Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258720 
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 12 [Article No.:] 617 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-22
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
stochastic volatility
Dirichlet process mixture
realized volatility
density forecast
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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