The Application of Garch Family Models for Agricultural Crop Products in Amhara Region, Ethiopia

Authors

  • Belayneh Debasu Kelkay Arba Minch University
  • Emmanuel G/Yohannes Ethiopian Civil Service University

Keywords:

Price volatility, Bean and pea, Amhara Region, Garch family models

Abstract

In the recent past, the price of general commodities has increased in Ethiopia as well as in the world. The main objective of this study is to identify and analyze the factors that affect the average monthly price volatility of pulses (bean and pea) in Amhara National Regional State over the period of December 2001 to June 2012 GC. The return series considered exhibited typical characteristics of financial time series such as volatility clustering, leptokurtic distributions and asymmetric effect and thus, can suitably modeled using GARCH family models. Among such models entertained in this study, ARMA(4,4)-EGARCH(2,3) with GED for bean and ARMA(1,0)-EGARCH(1,2) with student-t for pea were chosen to be the best fit models. From the results, exchange rate and general and food inflation rates were found to be an increasing effect on price volatility of bean and pea. On the other hand, rainfall was found to have a stabilizing effect on the price volatility of these crops. Moreover, saving interest rate has a decreasing effect on the price volatility of bean. The results also revealed that price volatility has seasonal variation. The asymmetric terms were found to be significant in all GARCH models considered. Thus, price volatility tends to over-react in response to bad news as compared to good news. Furthermore, the significance of the EGARCH terms provides strong evidence of volatility spillover from one period to another.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Belayneh Debasu Kelkay, Arba Minch University

 

 

Emmanuel G/Yohannes, Ethiopian Civil Service University

 

 

References

Akaike, H. (1974). Anew Look at Statistical Model Identification. IEEE Transactions on Automatic Control AC 19(6): 716-723.

Alisher Mirzabaev (2012). Climate volatility and change in central Asia: Economic impacts and adaptation.

Asteriou, D. and Hall, S.G. (2007). Applied Econometrics: A Modern Approach Using Eviews and Microfit: Revised Edition, Hampshire, Palgrave Macmillan.

Bollerslev, T. and J.M. Wooldridge (1992), Quasimaximum likelihood estimation and inference in dynamic models with time-varying covariances, Econometric Reviews 11: 143-173.

Bollerslev, T., Chou, R. Y and Kroner, K.F. (1992). ARCH Modeling in Finance. A Review of Theory and Empirical Evidence. Journal of Economics 52: 5-59.

Bollersslev. T. and Taylor (1986). Generalized Auto regressive Conditional Heteroskedasticity. Journal of Econometrics 31: 307-328.

Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis, Forecasting and Control. Revised Edition, Holden Day.

Dayton, C.M. (2003). Model Comparison Using Information Measures. Journal of Modern Applied Statistical Methods 2(2): 281-292.

Dickey, D.A. and Fuller, W.A. (1979). Distributions of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74: 427-431.

Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Journal of Econometrics 50: 987-1007.

FAO (2011). Responding to Global Food Price Volatility and its Impact on Food Security.

Fryzlewicz, P. (2007). Lecture Notes: Financial time series ARCH and GARCH models, Department of Mathematics, University of Bristol, Bristol BS8 1TW, UK. p.z.fryzlewicz@bristol.ac.uk, http://www.maths. bris.ac.uk /~mapzf/.

Hoddinott, J. (2006). Shocks and their consequences across and within households in rural Zimbabwe. Journal of Development Studies 42(2): 301-321.

IMF (2009). The Federal Democratic Republic of Ethiopia: Selected Issues Series, International Monetary Fund Country Report, No. 08/259, pp. 35f.

Jordaan, H., Jooste, B.A. and Alemu, Z.G. (2007). Measuring the Price Volatility of Certain Field Crops in South Africa: Using the ARCH/GARCH Approach. Journal of agriculture 46.

Lee, S.W., Hansen, B.E. (1994). Asymptotic properties of the maximum likelihood estimator and test of the stability of parameters of the GARCH and IGARCH models. Econometric Theory 10: 29-52.

Ljung, G. and Box, G. E. P. (1978): On a Measure of Lack of Fit in Time Series Models, Biometrika, 66, 67–72.

Nabeya, S. and Perron, P. (1994). Local asymptotic distribution related to the AR(1) model with dependent errors. Journal of Econometrics 62(2): 229-264. Nelson, D.B. (1991). Conditional Heteroskedasticity in Asset Returns. A New Approach Econometrician 59(2): 347-370.

Perron P. and Ng. S. (1996). Useful modifications to some unit root tests with dependent errors and their local asymptotic properties. Review of Economic Studies 63: 435-463.

Phillips, C.B. and Perron, P. (1987). Testing for a Unit Root in Time Series Regression. Biometrics 75: 335-346.

Shewartz, G. W. (1989). Why Does Stock Market Volatility Change over Time? Journal of Finance 44(5): 1115-1153.

Tsay, R.S. (2005). Analysis of Financial Time Series, 2nd Edition. John Wiley and Sons, New York.

Downloads

Published

18.12.2014

How to Cite

Kelkay, B. D., & G/Yohannes, E. (2014). The Application of Garch Family Models for Agricultural Crop Products in Amhara Region, Ethiopia. Journal of Science, Technology and Arts Research, 3(4), 49–58. Retrieved from https://journals.wgu.edu.et/index.php/star/article/view/465

Issue

Section

Original Research

Plaudit