​​​​​​​​​​​​​September 2018, Vol. 3, No. 9, pp. 190-195. 

​​​Forecasting Volatility of Processed Milk Products in the frameworks of ARCH Model

Md. Anwar Hossain*
Planning and Development Division, Bangladesh Council of Scientific and Industrial Research, Dr.Qudrat-I-Khuda Road, Dhanmondi, Dhaka-1205, Bangladesh.

​​*Corresponding author’s e-mail: anwarbcsir@yahoo.com

Abstract

Present work has explored the impact of type of food products on testing for ARCH effects and on the estimation of ARCH models for food products analysis data. Our sample comprises physiochemical and microbial analysis data for food products. The results of the food products forecasts reveal that processed milk products were forecasted to volatility of Tritratable Acidity (as lactic acid) (%) and Total Ash (on dry basis) (%) content which is highly volatile in this time period. The usual unit root tests results of the Dickey-Fuller test (DF) presented in study reject the null hypothesis of most of milk qualitative variable indicating that the series were stationary except Protein (%) and Total Ash (%). Hence, processed milk products qualitative analysis data are appropriate for this technique ARCH models of milk products analysis as expected.

Keywords: Physiochemical and Microbial analysis; ARCH effects; Forecasted to Volatility; Dickey–Fuller test.

References

  1. ​Star TD. Milk consumption lowest, prices highest in region, 2008. http://archive.thedailystar.net/newDesign/news-details.php?nid=66353.
  2. Hossain A. Quality Assessment of Processed Milk Products in Bangladesh in the framework of Six Sigma Analysis. Int J Ind Eng 2018;2:181-89.
  3. Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econom J Econom Soc 1982;50:987-1007.
  4. Ngailo E, Luvanda E, Massawe ES. Time Series Modelling with Application to Tanzania Inflation Data. Journal of Data Analysis and Information Processing 2014;2:49-59.
  5. Bollerslev T. Generalized autoregressive conditional heteroskedasticity. J Econom 1986;31:307-27.
  6. Taylor SJ. Modelling financial time series. Chichester: John Wiley & Sons, Ltd., 1986.
  7. Nelson DB. Conditional heteroskedasticity in asset returns: A new approach. Econom J Econom Soc 1991;59:347-70.
  8. Glosten LR, Jagannathan R, Runkle DE. On the relation between the expected value and the volatility of the nominal excess return on stocks. J Finance 1993;48:1779-801.
  9. Rabemananjara R, Zakoian J-M. Threshold ARCH models and asymmetries in volatility. J Appl Econom 1993;8:31-49.
  10. Baillie RT, Bollerslev T, Mikkelsen HO. Fractionally integrated generalized autoregressive conditional heteroskedasticity. J Econom 1996;74:3-30.
  11. Engle RF, Bollerslev T. Modelling the persistence of conditional variances. Econom Rev 1986;5:1-50.
  12. Bollerslev T. Glossary to arch (garch). 2007.
  13. Institute of Food Science and Technology (IFST), BCSIR D. Approval for adhoc data use, 2010
  14. Baillie RT, Bollerslev T. Common stochastic trends in a system of exchange rates. J Finance 1989;44:167-81.
  15. Kuwornu JKM, Mensah-Bonsu A, Ibrahim H. Analysis of foodstuff price volatility in Ghana: Implications for food security. Eur J Bus Manag 2011;3:100-18.
  16. Hye Q, Ali A. Money Supply, Food Prices and Manufactured Product Prices: A Causality Analysis for Pakistan Economy.
  17. Mahadeva L, Robinson P. Unit Root Testing to Help Model Building. Hand Book, Cent Bank, 2004.
  18. Pantelis A, Zehtabchi M. Testing for unit roots in the presence of structural change IRAN–GREECE CPI case. 2008.
  19. Iordanova T. Introduction to Stationary and Non-Stationary Processes | Investopedia, 2007. http://www.investopedia.com/articles/trading/07/stationary.asp.

ISSN 2456-0235

International Journal of Modern Science and Technology

INDEXED IN