Observations concerning the estimation of Heston’s stochastic volatility model using HF data

Ostap Okhrin (), Michael Rockinger () and Manuel Schmid ()
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Ostap Okhrin: Technische Universität Dresden, Institute of Economics and Transport, School of Transportation, Chair of Statistics and Econometrics esp. Transportation
Michael Rockinger: University of Lausanne, Faculty of Business and Economics
Manuel Schmid: Deutsche Bahn AG

Statistical Papers, 2025, vol. 66, issue 4, No 17, 23 pages

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.

Keywords: Spot volatility; Square-root process; Method of moments; Maximum-likelihood; High-frequency data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00362-025-01710-0

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