TY - JOUR
T1 - Sequential Monte Carlo for fractional stochastic volatility models
AU - Chronopoulou, Alexandra
AU - Spiliopoulos, Konstantinos
N1 - Funding Information:
Research of A.C. supported in part by a start-up fund from the University of Illinois at Urbana-Champaign and by the Simons Foundation [grant number 319216]. Research of K.S. supported in part by a start-up fund from Boston University and by the National Science Foundation [DMS 1550918].
PY - 2018/3/4
Y1 - 2018/3/4
N2 - 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.
AB - 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.
KW - Long memory stochastic volatility
KW - Parameter estimation
KW - Particle filtering
KW - Rough stochastic volatility
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U2 - 10.1080/14697688.2017.1327717
DO - 10.1080/14697688.2017.1327717
M3 - Article
AN - SCOPUS:85025451537
VL - 18
SP - 507
EP - 517
JO - Quantitative Finance
JF - Quantitative Finance
SN - 1469-7688
IS - 3
ER -