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generalized autoregressive conditional heteroskedasticity

Study About the Minimum Value at Risk of Stock Index Futures Hedging Applying Exponentially Weighted Moving Average - Generalized Autoregressive Conditional Heteroskedasticity Model

Study About the Minimum Value at Risk of Stock Index Futures Hedging Applying Exponentially Weighted Moving Average - Generalized Autoregressive Conditional Heteroskedasticity Model

... average-generalized autoregressive conditional heteroskedasticity (GARCH) (1,1)-M applicable to the real financial markets based on previous ...

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FORECASTING GOLD PRICES IN SRI LANKA USING GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY APPROACH

FORECASTING GOLD PRICES IN SRI LANKA USING GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY APPROACH

... as Autoregressive Integrated Moving Average (ARIMA) (Pitigalaarachchi et ...2013), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models (Mahalingam et ...

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Functional generalized autoregressive conditional heteroskedasticity

Functional generalized autoregressive conditional heteroskedasticity

... Modeling data as a collection of functions was popularized through the work of Ramsay and Silvermann (2005). While this approach has now started to become relevant for the analysis of high-frequency volatility data, much ...

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Estimation of Volatility and Correlation with Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models: An Application to Moroccan Stock Markets

Estimation of Volatility and Correlation with Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models: An Application to Moroccan Stock Markets

... Multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models were developed for this purpose and have known a great ...dynamic conditional correlation (DCC) and ...

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Assessment of dynamic linear and non-linear models on rainfall variations predicting of Iran

Assessment of dynamic linear and non-linear models on rainfall variations predicting of Iran

... the Autoregressive Integrated Moving Average (ARIMA) models and Autoregressive Conditional Heteroskedasticity (ARCH) family models have been used for predicting the monthly and annual rainfall ...

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An Empirical Investigation of Arima and Garch Models in Agricultural Price Forecasting

An Empirical Investigation of Arima and Garch Models in Agricultural Price Forecasting

... export. Autoregressive integrated moving average (ARIMA) models performed better than the structural model in predicting the wheat price (Moghaddasiand et ...and generalized autoregressive ...

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A Note on the Oil Price Trend and GARCH Shocks

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 ...

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On the Performance of Garch Family Models in the Presence of Additive Outliers

On the Performance of Garch Family Models in the Presence of Additive Outliers

... as autoregressive conditional heteroskedastcity (ARCH) model which was the first model to assume that the volatility is not ...as generalized autoregressive conditional ...

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Structural VAR analysis of monetary transmission mechanism and central bank’s response to equity volatility shock in Taiwan

Structural VAR analysis of monetary transmission mechanism and central bank’s response to equity volatility shock in Taiwan

... of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) volatility of TWSE on Taiwan’s daily exchange rate, overnight interbank loan rate, and Taiwan Government Bond Index ...

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Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

... (Periodic AutoRegressive Moving Average) models for sea- sonal streamflow ...of autoregressive conditional het- eroskedasticity ...(AutoRegressive Conditional Heteroskedasticity) ...

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Conditional heteroskedasticity in crypto asset returns

Conditional heteroskedasticity in crypto asset returns

... Keywords: Autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH), market volatility, nonlinear time ...

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Option Pricing Applications of Quadratic Volatility Models

Option Pricing Applications of Quadratic Volatility Models

... Thus, the conditional variance is a nonlinear function and hence the RCA model may be viewed as a non-linear time series model. Nicholls and Quinn [7] studied linear as well as some nonlinear (proposed) forecast ...

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Compare Value at Risk and Return of Assets Portfolio Stock, Gold, REIT, U.S. & Iran Market Indices

Compare Value at Risk and Return of Assets Portfolio Stock, Gold, REIT, U.S. & Iran Market Indices

... Nowadays investors follow investment in asset like, stocks, gold, and real-estate investment trusts in the world. This study surveys three types of main assets portfolio of stock, gold, and REIT. This article use from ...

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MEASURING INDEX VALUE-AT-RISK USING LAG OPTIMIZATION WITH STRESSED SCENARIOS 

MEASURING INDEX VALUE-AT-RISK USING LAG OPTIMIZATION WITH STRESSED SCENARIOS 

... Boonvorachote (nod) utilized the Trivariate Structured Vector Autoregressive Method (SVAR) method where the return, volatility and volume variables were used with optimal lags for each. The paper says that with ...

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Evaluating the Forecast Performance of Autoregressive Conditional Heteroscedasticity (ARCH) Family Models

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 conditional ...

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Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models

Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models

... GARCH (General Autoregressive Conditional Heteroskedasticity) by Bollerslev is a linear time series prediction method. It is a standard textbook material in econometrics and finance[6]. There are ...

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Rolling sampled parameters of ARCH and Levy stable models

Rolling sampled parameters of ARCH and Levy stable models

... asymmetric autoregressive conditional heteroskedasticity (ARCH) model and a Levy-stable distribution are applied to some well-known financial indices (DAX30, FTSE20, FTSE100 and SP500), using a ...

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Revisiting the Effects of Growth Uncertainty on Inflation in Iran:An Application of GARCH-in-Mean Models

Revisiting the Effects of Growth Uncertainty on Inflation in Iran:An Application of GARCH-in-Mean Models

... The paper contributes to the literature in several respects: First, this paper employs quarterly Iranian data, a country that has experienced significant uncertainty in inflation and economic growth over the last three ...

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Modelling Stock Return Volatility in India

Modelling Stock Return Volatility in India

... Others models like NARCH, NARCH with one shift, APARCH and NAPARCH, these models are measuring all those asymmetric volatility and symmetric relationship between of negative and positive shocks or innovations in the BSE ...

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Application of Generalized Autoregressive Conditional Heteroschedasticity Model on Inflation and Share Price Movement in Nigeria

Application of Generalized Autoregressive Conditional Heteroschedasticity Model on Inflation and Share Price Movement in Nigeria

... of generalized autoregressive conditional heteroschedasticity (GARCH) model is deemed necessary to examine if share price movement in Nigeria is depicted a variance autoregressive ...

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