Results 101 to 110 of about 1,114 (191)

Bayesian Estimation of R-Vine Copula with Gaussian-Mixture GARCH Margins: An MCMC and Machine Learning Comparison

Mathematics
This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions.
Rewat Khanthaporn, Nuttanan Wichitaksorn
doaj   +1 more source

Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models. [PDF]

Springerplus, 2015
Nortey EN, Ngoh DD, Doku-Amponsah K, Ofori-Boateng K.   +3 more
europepmc   +1 more source

Multivariate Autocontours for Specification Testing in Multivariate GARCH Models* [PDF]

, 2010
Gloria Gonzalez-Rivera, Emre Yoldas
openaire   +1 more source

Variance Persistence in the Greater China Region: A Multivariate GARCH Approach

Lahore Journal of Economics
This paper utilizes three Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) models to determine variance persistence in the Greater China region from 2009 to 2014.
John Francis Diaz, Peh Ying Qian, Genevieve Liao Tan   +2 more
doaj  

Forecasting with a Bivariate Hysteretic Time Series Model Incorporating Asymmetric Volatility and Dynamic Correlations. [PDF]

Entropy (Basel)
Than HT.
europepmc   +1 more source

Cross-market volatility spillovers between China and the United States: A DCC-EGARCH-t-Copula framework with out-of-sample forecasting. [PDF]

PLoS One
Zeng J, Wu J.
europepmc   +1 more source

AI-Carbon-Energy: Spillover effects and drivers in interconnected markets. [PDF]

iScience
Zhang M, Pan Y, Su B, Zhou D.
europepmc   +1 more source

Dynamic forecasting and mechanisms of volatility synchronization in complex financial systems. [PDF]

PLoS One
Li JC, Guo J, Ma R, Zhong G.
europepmc   +1 more source

FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty. [PDF]

Sci Rep
Wang TW, Shaikh ZA, Yong SL, Elmannai H, Por LY.   +4 more
europepmc   +1 more source