generalized autoregressive conditional heteroscedasticity (GARCH)
Exchange Rate Volatility and Central Bank Actions in Egypt: Generalized Autoregressive Conditional Heteroscedasticity Analysis
... using generalized autoregressive conditional heteroscedasticity (1,1) model under Gaussian normal distribution, considering monthly observations of Egyptian Pound against US Dollar, spanning ...
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Stock market volatility using GARCH models: Evidence from South Africa and China stock markets
... A Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to estimate volatility of the stock returns, namely, the Johannesburg Stock Exchange FTSE/JSE Albi index and ...
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Modelling and forecasting exchange rate of US dollar against Malaysian ringgit using hybrid ARIMA-GARCH and ARIMA-EGARCH models
... required. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family model is a well-known and frequently applied method especially in handling volatility for data ...
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Revisiting the Effects of Growth Uncertainty on Inflation in Iran:An Application of GARCH-in-Mean Models
... the conditional variance of growth. In this method, the generalized autoregressive conditional heteroscedasticity (GARCH) model is applied to estimate a time-varying conditional ...
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MEASURING INDEX VALUE-AT-RISK USING LAG OPTIMIZATION WITH STRESSED SCENARIOS
... (Generalized Autoregressive Conditional Heteroscedasticity Model) and EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity Model) models ...
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Option Pricing Applications of Quadratic Volatility Models
... coefficient autoregressive (RCA) models with quadratic generalized autoregressive conditional heteroscedasticity (GARCH) errors and study the mo- ments, mean, variance and ...
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Evaluating the Forecast Performance of Autoregressive Conditional Heteroscedasticity (ARCH) Family Models
... The Generalized Autoregressive conditional heteroscedasticity models were propounded by [7] and ...(Autoregressive conditional heteroscedaticity); this means that the ...
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Long Memory in Stock Market Volatility:Evidence from India
... Long memory in variance or volatility refers to a slow hyperbolic decay in auto-correlation functions of the squared or log-squared returns. GARCH models extensively used in empirical analysis do not account for long ...
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Effects of Liquidity Incentives on Performance of Listed Firms in Kenya
... the autoregressive conditional heteroscedasticity (ARCH) models, with its extension to generalized autoregressive conditional heteroscedasticity (GARCH) respectively which ...
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A Study of Conditional Volatilities in Financial Markets using Generalized Conditional Heteroscedasticity Jump Models
... Multivariate GARCH (MGARCH) models are veritable framework for examining the covariance matrix of asset returns. Prominent amongst these MGARCH models are the VECH (VEC) model of Bollerslev, Engle and Wooldridge (1998), ...
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Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico
... regime switching models. ARCH family models are used for modeling and forecasting conditional volatility of asset returns. Engle developed ARCH and its extensions consist of Generalized ARCH (GARCH) which ...
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The Glosten-Jagannathan-Runkle-Generalized Autoregressive Conditional Heteroscedastic Approach to Investigating the Foreign Exchange Forward Premium Volatility
... the heteroscedasticity and integrate the information content of the conditional variance that varies in ...the conditional variance effect in a univariate framework namely GARCH-in ...
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Generalized R estimators under Conditional heteroscedasticity
... There is a vast literature on the R-estimation of parameters in homoscedastic regression and autoregression models. For a glimpse, see Koul (1992, Section 4.4), Jureˇ ckov´ a and Sen (1996, Section 3.4, Chapter 6) and H´ ...
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Estimation of Volatility and Correlation with Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models: An Application to Moroccan Stock Markets
... the autoregressive conditional heteroskedasticity (ARCH) ...are autoregressive models in squared returns. The conditional part comes from the fact that next period volatility is ...
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An Empirical Investigation of Arima and Garch Models in Agricultural Price Forecasting
... Jenkins Autoregressive integrated moving average (ARIMA) and Generalized autoregressive conditional heteroscedastic (GARCH) models are studied and applied for modeling and forecasting of spot ...
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Modelling Stock Return Volatility in India
... the conditional heteroscedasticity, which explains the conditional standard deviations of the underlying asset ...(Autoregressive Conditional Heteroscedasticity) model was ...
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A Note on the Oil Price Trend and GARCH Shocks
... This paper investigates the trend in the monthly real price of oil between 1990 and 2008 with a generalized autoregressive conditional heteroskedasticity (GARCH) model. Trend and volatility are ...
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Application of Generalized Autoregressive Conditional Heteroschedasticity Model on Inflation and Share Price Movement in Nigeria
... contributed to the already existing literature on share prices movement and inflation which has been subjected to extensive research by academics, researchers, practitioners and policy makers world over since the 1990s. ...
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Inflation and Inflation Uncertainty in Turkey
... Abstract: In this study, the relationship between inflation and inflation uncertainty is analyzed using Granger causality tests with annual inflation series covering the time period 1923 to 2012 for Turkish Economy. ...
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Regime switching behavior of volatilities of Islamic equities: evidence from Markov Switching GARCH models for some selected broad based indices
... ˜st−1. This would require the integration over a number of (unobserved) regime paths that would grow exponentially with the sample size rendering the model essentially intractable and impossible to estimate. Therefore, a ...
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