Sequential Monte Carlo for fractional stochastic volatility models

Alexandra Chronopoulou, Konstantinos Spiliopoulos

Research output: Contribution to journalArticlepeer-review


In this paper, we consider a fractional stochastic volatility model, that is a model in which the volatility may exhibit a long-range dependent or a rough/antipersistent behaviour. We propose a dynamic sequential Monte Carlo methodology that is applicable to both long memory and antipersistent processes in order to estimate the volatility as well as the unknown parameters of the model. We establish a central limit theorem for the state and parameter filters and we study asymptotic properties (consistency and asymptotic normality) for the filter. We illustrate our results with a simulation study and we apply our method to estimate the volatility and the parameters of a long-range dependent model for S& P 500 data.

Original languageEnglish (US)
Pages (from-to)507-517
Number of pages11
JournalQuantitative Finance
Issue number3
StatePublished - Mar 4 2018


  • Long memory stochastic volatility
  • Parameter estimation
  • Particle filtering
  • Rough stochastic volatility

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)


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