Results 51 to 60 of about 1,203 (162)

The Information Content of Overnight Information for Volatility Forecasting: Evidence From China's Stock Market

Journal of Forecasting, Volume 44, Issue 8, Page 2331-2345, December 2025.
ABSTRACT Using overnight volatility as the proxy for overnight information, this paper models future Chinese stock market realized range–based volatility (RRV) within a class of heterogeneous autoregressive models augmented by this proxy. We confirm the important role of overnight information in volatility forecasting models with strong evidence from ...
Yi Zhang, Long Zhou, Zhidong Liu
wiley   +1 more source

Realized TGARCH Model Incorporating Continuous and Jump Components as Exogenous Variables

, 2023
Volatilitas adalah ukuran fluktuasi harga aset keuangan yang tak terpisahkan dari dinamika pasar, tidak hanya sebagai indikator risiko tetapi juga sebagai sumber informasi tentang peluang dan ketidakpastian bagi investor.
Hanafi, Fika Maula
core  

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

Contribution to the analysis of the Moroccan dirham exchange rate volatility : Econometric modeling using the asymmetric TGARCH model: Contribution à l’analyse de la volatilité du taux de change du dirham marocain : Modélisation économétrique à l’aide du modèle asymétrique TGARCH

, 2021
Résumé Le contexte générale de cette étude est lié directement à la réforme du régime de change appliqué au Maroc en passant vers la flexibilité graduelle basée sur l’élargissement des bandes de fluctuation du taux de change.
Salmi Yahya,, Ahmed, Khattab, Yahya, Salmi, Khattab Ahmed   +3 more
core   +1 more source

‘Take Back Control’: The implications of Brexit uncertainty on investor perception of ESG reputational events

European Financial Management, Volume 31, Issue 1, Page 72-114, January 2025.
Abstract This study examines the impact of Brexit on investor reactions to Environmental, Social and Governance (ESG) events in UK companies. Post‐Brexit, investors show reduced sensitivity to ESG incidents, suggesting relaxed corporate accountability for ESG disasters. We observe varied investor responses to different ESG events, with most having less
Erdinc Akyildirim, Thomas Conlon, Shaen Corbet, Les Oxley   +3 more
wiley   +1 more source

Forecasting Airtel Stock Prices Through Decomposition and Integration: A Novel VMD‐GARCH‐LSTM Framework

International Journal of Mathematics and Mathematical Sciences, Volume 2025, Issue 1, 2025.
Stock price forecasting is complex due to the nonlinear and nonstationary nature of financial time series. This study proposes a hybrid variational mode decomposition (VMD)–generalized autoregressive conditional heteroskedasticity (GARCH)–long short‐term memory (LSTM) model to predict Airtel’s stock prices, integrating VMD, GARCH, and LSTM networks ...
John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili, Andrzej Skowron   +3 more
wiley   +1 more source

Investor Sentiment, Unexpected Inflation, and Bitcoin Basis Risk

Journal of Futures Markets, Volume 44, Issue 11, Page 1807-1831, November 2024.
ABSTRACT The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data
Thomas Conlon, Shaen Corbet, Les Oxley
wiley   +1 more source

Threshold Network GARCH Model

Journal of Time Series Analysis, Volume 45, Issue 6, Page 910-930, November 2024.
Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐
Yue Pan, Jiazhu Pan
wiley   +1 more source

A COPULA-TGARCH APPROACH OF CONDITIONAL DEPENDENCE BETWEEN OIL PRICE AND STOCK MARKET INDEX: THE CASE OF MEXICO

Estudios Económicos, 2016
En este artículo se aplican las cópulas Clayton y Gumbel con el modelo TGARCH para la distribución marginal de los rendimientos con el ob- jeto de describir la dependencia condicional en las colas entre el precio del petróleo y el índice del mercado de ...
Arturo Lorenzo Valdés, Leticia Armenta Fraire, Rocío Durán Vázquez   +2 more
doaj  

A Hybrid GARCH and Deep Learning Method for Volatility Prediction

Journal of Applied Mathematics, Volume 2024, Issue 1, 2024.
Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse ...
Hailabe T. Araya, Jane Aduda, Tesfahun Berhane, Man Leung Wong   +3 more
wiley   +1 more source