Modeling Domestic Price Volatility for Cereal Crops in Ethiopia

Belay Belete Anjullo

doi:doi:10.11648/j.ijdsa.20210706.12

| Peer-Reviewed

Modeling Domestic Price Volatility for Cereal Crops in Ethiopia

Belay Belete Anjullo

Published in International Journal of Data Science and Analysis (Volume 7, Issue 6)

Received: 28 March 2021 Accepted: 2 November 2021 Published: 12 November 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The volatility in the domestic prices of maize and teff crops has been found to vary over time from month to month. Thus, Families of time series models namely, ARCH with their extensions to generalized ARCH, GARCH and EGARCH models with ARIMA mean equations were considered to the data. The best fitting model among each family of models was selected based on how well the model captures the variations in the data. The optimal lag specification for the models are accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric selected models were carried out based on the significance of asymmetric term in the EGARCH model. Thus, statistically significance of asymmetric term and least forecast error from the model established that the EGARCH model with GED for residuals was superior to the GARCH model. Therefore, the ARIMA(2,0,3)-EGARCH(1,1) and ARIMA(0,0,3)-EGARCH(2,3) were chosen to be the best fitting models among the ARIMA(p, d, q)-GARCH(P, Q) family for monthly domestic price volatility of maize and teff crops, respectively. However, the volatility in the domestic price of wheat and barley was found to be not changing over time. Hence, the variance of the ARIMA process was used as the measure of volatility in the prices of these two crops which were 0.00112 and 0.0004, respectively. Moreover, it was found that from candidate exogenous variables, import prices for maize crop, fuel oil price, exchange rate (dollar-birr), inflation from non-food items, past shock and volatility on the domestic price had statistically significant effect on the current month domestic price volatility for maize and teff crops.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 6)
DOI	10.11648/j.ijdsa.20210706.12
Page(s)	139-149
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Domestic Price Volatility, Time Series Data, ARCH, GARCH, EGARCH

References

[1]	World Bank (2007). Explaining Sources of Food Price Inflation in Ethiopia: A Just in Time Policy Note.
[2]	Rashid, S. and Meron, A. (2007). Cereal Price Instability in Ethiopia: an Analysis of Sources and Policy Options. Paper prepared for the Agricultural Economics Association for Africa, Ghana.
[3]	Yonas Alem and Mans Soderbom (2011). Household-Level Consumption in Urban Ethiopia: The Effects of a Large Food Price Shock, Working paper.
[4]	Klugman, J. (2007). Explaining Food Price Inflation Policy Note. Discussion paper, Ethiopia.
[5]	Myers, R. J. and Hanson, S. D. (1993). Pricing Commodity Options when the Underlying Futures Price Exhibits Time-Varying Volatility. American Journal of Agricultural Economics, vol. 75: p. 121–130.
[6]	Dehn, J. (2000). Commodity Price Uncertainty in Developing Countries. Working paper at the Centre for the Study of African Economies.
[7]	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, Vol. 46.
[8]	Box, G. and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, Revised Edition, Oakland, CA: Holden-Day.
[9]	Nelson, D. (1991). Conditional Heteroskedasticity in Returns: a New Approach. Journals of Econometrics, vol. 59: p. 347-70.
[10]	Phillips, C. B. and Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrics, vol. 75: p. 335–346.
[11]	Dickey, D. A. and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, vol. 74, p. 427–431.
[12]	Asteriou, D. and Hall, S. G. (2007). Applied Econometrics: A Modern Approach Using Eviews and Microfit: Revised Edition, Hampshire, Palgrave Macmillan.
[13]	Lee, C. L. (2009). Housing Price Volatility and its Determinants, School of Economics and Finance, University of Western Sydney, Australia.
[14]	Tsay, R. S. (2005). Analysis of Financial Time Series, 2^th Edition. John Wiley and Sons, New York.
[15]	Box, G. and Pierce, D. (1970). Distribution of Residual Autocorrelations in Autoregressive Moving Average Time Series Models. Journal of the American Statistical Association, vol. 65: p. 1509–1526.
[16]	Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation. Journals of Econometrics, vol. 50: P. 987–1007.
[17]	Bollerslev, T. and Taylor, S. (1986). Generalized Autoregressive Conditional Heteroskedast-icity. Journal of Econometrics, vol. 31: p. 307–327.
[18]	Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19, 716–723.
[19]	Schwarz, Gideon E. (1978). Estimating the dimension of a model, Annals of Statistics, 6 (2): 461-464, doi: 10.1214/aos/1176344136, MR 0468014.
[20]	McLeod, A. I. (1983). Diagnostic Checking ARMA Time Series Model Using Squared-Residual Autocorrelations. Journal of Time Series Analysis.
[21]	Chatfield, C. (1989) The Analysis of Time Series: An Introduction. 4^th Edition, Chapman and Hall, New York.
[22]	Loening, J., Durevall, D. and Birru, Y. A. (2009). Inflation Dynamics and Food Prices in an Agricultural Economy: The Case of Ethiopia. World Bank Policy Research Working Paper Series, No. 4969.
[23]	Gilbert, C. L. (1989). The Impact of Exchange Rates and Developing Country Debt on Commodity Prices. Journal of Economics, Vol. 99: p. 773–84.
[24]	Chambers, R. G. and Just, R. E. (1984). Effects of Exchange Rate Changes on U.S. Agriculture. Journals of Agricultural Economics, vol. 73: p. 33-43.
[25]	Swaray, R. (2007). How did the Demise of International Commodity Agreements Affect Volatility of Primary Commodity Prices? Applied Economics, No.#(17: p. 2253-2260.
[26]	Baffes, J. (2007). Oil Spills on Other Commodities. World Bank Policy Research Working Paper.
[27]	Ahmed, H. A. (2008). Structural Analysis of Price Drivers in Ethiopia. Addis Ababa: Paper for Ethiopian Development Research Institute.
[28]	Siourounis, G. D. (2002). Modelling Volatility and Testing for Efficiency in Emerging Capital Markets: the case of the Athens Stock Exchange, Applied Financial Economics, vol. 12: p. 47-55.

Cite This Article

Plain Text BibTeX RIS

APA Style

Belay Belete Anjullo. (2021). Modeling Domestic Price Volatility for Cereal Crops in Ethiopia. International Journal of Data Science and Analysis, 7(6), 139-149. https://doi.org/10.11648/j.ijdsa.20210706.12

Copy | Download

ACS Style

Belay Belete Anjullo. Modeling Domestic Price Volatility for Cereal Crops in Ethiopia. Int. J. Data Sci. Anal. 2021, 7(6), 139-149. doi: 10.11648/j.ijdsa.20210706.12

Copy | Download

AMA Style

Belay Belete Anjullo. Modeling Domestic Price Volatility for Cereal Crops in Ethiopia. Int J Data Sci Anal. 2021;7(6):139-149. doi: 10.11648/j.ijdsa.20210706.12

Copy | Download

@article{10.11648/j.ijdsa.20210706.12,
  author = {Belay Belete Anjullo},
  title = {Modeling Domestic Price Volatility for Cereal Crops in Ethiopia},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {6},
  pages = {139-149},
  doi = {10.11648/j.ijdsa.20210706.12},
  url = {https://doi.org/10.11648/j.ijdsa.20210706.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210706.12},
  abstract = {The volatility in the domestic prices of maize and teff crops has been found to vary over time from month to month. Thus, Families of time series models namely, ARCH with their extensions to generalized ARCH, GARCH and EGARCH models with ARIMA mean equations were considered to the data. The best fitting model among each family of models was selected based on how well the model captures the variations in the data. The optimal lag specification for the models are accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric selected models were carried out based on the significance of asymmetric term in the EGARCH model. Thus, statistically significance of asymmetric term and least forecast error from the model established that the EGARCH model with GED for residuals was superior to the GARCH model. Therefore, the ARIMA(2,0,3)-EGARCH(1,1) and ARIMA(0,0,3)-EGARCH(2,3) were chosen to be the best fitting models among the ARIMA(p, d, q)-GARCH(P, Q) family for monthly domestic price volatility of maize and teff crops, respectively. However, the volatility in the domestic price of wheat and barley was found to be not changing over time. Hence, the variance of the ARIMA process was used as the measure of volatility in the prices of these two crops which were 0.00112 and 0.0004, respectively. Moreover, it was found that from candidate exogenous variables, import prices for maize crop, fuel oil price, exchange rate (dollar-birr), inflation from non-food items, past shock and volatility on the domestic price had statistically significant effect on the current month domestic price volatility for maize and teff crops.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Modeling Domestic Price Volatility for Cereal Crops in Ethiopia
AU  - Belay Belete Anjullo
Y1  - 2021/11/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210706.12
DO  - 10.11648/j.ijdsa.20210706.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 139
EP  - 149
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210706.12
AB  - The volatility in the domestic prices of maize and teff crops has been found to vary over time from month to month. Thus, Families of time series models namely, ARCH with their extensions to generalized ARCH, GARCH and EGARCH models with ARIMA mean equations were considered to the data. The best fitting model among each family of models was selected based on how well the model captures the variations in the data. The optimal lag specification for the models are accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric selected models were carried out based on the significance of asymmetric term in the EGARCH model. Thus, statistically significance of asymmetric term and least forecast error from the model established that the EGARCH model with GED for residuals was superior to the GARCH model. Therefore, the ARIMA(2,0,3)-EGARCH(1,1) and ARIMA(0,0,3)-EGARCH(2,3) were chosen to be the best fitting models among the ARIMA(p, d, q)-GARCH(P, Q) family for monthly domestic price volatility of maize and teff crops, respectively. However, the volatility in the domestic price of wheat and barley was found to be not changing over time. Hence, the variance of the ARIMA process was used as the measure of volatility in the prices of these two crops which were 0.00112 and 0.0004, respectively. Moreover, it was found that from candidate exogenous variables, import prices for maize crop, fuel oil price, exchange rate (dollar-birr), inflation from non-food items, past shock and volatility on the domestic price had statistically significant effect on the current month domestic price volatility for maize and teff crops.
VL  - 7
IS  - 6
ER  -

Copy | Download

Author Information

Belay Belete Anjullo

Department of Statistics, Arba Minch University, Arba Minch, Ethiopia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Belay Belete Anjullo. (2021). Modeling Domestic Price Volatility for Cereal Crops in Ethiopia. International Journal of Data Science and Analysis, 7(6), 139-149. https://doi.org/10.11648/j.ijdsa.20210706.12

Copy | Download

ACS Style

Belay Belete Anjullo. Modeling Domestic Price Volatility for Cereal Crops in Ethiopia. Int. J. Data Sci. Anal. 2021, 7(6), 139-149. doi: 10.11648/j.ijdsa.20210706.12

Copy | Download

AMA Style

Belay Belete Anjullo. Modeling Domestic Price Volatility for Cereal Crops in Ethiopia. Int J Data Sci Anal. 2021;7(6):139-149. doi: 10.11648/j.ijdsa.20210706.12

Copy | Download

@article{10.11648/j.ijdsa.20210706.12,
  author = {Belay Belete Anjullo},
  title = {Modeling Domestic Price Volatility for Cereal Crops in Ethiopia},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {6},
  pages = {139-149},
  doi = {10.11648/j.ijdsa.20210706.12},
  url = {https://doi.org/10.11648/j.ijdsa.20210706.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210706.12},
  abstract = {The volatility in the domestic prices of maize and teff crops has been found to vary over time from month to month. Thus, Families of time series models namely, ARCH with their extensions to generalized ARCH, GARCH and EGARCH models with ARIMA mean equations were considered to the data. The best fitting model among each family of models was selected based on how well the model captures the variations in the data. The optimal lag specification for the models are accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric selected models were carried out based on the significance of asymmetric term in the EGARCH model. Thus, statistically significance of asymmetric term and least forecast error from the model established that the EGARCH model with GED for residuals was superior to the GARCH model. Therefore, the ARIMA(2,0,3)-EGARCH(1,1) and ARIMA(0,0,3)-EGARCH(2,3) were chosen to be the best fitting models among the ARIMA(p, d, q)-GARCH(P, Q) family for monthly domestic price volatility of maize and teff crops, respectively. However, the volatility in the domestic price of wheat and barley was found to be not changing over time. Hence, the variance of the ARIMA process was used as the measure of volatility in the prices of these two crops which were 0.00112 and 0.0004, respectively. Moreover, it was found that from candidate exogenous variables, import prices for maize crop, fuel oil price, exchange rate (dollar-birr), inflation from non-food items, past shock and volatility on the domestic price had statistically significant effect on the current month domestic price volatility for maize and teff crops.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Modeling Domestic Price Volatility for Cereal Crops in Ethiopia
AU  - Belay Belete Anjullo
Y1  - 2021/11/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210706.12
DO  - 10.11648/j.ijdsa.20210706.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 139
EP  - 149
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210706.12
AB  - The volatility in the domestic prices of maize and teff crops has been found to vary over time from month to month. Thus, Families of time series models namely, ARCH with their extensions to generalized ARCH, GARCH and EGARCH models with ARIMA mean equations were considered to the data. The best fitting model among each family of models was selected based on how well the model captures the variations in the data. The optimal lag specification for the models are accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric selected models were carried out based on the significance of asymmetric term in the EGARCH model. Thus, statistically significance of asymmetric term and least forecast error from the model established that the EGARCH model with GED for residuals was superior to the GARCH model. Therefore, the ARIMA(2,0,3)-EGARCH(1,1) and ARIMA(0,0,3)-EGARCH(2,3) were chosen to be the best fitting models among the ARIMA(p, d, q)-GARCH(P, Q) family for monthly domestic price volatility of maize and teff crops, respectively. However, the volatility in the domestic price of wheat and barley was found to be not changing over time. Hence, the variance of the ARIMA process was used as the measure of volatility in the prices of these two crops which were 0.00112 and 0.0004, respectively. Moreover, it was found that from candidate exogenous variables, import prices for maize crop, fuel oil price, exchange rate (dollar-birr), inflation from non-food items, past shock and volatility on the domestic price had statistically significant effect on the current month domestic price volatility for maize and teff crops.
VL  - 7
IS  - 6
ER  -

Copy | Download