Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches

ArXiv ID: 2401.02049 “View on arXiv”

Authors: Unknown

Abstract

This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it’s crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin’s volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.

Keywords: Historical Volatility, Implied Volatility, GARCH Models, Bitcoin Options, Cryptocurrency Volatility, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 5.5/10
• Quadrant: Holy Grail
• Why: The paper employs established econometric models (GARCH) with formal statistical comparisons, while also using real-world Bitcoin options data for implied volatility calculation, demonstrating a balance of mathematical modeling and data-driven implementation.

  flowchart TD
    A["Research Goal:<br>Compare Bitcoin Volatility Measures"] --> B["Data Preparation<br>Bitcoin Return Series"]
    B --> C["Methodology 1:<br>Historical Volatility<br>Sample Standard Deviation"]
    B --> D["Methodology 2:<br>Forecasted Volatility<br>GARCH-type Models"]
    B --> E["Methodology 3:<br>Implied Volatility<br>Bitcoin Options Market"]
    C --> F{"Key Findings & Outcomes"}
    D --> F
    E --> F
    F --> G["High Volatility Across Methods"]
    F --> H["Liquidity Constraints<br>Affect Implied Volatility Accuracy"]