Scale matters: The daily, weekly and monthly volatility and predictability of Bitcoin, Gold, and the S&P 500
Nassim Dehouche

TL;DR
This paper examines Bitcoin's volatility across different time scales, revealing that while Bitcoin exhibits high volatility and predictability, its heavy-tailed returns and distributional properties differ from traditional assets like Gold and S&P 500.
Contribution
It provides a nuanced analysis of Bitcoin's volatility and distributional characteristics across multiple time scales using advanced statistical methods.
Findings
Bitcoin's volatility is high but predictability is also high.
Bitcoin's returns are heavy-tailed with non-convergent moments.
Lower sampling frequencies reduce kurtosis in Bitcoin's returns.
Abstract
A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500. Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two…
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Taxonomy
TopicsMarket Dynamics and Volatility · Complex Systems and Time Series Analysis · Financial Risk and Volatility Modeling
