Asymmetric mean reversion of Bitcoin price returns

Abstract

Non-linearity is characterised by an asymmetric mean-reverting property, which has been found to be inherent in the short-term return dynamics of stocks. In this paper, we explore as to whether cryptocurrency returns, as represented by Bitcoin, exhibit similar asymmetric reverting patterns for minutely, hourly, daily and weekly returns between June 2010 and February 2018. We identify several differences in the behaviour of Bitcoin price returns in the pre-and post-$1,000 sub-periods and evidence of asymmetric reverting patterns in the Bitcoin price returns under all the ANAR models employed, regardless of the data frequency considered. We also present evidence indicating stronger reverting behaviour of negative price returns in terms of both reverting speed and magnitude compared to positive returns and evidence of positive serial correlation with prior positive price returns. Finally, we also investigated asymmetries in Bitcoin price return series' persistence by employing higher order ANAR models, finding evidence of a higher persistence of positive returns than negative returns, a result that further supports the existence of asymmetric reverting behaviour in the Bitcoin price returns.

Measuring Quantile Dependence and Testing Directional Predictability between Bitcoin, Altcoins and Traditional Financial Assets

“…Another topic that has been widely explored in the literature is the volatility of cryptocurrency price returns which has been studied by Katsiampa [2017], Ardia, Bluteau, and Rüede [2018], Phillip, Chan, and Peiris [2018], Corbet and Katsiampa [2018], and Baur and Dimpfl [2018], among others, all of whom employed different models to describe cryptocurrencies' volatility. Moreover, Blau [2018] investigated the volatility of Bitcoin across time while testing as to whether the unusual level of its volatility is attributed to speculative trading to find that this speculative trading did not have any relationship with the 2013 price increases nor the dramatic increases in volatility.…”

“…Opponents ask for more care and regulatory influence before creating a product that could potentially generate volatility and contagion effects upon unwilling and unsuspecting financial markets. The co-movement of cryptocurrency pricing structures with other more developed financial markets has been covered quite extensively in recent times (see, e.g., Corbet & Katsiampa, 2018), however, further analysis of the comovements of cryptocurrency pricing behaviour has begun to generate evidence of some substantial 'anomalies' (Griffins & Shams, 2018). Although the literature on interdependencies in cryptocurrency markets has emerged (see for example, Ciaian, Rajcaniova, et al, 2018;Corbet, Larkin, Lucey, Meegan, & Yarovaya, 2020;Katsiampa, 2018aKatsiampa, , 2018b, little is known about cryptocurrencies' quantile, including tail, dependence as well as about directional predictability between cryptocurrencies.…”

Asymmetric Mean Reversion in Low Liquid Markets: Evidence from BRVM

“…Empirical models that have been frequently used to capture the heteroskedasticity are the family of ARCH and GARCH models introduced by Engle (1982) and Bollerslev (1986), respectively. To account for the heteroskedasticity in the return dynamics in this paper, we use the exponential GARCH model (Corbet and Katsiampa 2018;Nam et al 2006). This model allows to capture the leverage effects.…”

“…This nonlinearity is characterized by an asymmetric mean-reverting property. Corbet and Katsiampa (2018) use an asymmetric mean-reverting analysis framework to explore whether a cryptocurrency's returns exhibit such similar asymmetric reverting patterns. Using an asymmetric nonlinear autoregressive model (ANAR), they find evidence of a higher persistence of positive returns than negative returns which supports the existence of asymmetric reverting property.…”

The effectiveness of technical trading rules in cryptocurrency markets

“…The evolution of cryptocurrencies has been the subject of varying research including their role as speculative trading products (Urquhart [2016]; Urquhart [2017]), the presence of inherent bubbles within the sector ), substantial price volatility (Katsiampa et al [2019]), pricing efficiency (Sensoy [2018]; Corbet and Katsiampa [2018]; Mensi et al [2019]), broad market behaviour (Feng et al [2018]; ; ; Vidal-Tomas et al [2018]; Mensi et al [2018]), price determination and predictability ; Giudici and Abu-Hashish [2019]; Panagiotidis et al [2018]), their relationship with other investment asset classes ; Liu [2018]) and derivatives ; Corbet et al [2017]). The recent studies also revealed a diminishing confidence in cryptocurrency market integrity and identified the presence of potential fraud and criminal behaviour within this system (Gandal et al [2018]; Griffin and Shams [2018]; Corbet et al [2019]).…”