Estimation of stochastic volatility models by nonparametric filtering

Shin Kanaya and Dennis Kristensen

No CWP09/15, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties of the proposed estimators.

Keywords: Realized spot volatility; stochastic volatility; kernel estimation; nonparametric; semi-parametric (search for similar items in EconPapers)
JEL-codes: C14 C32 C58 (search for similar items in EconPapers)
Date: 2015-03-05
New Economics Papers: this item is included in nep-ets and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: ESTIMATION OF STOCHASTIC VOLATILITY MODELS BY NONPARAMETRIC FILTERING (2016)
Working Paper: Estimation of stochastic volatility models by nonparametric filtering (2015)
Working Paper: Estimation of Stochastic Volatility Models by Nonparametric Filtering (2010)
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