Publication: Modelling the Asymmetric Volatility in Hog Prices in Taiwan:
The Impact of Joining the WTO
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2009-03-24
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Facultad de CC Económicas y Empresariales. Instituto Complutense de Análisis Económico
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Abstract
The hog industry, where prices are determined according to an auction system, is of vital importance to the agricultural industry in Taiwan by providing significant
production and employment. In particular, there were significant impacts on daily hog prices in the periods before, during and after joining the WTO, which we will refer to as periods of anticipation, adjustment and settlement. The purpose of the paper is to model
the growth rates and volatility in daily hog prices in Taiwan from 23 March 1999 to 30 June 2007, which enables an analysis of the effects of joining the WTO. The paper
provides a novel application of financial volatility models to agricultural finance. The empirical results have significant implications for risk management and policy
considerations in the agricultural industry in Taiwan, especially when significant structural changes, such as joining the WTO, are concerned. The three sub-samples
relating to the period before, during and after joining the WTO display significantly different volatility persistence, namely symmetry, asymmetry but not leverage, and
leverage, respectively, whereby negative shocks increase volatility but positive shocks of a similar magnitude decrease volatility.
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JEL Classifications: Q14, G18,G32
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Bai, J. and P. Perron (1998), Estimating and testing linear models with multiple structural changes, Econometrica, 66, 47-78.
Bai, J. and P. Perron (2003), Computation and analysis of multiple structural change models, Journal of Applied Econometrics, 18, 1-22.
Bollerslev, T. (1986), Generalised autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307-327.
Bollerslev, T., R.Y. Chou and K.F. Kroner (1992), ARCH modeling in finance – a review of the theory and empirical evidence, Journal of Econometrics, 52, 5-59.
Bollerslev, T. and J. Wooldridge (1992), Quasi maximum likelihood estimation and inference in dynamic models with time varying variances, Econometric Reviews, 11, 143-172.
Boussama, F. (2000), Asymptotic normality for the quasi-maximum likelihood estimator of a GARCH model, Comptes Rendus de l’Academie des Sciences, Serie I, 331, 81-84 (in French).
Chang, S.M (1999), An econometric analysis of changes in hog price and major pork price variability in Taiwan - an application of ARCH regression model, Journal of Agricultural Economics (Taiwan) (semiannual publication), 65, 117-140.
Dickey, D.A. and W.A. Fuller (1979), Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427-431.
Dickey, D.A. and W.A. Fuller (1981), Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica, 49, 1057-1072.
Egelkraut, T.M., P. Garcia, and B.J. Sherrick (2007), The term structure of implied forward volatility: Recovery and informational content in the corn options market, American Journal of Agricultural Economics, 89, 1-11.
Elie, L. and T. Jeantheau (1995), Consistency in heteroskedastic models, Comptes Rendus de l’Académie des Sciences, Série I, 320, 1255-1258 (in French).
Elliott, G., T.J. Rothenberg and J.H. Stock (1996), Efficient tests for an autoregressive unit root, Econometrica, 64, 813-836.
Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.
Glosten, L., R. Jagannathan and D. Runkle (1992), On the relation between the expected value and volatility of nominal excess return on stocks, Journal of Finance, 46, 1779-1801.
Jeantheau, T. (1998), Strong consistency of estimators for multivariate ARCH models, Econometric Theory, 14, 70-86.
Jin, H.J. and D.L. Frechette (2004), Fractional integration in agricultural futures price volatilities, American Journal of Agricultural Economics, 86, 432-443.
Koekebakker, S. and G. Lien (2004), Volatility and price jumps in agricultural futures prices - evidence from wheat options, American Journal of Agricultural Economics, 86, 1018-1031.
Langley, S.V., M.G. Giugale, W.H. Meyers and C. Hallahan (2000), International financial volatility and agricultural commodity trade: A primer, American Journal of Agricultural Economics, 82, 695-700.
Lence, S.H., and D.J. Hayes (2002), U.S. farm policy and the volatility of commodity prices and farm revenues, American Journal of Agricultural Economics, 84, 335-351.
Lee, S.W. and B.E. Hansen (1994), Asymptotic theory for the GARCH(1,1) quasimaximum likelihood estimator, Econometric Theory, 10, 29-52.
Li, W.K., S. Ling and M. McAleer (2002), Recent theoretical results for time series models with GARCH errors, Journal of Economic Surveys, 16, 245-269. Reprinted in M. McAleer and L. Oxley (eds.), Contributions to Financial Econometrics: Theoretical and Practical Issues, Blackwell, Oxford, 2002, pp. 9-33.
Lien, D. and D.A. Hennessy (2007), Cash flow effects of the Saskatchewan short-term hog loan program, Canada Journal of Agricultural Economics, 55, 83-96.
Ling, S. and W.K. Li (1997), On fractionally integrated autoregressive moving-average models with conditional heteroskedasticity, Journal of the American Statistical Association, 92, 1184-1194.
Ling, S. and M. McAleer (2002a), Stationarity and the existence of moments of a family of GARCH processes, Journal of Econometrics, 106, 109-117.
Ling, S. and M. McAleer (2002b), Necessary and sufficient moment conditions for the GARCH(r,s) and asymmetric power GARCH(r,s) models, Econometric Theory, 18, 722-729.
Ling, S. and M. McAleer, (2003a), Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory, 19, 278-308.
Ling, S. and M. McAleer (2003b), On adaptive estimation in nonstationary ARMA models with GARCH errors, Annals of Statistics, 31, 642-674.
McAleer, M. (2005), Automated inference and learning in modeling financial volatility, Econometric Theory, 21, 232-261.
McAleer, M. (2008), The Ten Commandments for optimizing value-at-risk, to appear in Journal of Economic Surveys.
McAleer, M., F. Chan and D. Marinova (2007), An econometric analysis of asymmetric volatility: Theory and application to patents, Journal of Econometrics, 139, 259-284.
McAleer, M., F. Chan, S. Hoti and O. Lieberman (2008), Generalized autoregressive conditional correlation, to appear in Econometric Theory.
Nelson, D.B. (1991), Conditional heteroscedasticity in asset returns: a new approach, Econometrica, 59, 347-370.
Ng, S. and P. Perron (2001), Lag length selection and the construction of unit root tests with good size and power, Econometrica, 69, 1519-1554.
Perron, P. and S. Ng (1996), Useful modifications to some unit root tests with dependent errors and their local asymptotic properties, Review of Economic Studies, 63, 435-463.
Phillips, P.C.B. and P. Perron (1988), Testing for a unit root in time series regression, Biometrika, 75, 335-346.
Schroeter, J. and A. Azzam (1991), Marketing margins, market power, and price uncertainty, American Journal of Agricultural Economics, 73, 990-999.
Shephard, N. (1996), Statistical aspects of ARCH and stochastic volatility, in O.E. Barndorff-Nielsen, D.R. Cox and D.V. Hinkley (eds.), Statistical Models in Econometrics, Finance and Other Fields, Chapman & Hall, London, pp. 1-67.
Shively, G.E. (2001), Price thresholds, price volatility, and the private costs of investment in a developing country grain market, Economic Modelling, 18, 399-414.
Yang, S.-R. and B.W. Brorsen (1992), Nonlinear dynamics of daily cash prices, American Journal of Agricultural Economics, 74, 706-715.