Results 21 to 30 of about 2,971 (265)

Hybrid Model for Stock Market Volatility

open access: yesJournal 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   +2 more
doaj   +1 more source

Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility

open access: yesRisks, 2023
Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility.
Rhenan G. S. Queiroz, Sergio A. David
doaj   +1 more source

GARCH models without positivity constraints: Exponential or log GARCH? [PDF]

open access: yesJournal of Econometrics, 2013
This paper provides a probabilistic and statistical comparison of the log-GARCH and EGARCH models, which both rely on multiplicative volatility dynamics without positivity constraints. We compare the main probabilistic properties (strict stationarity, existence of moments, tails) of the EGARCH model, which are already known, with those of an asymmetric
Francq, Christian   +2 more
openaire   +4 more sources

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction [PDF]

open access: yesPeerJ Computer Science
Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock
Sneha S. Bagalkot   +2 more
doaj   +2 more sources

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

open access: yesEnergies, 2020
We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil ...
Marcin Fałdziński   +2 more
doaj   +1 more source

Multivariate GARCH Models: A Survey [PDF]

open access: yesSSRN Electronic Journal, 2003
AbstractThis paper surveys the most important developments in multivariate ARCH‐type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research. Copyright © 2006 John Wiley & Sons, Ltd.
BAUWENS, Luc   +2 more
openaire   +3 more sources

Stochastic Volatility Modeling of Daily Streamflow Time Series

open access: yesWater Resources Research, 2023
Under the changing climate, the natural characteristics of hydrological processes are assumed to have been intensified, and the volatility of these processes to have increased significantly.
Huimin Wang   +4 more
doaj   +1 more source

Collateralised option pricing in a South African context: A Univariate GARCH approach

open access: yesCogent Economics & Finance, 2022
In this paper, the generalised autoregressive heteroskedasticity (GARCH) model is applied to the pricing of collateralised options in the South African equity market. Symmetric GARCH and nonlinear asymmetric GARCH (AGARCH) models are considered.
Pierre J Venter   +2 more
doaj   +1 more source

TESTING GARCH-X TYPE MODELS [PDF]

open access: yesEconometric Theory, 2017
We present novel theory for testing for reduction of GARCH-X type models with an exogenous (X) covariate to standard GARCH type models. To deal with the problems of potential nuisance parameters on the boundary of the parameter space as well as lack of identification under the null, we exploit a noticeable property of specific zero-entries in the ...
Pedersen, Rasmus Søndergaard   +1 more
openaire   +1 more source

Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19

open access: yesMathematics, 2023
Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations.
Özgür Ömer Ersin, Melike Bildirici
doaj   +1 more source

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