Sri Lankan Journal of Agricultural Economics 6(1) 2004

In this paper we study a fundamental issue related to the efficient price discovery process using time series data from seven international black tea markets. The major question studied is as follows: Is the price discovery process in black tea markets efficient? We use two statistical techniques as engines of analysis. First, we use time series methods to capture regularities in time lags among price series. Second, we forecast the tea prices in each market using the time series model we estimated followed by a comparison of the forecast with the forecasts from the random-walk (naïve) model. Weekly time series data on black tea prices from seven markets around the world are studied using time series methods. The study follows two paths. We study these prices in a common currency, the US dollar. We also study prices in each country’s local currency. Results from unit root tests suggest that prices from three Indian markets are not generated through random-walk like behavior. We conclude that the Indian markets are not weak-form efficient. However, prices from all non-Indian markets cannot be distinguished from random-walk like behavior. These latter markets are weak-form efficient. A Vector Autoregressions (VARs) on the non-Indian markets are studied in local currency and in US dollars. We use Theil’s U-statistic to test the forecasting ability of the VAR models. We find that for most markets in either dollars or in local currencies, that a random walk forecast outperforms the VAR generated forecasts. This last result suggests the non-Indian markets are both weak-form and semi-strong form efficient.

* The authors are, lecturer, Department of Agricultural Economics and Business Management, University of Peradeniya, Sri Lanka and Professor of Agricultural Economics, Texas A&M University, College Station, TX 77843-2124, USA, respectively.
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