Bayesian inference for non‐Gaussian Ornstein–Uhlenbeck stochastic volatility processes

By Gareth O. Roberts, Omiros Papaspiliopoulos, Petros Dellaportas. 2004

142 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6126111434bc95e23acb04cd3186494428031393

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Citations in the corpus

Bayesian analysis of single‐molecule experimental data (2005, methodology): “They suggested a non-centred reparameterization Xt→ X̃t=η.Xt ;φ/, η.x;φ/= ∫ x 0 1 σ.u;φ/ du; see also Roberts et al. (2004), Papaspiliopoulos et al. (2003) and Papaspiliopoulos (2003).”
HETEROSKEDASTIC TIME SERIES WITH A UNIT ROOT (2009, background): “…special case of the nonGaussian OU volatility model of Barndorff-Nielsen and Shephard (2001) where the homogeneous Lévy process driving volatility is a compound Poisson process with positive increments; see Roberts, Papaspiliopoulos, and Dellaportas (2004) and Duffie, Pan, and Singleton (2000).”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, unknown): “…(Geman and Geman 1984) and artiﬁcial intelligence, to dynamical models driven by ODE’s (Wilkinson 2011b) and diﬀusions (Roberts and Stramer 2001; Dellaportas et al. 2004; Beskos et al. 2006), hidden trees (Larget and Simon 1999; Huelsen-beck and Ronquist 2001; Chipman et al. 2008; Aldous et al.…”
Particle Markov chain Monte Carlo methods (2010, methodology): “These models were recently introduced in Barndorff-Nielsen and Shephard (2001a) and have become extremely popular in financial econometrics; see for example Creal (2008), Frühwirth-Schnatter and Sögner (2008), Gander and Stephens (2007) and Roberts et al. (2004). However, performing inference for Lé”
A General Framework for the Parametrization of Hierarchical Models (2007, background): “Specifically, X̃ can live on an infinite-dimensional space (while the dimension of X can still be finite) and this flexibility makes noncentering feasible to apply in very general contexts; see the example below for an illustration and [36] for a fully worked example.”
Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (CARMA) Stochastic Volatility Models (2006, methodology): “Roberts, Papaspiliopoulos, and Dellaportas (2004) avoided discretization bias in an MCMC context by using a shot noise decomposition as analyzed here, but only for OU models applied to low-frequency data.”
Stability of the Gibbs sampler for Bayesian hierarchical models (2007, methodology): “Thus, hierarchical models have been used in many areas of applied statistics such as geostatistics [8], longitudinal analysis [9], disease mapping [3] and financial econometrics [23], to name just a few.”
Bayesian computation: a summary of the current state, and samples backwards and forwards (2015, methodology): “1996) which were initially the focus, and graphical models that stemmed from image analysis (Geman and Geman 1984) and artificial intelligence, to dynamical models driven by ODE’s (Wilkinson 2011b) and diffusions (Roberts and Stramer 2001; Dellaportas et al. 2004; Beskos et al. 2006), hidden trees (”
Non-Centered Parameterisations for Hierarchical Models and Data Augmentation (2003, methodology): “It can easily be shown (see Dellaportas et al. (2001)) that the set of the resulting points {(cj, j)} has the same distribution as Ψ.”
Nonparametric inference for Lévy-driven Ornstein-Uhlenbeck processes (2005, background): “Another paper on estimation for OU processes is Roberts et al. (2004), in which Bayesian estimation for parametric models is considered.”
A multivariate Ornstein-Uhlenbeck type stochastic volatility model (2009, methodology): “…univariate non-Gaussian OU type stochastic volatility model proposed by Barndorff-Nielsen and Shephard (2001, 2002) and used and studied heavily since then (see e.g. Griffin and Steel, 2006, Rheinländer and Steiger, 2006, Roberts, Papaspiliopoulos, and Dellaportas, 2004 and references therein).”
Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo (2011, methodology): “For example, the work of Roberts et al. (2004), Griffin & Steel (2006) and Gander & Stephens (2007a) all require advanced MCMC techniques and reparameterizations.”
SMC2: an efficient algorithm for sequential analysis of state space models (2011, methodology): “This choice allows the exact simulation of the actual volatility process, and permits direct comparisons to the numerical results in Sections 4 of Roberts et al. (2004), 3.2 of Barndorﬀ-Nielsen and Shephard (2002) and 6 of Griﬃn and Steel (2006).”