On arbitrarily slow convergence rates for strong numerical approximations of Cox-Ingersoll-Ross processes and squared Bessel processes
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Publication:1711721
DOI10.1007/s00780-018-0375-5zbMath1425.91401arXiv1702.08761OpenAlexW2594037125WikidataQ129059795 ScholiaQ129059795MaRDI QIDQ1711721
Publication date: 18 January 2019
Published in: Finance and Stochastics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1702.08761
optimal approximationlower error boundCox-Ingersoll-Ross processsquared Bessel processstrong (pathwise) approximation
Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Interest rates, asset pricing, etc. (stochastic models) (91G30) Derivative securities (option pricing, hedging, etc.) (91G20)
Related Items (10)
On the performance of the Euler-Maruyama scheme for SDEs with discontinuous drift coefficient ⋮ An adaptive splitting method for the Cox-Ingersoll-Ross process ⋮ Semi-implicit Euler-Maruyama approximation for noncolliding particle systems ⋮ Multilevel Monte Carlo simulation for the Heston stochastic volatility model ⋮ Multilevel Monte Carlo using approximate distributions of the CIR process ⋮ Strong approximation of Bessel processes ⋮ High Order Splitting Methods for SDEs Satisfying a Commutativity Condition ⋮ Tamed Euler-Maruyama approximation for stochastic differential equations with locally Hölder continuous diffusion coefficients ⋮ Lower error bounds for strong approximation of scalar SDEs with non-Lipschitzian coefficients ⋮ Series Expansions and Direct Inversion for the Heston Model
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