Autoregressive Conditional Heteroscedasticity (ARCH)
Evaluating the Forecast Performance of Autoregressive Conditional Heteroscedasticity (ARCH) Family Models
... of ARCH family models used in the ...among autoregressive moving average (ARMA), Autoregressive conditional heteroscedasticity (ARCH), Generalized Autoregressive ...
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Changes in the Unconditional Variance and Autoregressive Conditional Heteroscedasticity
... called autoregressive conditional heteroscedasticity (ARCH) models and used them to estimate the variance of UK ...of ARCH models were proposed: GARCH, integrated GARCH (IGARCH), ...
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Exchange Rate Volatility and Central Bank Actions in Egypt: Generalized Autoregressive Conditional Heteroscedasticity Analysis
... generalized autoregressive conditional heteroscedasticity (1,1) model under Gaussian normal distribution, considering monthly observations of Egyptian Pound against US Dollar, spanning the period ...
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Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico
... models. ARCH family models are used for modeling and forecasting conditional volatility of asset ...developed ARCH and its extensions consist of Generalized ARCH (GARCH) which was proposed by ...
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Modelling Stock Return Volatility in India
... the conditional heteroscedasticity, which explains the conditional standard deviations of the underlying asset ...the ARCH (Autoregressive Conditional Heteroscedasticity) ...
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Examining the Value-at-risk Performance of Fractionally Integrated GARCH Models: Evidence from Energy Commodities
... generalised autoregressive conditional heteroscedasticity (GARCH), fractionally integrated GARCH (FIGARCH), hyperbolic GARCH (HYGARCH) and fractionally integrated, asymmetric power ARCH ...
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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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Conditional Heteroscedasticity in Streamflow Process: Paradox or Reality?
... of Autoregressive Conditional Het- eroscedasticity (ARCH) or volatility of streamflow processes, a form of nonlinear ...of conditional heteroscedasticity in streamflow processes is no ...
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Stock market volatility using GARCH models: Evidence from South Africa and China stock markets
... 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 the ...
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Modelling and forecasting exchange rate of US dollar against Malaysian ringgit using hybrid ARIMA-GARCH and ARIMA-EGARCH models
... 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 ...generalized autoregressive conditional heteroscedasticity (GARCH) model is applied to estimate a time-varying conditional residual ...
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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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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 were utilized ...
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Rolling sampled parameters of ARCH and Levy stable models
... one-day-ahead conditional volatility predictions for the trading days of 11 th January 2000 to 5 th July ...the ARCH model is estimated at each point in time, we use the maximum likelihood estimates at time ...
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Generalized R estimators under Conditional heteroscedasticity
... the conditional mean function of an autoregressive model through a three-steps procedure and then derive their asymp- totic ...Engel’s ARCH model and the threshold heteroscedastic ...
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A Range Based GARCH Model for Forecasting Volatility
... Auto-Regressive Conditional Heteroskedasticity Parkinson Range (GARCH-PARK-R) model can actually produce volatility estimates that are relatively superior than the ARCH class of models using inter-daily ...
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Recursive estimation of non-linear time series models
... A r.ecursive scheme for simultaneous optimal estimation of conditional mean and variance in a nonlinear ARCH (autoregressive con- ditional heteroscedastic) model is also proposed.. Keywo[r] ...
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The Glosten-Jagannathan-Runkle-Generalized Autoregressive Conditional Heteroscedastic Approach to Investigating the Foreign Exchange Forward Premium Volatility
... of Arch and GARCH parameters are statistically significant and have the same sign (whatever the 3, 6, 9 and 12 month horizon) in the presence of a GARCH term demonstrating a great ...the conditional ...
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Essays in financial econometrics : GMM and conditional heteroscedasticity
... The usual √ T consistent and asymptotically normal behavior of GMM estimators relies cru- cially upon the existence of the variance of the moment conditions. Unfortunately, moment conditions with infinite variance abound ...
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Functional generalized autoregressive conditional heteroskedasticity
... functional ARCH(1) processes, for which the conditional variance depends on the whole (intra-day) path of the previous obser- ...the conditional volatility function of the present observation is ...
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