[PDF] Top 20 Modelling and forecasting volatile data by using ARIMA and GARCH models
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Modelling and forecasting volatile data by using ARIMA and GARCH models
... Crude oil prices are volatile time series. The prices just like any other volatile commodity have huge price swings in periods of oversupply or shortage. The crude oil prices cycle may last over several ... See full document
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Modelling and forecasting exchange rate of US dollar against Malaysian ringgit using hybrid ARIMA-GARCH and ARIMA-EGARCH models
... between ARIMA model and GARCH family models in modelling and forecasting volatile data by using E-Views ...software. GARCH family models can be ... See full document
27
Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non linear models
... the GARCH family ...linear GARCH models seems to be confirmed by the Log likelihood values, although both the AIC and SC do not suggest a clear ...asymmetric GARCH models is further ... See full document
23
Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA GARCH Approach
... sample forecasting or say ex-post and ex- ante ...econometric models which is perfectly and methodologically ...conventionally. Forecasting in macroeconomic or financial data is widely ... See full document
20
Inflation dynamics in Jamaica: Evidence from the ARMA methodology
... Slovenia using ARMA models with a data set ranging from January 1994 to June 2006 and revealed that in terms of forecast ability ARMA models outperform AR models, when allowing for the ... See full document
9
Forecasting daily meteorological time series using ARIMA and regression models
... these models (Hoffmann et ...The forecasting of these two quantities using statistical methods is, therefore, of great ...series forecasting methods are based on the analysis of historical ... See full document
12
Modelling And Forecasting The Bandwidth Consumptions In Technology Campus Of UTeM By Using A Statistical Method (ARIMA)
... of data we used is a time series ...numerical data focuses in progressive order, normally happen in uniform ...The data collection will be carried out by daily for 8 months which is from January to ... See full document
24
Day ahead electricity price forecasting with emphasis on its volatility in Iran (GARCH combined with ARIMA models)
... price forecasting is an essential task for market ...volatility. ARIMA is suitable in forecasting, but it is not able to handle nonlinearity and volatility are existent in time ...Therefore, ... See full document
17
Predicting inflation in Sri Lanka using ARMA models
... Nigeria using Box-Jenkins ARIMA models with a data set ranging over the period January 2006 to December 2015 and showed that the ARIMA (0, 1, 1) model was the best model for ... See full document
10
The Comparison among ARIMA and hybrid ARIMA-GARCH Models in Forecasting the Exchange Rate of Iran
... the forecasting performance of the ARIMA model and hybrid ARMA-GARCH Models by using daily data of the Iran’s exchange rate against the ...remaining data were used to do ... See full document
20
Modelling of crude oil prices using hybrid arima-garch model
... years, modelling and forecasting volatility of a financial time series has become the area of ...applied models for this volatility are the conditional heteroscedastic ...these models is to ... See full document
29
GOLD PRICE FORECASTING IN INDIA USING ARIMA MODELLING
... about forecasting gold prices using time series ...price data for the period January 2012 to June 2012 was taken for ...new models are used to make accurate forecasts in the long run, ... See full document
20
Modelling and Forecasting Volatility of Value Added Tax Revenue in Kenya
... In Kenya, the tax revenue analysis and forecast is produced by a single agency, which is the Ministry of Finance in conjunction with the Kenya Institute for Public Policy Research and Analysis (KIPPRA). The ministry ... See full document
7
Modeling and forecasting inflation in Burundi using ARIMA models
... inflation using ARIMA models with quarterly data ranging over the period 1976 to 1998 and illustrated some practical issues in ARIMA time series ...Zimbabwe using GARCH ... See full document
10
Modeling and forecasting inflation in Philippines using ARIMA models
... inflation using Artificial Neural Network models with a data set ranging over the period March 1960 – December 2009 and established that direct forecasts are more accurate then their recursive ... See full document
14
Application of Arima and Garch models in forecasting crude oil prices
... In particular, we consider crude oil prices data as heteroscedastic time series models where the conditional variance given in the past is no longer constant(Palma, 2007). In a financial analysis, forecast ... See full document
25
Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach
... different data, ...sample data draw in the gap of 10 to 14 as converting to logarithm function and in the gap of -4 and 4 due to taking the first different ... See full document
19
Modeling and forecasting inflation in The Gambia: an ARMA approach
... Slovenia using ARMA models with a data set ranging from January 1994 to June 2006 and revealed that in terms of forecast ability ARMA models outperform AR models, when allowing for the ... See full document
10
An Empirical Investigation of Arima and Garch Models in Agricultural Price Forecasting
... for forecasting Gram prices and model gives reasonable and acceptable ...the data series. GARCH model was also fitted to forecast Gram ...The GARCH model performs better on account of its ... See full document
14
Modeling and forecasting inflation in Tanzania using ARIMA models
... and models by not using an inflation rate prediction can result in imprecise investment and saving decisions, potentially leading to economic instability (Enke & Mehdiyev, ...Tanzania using ... See full document
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