Results 231 to 240 of about 12,191 (249)

A realized EGARCH-MIDAS model with higher moments

Finance Research Letters, 2021
Abstract This paper proposes a realized EGARCH-MIDAS model with higher moments (REGARCH-MIDAS-SK) which combines the REGARCH-MIDAS model by Borup and Jakobsen (2019) and the REGARCH-SK model by Wu et al. (2019) to model volatility. A key feature of the proposed model is the ability to account for the high persistence of volatility and the time ...
Xinyu Wu, Haibin Xie
openaire   +1 more source

Testing for jumps in the EGARCH process

Mathematics and Computers in Simulation, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shi, Xiuhong, Kobayashi, Masahito
openaire   +2 more sources

Markov Switching Beta-skewed-t EGARCH

2019
This study extends the work of Harvey and Sucarrat [15] and present Markov regime-switching (MS) Beta-skewed-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model to predict the volatility. To examine the performance of our model, in-sample point forecast precision and AIC and BIC weights are conducted.
Woraphon Yamaka, Paravee Maneejuk, Songsak Sriboonchitta   +2 more
openaire   +1 more source

Application of EGARCH-GED model in VaR measurement

2010 International Conference on Financial Theory and Engineering, 2010
The GARCH model is used in simulating the volatility and VaR of the financial assets. The paper established an EGARCH-GED model to calculate the time varying VaR. Compared the VaR of the EGARCH-GED model and the GARCH model under the normal distribution and T distribution respectively, The paper checked the anticipated VaR in the previous step by ...
Tianjun Yu, Yang Wang
openaire   +1 more source

Pricing-to-Market Using EGARCH-Error Correction Model

International Journal of Strategic Decision Sciences, 2012
In this paper, the authors use an exponential generalized autoregressive conditional heteroscedastic (EGARCH) error-correction model (ECM), that is, EGARCH-ECM, to estimate the pass-through effects of foreign exchange (FX) rates and producers’ prices for 20 U.K. export sectors.
Baoying Lai, Nathan Lael Joseph
openaire   +1 more source

Analyzing cryptocurrency volatility: an EGARCH model

PANORAMA ECONÓMICO
The aim of this article is to examine the reasons why cryptocurrency volatility hinders its potential to replace fiat money as legal tender. We focus on Bitcoin and Ethereum for this analysis. By applying an augmented Dickey-Fuller stationarity test, we demonstrate that cryptocurrencies lack a long-term trend; instead, their movement is erratic and ...
Guillermo Arroyo Jiménez, Julia Juárez García   +1 more
openaire   +1 more source

Markov regime-switching Beta-t-EGARCH

Applied Economics, 2017
ABSTRACTWe suggest a Markov regime-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model for U.S. stock returns. We compare the in-sample statistical performance of the MS Beta-t-EGARCH model with that of the single-regime Beta-t-EGARCH model.
Szabolcs Blazsek, Han-Chiang Ho
openaire   +1 more source

Time-varying asymmetric volatility spillover between global markets and China’s A, B and H-shares using EGARCH and DCC-EGARCH models

The North American Journal of Economics and Finance, 2020
Abstract This paper investigates the volatility spillover and dynamic conditional correlation between three types of China’s shares including A, B and H-shares with 12 major emerging and developed markets from 2002 to 2017 using EGARCH and multivariate DCC-EGARCH models. Both models found that Chinese equities are more related with their neighbouring
A. Do, R. Powell, J. Yong, A. Singh
openaire   +1 more source

Model construction and empirical study of ARMA-EGARCH

2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009), 2009
This paper establishes an ARMA-EGARCH-M model by combining ARMA model with ARCH group models to study securities market volatility appraisal. The results based on examination of measuring indices for forecasting error using mass samples indicate that ARMA-EGARCH-M model surpasses ARCH group models on Shanghai securities market volatility fitting.
Bo Zhang, Zhong-min Yin
openaire   +1 more source

Estimating EGARCH-M models: Science or art?

The Quarterly Review of Economics and Finance, 1998
Abstract This paper shows that the EGARCH-M model should be estimated with caution. Regardless of the assumption made regarding the conditional error distribution, the EGARCH-M model is sensitive to the choice of starting values and the degree of computer precision.
openaire   +1 more source