کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417276 681479 2008 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models
چکیده انگلیسی

Filtering and smoothing algorithms that estimate the integrated variance in Lévy-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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 6, 20 February 2008, Pages 2863–2876
نویسندگان
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