A class of stochastic volatility models for environmental applications

DOI10.1111/j.1467-9892.2011.00735.xzbMath1294.62203OpenAlexW1781609209MaRDI QIDQ5495683

F. Jay Breidt, Wenying Huang, Ke Wang, Richard A. Davis

Publication date: 6 August 2014

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2011.00735.x

zbMATH Keywords

importance samplingcovariance functionGaussian processstochastic volatilitylatent processnon-stationaritynugget effect

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Gaussian processes (60G15) Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32)

Related Items (4)

Simulation methods and error analysis for trawl processes and ambit fields ⋮ Composite Gaussian process models for emulating expensive functions ⋮ Hybrid simulation scheme for volatility modulated moving average fields ⋮ Space‐time modelling of trends in temperature series

Uses Software

• spatial
• spBayes

Cites Work

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• Pseudo maximum likelihood estimation: Theory and applications
• Interpolation of spatial data. Some theory for kriging
• Spatio-temporal covariance functions generated by mixtures
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• Multiresolution models for nonstationary spatial covariance functions
• The Influence Curve and Its Role in Robust Estimation
• Monte Carlo maximum likelihood estimation for non-Gaussian state space models
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
• Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
• Weather Forecasting for Weather Derivatives
• Non-Gaussian Bayesian Geostatistical Modeling
• Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center

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