Bayesian estimation of stochastic volatility models based on OU processes with marginal gamma law

DOI10.1007/s10463-007-0130-8zbMath1294.62184OpenAlexW1977424816MaRDI QIDQ734413

Leopold Sögner, Sylvia Frühwirth-Schnatter

Publication date: 13 October 2009

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-007-0130-8

zbMATH Keywords

identificationMarkov chain Monte Carlomarked point processesdata augmentation

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Monte Carlo methods (65C05) Stochastic models in economics (91B70) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (8)

METHOD OF MOMENTS ESTIMATION FOR LÉVY-DRIVEN ORNSTEIN–UHLENBECK STOCHASTIC VOLATILITY MODELS ⋮ Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes ⋮ Stochastic volatility and stochastic leverage ⋮ Some recent developments in stochastic volatility modelling ⋮ Likelihood estimation of Lévy‐driven stochastic volatility models through realized variance measures ⋮ Gradient-based simulated maximum likelihood estimation for Lévy-driven Ornstein–Uhlenbeck stochastic volatility models ⋮ Particle Markov Chain Monte Carlo Methods ⋮ Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

Cites Work

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• The Pricing of Options and Corporate Liabilities
• Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility
• Estimating continuous-time stochastic volatility models of the short-term interest rate
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• A point process model for rainfall: further developments
• Practical Bayesian Density Estimation Using Mixtures of Normals
• Dealing With Label Switching in Mixture Models
• Bayesian Inference for Non-Gaussian Ornstein–Uhlenbeck Stochastic Volatility Processes
• Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models

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