On arbitrarily slow convergence rates for strong numerical approximations of Cox-Ingersoll-Ross processes and squared Bessel processes

Abstract

Cox-Ingersoll-Ross (CIR) processes are extensively used in state-of-the-art models for the approximative pricing of financial derivatives. In particular, CIR processes are day after day employed to model instantaneous variances (squared volatilities) of foreign exchange rates and stock prices in Heston-type models and they are also intensively used to model short-rate interest rates. The prices of the financial derivatives in the above mentioned models are very often approximately computed by means of explicit or implicit Euler- or Milstein-type discretization methods based on equidistant evaluations of the driving noise processes. In this article we study the strong convergence speeds of all such discretization methods. More specifically, the main result of this article reveals that each such discretization method achieves at most a strong convergence order of $\delta /2$, where $0<\delta <2$ is the dimension of the squared Bessel process associated to the considered CIR process. In particular, we thereby reveal that discretization methods currently employed in the financial industry may converge with arbitrarily slow strong convergence rates to the solution of the considered CIR process. We thereby lay open the need of the development of other more sophisticated approximation methods which are capable to solve CIR processes in the strong sense in a reasonable computational time and which thus can not belong to the class of algorithms which use equidistant evaluations of the driving noise processes.

References in corpus (4)

• Optimal pointwise approximation of SDEs based on brownian motion at discrete points
• On stochastic differential equations with arbitrary slow convergence rates for strong approximation
• Strong convergence of the symmetrized Milstein scheme for some CEV-like SDEs
• On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions

Cited by in corpus (7)

• Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equations
• An adaptive splitting method for the Cox-Ingersoll-Ross process
• High order splitting methods for SDEs satisfying a commutativity condition
• Assessing fluctuations of long-memory environmental variables based on the robustified dynamic Orlicz risk
• Stochastic differential equations with irregular coefficients:~mind the gap!
• A positivity preserving numerical scheme for the alpha-CEV process
• Strong convergence rates for Euler approximations to a class of stochastic path-dependent volatility models