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Agricultural Economics Research Review
Year : 2015, Volume : 28, Issue : 1
First page : ( 73) Last page : ( 82)
Print ISSN : 0971-3441. Online ISSN : 0974-0279.
Article DOI : 10.5958/0974-0279.2015.00005.1

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

Lama Achala,*, Jha Girish K.b, Paul Ranjit K.a, Gurung Bishala

aICAR-Indian Agricultural Statistics Research Institute, New Delhi - 110 012

bICAR-Division of Agricultural Economics, Indian Agricultural Research Institute, New Delhi - 110 012

*Author for correspondence Email: chllm6@gmail.com

§This paper is a part of master’s thesis “A study on agricultural commodity price volatility using dynamic neural networks” submitted in the year 2013 by the first author to Post Graduate School, Indian Agricultural Research Institute, New Delhi

JEL Classification: C 13, C 53, Q 13, Q 17

Online published on 23 June, 2015.

Abstract

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.

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Keywords

ARIMA, Cotlook A index, edible oils, EGARCH, GARCH, volatility, forecasting.

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