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Estimation of stochastic volatility models by nonparametric filtering

Kanaya, Shin and Kristensen, Dennis (2016) 'Estimation of stochastic volatility models by nonparametric filtering.' Econometric Theory, 32 (4). pp. 861-916. ISSN 0266-4666

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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.

Item Type: Article
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Economics, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 16 Mar 2020 15:50
Last Modified: 06 Jan 2022 14:11
URI: http://repository.essex.ac.uk/id/eprint/27132

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