Improving Ethiopia coffee productivity through price volatility forecasting

Research output: Contribution to conferencePaper

Abstract

Purpose
The general objective of this study is to model the future likely coffee prices paths to help producers make informed investments decisions and effectively manage their price risk exposure. An inherent price risk otherwise known as volatility in the coffee market possess serious challenges to more than 4 million smallholder farmers (hereafter referred to as producers) and approximately 15 million Ethiopians who directly and indirectly depend on coffee production as a means of livelihood.

Design/methodology/approach
We evaluate the forecast ability of the general autoregressive conditional heteroscedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) in terms of their ability to forecast seven-month-ahead conditional variance. The performance of the GARCH (1, 1) model is compared with that of the ARCH (1). We compare the models using monthly coffee spanning between the periods January 1976 to July 2018. The models are evaluated out-of-sample using five different loss functions namely the Mean Error (푀퐸), the Mean Squared Error (푀푆퐸), the Mean Absolute Error (푀퐴퐸), the Root Mean Squared Error (푅푀푆퐸) and the Standard Error form the regression model(푆퐸푅). Forecasting of primary agricultural commodities have been in previous studies to model rice yield and price forecasting (Shabri et al., 2009), price forecasting and milk productivity (Novotorv & Brikach, 2016), modelling and forecasting of edible oil price and cotton price using GARCH, EGARCH and ARIMA (Lama et al., 2015), improving rice productivity and crop rotation in Thailand (Ruekkaseem & Sasananan, 2018) hence, this study adopted similar research approach.

Findings
The result shows the persistence of volatility and takes a long time to die off and no remarkable difference between the predictive accuracies of both models. Using the standard error of the regression, it appears that the GARCH model might produce more robust forecasts than the ARCH model and can capture future price volatility.

Research limitations/Implications
The methodology employed in this paper can also be replicated in forecasting other agricultural commodities that exhibit similar volatility.

Practical Implications

Comparing the forecasted price volatility and actual prices indicates that producers can effectively manage price volatility by relying on forecasted volatility price to hedge price risk and plan coffee production.

Social Implications

Effective management of price volatility will improve producer’s returns, reduce poverty and contribute to coffee production sustainability.

Originality/Value of a paper
Contributing to the extant literature on coffee volatility in Ethiopia were gap was identified in forecasting future coffee price volatility.
Original languageEnglish
Pages78-79
Number of pages2
Publication statusPublished - 29 Aug 2019
Event Centre for African Research on Enterprise and Economic Development 4th Annual Conference - Universitt of the West of Scotland, Paisley, United Kingdom
Duration: 29 Aug 201930 Aug 2019

Conference

Conference Centre for African Research on Enterprise and Economic Development 4th Annual Conference
Abbreviated titleCAREED 2019
CountryUnited Kingdom
CityPaisley
Period29/08/1930/08/19

Fingerprint

Volatility forecasting
Ethiopia
Productivity
Coffee
Price volatility
Autoregressive conditional heteroscedasticity
Price risk
Mean squared error
Price forecasting
Standard error
Agricultural commodities
Coffee prices
Modeling
Loss function
Conditional variance
Milk
Livelihoods
Poverty
Risk exposure
Design methodology

Keywords

  • Coffee price volatility
  • Ethiopia
  • Producers
  • Price forecasting
  • GARCH/ARCH

Cite this

Emeana, K. N. (2019). Improving Ethiopia coffee productivity through price volatility forecasting. 78-79. Paper presented at Centre for African Research on Enterprise and Economic Development 4th Annual Conference, Paisley, United Kingdom.
Emeana, Kingsley N. / Improving Ethiopia coffee productivity through price volatility forecasting. Paper presented at Centre for African Research on Enterprise and Economic Development 4th Annual Conference, Paisley, United Kingdom.2 p.
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Emeana, KN 2019, 'Improving Ethiopia coffee productivity through price volatility forecasting' Paper presented at Centre for African Research on Enterprise and Economic Development 4th Annual Conference, Paisley, United Kingdom, 29/08/19 - 30/08/19, pp. 78-79.

Improving Ethiopia coffee productivity through price volatility forecasting. / Emeana, Kingsley N.

2019. 78-79 Paper presented at Centre for African Research on Enterprise and Economic Development 4th Annual Conference, Paisley, United Kingdom.

Research output: Contribution to conferencePaper

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N2 - PurposeThe general objective of this study is to model the future likely coffee prices paths to help producers make informed investments decisions and effectively manage their price risk exposure. An inherent price risk otherwise known as volatility in the coffee market possess serious challenges to more than 4 million smallholder farmers (hereafter referred to as producers) and approximately 15 million Ethiopians who directly and indirectly depend on coffee production as a means of livelihood.Design/methodology/approachWe evaluate the forecast ability of the general autoregressive conditional heteroscedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) in terms of their ability to forecast seven-month-ahead conditional variance. The performance of the GARCH (1, 1) model is compared with that of the ARCH (1). We compare the models using monthly coffee spanning between the periods January 1976 to July 2018. The models are evaluated out-of-sample using five different loss functions namely the Mean Error (푀퐸), the Mean Squared Error (푀푆퐸), the Mean Absolute Error (푀퐴퐸), the Root Mean Squared Error (푅푀푆퐸) and the Standard Error form the regression model(푆퐸푅). Forecasting of primary agricultural commodities have been in previous studies to model rice yield and price forecasting (Shabri et al., 2009), price forecasting and milk productivity (Novotorv & Brikach, 2016), modelling and forecasting of edible oil price and cotton price using GARCH, EGARCH and ARIMA (Lama et al., 2015), improving rice productivity and crop rotation in Thailand (Ruekkaseem & Sasananan, 2018) hence, this study adopted similar research approach.FindingsThe result shows the persistence of volatility and takes a long time to die off and no remarkable difference between the predictive accuracies of both models. Using the standard error of the regression, it appears that the GARCH model might produce more robust forecasts than the ARCH model and can capture future price volatility.Research limitations/ImplicationsThe methodology employed in this paper can also be replicated in forecasting other agricultural commodities that exhibit similar volatility.Practical ImplicationsComparing the forecasted price volatility and actual prices indicates that producers can effectively manage price volatility by relying on forecasted volatility price to hedge price risk and plan coffee production.Social ImplicationsEffective management of price volatility will improve producer’s returns, reduce poverty and contribute to coffee production sustainability.Originality/Value of a paperContributing to the extant literature on coffee volatility in Ethiopia were gap was identified in forecasting future coffee price volatility.

AB - PurposeThe general objective of this study is to model the future likely coffee prices paths to help producers make informed investments decisions and effectively manage their price risk exposure. An inherent price risk otherwise known as volatility in the coffee market possess serious challenges to more than 4 million smallholder farmers (hereafter referred to as producers) and approximately 15 million Ethiopians who directly and indirectly depend on coffee production as a means of livelihood.Design/methodology/approachWe evaluate the forecast ability of the general autoregressive conditional heteroscedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) in terms of their ability to forecast seven-month-ahead conditional variance. The performance of the GARCH (1, 1) model is compared with that of the ARCH (1). We compare the models using monthly coffee spanning between the periods January 1976 to July 2018. The models are evaluated out-of-sample using five different loss functions namely the Mean Error (푀퐸), the Mean Squared Error (푀푆퐸), the Mean Absolute Error (푀퐴퐸), the Root Mean Squared Error (푅푀푆퐸) and the Standard Error form the regression model(푆퐸푅). Forecasting of primary agricultural commodities have been in previous studies to model rice yield and price forecasting (Shabri et al., 2009), price forecasting and milk productivity (Novotorv & Brikach, 2016), modelling and forecasting of edible oil price and cotton price using GARCH, EGARCH and ARIMA (Lama et al., 2015), improving rice productivity and crop rotation in Thailand (Ruekkaseem & Sasananan, 2018) hence, this study adopted similar research approach.FindingsThe result shows the persistence of volatility and takes a long time to die off and no remarkable difference between the predictive accuracies of both models. Using the standard error of the regression, it appears that the GARCH model might produce more robust forecasts than the ARCH model and can capture future price volatility.Research limitations/ImplicationsThe methodology employed in this paper can also be replicated in forecasting other agricultural commodities that exhibit similar volatility.Practical ImplicationsComparing the forecasted price volatility and actual prices indicates that producers can effectively manage price volatility by relying on forecasted volatility price to hedge price risk and plan coffee production.Social ImplicationsEffective management of price volatility will improve producer’s returns, reduce poverty and contribute to coffee production sustainability.Originality/Value of a paperContributing to the extant literature on coffee volatility in Ethiopia were gap was identified in forecasting future coffee price volatility.

KW - Coffee price volatility

KW - Ethiopia

KW - Producers

KW - Price forecasting

KW - GARCH/ARCH

M3 - Paper

SP - 78

EP - 79

ER -

Emeana KN. Improving Ethiopia coffee productivity through price volatility forecasting. 2019. Paper presented at Centre for African Research on Enterprise and Economic Development 4th Annual Conference, Paisley, United Kingdom.