Results 51 to 60 of about 3,916 (205)

Extended Multivariate EGARCH Model: A Model for Zero‐Return and Negative Spillovers

Journal of Forecasting, Volume 44, Issue 4, Page 1266-1279, July 2025.
ABSTRACT This paper introduces an extended multivariate EGARCH model that overcomes the zero‐return problem and allows for negative news and volatility spillover effects, making it an attractive tool for multivariate volatility modeling. Despite limitations, such as noninvertibility and unclear asymptotic properties of the QML estimator, our Monte ...
Yongdeng Xu
wiley   +1 more source

Application of Copula Models in Stock Market Analysis

Informatika
Objectives. The objective of the study is to use copula models to analyze shares of the Russian stock market and describe changes in the relationship between the shares before and during the coronavirus infection (COVID-19).Methods.
A. M. Kendys, M. M. Troush
doaj   +1 more source

Hybrid Model for Stock Market Volatility

Journal of Probability and Statistics, 2023
Empirical evidence suggests that the traditional GARCH-type models are unable to accurately estimate the volatility of financial markets. To improve on the accuracy of the traditional GARCH-type models, a hybrid model (BSGARCH (1, 1)) that combines the ...
Kofi Agyarko, Nana Kena Frempong, Eric Neebo Wiah   +2 more
doaj   +1 more source

Estimation of tail thickness parameters from GJR-GARCH models [PDF]

, 2009
We propose a method of estimating the Pareto tail thickness parameter of the unconditional distribution of a financial time series by exploiting the implications of a GJR-GARCH volatility model. The method is based on some recent work on the extremes of GARCH-type processes and extends the method proposed by Berkes, Horváth and Kokoszka (2003). We show
Linton, Oliver, Iglesias, Emma M.
openaire   +1 more source

An analysis of Ramadan effect by GJR-GARCH model: case of Borsa Istanbul

Oeconomia Copernicana, 2016
Although there are a lot of studies testing the calendar effect in BIST, there are limited numbers of studies testing the Ramadan effect. In this study, the period of 05 August 1997–24 October 2014 is tested by the GJR-GARCH(1,1) model on the basis of BIST 30, 100, all, second national, sectors and sub-sectors. In some of the models, the dummy variable
K. Batu Tunay, Murat Akbalik
openaire   +2 more sources

A note on the determinants of non‐fungible tokens returns

International Journal of Finance &Economics, Volume 30, Issue 3, Page 3201-3211, July 2025.
Abstract We aim to identify the determinants of non‐fungible tokens (NFTs) returns. The 10 most popular NFTs based on their price, trading volume, and market capitalisation are examined. Twenty‐three potential drivers of the returns of each NFT are considered.
Theodore Panagiotidis, Georgios Papapanagiotou   +1 more
wiley   +1 more source

Risk measurement of global stock markets: a factor copula-based GJR-GARCH approach

Journal of Physics: Conference Series, 2019
AbstractFinancial crisis in 2008 caused huge loss and one of the accusations is the misprediction of risk measurement. Considering the important role the stock markets play, and the trend of globalization in economy, we propose forecasting Value at Risk of G20’s (except European Union) stock indexes in three periods, pre-crisis, during crisis and post ...
Quanrui Song, Jianxu Liu, Songsak Sriboonchitta   +2 more
openaire   +1 more source

Spatial and spatiotemporal volatility models: A review

Journal of Economic Surveys, Volume 39, Issue 3, Page 1037-1091, July 2025.
Abstract Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, in the case of positive spatial dependence, if two locations are in close proximity ...
Philipp Otto, Osman Doğan, Süleyman Taşpınar, Wolfgang Schmid, Anil K. Bera   +4 more
wiley   +1 more source

Modelling Volatility Cycles: The MF2‐GARCH Model

Journal of Applied Econometrics, Volume 40, Issue 4, Page 438-454, June/July 2025.
ABSTRACT We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors.
Christian Conrad, Robert F. Engle
wiley   +1 more source

Inflación e incertidumbre inflacionaria: la postura del Banco de México, 1969-2017

Revista Finanzas y Política Económica, 2018
Este artículo examina la relación entre inflación e incertidumbre inflacionaria para la economía de México durante el periodo que comprende enero de 1969 a febrero de 2017, utilizando modelos SARMA-GARCH y sus extensiones GJR-GARCH-M y E-GARCH-M.
Eduardo Rosas Rojas, Teresa López González   +1 more
doaj   +1 more source