کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
417528 | 681534 | 2012 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
A methodology for fitting general stochastic volatility (SV) models that are naturally cast in terms of a positive volatility process is developed. Two well known methods for evaluating the likelihood function, sequential importance sampling and Laplace importance sampling, are combined. The statistical properties of the resulting estimator are investigated by simulation for an ensemble of SV models. It is found that the performance is good compared to the efficient importance sampling (EIS) algorithm. Finally, the computational framework, building on automatic differentiation (AD), is outlined. The use of AD makes it easy to implement other SV models with non-Gaussian latent volatility processes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 11, November 2012, Pages 3105–3119
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 11, November 2012, Pages 3105–3119
نویسندگان
Tore Selland Kleppe, Hans Julius Skaug,