The Analysis of Weak-Form Efficiency in the Market of Crude Oil

Authors

  • Anna Górska Warsaw University of Life Sciences, Department of Agricultural Economics and International Economic Relations Author
  • Monika Krawiec Author

DOI:

https://doi.org/10.26417/ejes.v5i1.p101-112

Keywords:

crude oil, weak-form market efficiency, statistical tests

Abstract

Crude oil is the strategic commodity whose market has become the biggest commodity market in the world over the past 40 years. The main actors in the market, such as producers, refiners, financial institutions, and individual traders are interested in recognizing some trends, patterns or anomalies in performance of oil prices and returns, they could benefit from. Such anomalies among others are calendar effects, for example the day-of-the week effect, the month-of-the year effect, holidays effect or the turn-of-the month effect. Either the calendar effects are observed for stock prices, or for commodity prices, they make the markets inefficient. According to classical Fama’s definition: a market in which prices always fully reflect available information is called efficient. However, there are 3 types of market efficiency: weak-form efficiency, semistrong-form efficiency, strong-form efficiency. The weak-form market efficiency is tested the most often. There are several tools used for its verification, for example: some statistical tests (unit root tests, autocorrelation tests, variance ratio tests), long-run relationships and correlation analysis, calendar effects analysis. Our previous research focused on searching for calendar effects in the market of crude oil (Górska, Krawiec 2015), shows the existence of the-day-of-the week and the month effects. It may imply market inefficiency. That is why the present paper is aimed at further testing weak-form market efficiency. The empirical data covers daily closing prices of crude oil in USD per barrel from 2000 to 2015 and includes, both West Texas Intermediate (WTI) and Brent quotations. Having calculated their logarithmic returns, we apply the following tests: runs test, variance ratio, autocorrelation test.

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Published

2016-08-30