A fast and efficient Markov chain Monte Carlo method for market microstructure model

DOI10.1155/2021/5523468zbMath1486.62249OpenAlexW3210555645MaRDI QIDQ2244387

Peng Hui, Sun Yapeng, Xie Wenbiao

Publication date: 12 November 2021

Published in: Discrete Dynamics in Nature and Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2021/5523468

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Monte Carlo methods (65C05)

Uses Software

• SsfPack

Cites Work

• Fast Sampling of Gaussian Markov Random Fields
• Stochastic volatility with leverage: fast and efficient likelihood inference
• Simulation smoothing for state-space models: a computational efficiency analysis
• Simulated likelihood inference for stochastic volatility models using continuous particle filtering
• The HESSIAN method: highly efficient simulation smoothing, in a nutshell
• Stochastic volatility in mean models with scale mixtures of normal distributions and correlated errors: a Bayesian approach
• Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
• Leverage, heavy-tails and correlated jumps in stochastic volatility models
• Block sampler and posterior mode estimation for asymmetric stochastic volatility models
• The diffuse Kalman filter
• Estimation of stochastic volatility models with diagnostics
• Generalized autoregressive conditional heteroscedasticity
• Markov chain Monte Carlo methods for stochastic volatility models.
• Markov chains for exploring posterior distributions. (With discussion)
• Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets
• Modeling financial time series based on a market microstructure model with leverage effect
• Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture
• Bayesian nonparametric modelling of the return distribution with stochastic volatility
• Efficient simulation and integrated likelihood estimation in state space models
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Likelihood analysis of non-Gaussian measurement time series
• Monte Carlo maximum likelihood estimation for non-Gaussian state space models
• MODELING STOCHASTIC VOLATILITY: A REVIEW AND COMPARATIVE STUDY
• A simple and efficient simulation smoother for state space time series analysis
• Particle Markov Chain Monte Carlo Methods
• Disturbance smoother for state space models
• Statistical algorithms for models in state space using SsfPack 2.2
• The simulation smoother for time series models
• A Survey of Sequential Monte Carlo Methods for Economics and Finance
• Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems
• Accept–reject Metropolis–Hastings sampling and marginal likelihood estimation
• A multi-move sampler for estimating non-Gaussian time series models: Comments on Shephard & Pitt (1997)
• Modelling and asset allocation for financial markets based on a stochastic volatility microstructure model
• Stochastic Gradient Markov Chain Monte Carlo
• Nonlinear autoregressive models with optimality properties
• Bayesian analysis of moving average stochastic volatility models: modeling in-mean effects and leverage for financial time series

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