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Decisions in Economics and Finance

, Volume 42, Issue 2, pp 503–525 | Cite as

On parameter estimation of Heston’s stochastic volatility model: a polynomial filtering method

  • F. Cacace
  • A. Germani
  • M. PapiEmail author
Article

Abstract

In this paper, we investigate the problem of estimating the volatility from the underlying asset price for discrete-time observations. This topic has attracted much research interest due to the key role of the volatility in finance. In this paper, we consider the Heston stochastic volatility model with jumps and we develop a new polynomial filtering method for the estimation of the volatility. The method relies on a linear filter which uses a polynomial state-space formulation of the discrete version of the continuous-time model. We demonstrate that a higher-order polynomial filtering method can be efficiently applied in the context of stochastic volatility models. Then, we compare our approach with some, well-established, techniques in the literature.

Keywords

Stochastic volatility Variance gamma Polynomial filtering Likelihood 

Notes

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Copyright information

© Associazione per la Matematica Applicata alle Scienze Economiche e Sociali (AMASES) 2019

Authors and Affiliations

  1. 1.School of EngineeringUniversità CBMRomeItaly
  2. 2.Department of Electrical and Information EngineeringUniversita’ dell’AquilaL’AquilaItaly

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