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Analyzing the Effect of Dual Long Memory Process in Forecasting Agricultural Prices in Different Markets of India

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  • Ranjit Kumar Paul
  • Bishal Gurung
  • Sandipan Samanta

Abstract

The potential presence of long memory (LM) properties in mean and volatility of the spot price of wheat and mustard in different markets of India has been investigated. The findings revealed the evidence of long range dependence in price series as well as in the volatility. Accordingly, Autoregressive fractionally integrated moving average (ARFIMA) with error following Fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) model has been applied for forecasting the price of commodities in different markets of India. To this end, evaluation of forecasting is carried out with root mean squares error (RMSE), mean absolute error (MAE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models were used for diagnostic checking.

Suggested Citation

  • Ranjit Kumar Paul & Bishal Gurung & Sandipan Samanta, 2015. "Analyzing the Effect of Dual Long Memory Process in Forecasting Agricultural Prices in Different Markets of India," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(4), pages 235-249.
    • Handle: RePEc:rss:jnljef:v4i4p4
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    1. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    2. A. Assaf, 2007. "Fractional integration in the equity markets of MENA region," Applied Financial Economics, Taylor & Francis Journals, vol. 17(9), pages 709-723.
    3. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    4. Philipp Sibbertsen, 2004. "Long memory in volatilities of German stock returns," Empirical Economics, Springer, vol. 29(3), pages 477-488, September.
    5. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 397-417.
    6. Ignacio N. Lobato & Peter M. Robinson, 1998. "A Nonparametric Test for I(0)," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 475-495.
    7. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    8. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssiere, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," Journal of Econometrics, Elsevier, vol. 112(2), pages 265-294, February.
    9. Paul, R.K., 2014. "Forecasting Wholesale Price of Pigeon Pea Using Long Memory Time-Series Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 27(2).
    10. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    11. Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. GIRAITIS, Liudas & KOKOSZKA, Piotr & LEIPUS, Remigijus & TEYSSIÈRE, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," LIDAM Reprints CORE 1594, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    15. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
    16. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    17. Hyun J. Jin & Darren L. Frechette, 2004. "Fractional Integration in Agricultural Futures Price Volatilities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 432-443.
    18. Kyongwook Choi & Shawkat Hammoudeh, 2009. "Long Memory in Oil and Refined Products Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-116.
    19. Weiss, Andrew A., 1986. "Asymptotic Theory for ARCH Models: Estimation and Testing," Econometric Theory, Cambridge University Press, vol. 2(1), pages 107-131, April.
    20. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    21. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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