Comparing stochastic volatility models through Monte Carlo simulations
Computational Statistics & Data Analysis
Inference in Hidden Markov Models
Inference in Hidden Markov Models
Editorial: Special Issue on Statistical and Computational Methods in Finance
Computational Statistics & Data Analysis
Implied volatility in oil markets
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Nonlinear tracking in a diffusion process with a Bayesian filter and the finite element method
Computational Statistics & Data Analysis
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Filtering and smoothing algorithms that estimate the integrated variance in Levy-driven stochastic volatility models are analyzed. Particle filters are algorithms designed for nonlinear, non-Gaussian models while the Kalman filter remains the best linear predictor if the model is linear but non-Gaussian. Monte Carlo experiments are performed to compare these algorithms across different specifications of the model including different marginal distributions and degrees of persistence for the instantaneous variance. The use of realized variance as an observed variable in the state space model is also evaluated. Finally, the particle filter's ability to identify the timing and size of jumps is assessed relative to popular nonparametric estimators.