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AbstractPolicy-makers are struggling to accommodate cryptocurrencies within tax systems not designed to handle them; this paper reviews the issues that arise. The greatest challenges are for implementation: crypto's pseudonymity is an inherent obstacle to third-party reporting. Design problems arise from cryptocurrencies' dual nature as investment assets and means of payment: more straightforward is a compelling case for corrective taxation of carbon-intensive mining. Ownership is highly concentrated at the top, but many crypto investors have only moderate incomes. The capital gains tax revenue at stake worldwide may be in the tens of billions of dollars, but the more profound risks may ultimately be for VAT/sales taxes.

In the introductory part of the paper, the author briefly explores the emergence of the first cryptocurrency (Bitcoin), which was initially devised for the purpose of securing easier transactions without intermediaries. Criminals soon realised that cryptocurrencies, due to their inherent characteristics, could provide them with anonymity. As other cryptocurrencies (altcoins) emerged, it was necessary to define their conceptual framework. While cryptocurrencies were initially used in illegal sales of narcotics, their application soon spread to a number of other criminal activities. In that context, the author first presents the reasons that led criminals to turn to cryptocurrencies in their financial transactions, and then explains the possible uses of cryptocurrencies in the commission of crime. The central part of the paper provides examples of criminal activities committed by using cryptocurrencies. It is reasonable to expect that, in the future, the use of cryptocurrencies will extend to other criminal activities, which are still unaffected by the trend that has existed for the last ten years.

This panel includes three presentations: Cryptocurrencies and Financial Crimes by Marie Vasek; Cryptocurrencies and Specific Drug Types by Eric Jardine; and Bitcoin and OPSEC for Terrorists by Aaron Brantly. (Please note that due to technical difficulties, slides for the last presentation by Aaron Brantly were not captured on the video). The presentations were given as part of the conference "Understanding the Dark Web and Its Implications for Policy" held on May 18, 2018 at the Virginia Tech Executive Briefing Center in Arlington, Virginia. ; Virginia Tech. Department of Political Science ; Virginia Tech. Institute for Society, Culture, and Environment ; Virginia Tech. National Capital Region ; Infragard ; Bluestone Analytics ; Virginia Tech. Integrated Security Destination Area ; Government Technology & Services Coalition

Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.

Current cryptocurrencies, starting with Bitcoin, build a decentralized blockchain-based transaction ledger, maintained through proofs-of-work that also serve to generate a monetary supply. Such decentralization has benefits, such as independence from national political control, but also significant limitations in terms of computational costs and scalability. We introduce RSCoin, a cryptocurrency framework in which central banks maintain complete control over the monetary supply, but rely on a distributed set of authorities, or mintettes, to prevent double-spending. While monetary policy is centralized, RSCoin still provides strong transparency and auditability guarantees. We demonstrate, both theoretically and experimentally, the benefits of a modest degree of centralization, such as the elimination of wasteful hashing and a scalable system for avoiding double-spending attacks.