Department of Economics and Business Economics

Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

Research output: Working paperResearch

Standard

Estimating Stochastic Volatility Models using Prediction-based Estimating Functions. / Lunde, Asger; Brix, Anne Floor.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2013.

Research output: Working paperResearch

Harvard

Lunde, A & Brix, AF 2013 'Estimating Stochastic Volatility Models using Prediction-based Estimating Functions' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Lunde, A., & Brix, A. F. (2013). Estimating Stochastic Volatility Models using Prediction-based Estimating Functions. Aarhus: Institut for Økonomi, Aarhus Universitet. CREATES Research Papers, No. 2013-23

CBE

Lunde A, Brix AF. 2013. Estimating Stochastic Volatility Models using Prediction-based Estimating Functions. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Lunde, Asger and Anne Floor Brix Estimating Stochastic Volatility Models using Prediction-based Estimating Functions. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2013-23). 2013., 38 p.

Vancouver

Lunde A, Brix AF. Estimating Stochastic Volatility Models using Prediction-based Estimating Functions. Aarhus: Institut for Økonomi, Aarhus Universitet. 2013 Jul 5.

Author

Lunde, Asger ; Brix, Anne Floor. / Estimating Stochastic Volatility Models using Prediction-based Estimating Functions. Aarhus : Institut for Økonomi, Aarhus Universitet, 2013. (CREATES Research Papers; No. 2013-23).

Bibtex

@techreport{23a6158077db4081885387ef2769973c,
title = "Estimating Stochastic Volatility Models using Prediction-based Estimating Functions",
abstract = "In this paper prediction-based estimating functions (PBEFs), introduced in S{\o}rensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF based estimator in practice.",
keywords = "GMMestimation, Heston model, high-frequency data, integrated volatility, market microstructure noise, prediction-based estimating functions, realized variance, realized kernel",
author = "Asger Lunde and Brix, {Anne Floor}",
year = "2013",
month = "7",
day = "5",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2013-23",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

AU - Lunde, Asger

AU - Brix, Anne Floor

PY - 2013/7/5

Y1 - 2013/7/5

N2 - In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF based estimator in practice.

AB - In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF based estimator in practice.

KW - GMMestimation, Heston model, high-frequency data, integrated volatility, market microstructure noise, prediction-based estimating functions, realized variance, realized kernel

M3 - Working paper

T3 - CREATES Research Papers

BT - Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -