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Paul, A. K.
- The volatility spillover of potato prices in different markets of India
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PDF Views:76
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Source
Current Science, Vol 123, No 3 (2022), Pagination: 482-487Abstract
Agricultural commodity prices, particularly the prices of perishable commodities, are volatile. The interdependency of market prices of agricultural commodities makes it difficult for accurate modelling. In the present study, two variants of multivariate generalized autoregressive conditional heteroscedastic models, namely DCC and BEKK, have been applied for modelling the price volatility of potato in five major markets in India, i.e. Agra, Delhi, Bengaluru, Mumbai and Ahmedabad. It is observed that the Agra market has the highest price variability, whereas Mumbai has the least. All the studied market prices showed a significant presence of conditional heteroscedasticity. To this end, Volatility Impulse Response Function has been used to assess the impacts of a specific shock on the price volatility spillovers of potatoes among the studied markets. The volatility spillover has been computed for all the markets.Full Text
References
- Bellemare, M. F., Barrett, C. B. and Just, D. R., The welfare impacts of commodity price volatility: evidence from rural Ethiopia. Am. J. Agric. Econ., 2013, 95, 877–899.
- Engle, R. F., Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 1982, 987–1007.
- Newbery, D. M., The theory of food price stabilisation. Econ. J., 1989, 99(398), 1065–1082.
- Zheng, Y., Kinnucan, H. W. and Thompson, H., News and volatility of food prices. Appl. Econ., 2008, 40, 1629–1635.
- Apergis, N. and Rezitis, A., Food price volatility and macroeconomic factors: evidence from GARCH and GARCH-X estimates. J. Agric. Appl. Econ., 2011, 43, 95–110.
- Candila, V. and Farace, S., On the volatility spillover between agricultural commodities and Latin American stock markets. Risks, 2018, 6, 116.
- Paul, R. K., Prajeshnu and Himadri, G., GARCH nonlinear time series analysis for modelling and forecasting of India’s volatile spices export data. J. Indian Soc. Agric. Stat., 2009, 63, 123–131.
- Paul, R. K., Saxena, R., Chaurasia, S. and Rana, S., Examining export volatility, structural breaks in price volatility and linkages between domestic and export prices of onion in India. Agric. Econ. Res. Rev., 2015, 28, 101–116.
- Paul, R. K., Bhardwaj, S. P., Singh, D. R., Kumar, A., Arya, P. and Singh, K. N., Price volatility in food commodities in India – an empirical investigation. Int. J. Agric. Stat. Sci., 2015, 11(2), 395–401.
- Paul, R. K., Rana, S. and Saxena, R., Effectiveness of price forecasting techniques for capturing asymmetric volatility for onion in selected markets of Delhi. Indian J. Agric. Sci., 2016, 86, 303–309.
- Singla, S., Paul, R. K. and Shekhar, S., Modelling price volatility in onion using wavelet based hybrid models. Indian J. Econ. Dev., 2021, 17, 256–265.
- Hafner, C. and Herwartz, H., Time-Varying Market Price of Risk in the CAPM, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 1999.
- Sinha, K. et al., Volatility spillover using multivariate GARCH model: an application in futures and spot market price of black pepper. J. Indian Soc. Agric. Stat., 2017, 71(1), 21–28.
- Paul, R. K., Das, T., Panwar, S., Paul, A. K. and Bhar, L. M., Volatility and spillover in onion prices in major markets of Karnataka, India. Indian J. Agric. Market., 2019, 33, 65–76.
- Koop, G., Pesaran, M. H. and Potter, S. M., Impulse response analysis in nonlinear multivariate models. J. Econ., 1996, 74, 119–147.
- Baba, Y., Engle, R. F., Kraft, D. F. and Kroner, K. F., Multivariate simultaneous generalized ARCH. Manuscript, Department of Economics, University of California, San Diego, 1990.
- Engle, R. F. and Kroner, K. F., Multivariate simultaneous generalized ARCH. Econ Theory, 1995, 11, 122–150.
- Deep Learning Technique for Forecasting the Price of Cauliflower
Abstract Views :94 |
PDF Views:46
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
Source
Current Science, Vol 124, No 9 (2023), Pagination: 1065-1073Abstract
Vegetables are the staple food in our diets. Vegetable prices are difficult to forecast because they are influenced by a variety of factors, including weather, demand and supply chain, Government policies, etc. and exhibit volatile fluctuations. Marketing of vegetables is complex, especially because of their perishability, seasonality and bulkiness. An accurate and timely forecast of vegetables is essential to help its stakeholders. Previous studies observed that traditional statistical models are unable to capture the complex behaviour of vegetable markets. In this study, a comparative assessment has been carried out among the traditional time-series model, machine learning and deep learning techniques in order to find the best-suited model. For empirical illustration, cauliflower markets have been chosen as it is one of India’s most important and popular winter. In order to identify the complexity in the price of cauliflower, the machine learning technique, i.e. artificial neural network and deep learning technique, i.e. long short-term memory model have been implemented. In addition, the traditional stochastic time-series model, i.e. autoregressive integrated moving average model, was used to compare the prediction accuracy of the above models. To this end, the moving window forecast approach was also implemented to evaluate the sensitivity of these models with respect to forecast length. It can be concluded that the deep learning model outperforms the traditional time-series model and the machine learning technique for both short- and long-term forecasting.Keywords
Cauliflower, Deep Learning Technique, Machine Learning, Statistical Models, Vegetable Prices.Full Text
References
- Dias, J. S., Nutritional quality and effect on disease prevention of vegetables. Food Nutr. Sci., 2019, 10(4), 369–402.
- Kumar, V., Sharma, H. R. and Singh, K., Behaviour of market arri-vals and prices of selected vegetable crops: a study of four metro-politan markets. Agric. Econ. Res. Rev., 2005, 18(2), 271–290.
- https://www.fao.org/faostat/es/#home (accessed on 13 January 2022).
- Paul, R. K., Das, T. and Yeasin, M., Ensemble of time series and machine learning model for forecasting volatility in agricultural prices. Natl. Acad. Sci. Lett., 2023; https://doi.org/10.1007/s40009-023-01218-x
- Paul, R. K. and Garai, S., Wavelets based artificial neural network technique for forecasting agricultural prices. J. Indian Soc. Prob. Stat., 2022, 23(1), 47–61.
- Paul, R. K., Rana, S. and Saxena, R., Effectiveness of price fore-casting techniques for capturing asymmetric volatility for onion in selected markets of Delhi. Indian J. Agric. Sci., 2016, 86(3), 303– 309.
- Rakshit, D., Paul, R. K. and Sanjeev, P., Asymmetric price volatility of onion in India. Indian J. Agric. Econ., 2021, 76(2), 245–260.
- Paul, R. K., Yeasin, M. and Paul, A. K., The volatility spillover of potato prices in different markets of India. Curr. Sci., 2022, 123(3), 482–487.
- Paul, R. K. et al., Machine learning techniques for forecasting agri-cultural prices: a case of brinjal in Odisha, India. PLoS ONE, 2022, 17(7), e0270553.
- Dieng, A., Alternative forecasting techniques for vegetable, Rev. Sénégalais Rec. Agric. Agroallementalress, 2008, 1(3), 5–10.
- Luo, C., Wei, Q., Zhou, L., Zhang, J. and Sun, S., Prediction of vegetable price based on neural network and genetic algorithm. IFIP Adv. Infor. Commun. Technol., 2011, 346, 672–681.
- Nasira, G. M., Professor, A. and Hemageetha, N., Forecasting model for vegetable price using back propagation neural network. Int. J. Comput. Intel. Informat., 2012, 2(2), 110–115.
- Xiong, T., Li, C. and Bao, Y., Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: evidence from the vegetable market in China. Neurocomputing, 2018, 275, 2831–2844.
- Kyriazi, F., Thomakos, D. D. and Guerard, J. B., Adaptive learning forecasting, with applications in forecasting agricultural prices. Int. J. Forecast., 2019, 35(4), 1356–1369.
- Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z. and Zhang, H., Deep learning with long short-term memory for time series prediction. IEEE Commun. Mag., 2019, 57(6), 114–119.
- Ma, Q., Comparison of ARIMA, ANN and LSTM for stock price prediction. E3S Web of Conf., 2020, 218.
- Chen, Q., Lin, X., Zhong, Y. and Xie, Z., Price prediction of agri-cultural products based on wavelet analysis – LSTM. IN Proceed-ings – 2019 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, Singapore, 2019, pp. 984–990.
- Yin, H., Jin, D., Gu, Y. H., Park, C. J., Han, S. K. and Yoo, S. J., STL-ATTLSTM: vegetable price forecasting using STL and atten-tion mechanism-based LSTM. Agriculture, 2020, 10(12), 612.
- Zhang, G., Eddy Patuwo, B. and Hu, Y. M., Forecasting with artifi-cial neural networks: the state of the art. Int. J. Forecast., 1998, 14(1), 35–62.
- Rumelhart, D. E., Hinton, G. E. and Williams, R. J., Learning In-ternal Representations by Error Propagation, California University, San Diego, La Jolla Institute for Cognitive Science, USA, 1985.
- Hochreiter, S. and Schmidhuber, J., Long short-term memory. Neural Comput., 1997, 9(8), 1735–1780.
- Ta, V. D., Liu, C. M. and Tadesse, D. A., Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Appl. Sci., 2020, 10(2), 437.
- Han, J., Kamber, M. and Pei, J., Data mining: concepts and techni-ques. Data Mining: Concepts and Techniques, Elsevier, New York, 2012.
- Diebold, F. X. and Mariano, R. S., Comparing predictive accuracy. J. Bus. Econ. Stat., 1995, 13, 253–263.