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Interactions of Source State and Market Price Trends for Cattle of Korean, Japanese and USA Market Specifications

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Abstract

This study analyses the trends in the real prices of steers destined for the Japanese and Korean market, and cows destined for the USA market when sold in Queensland (QLD) or New South Wales (NSW). The aim of this paper is to explore how these prices have influenced each other when faced with the same overall economic and climatic conditions. A Vector Autoregressive model is first estimated to find linkages across six price series defined by source and destination. A Seemingly Unrelated Regressions Model for the real price series is then estimated including lagged prices of linked markets and indicators of macroeconomic and climatic conditions. From our empirical analysis, we find strong evidence of mean-reverting real prices, indicating they can be predicted by their historical mean. Further, the historical mean prices paid in QLD are higher than those paid in NSW for the Korean and US market cattle specifications. The price of cattle of Japanese market specification sold in NSW is solely determined by world conditions and historical values, and it influences directly or indirectly all other markets. The price trends for cattle of US market specifications do not seem to predict movements in the Japanese or Korean markets. This is expected as the Japanese market is a premium market while the US market accepts cattle from a wider range of specifications. This study does not find a systematic relationship between these prices and movements, the Southern Oscillation Index, the Asian financial crises, or the Australian Business Cycle.

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  • Dr Alicia Rambaldi & Bortolussi, 2004. "Interactions of Source State and Market Price Trends for Cattle of Korean, Japanese and USA Market Specifications," Discussion Papers Series 334, School of Economics, University of Queensland, Australia.
  • Handle: RePEc:qld:uq2004:334
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    File URL: http://www.uq.edu.au/economics/abstract/334.pdf
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    1. Layton, Allan P, 1997. "Do Leading Indicators Really Predict Australian Business Cycle Turning Points?," The Economic Record, The Economic Society of Australia, vol. 73(222), pages 258-269, September.
    2. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    3. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    4. Rosen, Sherwin & Murphy, Kevin M & Scheinkman, Jose A, 1994. "Cattle Cycles," Journal of Political Economy, University of Chicago Press, vol. 102(3), pages 468-492, June.
    5. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    6. Philip M. Bodman & Mark Crosby, 2002. "The Australian Business Cycle: Joe Palooka or Dead Cat Bounce?," Australian Economic Papers, Wiley Blackwell, vol. 41(2), pages 191-207, June.
    7. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
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