Results 181 to 190 of about 11,730 (214)
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Full Bayesian Inference for GARCH and EGARCH Models
Journal of Business & Economic Statistics, 2000A full Bayesian analysis of GARCH and EGARCH models is proposed consisting of parameter estimation, model selection, and volatility prediction. The Bayesian paradigm is implemented via Markov-chain Monte Carlo methodologies. We provide implementation details and illustrations using the General Index of the Athens stock exchange.
Vrontos, Ioannis D. +5 more
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Volatility Forecasting With Range-Based EGARCH Models
Journal of Business & Economic Statistics, 2006We provide a simple, yet highly effective framework for forecasting return volatility by combining exponential generalized autoregressive conditional heteroscedasticity models with data on the range. Using Standard and Poor's 500 index data for 1983–2004, we demonstrate the importance of a long-memory specification, based on either a two-factor ...
Brandt, Michael W. +1 more
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Financial Contagion in South Asia: An EGARCH Approach
SSRN Electronic Journal, 2013This study examines financial contagion in stock markets of India, Sri Lanka and Pakistan during various financial crises. These markets represent a significant part of South Asian economies; therefore, the results obtained can be generalized to the region. The paper employs an Exponential GARCH model in an event study approach.
Syed Kashif Saeed +2 more
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Testing for jumps in the EGARCH process
Mathematics and Computers in Simulation, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shi, Xiuhong, Kobayashi, Masahito
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Markov Switching Beta-skewed-t EGARCH
2019This 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 +2 more
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Application of EGARCH-GED model in VaR measurement
2010 International Conference on Financial Theory and Engineering, 2010The 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
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Pricing-to-Market Using EGARCH-Error Correction Model
International Journal of Strategic Decision Sciences, 2012In 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
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Analyzing cryptocurrency volatility: an EGARCH model
PANORAMA ECONÓMICOThe 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 +1 more
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Markov regime-switching Beta-t-EGARCH
Applied Economics, 2017ABSTRACTWe 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
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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
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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
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