Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes

DOI10.1016/j.csda.2009.06.008zbMath1284.91586OpenAlexW2123108910MaRDI QIDQ2445712

Jim E. Griffin, Mark F. J. Steel

Publication date: 14 April 2014

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.06.008

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Bayesian inference (62F15) Economic time series analysis (91B84) Stochastic models in economics (91B70)

Related Items (10)

METHOD OF MOMENTS ESTIMATION FOR LÉVY-DRIVEN ORNSTEIN–UHLENBECK STOCHASTIC VOLATILITY MODELS ⋮ Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models ⋮ A Bayesian analysis of exogeneity in models pooling time-series and cross-sectional data ⋮ Indirect inference methods for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes ⋮ The unusual properties of aggregated superpositions of Ornstein-Uhlenbeck type processes ⋮ Weak dependence and GMM estimation of supOU and mixed moving average processes ⋮ Superposition of COGARCH processes ⋮ The Ornstein-Uhlenbeck Dirichlet process and other time-varying processes for Bayesian nonparametric inference ⋮ A Bayesian analysis of simultaneous equation models by combining recursive analytical and numerical approaches ⋮ Tail behavior of multivariate lévy-driven mixed moving average processes and supOU Stochastic Volatility Models

Cites Work

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