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Erscheinungsjahr: 
2021
Quellenangabe: 
[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
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
stochastic volatility
Dirichlet process mixture
realized volatility
density forecast
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