A new variant of estimation approach to asymmetric stochastic volatilitymodel<br />

Zhongxian Men; Tony S. Wirjanto; Zhongxian Men; Tony S. Wirjanto

doi:10.3934/QFE.2018.2.325

Quantitative Finance and Economics

2018, Volume 2, Issue 2: 325-347. doi: 10.3934/QFE.2018.2.325

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Research article Special Issues

A new variant of estimation approach to asymmetric stochastic volatilitymodel

Zhongxian Men ^{1
,
,},
Tony S. Wirjanto ^2,3

1.
Quantitative Research, JPMorgan Chase & Co., 277 Park Avenue, New York, USA
2.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University AvenueWest, Waterloo, Ontario, Canada
3.
School of Accounting and Finance, University of Waterloo, 200 University Avenue West, Waterloo,Ontario, Canada

Received: 31 August 2017 Accepted: 23 April 2018 Published: 09 May 2018
JEL Codes: C10, C11, C13, C15, C22, C58, C53

This paper proposes a novel simulation-based inference for an asymmetric stochastic volatility model. An acceptance-rejection Metropolis-Hastings algorithm is developed for the simulation of latent states of the model. A simple and e cient algorithm is also developed for estimation of a heavy-tailed stochastic volatility model. Simulation studies show that our proposed methods give rise to reasonable parameter estimates. Our proposed estimation methods are then used to analyze a benchmark data set of asset returns.
- stochastic volatility,
- leverage effect,
- Bayesian inference,
- acceptance-rejection,
- Metropolis-Hastings,
- slice sampler
Citation: Zhongxian Men, Tony S. Wirjanto. A new variant of estimation approach to asymmetric stochastic volatilitymodel[J]. Quantitative Finance and Economics, 2018, 2(2): 325-347. doi: 10.3934/QFE.2018.2.325

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Abstract

This paper proposes a novel simulation-based inference for an asymmetric stochastic volatility model. An acceptance-rejection Metropolis-Hastings algorithm is developed for the simulation of latent states of the model. A simple and e cient algorithm is also developed for estimation of a heavy-tailed stochastic volatility model. Simulation studies show that our proposed methods give rise to reasonable parameter estimates. Our proposed estimation methods are then used to analyze a benchmark data set of asset returns.

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