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The Transmission Effects of Price and Volatility Among Agricultural Markets: Three Essays on International Grain Futures Prices, Broiler Feed and Farm Prices in Taiwan
|引用:||Reference Aradhyula, S. V. and M. T. Holt, (1989). Risk Behavior and Rational Expectations in the U.S. Broiler Market, American Journal of Agricultural Economics, 7(4), 892-902. Baillie, R.T. and R.J. Myers, (1991). Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge, Journal of Applied Econometrics, 6, 109-124. Bera, A. K., and M. L. Higgins, (1993). ARCH Models: Properties, Estimation and Testing, Journal of Economic Surveys, 7, 305-366. Bera, A.K. and S. Kim, (2002). Testing Constancy of Correlation and Other Specifications of the garch Model with an Application to International Equity Return, Journal of Empirical Finance, 9, 171-195. Bera, A.k., P. Garcia and J.S. Roh, (1997). Estimation of Time-Varying Hedgeing Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches, Sankhya: Series B, 59, 346-368. Bollerslev, T., (1986). Generalized Autoregressive Conditional Heteroskedasicity, Journal of Econometrics, 31, 307-327. Bollerslev, T., (1987). A Conditional Heteroskedasicity Time Series Model for Speculative Prices and Rates of Return, Reviews of Economics and Statistics, 69, 542-547. Bollerslev, T., R. F. Engle and J.M. Wooldridge, (1988). A Capital Asset Pricing Model with Time-Varying Covariance, Journal of political Economy, 96, 116-131. Bollerslev, T., (1990). Modelling the Coherence in the Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model, Review of Economics and Statistics, 72, 498-505. Bollerslev, T. and J.M. Wooldridge, (1992). Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariance, Econometric Reviews, 11, 143-172. 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. Caporin, M. and M. McAleer, (2008). Scalar BEKK and Indirect DCC, Journal of Forecasting, 27(6), 537-549. Cecchetti, S., R. Cumby, and S. Figlewski, (1988). Estimation of the Optimal Futures Hedge, Review of Economics and Statistics, 70, 623-630. Chan, K., (1992). A further analysis of the lead-lag relationship between the cash market and stock index futures market, Review of Financial Studies, 5(1), 123-152. Chuang, Y., J. Lu and K. Tswei, (2007). Interdependence of international equity variance:Evidence from East Asian markets, Emerging Markets Review, 5, 427-446. Connolly, R. and C. Stivers, (1999). Conditional Return Autocorrelation and Price Formation: Evidence from Six Major Equity Markets, Working Paper, University of Georgia and the University of North Carolina at Chapel Hill. Dickey, D. A. and W. Fuller, (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root, J. Amer. Stat Asso. 74, 427-431. Dickey, D. A. and W. Fuller,1981, Likelihood Ratio Test for Autoregressive Time Series with a Unit Root, Econometrica, 49, 1057-1072. Eales, J. S. and L. J. Unnevehr, (1988). Demand for Beef and Chicken Products: Separability and Structural Change, American Journal Agricultural Economics, 70(3), 521-532. Engle, R. F, and K. F. Kroner, (1995). Multivariate Simultaneous Generalized ARCH, Econometric Theory, 11, 122-150. Engle, R. F., (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50, 987-1007. Engle, R. F. and T. Bollerslev, (1986). Modelling the Persistence of Conditional Variances, Econometric Reviews, 5(1), 81-87. Engle, R. F., D. M. Lilien and R. P. Robins, (1987). Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model, Econometrica, 55(2), 391-407. Engle, R. F., (2002). Dynamic Conditional Correlation - A Simple Class of Multivariate Garch Models, Journal of Business and Economic Statistics, 20, 339-350. Engle, R. F. and V. Ng, (1993). Measuring and Testing the Impact of News on Volatility, Journal of Finance, 48, 1749-1778. Emerson, P.M. and W.G. Tomek, (1969). Did Futures Trading Influence Potato Prices? , American Journal of Agricultural Economics, 51( 3), 666-672. Fama, E. F., (1965). The Behavior of Stock-Market Prices, The Journal of Business, 38(1), 34-53. Gagnon, L. and G. Lypny, (1995). Hedging Short-Term Interest Risk under Time Varying Distribution, The Journal of Futures Markets, 15, 767–783. Golan, A., J. M. Perloff and E. Z. Shen, (2001). Estimating a Demand System with Nonnegativity Constraints:Mexican Meat Demand, The Review of Economics and Statistics, 83(3), 541-550. Gourieroux, C., (1997). ARCH Models and Financial Applications, New York: Springer-Verla. Gray, R. W., (1964). The Attack Upon Potato Futures Trading in the United States, Food Research Institute Studies, 1(2), 65-78. Heien, D., (1976). An Economic Analysis of the U.S. Poultry Sector, American Journal Agricultural Economics, 58(2), 311-316. Holt, M. T. and S.V. Aradhyula, (1990). Price Risk in Supply Equation:An Application of GARCH Time-Series Models to the U.S. Broiler Market, Southern Economic Journal, 57(1), 230-242. Holt, M. T. and S.V. Aradhyula, (1998). Endogenous Risk in Rational-Expectations Commodity models: A Multivariate Generalized ARCH-M Approach, Journal of Empirical Finance, 5, 99-129. Jiang, Y., (2004). Government Policy and Price Comovements in Commodity Futures Markets, American Business Review, 22,1-10. Johansen, S., (1988). Statistical analysis of co-integration vectors, Journal of Economic Dynamics and Control, 12, 231-254. Johansen, S., (1991). Estimation and hypothesis testing of co-integration vectors in Gaussian vector autoregressive models, Econometrica, 59, 1551-1580. Johansen, S. and K. Juselius, (1990). Maximum Likelihood Estimation and Inference on Co-integration - With Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52, 169-210. Kapombe, C. M. and D. Colyer, (1999). A structural Time Series Analysis of US Broiler Exports, American Journal Agricultural Economics, 21, 295-307. Karagiannis, G., S. Katranidis and K. Velentzas, (2000). An Error Correction almost ideal Demand System for Meat in Greece, Agricultural Economics, 22, 29-35. Karolyi, G. A., (1995). A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada, Journal of Business and Economic Statistics, 13, 11-25. Kawai, M., (1983). Price Volatility of Storable Commodities under Rational Expectations in Spot and Futures Markets, International Economic Review, 24, 435-459. Kawaller, I. G., P. D. Koch and T. W. Koch, (1987). The temporal price relationship between S&P 500 futures and the S&P 500 Index, Journal of Finance, 42, 1309-1329. Kim, Y. and J. Shin, (2000). Interactions among China-Related Stocks, Asia-Pacific Financial Markets, 7, 97-115. Kroner, K.F. and S. Claessens, (1991). Optimal Dynamic Hedging Portfolios and the Currency Composition of External Debt, Journal of International Money and Finance, 10, 131-148. Kroner, K.F. and J. Sultan, (1993). Time Varying Distributions and Dynamic Hedging with Foreign Currency Futures, Journal of Financial and Quantitative Analysis, 28, 535-551. Lien, D. and L. Yang, (2005). Spot-futures spread, time-varing correlation, and hedging with currency futures, The Journal of Futures Markets, 26, 1019-1038. Lien, D. and X. Luo, (1994). Multiperiod Hedging in the Presence of Conditional Heteroscedasticity, Journal of Futures Markets, 14, 927-955. Lien, D., Y.K. Tse and A.K.C. Tsui, (1999). Evaluating the Hedging Performance of GARCH Strategies, Mimeo, presented at the Tenth Annual Asia-Pacific Futures Research Symposium of the Chicago Board of Trade. Malliaris, A.G. and J.L. Urrutia, (1998). Volume and Price Relationships:Hypotheses and Testing for Agricultural Futures, The Journal of Futures Markets, 18, 53-72. Mananyi, A. and J.J. Struthers, (1997). Cocoa Market Efficiency - a co-integration Approach, Journal of Economic Studies, 24, 141-151. Mandelbrot, B., (1963). The Change of Certain Speculative Prices, The Journal of Business, 36(4), 394-412. Manera, M., M. McAleer and M. Grasso, (2006). Modeling Time-Varying Conditional Correlations in the Volatility of Tapis Oil Spot and Forward Returns, Applied Financial Economics, 16, 525-533. McAleer, M., (2005). Automated inference and learning in modeling financial volatility, Econometric Theory,21, 232-261. Myers, R. J, (1991). Estimating Time-Vary Optimal Hedge Rations on Futures Markets, Journal of Futures Market,11, 139-153. Myers, R. J. and T. R. Stanley, (1989). Generalized Optimal Hedge Ratio Estimation, American Journal of Agricultural Economics, 71(4), 858-868. Park, T. and L. Switzer, (1995). Time-Varying Distribution and The Optimal Hedge Ratios for Stock Index Futures, Financial Economics, 5, 131-137. Poon, W.P.H. and H.G. Fung, (2000). Red Chips or H Shares: Which China-backed Securities Process Information the Fastest?, Journal of Multinational Financial Management, 10, 351-343. Powers, M.J., (1970). Does Futures Trading Reduce Price Fluctuations in the Cash Markets? , The American Economic Review, 60( 3), 460-464. Rezitis, A., (2003). Meat and Volatility Spillover Effects in Greek Producer-Consumer Meat Prices, Applied Economics Letters, 10,381-384. Rezitis, A., (2003). Volatility Spillover Effects in Greek Consumer Meat Prices, Agriculture Economics Reviews, 4(1), 29-36. Scruggs, J. T. and P. Glabdanidis, (2003). Risk premia and the dynamic covariance between stock and bond return, Journal of Financial and Quantitative Analysis, 38,295-316. Taylor, G. and R. Leuthold, (1974). The Influence of Futures Trading on Cash Cattle Price Changes, Food Research Institute Studies, 13, 125-142. Tian, G.G. and G.H.Wan, (2004). Interaction among China-related Stocks: Evidence from a Causality Test with a New Procedure, Applied Financial Economics, 14, 67-72. Tse, Y.K., (2000). A Test for Constant Correlation in a Multivariate GARCH Model, Journal of Economics, 98, 107-127. Turnovsky, S. J., (1979). Futures Markets, Private Storage, and Price Stabilization, Journal of Public Economics, 12, 301-327. Wang, K. M. and B. N. Huang, (2004). Discussing the Link Between the Stock and Exchange Market of Taiwan and the Stock Market of US, Taiwan Journal of Economic Forecasts and Policy, 34, 31-72。 Zhu, H., Z. Lu and S. Wang, (2004). Causal Linkage among Shanghai, Shenzhen, and Hong Kong Stock Markets, International Journal of Theoretical and Applied Finance, 7, 135-149.|
|摘要:||Abstract Countries around the world often face agricultural products price volatility problem. Agriculture products' price level directly influences producers' revenue, and agriculture products' price volatility will further reveal price risks taken by producers throughout the production. The empirical result of this dissertation can be divided into three parts. The first part adopts BEKK-MGARCH model to analyze U.S. wheat, corns, and soybeans futures price and volatility transmission effect. Next, corns and soybeans are raw materials of domestic broiler's forage. Therefore, the second part of this dissertation will adopt DCC-MGARCH model to analyze direct influence of U.S. corns and soybeans futures prices volatility on domestic broiler's forage price. Also using the influential effect from indirect influence of domestic broiler's forage price volatility on broiler's farm price, this thesis will further analyze domestic broiler forage and farm price volatility transmission effect. Lastly, the third part of this dissertation also adopts BEKK-MGARCH model, and conducts price and volatility transmission effect analysis on domestic broiler's farm price and chicken retailing price, chicken and pork's retailing price, and pork retailing and hog's farm price volatility, respectively. The estimated result of dissertation is as following: 1. Current period US wheat, corns, and soybeans futures price change' are influenced by prior period self futures price change's. And from different grains futures price change's transmission effect, prior period futures price change's of US wheat, corns, and soybeans would mutually influence current period futures price change's. Next, US wheat, corns, and soybeans futures price change's not only is influenced by self prior period price change's, but also is influenced by crossing market different level's prior period futures price change's short-run shock. US wheat, corns, and soybeans futures price change's volatility not only is influenced by self prior period futures price change's volatility long-run persistence, but also is influenced by crossing market prior period futures price change's volatility long-run persistence. 2. There are significant effects of previous broiler feed and farm prices on current price reactions. As for the interaction between broiler feed and farm price, there are significant effects of previous broiler feed price on current broiler farm price, and of previous broiler farm price on current broiler feed price. As for the price volatility analysis of broiler feed and farm price, the short-run shock and volatility long-run persistent effects of broiler feed farm price are significant. 3. In price transmission effect analysis, prior period hog's farm (pork retailing), not only price are influenced by current period self price volatility, but also current period hog's farm (pork retailing) price is deeply influenced by prior period pork retailing (hog's farm) price. For price volatility short-run shock and long-run persistence transmission effect, current period hog's farm and pork retailing price change's volatility is influenced by prior period self-price change's volatility short-run shock and long-run persistence effect; in crossing market price change's volatility short-run shock and long-run persistence transmission effect, short-run shock and long-run persistence of current retailing price change's volatility also reacts greater to prior period hog's farm price change's volatility.|
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