Work

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

Year: 2025

Type: article

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.

Source: Statistical Papers

DOI: 10.1007/s00362-025-01710-0

Authors: Ostap Okhrin, Michael Rockinger, Manuel Schmid

Institutions: Technische Universität Dresden, University of Lausanne, Deutsche Bahn (Germany)

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,890

Funders: Deutsche Forschungsgemeinschaft, Technische Universität Dresden