Results 11 to 20 of about 3,197 (255)

GARCH Modeling of Cryptocurrencies [PDF]

open access: yesSSRN Electronic Journal, 2017
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria.
Chu, Jeffrey   +3 more
openaire   +2 more sources

Multivariate GARCH Models [PDF]

open access: yesSSRN Electronic Journal, 2008
This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed.
Silvennoinen, Annastiina   +1 more
openaire   +3 more sources

Multivariate GARCH Models: A Survey [PDF]

open access: yesJournal of Applied Econometrics, 2003
This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications, the inference methods, and the main areas of application of these models in financial econometrics.
Luc Bauwens   +2 more
openaire   +3 more sources

Closing the GARCH gap: Continuous time GARCH modeling [PDF]

open access: yesJournal of Econometrics, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Werker, B.J.M., Drost, F.C.
openaire   +8 more sources

Regime Switching GARCH Models [PDF]

open access: yesSSRN Electronic Journal, 2006
We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for stationarity and existence of moments.
Luc, BAUWENS   +2 more
openaire   +4 more sources

Comparison and Forecasting of VaR Models for Measuring Financial Risk: Evidence from China

open access: yesDiscrete Dynamics in Nature and Society, 2022
With increasing extremal risk, VaR has been becoming a popular methodology because it is easy to interpret and calculate. For comparing the performance of extant VaR models, this paper makes an empirical analysis of five VaR models: simple VaR, VaR based
Yuling Wang, Yunshuang Xiang, Huan Zhang
doaj   +1 more source

Empirical Testing of Models of Autoregressive Conditional Heteroscedasticity Used for Prediction of the Volatility of Bulgarian Investment Funds

open access: yesRisks, 2023
The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment ...
Mariana Petrova, Teodor Todorov
doaj   +1 more source

The application of the hybrid copula-GARCH approach in the simulation of extreme discharge values

open access: yesApplied Water Science, 2022
Statistical analysis and simulation of annual maximum discharge values, while considering the corresponding maximum daily rainfall, provide a comprehensive view of flood management.
Mohammad Nazeri Tahroudi   +2 more
doaj   +1 more source

A Copula-Garch Modelcopula-Garch Model [PDF]

open access: yesEconomic Research-Ekonomska Istraživanja, 2010
AbstractIn the present study we develop a new two-dimensional Copula-GARCH model. This type of two-dimensional process is characterized by a dependency structure modeled using a copula function. For the marginal densities we employ a GARCH(1,1) model with innovations drawn from a t-Student distribution.
openaire   +1 more source

Modeling crude oil price volatility in Nigeria: using GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) models [PDF]

open access: yesمجلة العلوم التجارية والبيئية
This study investigates the performance of various GARCH models for volatility forecasting, focusing on the GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) frameworks, each tested with normal and Student’s t-distributions.
Frederick A. Omoruyi   +2 more
doaj   +1 more source

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