Results 131 to 140 of about 3,586 (227)

Multivariate ARCH with spatial effects for stock sector and size [PDF]

This paper applies a new spatial approach for the specfication of multivariate GARCH models, called Spatial Effects in ARCH, SEARCH. We consider spatial dependence associated with industrial sectors and capitalization size.
Caporin Massimiliano, Paruolo Paolo
core  

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

Nuisance parameters, composite likelihoods and a panel of GARCH models [PDF]

We investigate the properties of the composite likelihood (CL) method for (T ×N_T ) GARCH panels. The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across N_T series, other parameters ...
Neil Shephard, Kevin Sheppard, Cavit Pakel   +2 more
core  

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

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

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

Forecasting the time-varying beta of UK firms: GARCH models vs Kalman filter method

, 2007
This paper forecast the weekly time-varying beta of 20 UK firms by means of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model.
Wu, Hao, Choudhry, Taufiq
core  

Risk contagion of COVID-19 to oil prices: A Markov switching GARCH and PCA approach. [PDF]

PLoS One
Siddiqui N, Mohamad Hasim H.
europepmc   +1 more source

Structure and Asymptotic Theory for Nonlinear Models with GARCH Errors [PDF]

Nonlinear time series models, especially those with regime-switching and conditionally heteroskedastic errors, have become increasingly popular in the economics and finance literature.
Michael McAleer, Marcelo C. Medeiros, Felix Chan   +2 more
core  

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

Value at Risk long memory volatility models with heavy-tailed distributions for cryptocurrencies. [PDF]

Front Appl Math Stat
Subramoney SD, Chinhamu K, Chifurira R.
europepmc   +1 more source