Modelling financial time series based on heavy-tailed market microstructure models with scale mixtures of normal distributions

DOI10.1080/00207721.2018.1464607zbMath1482.91202OpenAlexW2800172186MaRDI QIDQ5027559

Yanhui Xi, Hui Peng

Publication date: 4 February 2022

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2018.1464607

zbMATH Keywords

Markov chain Monte Carlooutlierheavy tailscale mixtures of normal distributionsmarket microstructure model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistics of extreme values; tail inference (62G32) Financial markets (91G15)

Uses Software

• BUGS

Cites Work

• Unnamed Item
• Understanding predictive information criteria for Bayesian models
• A robust extension of the bivariate Birnbaum-Saunders distribution and associated inference
• Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions
• On estimation of a heteroscedastic measurement error model under heavy-tailed distributions
• Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: estimation and case influence diagnostics
• Stochastic volatility models with leverage and heavy-tailed distributions: a Bayesian approach using scale mixtures
• Skew scale mixtures of normal distributions: properties and estimation
• Stochastic volatility in mean models with scale mixtures of normal distributions and correlated errors: a Bayesian approach
• Leverage, heavy-tails and correlated jumps in stochastic volatility models
• State space mixed models for binary responses with scale mixture of normal distributions links
• Partially linear censored regression models using heavy-tailed distributions: a Bayesian approach
• Generalized autoregressive conditional heteroscedasticity
• Markov chain Monte Carlo methods for stochastic volatility models.
• Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets
• Modeling volatility using state space models with heavy tailed distributions
• Matrix variate slash distribution
• Robust adaptive rate-optimal testing for the white noise hypothesis
• Multivariate generalized Poisson geometric process model with scale mixtures of normal distributions
• Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions
• A model selection method based on maximal information coefficient of residuals
• BUGS for a Bayesian analysis of stochastic volatility models
• Bayesian student-t stochastic volatility models via scale mixtures
• Bayesian Measures of Model Complexity and Fit
• Structured parameter optimization method for the radial basis function-based state-dependent autoregressive model
• Modelling and asset allocation for financial markets based on a stochastic volatility microstructure model