Effectiveness of price forecasting techniques for capturing asymmetric volatility for onion in selected markets of Delhi
Abstract views: 251 / PDF downloads: 132
https://doi.org/10.56093/ijas.v86i3.56843
Keywords:
EGARCH model, GARCH model, Onion, Onion prices, VolatilityAbstract
Onion prices exhibit very high instability/volatility in all the selected markets of Delhi. The present study aimed to forecast the prices of onion for three markets of Delhi, viz. Azadpur, Keshopur and Shahdara using different foresting techniques. The study was based on times series secondary data on monthly wholesale price of onion from April 2005 to February 2015. After ensuring the stationarity of series after seasonal adjustment and differencing, the best ARIMA model was chosen for individual series. The residuals were checked for the presence of autocorrelation, it was found that the residuals are correlated implying improper specification of the models. Also, the plots of prices in the selected markets also exhibited nonlinearity in the series, which necessitated the application of non-linear
models to the data. Considering this, squared residuals were checked for the presence of conditional heteroscedasticity. The presence of conditional heteroscedasticity was found in all the three price series. A significant ARCH-LM test and high value of skewness and kurtosis coefficients justify the selection of EGARCH models as the best fit models in these markets. The out-of-sample forecast of onion price has been carried out by using the best fitted EGARCH/GARCH model and it is projected that the prices of onion will be between rupees 1800-1950 per quintal in Azadpur and Shahdara market; while the prices will remain between rupees 2178 to 2413 per quintal in Keshopur market during March to July, 2015.
Downloads
References
Bollerslev T. 1986. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 31: 307–27. DOI: https://doi.org/10.1016/0304-4076(86)90063-1
Box G E P, Jenkins G M and Reinsel G C. 2007. Time-Series Analysis: Forecasting and Control, 3rd edition. Pearson education, India. DOI: https://doi.org/10.1002/9781118619193.ch5
Chengappa P G, Arun M, Yadava C G and Kumar H M P. 2012. IT Application in Agricultural Marketing Service Delivery – Electronic Tender System in Regulated Markets, Agricultural Economics Research Review 25: 359–72
Engle R F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of U K inflation. Econometrica 50: 987-1 008. DOI: https://doi.org/10.2307/1912773
Fan J and Yao Q. 2003. Nonlinear Time Series: Nonparametric and Parametric Methods. Springer, USA.
Ghosh H, Paul R K and Prajneshu 2010. The GARCH and EGARCH nonlinear time-series models for volatile data: An Application. Journal of Statistics and Applications 5(2): 177– 93. DOI: https://doi.org/10.3233/MAS-2010-0147
Nelson D B and Cao C Q. 1992. Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics 10: 229–35. DOI: https://doi.org/10.1080/07350015.1992.10509902
Paul R K and Das M K. 2010. Statistical modelling of inland fish production in India. Journal of the Inland Fisheries Society of India 42: 1–7.
Paul R K and Das M K. 2013. Forecasting of average annual fish landing in Ganga Basin. Fishing Chimes 33 (3): 51–4
Paul R K, Panwar S, Sarkar S K, Kumar A, Singh K N, Farooqi S and Chaudhary V K. 2013. Modelling and forecasting of meat exports from India. Agricultural Economics Research Review 26 (2): 249–56.
Paul R K. 2014. Forecasting Wholesale Price of pigeon pea using long memory time-series models. Agricultural Economics Research Review 27(2): 167–76 DOI: https://doi.org/10.5958/0974-0279.2014.00021.4
Paul, R K, Gurung, B and Paul, A K. 2015. Modeling and forecasting of retail price of arhar dal in Karnal, Haryana. Indian Journal of Agricultural Sciences 85(1): 69–72.
Sekhar 2004. Agricultural price volatility in international and Indian markets. Economic and Political Weekly 39: 4 729–36
Straumann D. 2005. Estimation in Conditionally Heteroscedastic Time Series Models. Springer, Germany.
Tsay R S. 2005. Analysis of Financial Time Series, 2nd Ed. John Wiley, USA. DOI: https://doi.org/10.1002/0471746193
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2016 The Indian Journal of Agricultural Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.