A semiparametric stochastic volatility model

In this paper the correlation structure in the classical leverage stochastic volatility (SV) model is generalized based on a linear spline. In the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the...

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Main Author: YU, Jun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/soe_research/1347
https://ink.library.smu.edu.sg/context/soe_research/article/2346/viewcontent/SemiparametricStochasticVolatilityModel_2010.pdf
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spelling sg-smu-ink.soe_research-23462020-03-31T03:07:35Z A semiparametric stochastic volatility model YU, Jun In this paper the correlation structure in the classical leverage stochastic volatility (SV) model is generalized based on a linear spline. In the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the market. Theoretical properties of the proposed model are examined. The model estimation and comparison are conducted by Bayesian methods. The performance of the estimates are examined in simulations. The new model is fitted to daily and weekly US data and compared with the classical SV and GARCH models in terms of their in-sample and out-of-sample performances. Empirical results suggest evidence in favor of the proposed model. In particular, the new model finds strong evidence of time varying leverage effect in individual stocks when the classical model fails to identify the leverage effect. 2012-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1347 info:doi/10.1016/j.jeconom.2011.09.029 https://ink.library.smu.edu.sg/context/soe_research/article/2346/viewcontent/SemiparametricStochasticVolatilityModel_2010.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Leverage effect Simulated maximum likelihood Laplace approximation Spline Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Leverage effect
Simulated maximum likelihood
Laplace approximation
Spline
Econometrics
spellingShingle Leverage effect
Simulated maximum likelihood
Laplace approximation
Spline
Econometrics
YU, Jun
A semiparametric stochastic volatility model
description In this paper the correlation structure in the classical leverage stochastic volatility (SV) model is generalized based on a linear spline. In the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the market. Theoretical properties of the proposed model are examined. The model estimation and comparison are conducted by Bayesian methods. The performance of the estimates are examined in simulations. The new model is fitted to daily and weekly US data and compared with the classical SV and GARCH models in terms of their in-sample and out-of-sample performances. Empirical results suggest evidence in favor of the proposed model. In particular, the new model finds strong evidence of time varying leverage effect in individual stocks when the classical model fails to identify the leverage effect.
format text
author YU, Jun
author_facet YU, Jun
author_sort YU, Jun
title A semiparametric stochastic volatility model
title_short A semiparametric stochastic volatility model
title_full A semiparametric stochastic volatility model
title_fullStr A semiparametric stochastic volatility model
title_full_unstemmed A semiparametric stochastic volatility model
title_sort semiparametric stochastic volatility model
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/soe_research/1347
https://ink.library.smu.edu.sg/context/soe_research/article/2346/viewcontent/SemiparametricStochasticVolatilityModel_2010.pdf
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