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Cryptocurrency, Delivery Lag, and Double Spending History

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  • Kang, Kee-Youn

Abstract

We develop a general equilibrium model of cryptocurrency to study the optimal design of a cryptocurrency system. Agents trade cryptocurrency using digital wallets, and the cryptocurrency system provides verification of a digital wallet’s history of double spending attempts. Delaying the delivery of goods until payment information is confirmed in the blockchain prevents double spending. However, double spending can be prevented without a delivery lag under some conditions if a wallet has a good reputation in terms of its history of double spending attempts. In particular, as the difficulty of mining work rises, the incentive to engage in double spending with a good wallet decreases. We study the optimal design of the cryptocurrency system in terms of the difficulty of mining work and the supply of cryptocurrency and evaluate the welfare gain that would be captured if the current Bitcoin system adopted the optimal cryptocurrency system.

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  • Kang, Kee-Youn, 2019. "Cryptocurrency, Delivery Lag, and Double Spending History," MPRA Paper 93598, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:93598
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    References listed on IDEAS

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    Cited by:

    1. Kee‐Youn Kang & Seungduck Lee, 2024. "Money, Bitcoin, and Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(1), pages 225-256, February.

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    More about this item

    Keywords

    Blockchain; Cryptocurrency; Delivery lag; Double spending; Trade history;
    All these keywords.

    JEL classification:

    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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