Rate of convergence for parametric estimation in a stochastic volatility model.
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Publication:1766043
DOI10.1016/S0304-4149(01)00130-2zbMath1058.62024MaRDI QIDQ1766043
Publication date: 25 February 2005
Published in: Stochastic Processes and their Applications (Search for Journal in Brave)
Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Markov processes: estimation; hidden Markov models (62M05)
Related Items (10)
Stochastic volatility models including open, close, high and low prices ⋮ Estimation of integrated volatility of volatility with applications to goodness-of-fit testing ⋮ Inference on the maximal rank of time-varying covariance matrices using high-frequency data ⋮ Nonparametric estimation for stochastic volatility models ⋮ ANOVA for diffusions and Itō processes ⋮ Efficient estimation of drift parameters in stochastic volatility models ⋮ Stochastic volatility and fractional Brownian motion ⋮ Linear‐representation Based Estimation of Stochastic Volatility Models ⋮ Realised volatility and parametric estimation of Heston SDEs ⋮ Spot volatility estimation using delta sequences
Cites Work
- Limit theorems for discretely observed stochastic volatility models
- Limit of the quadratic risk in density estimation using linear methods
- Parameter estimation for discretely observed stochastic volatility models
- Mouvement brownien et espaces de besov
- Estimation du coefficient de diffusion de la volatilité d'un modèle à volatilité stochastique
- A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
- Stochastic volatility models as hidden Markov models and statistical applications
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