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A Comparative Study on Time-delay Neural Network and GARCH Models for Forecasting Agricultural Commodity Price Volatility

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Title A Comparative Study on Time-delay Neural Network and GARCH Models for Forecasting Agricultural Commodity Price Volatility
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Creator Achal Lama
Girish K. Jha
Bishal Gurung
Ranjit Kumar Paul
Anshu Bharadwaj
Rajender Parsad
 
Subject Time-delay neural network
GARCH
Non-parametric
Combining forecasts
 
Description Not Available
In this paper, forecasting performance of time-delay neural network and GARCH models for predicting the volatility using monthly price series of edible oils in domestic and international markets is evaluated. An attempt has also been made to investigate whether the forecasting performance of two competing models can be improved by combining their individual forecasts. For this purpose, the individual models were combined to produce improved forecasts using non-parametric approach through the use of
kernel. Further, the models were evaluated on their ability to predict the correct change of direction (CCD) for future values.
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Date 2017-04-13T05:44:50Z
2017-04-13T05:44:50Z
2016-04-30
 
Type Research Paper
 
Identifier Achal Lama, Girish K Jha, Bishal Gurung, Ranjit Kumar Paul, Anshu Bharadwaj and Rajender Parsad (2016). A comparative study on time-delay neural network and GARCH models for forecasting agricultural commodity price volatility. Journal of the Indian Society of Agricultural Statistics, 70(1), 7-18.
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http://krishi.icar.gov.in/jspui/handle/123456789/3606
 
Language English
 
Relation Not Available;
 
Publisher Indian Society of Agricultural Statistics