  Recent studies estimate around 70% of traded value on off-chain crypto
exchanges like Binance is wash trading. This paper turns to NFT markets, where
the on-chain nature of transactions-a key tenet of Web3 innovation-enables more
direct estimation methods to be applied. Focusing on three of the largest NFT
marketplaces, we find 30-40% of NFT volume and 25-95% of traded value involve
wash trading. We leverage this direct approach to critically evaluate recent
indirect estimation methods suggested in the literature, revealing major
differences in effectiveness, with some failing altogether. Trade-roundedness
filters, as suggested in Cong et al. (2023), emerge as the most accurate
indirect estimation method. In fact, we show how direct and indirect approaches
can be closely aligned via hyper-parameter fine-tuning. Our findings underscore
the crucial role of technological innovation in detecting and regulating
financial misconduct in digital finance.
