Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323270 
Year of Publication: 
2025
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 66 [Issue:] 4 [Article No.:] 90 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2025
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
Abstract This paper presents a comprehensive simulation study on estimating parameters for the popular Heston stochastic volatility model. Leveraging high-frequency data, we explore, in a data-science type exercise, various spot-volatility estimation and sampling techniques, improving existing methods to enhance parameter accuracy. Through extensive simulations, we report difficulties in generating correct parameter estimates for realistic parameter settings where the volatility dynamic does not satisfy the Feller condition. This study contributes valuable insights into the practical implementation of the Heston model and its applicability to high-frequency data. We find that the scheme of Azencott et al. ( 2020 ) with uniform kernel weighting provides reliable and efficient parameter estimates. It is advised to also apply a Jackknife estimation to corroborate the findings.
Subjects: 
Spot volatility
Square-root process
Method of moments
Maximum-likelihood
High-frequency data
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version
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