International Journal of Modern Computation, Information and Communication Technology

ISSN 2581-5954

July-August 2020, Vol. 3, Issue 7-8, p. 70-76.​​

Forecasting volatility of quality for wheat soya blend products in the statistical model of ARCH
Md. Anwar Hossain*¹, Kalipada Sen², Nitai Chakraborty²
¹Planning and Development Division, Bangladesh Council of Scientific and Industrial Research, Dr. Qudrat-I-Khuda Road, Dhanmondi, Dhaka-1205, Bangladesh.
²Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh.
*Corresponding author’s e-mail:


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. In our analysis the different value for different variables of parameters of the ARCH-LM test; the lags are 1. The corresponding p-value is >0.05, which is very high. So we have no difficulty to accept the null hypothesis of no ARCH error in the analysis series. The parameters of Wheat Soya Blend (WSB) analysis are insignificant that means no ARCH effects of the models. The estimation results are given in the table 3 shows that the values of Dickey–Fuller (DF) test for all variables p-value<0.05 at 5%, level of significance except Sugar (as sucrose) (%) and Standard Plate Count (cfu/g) which implies that the variables series is stationary. An outcome of DF test confirms that the physiochemical analysis variables series is stationary. Our results revealed that the ARCH model satisfactorily explains volatility and is the most appropriate model for explaining volatility in the series under analysis.

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


  1. WFP. Nutrition at the World Food Programme Programming for Nutrition-Specific Interventions. Programming for Nutrition-Specific Interventions. Nutr Worl Food Programme 2012;1–38.
  2. Alimentarius C, Control C. Super cereal Plus. 1987.
  3. Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econom J Econom Soc 1982;50:987–1007.
  4. Edward N. Modelling and forecasting using time series GARCH models: An application of Tanzania inflation rate data. 2011.
  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). Duke University, CREATES and NBER, 2009.
  13. Institute of Food Science and Technology (IFST) BC of S and IR (BCSIR), Institute of Food Science and Technology (IFST), BCSIR D. Approval for adhoc data use. Dhaka: Institute of Food Science and Technology (IFST), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, 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. 2009.
  17. Pantelis A, Zehtabchi M. Testing for unit roots in the presence of structural change IRAN–GREECE CPI case. 2008.
  18. Mahadeva L, Robinson P. Unit Root Testing to Help Model Building. London: Issued by the Centre for Central Banking Studies, Bank of England, London EC2R 8AH, 2004.
  19. Iordanova, Tzveta  and CRD. Introduction to stationary and non-stationary processes. 2009;1–4.
  20. WFP. Super cereal plus. 2014; 1987: 1–8.
  21. Hole G. Statistics Summarise the Data, Making Clear Any Trends, Patterns Etc. Which May Be Lurking Within Them; They Consist of Visual Displays Such As Graphs, and Summary Statistics Such As Means. 2000;1–6.