Quadrature integration techniques for random hyperbolic PDE problems

In this paper, we consider random hyperbolic partial differential equation (PDE) problems following the mean square approach and Laplace transform technique. Randomness requires not only the computation of the approximating stochastic processes, but also its statistical moments. Hence, appropriate numerical methods should allow for the efficient computation of the expectation and variance. Here, we analyse different numerical methods around the inverse Laplace transform and its evaluation by using several integration techniques, including midpoint quadrature rule, Gauss?Laguerre quadrature and its extensions, and the Talbot algorithm. Simulations, numerical convergence, and computational process time with experiments are shown.This research has been funded by the Spanish Ministerio de Economía, Industria y Competitividad (MINECO), the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-

An ETD method for multi-asset American option pricing under jump-diffusion model

In this paper, we propose a numerical method for American multi-asset options under jump-diffusion model based on the combination of the exponential time differencing (ETD) technique for the differential operator and Gauss-Hermite quadrature for the integral term. In order to simplify the computational stencil and improve characteristics of the ETD-scheme mixed derivative eliminating transformation is applied. The results are compared with recently proposed methods.Ministerio de Ciencia, Innovación y Universidades, Grant/Award Number: MTM2017- 89664-P; Ministerio de Economía y Competitividad, Grant/Award Number: PID2019-107685RB-I0

Conditional full stability of positivity-preserving finite difference scheme for diffusion-advection-reaction models

The matter of the stability for multidimensional diffusion-advection-reaction problems treated with the semi-discretization method is remaining challenge because when all the stepsizes tend simultaneously to zero the involved size of the problem grows without bounds. Solution of such problems is constructed by starting with a semi-discretization approach followed by a full discretization using exponential time differencing and matrix quadrature rules. Analysis of the time variation of the numerical solution with respect to previous time level together with the use of logarithmic norm of matrices is the basis of the stability result. Sufficient stability conditions on stepsizes, that also guarantee positivity and boundedness of the solution, are found. Numerical examples in different fields prove its competitiveness with other relevant methods.This work has been partially supported by the Ministerio de Economía y Competitividad Spanish grant MTM2017-89664-P

An efficient method for solving spread option pricing problem: numerical analysis and computing

This paper deals with numerical analysis and computing of spread option pricing problem described by a two-spatial variables partial differential equation. Both European and American cases are treated. Taking advantage of a cross derivative removing technique, an explicit difference scheme is developed retaining the benefits of the one-dimensional finite difference method, preserving positivity, accuracy, and computational time efficiency. Numerical results illustrate the interest of the approach.This work has been partially supported by the European Union in the FP7- PEOPLE-2012-ITN Program under Grant Agreement no. 304617 (FP7 Marie Curie Action, Project Multi-ITN STRIKE-Novel Methods in Computational Finance) and the Ministerio de Economía y Competitividad Spanish Grant MTM2013-41765-P

Solving American option pricing models by the front fixing method: numerical analysis and computing

This paper presents an explicit finite-difference method for nonlinear partial differential equation appearing as a transformed Black-Scholes equation for American put option under logarithmic front fixing transformation. Numerical analysis of the method is provided. The method preserves positivity and monotonicity of the numerical solution. Consistency and stability properties of the scheme are studied. Explicit calculations avoid iterative algorithms for solving nonlinear systems. Theoretical results are confirmed by numerical experiments. Comparison with other approaches shows that the proposed method is accurate and competitive

A New Efficient Numerical Method for Solving American Option under Regime Switching Model

[EN] A system of coupled free boundary problems describing American put option pricing under regime switching is considered. In order to build numerical solution firstly a front-fixing transformation is applied. Transformed problem is posed on multidimensional fixed domain and is solved by explicit finite difference method. The numerical scheme is conditionally stable and is consistent with the first order in time and second order in space. The proposed approach allows the computation not only of the option price but also of the optimal stopping boundary. Numerical examples demonstrate efficiency and accuracy of the proposed method. The results are compared with other known approaches to show its competitiveness.This work has been partially supported by the European Union in the FP7- PEOPLE-2012-ITN program under Grant Agreement Number 304617 (FP7 Marie Curie Action, Project Multi-ITN STRIKE-Novel Methods in Computational Finance) and the Ministerio de Economia y Competitividad Spanish grant MTM2013-41765-P.Egorova, V.; Company Rossi, R.; Jódar Sánchez, LA. (2016). A New Efficient Numerical Method for Solving American Option under Regime Switching Model. Computers and Mathematics with Applications. 71:224-237. https://doi.org/10.1016/j.camwa.2015.11.019S2242377

Solving partial integro-differential option pricing problems for a wide class of infinite activity Lévy processes

[EN] In this paper, numerical analysis of finite difference schemes for partial integro-differential models related to European and American option pricing problems under a wide class of Lévy models is studied. Apart from computational and accuracy issues, qualitative properties such as positivity are treated. Consistency of the proposed numerical scheme and stability in the von Neumann sense are included. Gauss Laguerre quadrature formula is used for the discretization of the integral part. Numerical examples illustrating the potential advantages of the presented results are included.This work has been partially supported by the European Union in the FP7-PEOPLE-2012-ITN program under Grant Agreement Number 304617 (FP7 Marie Curie Action, Project Multi-ITN STRIKE-Novel Methods in Computational Finance) and the Ministerio de Economia y Competitividad Spanish grant MTM2013-41765-P.El-Fakharany, M.; Company Rossi, R.; Jódar Sánchez, LA. (2016). Solving partial integro-differential option pricing problems for a wide class of infinite activity Lévy processes. Journal of Computational and Applied Mathematics. 296:739-752. https://doi.org/10.1016/j.cam.2015.10.027S73975229

Numerical difference solution of moving boundary random Stefan problems

[EN] This paper deals with the construction of numerical solutions of moving boundary random problems where the uncertainty is limited to a finite degree of randomness in the mean square framework. Using a front fixing approach the problem is firstly transformed into a fixed boundary one. Then a random finite difference scheme for both the partial differential equation and the Stefan condition, allows the discretization. Since statistical moments of the approximate stochastic process solution are required, we combine the sample approach of the difference schemes together with Monte Carlo technique to perform manageable approximations of the expectation and variance of both the approximating stochastic process solution and the stochastic moving boundary solution. Qualitative and reliability properties such as positivity, monotonicity and stability in the mean square sense are treated. Feasibility of the proposed method is checked with illustrative examples of a melting problem and a binary metallic alloys problems.This work was supported by the Spanish Ministerio de EconomIa, Industria y Competitividad (MINECO), Spain, the Agencia Estatal de Investigacion (AEI), Spain and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P.Casabán, M.; Company Rossi, R.; Jódar Sánchez, LA. (2023). Numerical difference solution of moving boundary random Stefan problems. Mathematics and Computers in Simulation. 205:878-901. https://doi.org/10.1016/j.matcom.2022.10.02687890120

Reliable Efficient Difference Methods for Random Heterogeneous Diffusion Reaction Models with a Finite Degree of Randomness

[EN] This paper deals with the search for reliable efficient finite difference methods for the numerical solution of random heterogeneous diffusion reaction models with a finite degree of randomness. Efficiency appeals to the computational challenge in the random framework that requires not only the approximating stochastic process solution but also its expectation and variance. After studying positivity and conditional random mean square stability, the computation of the expectation and variance of the approximating stochastic process is not performed directly but through using a set of sampling finite difference schemes coming out by taking realizations of the random scheme and using Monte Carlo technique. Thus, the storage accumulation of symbolic expressions collapsing the approach is avoided keeping reliability. Results are simulated and a procedure for the numerical computation is given.This work was supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-PCasabán, M.; Company Rossi, R.; Jódar Sánchez, LA. (2021). Reliable Efficient Difference Methods for Random Heterogeneous Diffusion Reaction Models with a Finite Degree of Randomness. Mathematics. 9(3):1-15. https://doi.org/10.3390/math9030206S1159

Non-Gaussian Quadrature Integral Transform Solution of Parabolic Models with a Finite Degree of Randomness

[EN] In this paper, we propose an integral transform method for the numerical solution of random mean square parabolic models, that makes manageable the computational complexity due to the storage of intermediate information when one applies iterative methods. By applying the random Laplace transform method combined with the use of Monte Carlo and numerical integration of the Laplace transform inversion, an easy expression of the approximating stochastic process allows the manageable computation of the statistical moments of the approximation.This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P.Casabán Bartual, MC.; Company Rossi, R.; Jódar Sánchez, LA. (2020). Non-Gaussian Quadrature Integral Transform Solution of Parabolic Models with a Finite Degree of Randomness. Mathematics. 8(7):1-16. https://doi.org/10.3390/math8071112S11687Bharucha-Reid, A. T. (1964). On the theory of random equations. Proceedings of Symposia in Applied Mathematics, 40-69. doi:10.1090/psapm/016/0189071Ernst, O. G., Sprungk, B., & Tamellini, L. (2018). Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables (with Application to Elliptic PDEs). SIAM Journal on Numerical Analysis, 56(2), 877-905. doi:10.1137/17m1123079Casaban, M.-C., Cortes, J.-C., & Jodar, L. (2018). Analytic-Numerical Solution of Random Parabolic Models: A Mean Square Fourier Transform Approach. Mathematical Modelling and Analysis, 23(1), 79-100. doi:10.3846/mma.2018.006Casabán, M. C., Company, R., & Jódar, L. (2019). Numerical Integral Transform Methods for Random Hyperbolic Models with a Finite Degree of Randomness. Mathematics, 7(9), 853. doi:10.3390/math7090853Nouri, K., & Ranjbar, H. (2014). Mean Square Convergence of the Numerical Solution of Random Differential Equations. Mediterranean Journal of Mathematics, 12(3), 1123-1140. doi:10.1007/s00009-014-0452-8Casabán, M.-C., Cortés, J.-C., & Jódar, L. (2015). A random Laplace transform method for solving random mixed parabolic differential problems. Applied Mathematics and Computation, 259, 654-667. doi:10.1016/j.amc.2015.02.091Iserles, A. (2004). On the numerical quadrature of highly-oscillating integrals I: Fourier transforms. IMA Journal of Numerical Analysis, 24(3), 365-391. doi:10.1093/imanum/24.3.365Consuelo Casabán, M., Company, R., Egorova, V. N., & Jódar, L. (2020). Integral transform solution of random coupled parabolic partial differential models. Mathematical Methods in the Applied Sciences, 43(14), 8223-8236. doi:10.1002/mma.6492Casabán, M.-C., Company, R., Cortés, J.-C., & Jódar, L. (2014). Solving the random diffusion model in an infinite medium: A mean square approach. Applied Mathematical Modelling, 38(24), 5922-5933. doi:10.1016/j.apm.2014.04.063Casabán, M.-C., Cortés, J.-C., & Jódar, L. (2018). Solving linear and quadratic random matrix differential equations using: A mean square approach. The non-autonomous case. Journal of Computational and Applied Mathematics, 330, 937-954. doi:10.1016/j.cam.2016.11.049Davies, B., & Martin, B. (1979). Numerical inversion of the laplace transform: a survey and comparison of methods. Journal of Computational Physics, 33(1), 1-32. doi:10.1016/0021-9991(79)90025-1Ng, E. W., & Geller, M. (1969). A table of integrals of the Error functions. Journal of Research of the National Bureau of Standards, Section B: Mathematical Sciences, 73B(1), 1. doi:10.6028/jres.073b.001Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69-80. doi:10.1016/0169-2070(92)90008-