Fast particle smoothing: if I had a million particles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Simulation smoothing for state-space models: A computational efficiency analysis
Computational Statistics & Data Analysis
Nonlinear tracking in a diffusion process with a Bayesian filter and the finite element method
Computational Statistics & Data Analysis
Editorial: The third special issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
RcppArmadillo: Accelerating R with high-performance C++ linear algebra
Computational Statistics & Data Analysis
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A smoothing algorithm based on the unscented transformation is proposed for the nonlinear Gaussian system. The algorithm first implements a forward unscented Kalman filter and then evokes a separate backward smoothing pass by only making Gaussian approximations in the state but not in the observation space. The method is applied to volatility extraction in a diffusion option pricing model. Both simulation study and empirical applications with the Heston stochastic volatility model indicate that in order to accurately capture the volatility dynamics, both stock prices and options are necessary.