Stochastic Risk Volatility Forecasting in Poultry Agribusiness in Delta State, Nigeria

Achoja Felix Odemero; Okoh Rosemary Ngozi

Nijerya Delta Eyaleti Tavukçuluk Endüstrisinde Stokastik Risk Olarak Fiyat Değişimlerinin Tahmini

Year 2014, Volume: 1 Issue: 1, 1 - 9, 26.07.2014

Achoja Felix Odemero Okoh Rosemary Ngozi

Abstract

The dearth of information on financial risk has negative effect on the growth of poultry agribusiness. The purpose of the study was to determine the mean financial risk volatility in poultry agribusiness in Delta state, Nigeria. Six years panel data (2004 – 2009) were collected from 200 poultry farms using structured questionnaire. Collected data were analyzed using ARCH(5,5) Model and Time Response Model. Test of hypothesis using Durbin Watson statistics indicated that there is no volatility clustering of financial returns. The result of ARCH model showed a random walk (i.e. upward and downward swings) of standard error of financial risk with a mean volatility of 7.5% in poultry agribusiness over the 6 years period. Time Response prediction model gave an impression that short run forecast of financial risk volatility in poultry agribusiness is feasible. The study recommends early warning / early mitigate for measures of the short run to manage financial risk in poultry industry.

Keywords

Financial, Risk, Volatility, Forecasting, Poultry, Enterprise.

References

Ahmad, A.K., Ghalamreze, S.M., Renato, V., 2005. Agricultural risk analysis in Fars province of Iran: A Risk-Programming Approach: Agricultural and resources Economics; U.K: University of New England.
Aihonsu, J., 1999. Optimal Laying Period for Profitable and Sustainable Egg Production. Ife Journal of Agriculture, 20(1&2), 67-80.
Akanni, K.A., Akinleye, S.O., 2004. Marketing Margins and Risks in Small Scale Poultry Business in Abeokuta Metropolis: An Empirical Analysis. The Ogun Journal of Agricultural Science 3(1) 86-88.
Bamire, O.M., 200 Economics of Vertical Integration in Ogun and Oyo States of Nigeria. Ph.D Thesis (Unpublished), University of Agriculture, Abeokuta (UNAAB). Beckers, S., 1983. Variance of Security Price Returns Based on High, Low and Closing Prices. Journal of Business, 56, 97-112.
Bollerslev, T., 1986. Generalized Autoregressive Conditional. Heteroscedasticity Journal of Econometrics vol. 31.
Chamberlain, G., 1984. Panel Data In Handbook of Econometrics vol. 112.
Downey, W.D., Erickson, S.P., 1987. Agribusiness Management, New York: McGraw Hill Inc.
Duer, W.A., 1976. Introduction to Forest Resource Economics; Singapore: McGraw – Hill International Edition
Ederington, L., Guan, W., 2000. Forecasting Volatility, Norman: University of Oklahoma.
Ekanem, O.T., Iyoha, M.A., 2003. Managerial Economics, Benin City: Moreh Publishers.
Engle, R., 1992. Auto Regressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica. Vol. 50 No 1.
FAO STAT 2006. Statistics Database; Food and Agricultural Organization of United Nations Rome, Italy.
Garman, M.B., Klass, M.J., 1980. On the Estimation of Price Volatility from historical data. Journal of Business, 53, 67 – 78.
Goodwin, H.L., Ahrendsen, B.L., Barton, T.L., Denton, J.H., 2005. Estimated Returns for Contract Broiler Production in Arkansas, Missouri, and Oklahoma: Historical and Future Perspectives. In Journal of Applied Poultry Resources, 14:106-115.
Gujarati, D.N., 2001. Basic Econometrics, New Delhi: Tata McGraw – Hill Publishing Company Ltd. Hull, J., White, A., 1987. Pricing of options on assets with stochastic variance volatilities. Journal of Finance, 42, 281 – 300.
Oduh, M.O., Agu, C., Eboh, E.C, Urama, N.E., Nwosu, E., 2009. Designing and Operationalizing Macroeconomic Forecast Models for Nigeria, Context and Prospects, Enugu: African Institute for Applied Economics
Ord, H.W., Livingstone, L., 1976. An Introduction to West African Economics, London: Heinemann Educational Book Ltd.
Parkinson, M., 1980. The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53, 61- 65.
Samuelson, P.A., Nordhaus, W.D., 2003. Economics, New Delhi: Tata McGraw-Hill Publishing Company Limited.
Umebali, E.E., 2005. Role of Rural Association in farm Credit delivery in Enugu State: in Ogisi, O.D. Okuneye, P.B and Oyaide, W.J. (eds.) Economic Reforms and Management of Nigerian Agriculture Proceedings of the FARM management Association of Nigeria (Faman) Pp.316 – 321.
Umoh, G.S., 2008. Programming Risks in Wetlands Farming: Evidence from Nigeria’s Flood Plains. Journal of Human Ecology. 24(2) 85 – Valderrama, D., Engle, C.R., 2009. A Risk Programming Model for Shrimp Farming in Honduras. (Unpublished)
Yamane, T., 1973. Statistics: An Introduction Analysis. New York: Harper and Row.
Yotopoulos, P.A., Lau, L.J., 1973. A Test for Relative Economic Efficiency: Some Further Results American Economic Review. 63(1).
Yu, J., 2002. Forecasting Volatility in the New Zealand Stock Market, Applied Financial Economics, 12, 193-202
Yusuf, B.L., Baba, K.M., Mohammed, I., Bello, H.M., 200 Impact of Inflation on Farm Families in Sokoto State. Proceedings of the 23 rd Annual National Conference of Farm Management Association of Nigeria.