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What Do Bitcoin Premiums Measure? Evidence from Global P2P Markets

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  • Yanan Niu

Abstract

This paper studies what Bitcoin (BTC) premiums in peer-to-peer (P2P) markets measure. Using transaction-level data from LocalBitcoins, we construct BTC premiums for 80 currencies relative to the U.S. dollar and relate them to blockchain transaction conditions, centralized crypto market (CEX) conditions, cross-border payment frictions, and foreign exchange (FX) markets. We show that these premiums reflect both trading frictions within crypto markets and local frictions in access to cross-border payments. They vary systematically with blockchain conditions and broader crypto market conditions, including BTC returns and volatility, and they are larger in countries facing greater frictions in conventional cross-border payment channels. This pattern is especially pronounced in economies with binding institutional constraints, i.e., tight capital controls and non-floating exchange-rate regimes, consistent with greater reliance on P2P crypto markets as an alternative cross-border payment channel. We further show that rising FX pressure is absorbed mainly through prices rather than trading volumes, and that P2P BTC premiums predict subsequent official exchange rate depreciation. Although premium levels differ across countries, their predictive content remains broadly similar. Overall, P2P BTC premiums reflect limits to arbitrage across crypto trading venues, especially where formal cross-border payment channels are more constrained, and they also embed forward-looking information about currency depreciation.

Suggested Citation

  • Yanan Niu, 2024. "What Do Bitcoin Premiums Measure? Evidence from Global P2P Markets," Papers 2410.22443, arXiv.org, revised May 2026.
    • Handle: RePEc:arx:papers:2410.22443
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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    3. Annamaria Kokenyne & Mr. Romain M Veyrune & Mr. Karl F Habermeier & Mr. Harald J Anderson, 2009. "Revised System for the Classification of Exchange Rate Arrangements," IMF Working Papers 2009/211, International Monetary Fund.
    4. Gagnon, Louis & Andrew Karolyi, G., 2010. "Multi-market trading and arbitrage," Journal of Financial Economics, Elsevier, vol. 97(1), pages 53-80, July.
    5. Bodurtha, James N, Jr & Kim, Dong-Soon & Lee, Charles M C, 1995. "Closed-End Country Funds and U.S. Market Sentiment," The Review of Financial Studies, Society for Financial Studies, vol. 8(3), pages 879-918.
    6. Oana Andreea ION, 2022. "Migration Narratives: From Integration To Incorporation," The USV Annals of Economics and Public Administration, Stefan cel Mare University of Suceava, Romania, Faculty of Economics and Public Administration, vol. 22(2(36)), pages 155-166, December.
    7. Abe De Jong & Leonard Rosenthal & Mathijs A. Van Dijk, 2009. "The Risk and Return of Arbitrage in Dual-Listed Companies," Review of Finance, European Finance Association, vol. 13(3), pages 495-520.
    8. Graf von Luckner, Clemens & Reinhart, Carmen M. & Rogoff, Kenneth, 2023. "Decrypting new age international capital flows," Journal of Monetary Economics, Elsevier, vol. 138(C), pages 104-122.
    9. LI,Longling, . "Impacts of Rural-urban Migration on Food Consumption," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 14(12).
    10. Luis E C Rocha & Petter Holme & Claudio D G Linhares, 2022. "The global migration network of sex-workers," Journal of Computational Social Science, Springer, vol. 5(1), pages 969-985, May.
    11. El Mehdi Er Raqabi & Wenkai Li, 2022. "An Electric Vehicle Migration Framework," Working Papers EMS_2022_03, Research Institute, International University of Japan.
    12. Froot, Kenneth A. & Dabora, Emil M., 1999. "How are stock prices affected by the location of trade?," Journal of Financial Economics, Elsevier, vol. 53(2), pages 189-216, August.
    13. Andrés Fernández & Michael W Klein & Alessandro Rebucci & Martin Schindler & Martín Uribe, 2016. "Capital Control Measures: A New Dataset," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 64(3), pages 548-574, August.
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