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Optimal pointwise approximation of stochastic differential equations driven by fractional Brownian motion

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  • Neuenkirch, Andreas

Abstract

We study the approximation of stochastic differential equations driven by a fractional Brownian motion with Hurst parameter H>1/2. For the mean-square error at a single point we derive the optimal rate of convergence that can be achieved by arbitrary approximation methods that are based on an equidistant discretization of the driving fractional Brownian motion. We find that there are mainly two cases: either the solution can be approximated perfectly or the best possible rate of convergence is n-H-1/2, where n denotes the number of evaluations of the fractional Brownian motion. In addition, we present an implementable approximation scheme that obtains the optimal rate of convergence in the latter case.

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  • Neuenkirch, Andreas, 2008. "Optimal pointwise approximation of stochastic differential equations driven by fractional Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 118(12), pages 2294-2333, December.
    • Handle: RePEc:eee:spapps:v:118:y:2008:i:12:p:2294-2333
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    1. Peter F. Craigmile, 2003. "Simulating a class of stationary Gaussian processes using the Davies–Harte algorithm, with application to long memory processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(5), pages 505-511, September.
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    4. Fred Espen Benth, 2003. "On arbitrage-free pricing of weather derivatives based on fractional Brownian motion," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(4), pages 303-324.
    5. Dorje Brody & Joanna Syroka & Mihail Zervos, 2002. "Dynamical pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 2(3), pages 189-198.
    6. Nourdin, Ivan & Simon, Thomas, 2006. "On the absolute continuity of one-dimensional SDEs driven by a fractional Brownian motion," Statistics & Probability Letters, Elsevier, vol. 76(9), pages 907-912, May.
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    Cited by:

    1. Kęstutis Kubilius & Aidas Medžiūnas, 2022. "Pathwise Convergent Approximation for the Fractional SDEs," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    2. Neuenkirch, A. & Tindel, S. & Unterberger, J., 2010. "Discretizing the fractional Lévy area," Stochastic Processes and their Applications, Elsevier, vol. 120(2), pages 223-254, February.
    3. Peter Kloeden & Andreas Neuenkirch & Raffaella Pavani, 2011. "Multilevel Monte Carlo for stochastic differential equations with additive fractional noise," Annals of Operations Research, Springer, vol. 189(1), pages 255-276, September.

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