Stochastic Patterns of Bitcoin Volatility: Evidence across Measures

Georgia Zournatzidou, Dimitrios Farazakis, Ioannis Mallidis and Christos Floros ()
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Georgia Zournatzidou: Department of Accounting and Finance, Hellenic Mediterranean University, 71005 Heraklion, Greece
Dimitrios Farazakis: Department of Mathematics, University of Western Macedonia, 52100 Kastoria, Greece
Ioannis Mallidis: Department of Statistics and Insurance Science, University of Western Macedonia, 52100 Grevena, Greece
Christos Floros: Department of Accounting and Finance, Hellenic Mediterranean University, 71005 Heraklion, Greece

Mathematics, 2024, vol. 12, issue 11, 1-16

Abstract: This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin’s susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin’s volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin.

Keywords: Bitcoin; cryptocurrency; econometric analysis; volatility estimators; fractional Brownian motion; trading strategies; adjusted rescaled range analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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