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Semiparametric Estimation of Locally Stationary Diffusion Models

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  • Bonsoo Koo
  • Oliver Linton

Abstract

This paper proposes a class of locally stationary diffusion processes. The modelhas a time varying but locally linear drift and a volatility coefficient that is allowed tovary over time and space. We propose estimators of all the unknown quantitiesbased on long span data. Our estimation method makes use of the localstationarity. We establish asymptotic theory for the proposed estimators as thetime span increases. We apply this method to the real financial data to illustrate thevalidity of our model. Finally, we present a simulation study to provide the finitesampleperformance of the proposed estimators.

Suggested Citation

  • Bonsoo Koo & Oliver Linton, 2010. "Semiparametric Estimation of Locally Stationary Diffusion Models," STICERD - Econometrics Paper Series 551, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:551
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    References listed on IDEAS

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    Cited by:

    1. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Aslanidis, Nektarios & Casas, Isabel, 2013. "Nonparametric correlation models for portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2268-2283.

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    More about this item

    Keywords

    diffusion processes; local stationarity; term structure dynamics; density matching; option pricing.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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