In the evolving cryptocurrency market, accurate token valuation is crucial for investment decisions and policy development. This study refines the equation of exchange model using empirical data from CoinGecko to enhance insights into token valuation methodologies. By focusing on velocity and holding time, innovative equations are introduced to potentially revolutionize cryptocurrency analytics.
Traditional valuation methods like DCF models are not directly applicable to cryptoassets due to their unique characteristics. The Quantity Theory of Money applied through the equation of exchange offers a quantitative approach based on token supply, velocity, and transaction volume. However, flaws in this model necessitate refinement through empirical data analysis.
The study uses historical data from key cryptoassets like BTC and ETH to identify distributional fits for velocity and holding time. A log-linear model is developed to predict price based on these factors, achieving high explanatory power. The research also addresses limitations such as lack of granular data on individual token velocities and suggests future work directions for improved valuation models.
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by Stylianos Ka... at arxiv.org 03-11-2024
https://arxiv.org/pdf/2403.04914.pdfDeeper Inquiries