Abstract
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. DOI: http://dx.doi.org/10.4038/sjae.v6i1.3467 <em>SJAE </em>2004; 6(1): 1-24
Highlights
Black tea is traded primarily in spot markets.These markets are located mainly in the following countries: Sri Lanka (Colombo), India (Calcutta, Coimbatore, Cochin, Guwahati, Coonoor, and Siliguri), Bangladesh (Chittagong), Indonesia (Jakarta), Kenya (Mombasa) and Malawi (Limbe
The augmented DF test (ADF) shows that all Indian markets again are mean stationary in levels and the rest of the markets are mean nonstationary in levels. As residuals from these markets are better behaved under the ADF, we suggest the use of results from augmented tests
We have studied weekly black tea auction prices from eight auction markets over the period December 1999 through June 2002
Summary
Black tea is traded primarily (about 80percent) in spot markets. These markets are located mainly in the following countries: Sri Lanka (Colombo), India (Calcutta, Coimbatore, Cochin, Guwahati, Coonoor, and Siliguri), Bangladesh (Chittagong), Indonesia (Jakarta), Kenya (Mombasa) and Malawi (Limbe). The general problem addressed in this study is to determine whether black tea markets are efficient in terms of price discovery. Even though the Indian government does not directly influence the price in auction centers, the tea marketing control order requires all the manufacturers to sell 75percent of their tea (excluding exports and packet sales) through auction houses. This paper primarily focuses on the use of time series techniques in understanding the time related properties of black tea auction market prices around the world and to compare it with the naïve model. The VAR is an atheoretic analysis (non-structural analysis) that summarizes the regularities in a set of variables which theory suggests as important (Bessler, 1984) These models are useful in the analysis of observational data; i.e. data that are collected without experimental controls.
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