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Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models

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Title Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models
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Creator Achal Lama
Girish K Jha
Ranjit K Paul
Bishal gurung
 
Subject ARIMA
Cotlook A index
edible oils
EGARCH
GARCH
volatility
forecasting
 
Description Not Available
This paper has studied the autoregressive integrated moving-average (ARIMA) model, generalized
autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model
along with their estimation procedures for modelling and forecasting of three price series, namely domestic
and international edible oils price indices and the international cotton price ‘Cotlook A’ index. The
Augmented Dickey-Fuller (ADF) and Philips Peron (PP) tests have been used for testing the stationarity
of the series. Lagrange multiplier test has been applied to detect the presence of autoregressive conditional
heteroscedastic (ARCH) effect. A comparative study of the above three models has been done in terms of
root mean square error (RMSE) and relative mean absolute prediction error (RMAPE). The residuals of
the fitted models have been used for diagnostic checking. The study has revealed that the EGARCH
model outperformed the ARIMA and the GARCH models in forecasting the international cotton price
series primarily due to its ability to capture asymmetric volatility pattern. The SAS software version 9.3
has been used for data analysis.
Not Available
 
Date 2018-09-22T09:39:20Z
2018-09-22T09:39:20Z
2015-06-01
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/7150
 
Language English
 
Relation Not Available;
 
Publisher Agricultural Economics Research Review