Abstract
This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.
Highlights
The substantial growth of cryptocurrencies has attracted considerable attention from investors and policymakers in recent years
One of the critical issues yet to be analyzed is whether the dynamic behavior of cryptocurrencies is predictable, which would be inconsistent with the efficient market hypothesis (EMH), according to which prices should follow a random walk
To assess any variation in market efficiency and check the market’s evolving behavior over time, we applied a rolling-sample technique with diverse window lengths that was consistent with adaptive market hypothesis (AMH) implications
Summary
The substantial growth of cryptocurrencies has attracted considerable attention from investors and policymakers in recent years. Noda (2020) investigated whether the market efficiency of selected cryptocurrencies (Bitcoin and Ethereum) changed over time based on the AMH He measured the extent of market efficiency by applying a time-varying model that did not have any type of dependency upon sample size, unlike prior studies that utilized common approaches. This study’s main contributions are as follows: First, we tested the AMH on benchmark cryptocurrencies and gold and WTI crude oil to analyze the return predictability of the data by employing well-known linear and nonlinear statistical techniques These techniques could distinguish any time-varying serial dependence in the conditional mean, support an unidentified form of conditional heteroskedasticity, and confirm the fluctuating conduct of efficiency. The analysis of the research results is given in “Analysis of the empirical results” section, and “Conclusion” section concludes the study
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