Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage

DOI10.1007/978-3-030-30611-3_8zbMath1435.62109arXiv1901.11491OpenAlexW2997145801MaRDI QIDQ3297248

Darjus Hosszejni, Gregor Kastner

Publication date: 3 July 2020

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.11491

zbMATH Keywords

state-space modelBayesian inferenceMarkov chain Monte Carlo (MCMC)auxiliary mixture samplingancillarity-sufficiency interweaving strategy (ASIS)

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15)

Related Items (5)

Review of statistical approaches for modeling high-frequency trading data ⋮ Wavelet-\(L_2 E\) stochastic volatility models: an application to the water-energy nexus ⋮ MCMC interweaving strategy for estimating stochastic volatility model and its application ⋮ Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity ⋮ Fast inference for time-varying quantiles via flexible dynamic models with application to the characterization of atmospheric rivers

Uses Software

• Unnamed Item
• CODA
• JAGS
• Rcpp
• Stan
• tidyverse
• stochvol

Cites Work

• Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models
• Stochastic volatility with leverage: fast and efficient likelihood inference
• Simulation smoothing for state-space models: a computational efficiency analysis
• Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
• Leverage, heavy-tails and correlated jumps in stochastic volatility models
• Stochastic model specification search for Gaussian and partial non-Gaussian state space models
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem
• On Gibbs sampling for state space models

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