Department of Economics and Business Economics

Estimation of Stochastic Volatility Models by Nonparametric Filtering

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Estimation of Stochastic Volatility Models by Nonparametric Filtering. / Kanaya, Shin; Kristensen, Dennis.

In: Econometric Theory, Vol. 32, No. 4, 2016, p. 861-916.

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@article{207a4e2849a04b1c93928747c1e627b7,
title = "Estimation of Stochastic Volatility Models by Nonparametric Filtering",
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.",
author = "Shin Kanaya and Dennis Kristensen",
year = "2016",
doi = "10.1017/S0266466615000079",
language = "English",
volume = "32",
pages = "861--916",
journal = "Econometric Theory",
issn = "0266-4666",
publisher = "Cambridge University Press",
number = "4",

}

RIS

TY - JOUR

T1 - Estimation of Stochastic Volatility Models by Nonparametric Filtering

AU - Kanaya, Shin

AU - Kristensen, Dennis

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1017/S0266466615000079

DO - 10.1017/S0266466615000079

M3 - Journal article

VL - 32

SP - 861

EP - 916

JO - Econometric Theory

JF - Econometric Theory

SN - 0266-4666

IS - 4

ER -