Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns

1996 ◽  
Vol 14 (4) ◽  
pp. 429 ◽  
Author(s):  
Andrew C. Harvey ◽  
Neil Shephard
Keyword(s):  
Stochastic Volatility ◽  
Asset Returns ◽  
Stochastic Volatility Model ◽  
Volatility Model

scholarly journals A Bayesian Semiparametric Realized Stochastic Volatility Model

2021 ◽  
Vol 14 (12) ◽  
pp. 617
Author(s):  
Jia Liu
Keyword(s):  
Stochastic Volatility ◽  
Asset Returns ◽  
Realized Volatility ◽  
Stochastic Volatility Model ◽  
Estimation Bias ◽  
Bayesian Nonparametric ◽  
Proposed Model ◽  
Volatility Model ◽  
Flexible Framework ◽  
Density Forecasts

This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework offered by Bayesian nonparametric mixtures not only improves the fitting of asymmetric and leptokurtic densities of asset returns and logarithmic realized volatility but also enables flexible adjustments for estimation bias in realized volatility. Applications to equity data show that the proposed model offers superior density forecasts for returns and improved estimates of parameters and latent volatility compared with existing alternatives.

scholarly journals Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects

2021 ◽  
Vol 14 (5) ◽  
pp. 225
Author(s):  
Zhongxian Men ◽  
Tony S. Wirjanto ◽  
Adam W. Kolkiewicz
Keyword(s):  
Stochastic Volatility ◽  
Heavy Tails ◽  
Foreign Currency ◽  
Asset Returns ◽  
Stochastic Volatility Model ◽  
Stochastic Volatility Models ◽  
Asymmetric Effects ◽  
Volatility Models ◽  
Volatility Model ◽  
T Distribution

This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market. The model fits are assessed, and the proposed models are shown to compare favorably to the one-component asymmetric stochastic volatility models with Gaussian and Student t distributed innovation terms.

A non-Gaussian stochastic volatility model

1998 ◽  
Vol 2 (2) ◽  
pp. 33-47 ◽  
Author(s):  
Yuichi Nagahara ◽  
Genshiro Kitagawa
Keyword(s):  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Volatility Model ◽  
Non Gaussian
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