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<p>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. (<CitationRef CitationID="CR1">2020</CitationRef>) with uniform kernel weighting provides reliable and efficient parameter estimates. It is advised to also apply a Jackknife estimation to corroborate the findings.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Ostap Okhrin,
  • Michael Rockinger,
  • Manuel Schmid

摘要

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.