A Fourier interpolation method for numerical solution of FBSDEs: Global convergence, stability, and higher order discretizations

The implementation of the convolution method for the numerical solution of backward stochastic differential equations (BSDEs) introduced in [19] uses a uniform space grid. Locally, this approach produces a truncation error, a space discretization error, and an additional extrapolation error. Even if the extrapolation error is convergent in time, the resulting absolute error may be high at the boundaries of the uniform space grid. In order to solve this problem, we propose a tree-like grid for the space discretization which suppresses the extrapolation error leading to a globally convergent numerical solution for the (F)BSDE. On this alternative grid the conditional expectations involved in the BSDE time discretization are computed using Fourier analysis and the fast Fourier transform (FFT) algorithm as in the initial implementation. The method is then extended to higher-order time discretizations of FBSDEs. Numerical results demonstrating convergence are also presented.Comment: 28 pages, 8 figures; Previously titled 'Global convergence and stability of a convolution method for numerical solution of BSDEs' (1410.8595v1

Exact Simulation of Wishart Multidimensional Stochastic Volatility Model

In this article, we propose an exact simulation method of the Wishart multidimensional stochastic volatility (WMSV) model, which was recently introduced by Da Fonseca et al. \cite{DGT08}. Our method is based onanalysis of the conditional characteristic function of the log-price given volatility level. In particular, we found an explicit expression for the conditional characteristic function for the Heston model. We perform numerical experiments to demonstrate the performance and accuracy of our method. As a result of numerical experiments, it is shown that our new method is much faster and reliable than Euler discretization method.Comment: 27 page

Discretization of Fractional Differential Equations by a Piecewise Constant Approximation

There has recently been considerable interest in using a nonstandard piecewise approximation to formulate fractional order differential equations as difference equations that describe the same dynamical behaviour and are more amenable to a dynamical systems analysis. Unfortunately, due to mistakes in the fundamental papers, the difference equations formulated through this process do not capture the dynamics of the fractional order equations. We show that the correct application of this nonstandard piecewise approximation leads to a one parameter family of fractional order differential equations that converges to the original equation as the parameter tends to zero. A closed formed solution exists for each member of this family and leads to the formulation of a difference equation that is of increasing order as time steps are taken. Whilst this does not lead to a simplified dynamical analysis it does lead to a numerical method for solving the fractional order differential equation. The method is shown to be equivalent to a quadrature based method, despite the fact that it has not been derived from a quadrature. The method can be implemented with non-uniform time steps. An example is provided showing that the difference equation can correctly capture the dynamics of the underlying fractional differential equation

Integrable discretization of recursion operators and unified bilinear forms to soliton hierarchies

In this paper, we give a procedure of how to discretize the recursion operators by considering unified bilinear forms of integrable hierarchies. As two illustrative examples, the unified bilinear forms of the AKNS hierarchy and the KdV hierarchy are presented from their recursion operators. Via the compatibility between soliton equations and their auto-B\"acklund transformations, the bilinear integrable hierarchies are discretized and the discrete recursion operators are obtained. The discrete recursion operators converge to the original continuous forms after a standard limit.Comment: 11Page

New definitions of exponential, hyperbolic and trigonometric functions on time scales

We propose two new definitions of the exponential function on time scales. The first definition is based on the Cayley transformation while the second one is a natural extension of exact discretizations. Our eponential functions map the imaginary axis into the unit circle. Therefore, it is possible to define hyperbolic and trigonometric functions on time scales in a standard way. The resulting functions preserve most of the qualitative properties of the corresponding continuous functions. In particular, Pythagorean trigonometric identities hold exactly on any time scale. Dynamic equations satisfied by Cayley-motivated functions have a natural similarity to the corresponding diferential equations. The exact discretization is less convenient as far as dynamic equations and differentiation is concerned.Comment: 27 page

A partial Fourier transform method for a class of hypoelliptic Kolmogorov equations

We consider hypoelliptic Kolmogorov equations in $n+1$ spatial dimensions, with $n\geq 1$, where the differential operator in the first $n$ spatial variables featuring in the equation is second-order elliptic, and with respect to the $(n+1)$st spatial variable the equation contains a pure transport term only and is therefore first-order hyperbolic. If the two differential operators, in the first $n$ and in the $(n+1)$st co-ordinate directions, do not commute, we benefit from hypoelliptic regularization in time, and the solution for $t>0$ is smooth even for a Dirac initial datum prescribed at $t=0$. We study specifically the case where the coefficients depend only on the first $n$ variables. In that case, a Fourier transform in the last variable and standard central finite difference approximation in the other variables can be applied for the numerical solution. We prove second-order convergence in the spatial mesh size for the model hypoelliptic equation $\frac{\partial u}{\partial t} + x \frac{\partial u}{\partial y} = \frac{\partial^2 u}{\partial x^2}$ subject to the initial condition $u(x,y,0) = \delta (x) \delta (y)$, with $(x,y) \in \mathbb{R} \times\mathbb{R}$ and $t>0$, proposed by Kolmogorov, and for an extension with $n=2$. We also demonstrate exponential convergence of an approximation of the inverse Fourier transform based on the trapezium rule. Lastly, we apply the method to a PDE arising in mathematical finance, which models the distribution of the hedging error under a mis-specified derivative pricing model