen, Z. and Le, A. (2011) A Robust and Accurate Finite Difference Method for a Generalized Black-Scholes Equation. Journal of Computational and Applied Mathematics, 235, 3728-3733. https://doi.org/10.1016/j.cam.2011.01.018
Warne, P.G., Warne, D.A., Sochacki, J.S., Parker, G.E. and Carothers, D.C. (2006) Explicit A-Priori Error Bounds and Adaptive Error Control for Approximation of Nonlinear Initial Value Differential Systems. Computers & Mathematics with Applications, 52, 1695-1710. https://doi.org/10.1016/j.camwa.2005.12.004
Carothers, D.C., Parker, G.E., Sochacki, J.S. and Warne, P.G. (2005) Some Properties of Solutions to Polynomial Systems of Differential Equations. Electronic Journal of Differential Equations, 40, 1-17.
Carothers, D.C., Lucas, S.K., Parker, G.E., Rudmin, J.D., Sochacki, J.S., Thelwell, R.J., Tongen, A. and Warne, P.G. (2012) Connections between Power Series Methods and Automatic Differentiation. Recent Advancements in Algorithmic Differentiation, 87, 175-186. https://doi.org/10.1007/978-3-642-30023-3_16
Duffie, D. (2006) Finite Difference Methods in Financial Engineering: A Partial Differential Equation Approach. John Willy & Sons, Hoboken. https://doi.org/10.1002/9781118673447
Brennan, M. and Schwartz, E. (1978) Finite Difference Methods and Jump Processes Arising in the Pricing of Contingent Claims: A Synthesis. Journal of Financial and Quantitative Analysis, 13, 461-474. https://doi.org/10.2307/2330152
Company, R., Jodar, L. and Pintos, J.R. (2009) A Numerical Method for European Option Pricing with Transaction Costs Nonlinear Equation. Mathematical and Computer Modelling, 50, 910-920. https://doi.org/10.1016/j.mcm.2009.05.019
Forsyth, P. and Labahn, G. (2007) Numerical Methods for Controlled Hamilton-Jacobi-Bellman PDEs in Finance. Journal of Computational Finance, 11, 1-44. https://doi.org/10.21314/JCF.2007.163
Tangman, D., Gopaul, A. and Bhuruth, M. (2008) Numerical Pricing of Options Using Higher-Order Compact FD Schemes. Journal of Computational and Applied Mathematics, 218, 270-280. https://doi.org/10.1016/j.cam.2007.01.035
Buetow, G. and Sochacki, J. (1995) A Finite Difference Approach to the Pricing of Options Using Absorbing Boundary Conditions. Journal of Financial Engineering, 4, 263-280.
Buetow, G. and Sochacki, J. (1998) A More Accurate Finite Difference Approach to the Pricing of Contingent Claims. Applied Mathematics and Computation, 91, 111-126. https://doi.org/10.1016/S0096-3003(97)10029-7
Buetow, G. and Sochacki, J. (2000) The Tradeoffs between Alternative Finite Difference Techniques Used to Price Derivative Securities. Applied Mathematics and Computation, 115, 177-190. https://doi.org/10.1016/S0096-3003(99)00141-1
Wang, J. and Forsyth, P. (2008) Maximal Use of Central Differencing for Hamilton-Bellman PDE’s in Finance. SIAM Journal of Numerical Analysis, 46, 1580-1601. https://doi.org/10.1137/060675186
Leland, H. (1985) Option Pricing and Replication with Transactions Costs. The Journal of Finance, 40, 1283-1301. https://doi.org/10.1111/j.1540-6261.1985.tb02383.x
Boyle, P. and Vorst, T. (1992) Option Replication in Discrete Time with Transaction Costs. The Journal of Finance, 47, 271-293. https://doi.org/10.1111/j.1540-6261.1992.tb03986.x
Hoggard, T., Whaley, A. and Wilmott, P. (1994) Hedging Option Portfolios in the Presence of Transaction Costs. Advances in Futures and Options Research, 7, 21-35.
Kratka, M. (1998) No Mystery behind the Smile. Risk, 9, 67-71.
Jandacka, M. and Sevcovic, D. (2005) On the Risk-Adjusted Pricing-Methodology-Based Valuation of Vanilla Options and Explanation of the Volatility Smile. Journal of Applied Mathematics, 3, 235-258. https://doi.org/10.1155/JAM.2005.235
Barles, G. and Soner, H.M. (1998) Option Pricing with Transaction Costs and a Nonlinear Black-Scholes Equation. Finance and Stochastics, 2, 369-397. https://doi.org/10.1007/s007800050046
Kutik, P. and Mikula, K. (2011) Finite Volume Schemes for Solving Nonlinear Partial Differential Equations in Financial Mathematics. In: Finite Volumes for Complex Applications VI and Perspectives, Springer, Berlin, 643-651. https://doi.org/10.1007/978-3-642-20671-9_68
Lesmana, D. and Wang, S. (2013) An Upwind Finite Difference Method for a Nonlinear Black-Scholes Equation Governing European Option Valuation under Transaction Costs. Applied Mathematics and Computation, 219, 8811-8828. https://doi.org/10.1016/j.amc.2012.12.077
Frey, R. (2000) Market Illiquidity as a Source of Model Risk in Dynamic Hedging. In: Gibson, R., Ed., Model Risk, Risk Publications, London, 125-136.
Frey, R. and Patie, P. (2002) Risk Management for Derivatives in Illiquid Markets: A Simulation Study. In: Sandmann, K. and Schnbucher, P., Eds., Advances in Finance and Stochastics, Springer, Berlin, 137-159. https://doi.org/10.1007/978-3-662-04790-3_8
Frey, R. and Stremme, A. (1997) Market Volatility and Feedback Effects from Dynamic Hedging. Mathematical Finance, 7, 351-374. https://doi.org/10.1111/1467-9965.00036
Liu, H. and Yong, J. (2005) Option Pricing with an Illiquid Underlying Asset Market. Journal of Economic Dynamics and Control, 29, 2125-2156. https://doi.org/10.1016/j.jedc.2004.11.004
Bakstein, D. and Howison, S. (2003) A Non-Arbitrage Liquidity Model with Observable Parameters for Derivatives. Working Paper, Oxford Centre for Industrial and Applied Mathematics, Oxford, 1-52.
Pruett, C.D., Rudmin, J.W. and Lacy, J.M. (2003) An Adaptive N-Body Algorithm of Optimal Order. Journal of Computational Physics, 187, 298-317. https://doi.org/10.1016/S0021-9991(03)00101-3
Nurminskii, E. and Buryi, A. (2011) Parker-Sochacki Method for Solving Systems of Ordinary Differential Equations Using Graphics Processors. Numerical Analysis and Applications, 4, 223. https://doi.org/10.1134/S1995423911030049
Pruett, C.D., Ingham, W.H. and Herman, R.D. (2011) Parallel Implementation of an Adaptive and Parameter-Free n-Body Integrator. Computer Physics Communications, 182, 1187-1198. https://doi.org/10.1016/j.cpc.2011.01.014
Szynkiewicz, P. (2016) A Novel GPU-Enabled Simulator for Large Scale Spiking Neural Networks. Journal Telecommunications and Information Technology, 2, 34-42.
Yudanov, D., Shaaban, M., Melton, R. and Reznik, L. (2010) GPU-Based Simulation of Spiking Neural Networks with Real-Time Performance & High Accuracy. The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, 18-23 July 2010, 1-8. https://doi.org/10.1109/IJCNN.2010.5596334
Money, J.H. (2006) Variational Methods for Image Deblurring and Discretized Picard’s Method. PhD Thesis, University of Kentucky, Lexington.