Serial autocorrelation, regardless of its directionality, pertains to the statistical association observed between successive data points within a given time series. Positive serial autocorrelation refers to a situation where there is a positive correlation between consecutive data points. This implies that a high value is more likely to be followed by another high value, while a low value is more likely to be followed by another low value. Negative serial autocorrelation refers to a situation where there is a negative correlation between consecutive data points. This implies that a high value is likely to be followed by a low value, and vice versa.
Published in Chapter:
Overreaction, Underreaction, and Short-Term Efficient Reaction Evidence for Cryptocurrencies
Copyright: © 2023
|Pages: 25
DOI: 10.4018/978-1-6684-9039-6.ch014
Abstract
This chapter aims to analyze the price efficiency of Bitcoin (BTC), DASH, EOS, Ethereum (ETH), LISK, Litecoin (LTC), Monero, NEO, QUANTUM, RIPPLE, STELLAR, and ZCASH in their weak form between March 1, 2018 and March 1, 2023 and determine whether they experience overreactions. The results show that cryptocurrencies exhibit positive and negative autocorrelations, which can reduce volatility and moderate price fluctuations. The results also show persistence in cryptocurrency returns, suggesting long-term trends or market patterns that individual and institutional investors can exploit. It is essential to recognize that cryptocurrencies are characterized by a high degree of complexity and instability. Investors need to monitor market trends and make the necessary adjustments to their investment strategies to anticipate market changes.