3 research outputs found
Strong convergence of an adaptive time-stepping Milstein method for SDEs with one-sided Lipschitz drift
We introduce explicit adaptive Milstein methods for stochastic differential
equations with one-sided Lipschitz drift and globally Lipschitz diffusion with
no commutativity condition. These methods rely on a class of path-bounded
timestepping strategies which work by reducing the stepsize as solutions
approach the boundary of a sphere, invoking a backstop method in the event that
the timestep becomes too small. We prove that such schemes are strongly
convergent of order one. This convergence order is inherited by an explicit
adaptive Euler-Maruyama scheme in the additive noise case. Moreover we show
that the probability of using the backstop method at any step can be made
arbitrarily small. We compare our method to other fixed-step Milstein variants
on a range of test problems.Comment: 20 pages, 2 figure
Strong Convergence of a GBM Based Tamed Integrator for SDEs and an Adaptive Implementation
We introduce a tamed exponential time integrator which exploits linear terms
in both the drift and diffusion for Stochastic Differential Equations (SDEs)
with a one sided globally Lipschitz drift term. Strong convergence of the
proposed scheme is proved, exploiting the boundedness of the geometric Brownian
motion (GBM) and we establish order 1 convergence for linear diffusion terms.
In our implementation we illustrate the efficiency of the proposed scheme
compared to existing fixed step methods and utilize it in an adaptive time
stepping scheme. Furthermore we extend the method to nonlinear diffusion terms
and show it remains competitive. The efficiency of these GBM based approaches
are illustrated by considering some well-known SDE models
Mean-square convergence rates of implicit Milstein type methods for SDEs with non-Lipschitz coefficients: applications to financial models
A novel class of implicit Milstein type methods is devised and analyzed in
the present work for stochastic differential equations (SDEs) with non-globally
Lipschitz drift and diffusion coefficients. By incorporating a pair of method
parameters into both the drift and diffusion parts,
the new schemes can be viewed as a kind of double implicit methods, which also
work for non-commutative noise driven SDEs. Within a general framework, we
offer upper mean-square error bounds for the proposed schemes, based on certain
error terms only getting involved with the exact solution processes. Such error
bounds help us to easily analyze mean-square convergence rates of the schemes,
without relying on a priori high-order moment estimates of numerical
approximations. Putting further globally polynomial growth condition, we
successfully recover the expected mean-square convergence rate of order one for
the considered schemes with , solving general SDEs in
various circumstances. As applications, some of the proposed schemes are also
applied to solve two scalar SDE models arising in mathematical finance and
evolving in the positive domain . More specifically, the
particular drift-diffusion implicit Milstein method () is
utilized to approximate the Heston -volatility model and the
semi-implicit Milstein method () is used to solve the
Ait-Sahalia interest rate model. With the aid of the previously obtained error
bounds, we reveal a mean-square convergence rate of order one of the positivity
preserving schemes for the first time under more relaxed conditions, compared
with existing relevant results for first order schemes in the literature.
Numerical examples are finally reported to confirm the previous findings.Comment: 36 pages, 3 figure