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Maximum likelihood estimation for small noise multiscale diffusions

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  • Konstantinos Spiliopoulos
  • Alexandra Chronopoulou

Abstract

We study the problem of parameter estimation for stochastic differential equations with small noise and fast oscillating parameters. Depending on how fast the intensity of the noise goes to zero relative to the homogenization parameter, we consider three different regimes. For each regime, we construct the maximum likelihood estimator and we study its consistency and asymptotic normality properties. A simulation study for the first order Langevin equation with a two scale potential is also provided. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Konstantinos Spiliopoulos & Alexandra Chronopoulou, 2013. "Maximum likelihood estimation for small noise multiscale diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 16(3), pages 237-266, October.
    • Handle: RePEc:spr:sistpr:v:16:y:2013:i:3:p:237-266
    DOI: 10.1007/s11203-013-9088-8
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    References listed on IDEAS

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    1. Papavasiliou, A. & Pavliotis, G.A. & Stuart, A.M., 2009. "Maximum likelihood drift estimation for multiscale diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(10), pages 3173-3210, October.
    2. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    3. Dupuis, Paul & Spiliopoulos, Konstantinos, 2012. "Large deviations for multiscale diffusion via weak convergence methods," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1947-1987.
    4. Jin Feng & Jean-Pierre Fouque & Rohini Kumar, 2010. "Small-time asymptotics for fast mean-reverting stochastic volatility models," Papers 1009.2782, arXiv.org, revised Aug 2012.
    5. Freidlin, Mark I. & Sowers, Richard B., 1999. "A comparison of homogenization and large deviations, with applications to wavefront propagation," Stochastic Processes and their Applications, Elsevier, vol. 82(1), pages 23-52, July.
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    Cited by:

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    2. Kang, Kai & Maroulas, Vasileios & Schizas, Ioannis & Bao, Feng, 2018. "Improved distributed particle filters for tracking in a wireless sensor network," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 90-108.

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