Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 507-517 |
| Number of pages | 11 |
| Journal | Quantitative Finance |
| Volume | 18 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 4 2018 |
Keywords
- Long memory stochastic volatility
- Parameter estimation
- Particle filtering
- Rough stochastic volatility
ASJC Scopus subject areas
- Finance
- Economics, Econometrics and Finance(all)
Fingerprint Dive into the research topics of 'Sequential Monte Carlo for fractional stochastic volatility models'. Together they form a unique fingerprint.
Cite this
Chronopoulou, A., & Spiliopoulos, K. (2018). Sequential Monte Carlo for fractional stochastic volatility models. Quantitative Finance, 18(3), 507-517. https://doi.org/10.1080/14697688.2017.1327717