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volatile one is MBOX (27% daily), even though they have been listed on the crypto exchange during the same calendar time. The annualized volatility is explosive 174% (median 176%), an order of magnitude higher than the volatility of the US listed equity. [Place Figure 5 about here] Figure 5 plots the relationship between daily values of return and volatility. As depicted in Panel A, the volatility clusters around the 11% level with the exception of the three outliers MBOX, WAX, and GHST, all of which began trading in August 2021. When we remove the outliers, the relationship between risk and return becomes parabolic (Panel B). It appears that, top performers are most volatile but so are the worst performers. It seems that among the riskiest NFTs there are either big winners or losers. The alternative explanation may be that the failing projects increase return volatility, due to extreme downside moves. Because the returns are scaled (per unit of time), the identity of the winners are different than those depicted in Figure 4, Panel A. The identity of losers remains the same, because these are the only projects in the sample that generate negative returns. 4.2. NFT risk-adjusted performance In this section, we analyze returns to NFTs on the risk-adjusted basis. To this end, we select two standard measures: Sharpe ratio and market-adjusted return. We use ex-post Sharpe ratio defined as historic average differential return per unit of historic variability of the differential return as in Sharpe (1994). Then, for purposes of standardization we annualize both nominator and denominator (daily returns and daily volatilities). As seen in Table 3, the average Sharpe ratio for the entire sample of NFTs - after excluding an extreme outlier - is 0.32 (median 0.37). Interestingly, this figure compares with the historical average of 0.3 for publicly traded equities (see e.g., Fama and French, 2002; Ferreira and Santa-Clara, 2011). [Place Table 3 about here] 16PDF Image | NFT The Analysis of Risk and Return
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