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On the Performance of Cryptocurrency Funds

Author

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  • Bianchi, Daniele

    (School of Economics and Finance, Queen Mary University of London)

  • Babiak, Mykola

    (Lancaster University Management School)

Abstract

We investigate the performance of funds that specialise in cryptocurrency markets and contribute to a grow ing literature that aims to understand the value of digital assets as investments. The main empirical results support the idea that cryptocurrency funds generate significantly alphas compared to passive benchmarks or conventional risk factors. We compare the actual fund alphas against the simulated values from a panel semi-parametric bootstrap approach. The analysis shows that the extreme outperformance is unlikely to be explained by the luck of fund managers. However, the significance of the alphas becomes statistically weaker after considering the cross-sectional correlation in fund returns.

Suggested Citation

  • Bianchi, Daniele & Babiak, Mykola, 2021. "On the Performance of Cryptocurrency Funds," Working Paper Series 408, Sveriges Riksbank (Central Bank of Sweden).
    • Handle: RePEc:hhs:rbnkwp:0408
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    Cited by:

    1. Victoria Dobrynskaya & Mikhail Dubrovskiy, 2022. "Cryptocurrencies Meet Equities: Risk Factors And Asset Pricing Relationships," HSE Working papers WP BRP 86/FE/2022, National Research University Higher School of Economics.
    2. Khaki, Audil & Prasad, Mason & Al-Mohamad, Somar & Bakry, Walid & Vo, Xuan Vinh, 2023. "Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Dombrowski, Niclas & Drobetz, Wolfgang & Momtaz, Paul P., 2023. "Performance measurement of crypto funds," Economics Letters, Elsevier, vol. 228(C).
    4. Ben Khelifa, Soumaya & Guesmi, Khaled & Urom, Christian, 2021. "Exploring the relationship between cryptocurrencies and hedge funds during COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 76(C).
    5. Bianchi, Daniele & Babiak, Mykola & Dickerson, Alexander, 2022. "Trading volume and liquidity provision in cryptocurrency markets," Journal of Banking & Finance, Elsevier, vol. 142(C).
    6. Kim, Jang Ho, 2022. "Analyzing diversification benefits of cryptocurrencies through backfill simulation," Finance Research Letters, Elsevier, vol. 50(C).
    7. Andreas Renard Widarto & Harjum Muharam & Sugeng Wahyudi & Irene Rini Demi Pangestuti, 2022. "ASEAN-5 and Crypto Hedge Fund: Dynamic Portfolio Approach," SAGE Open, , vol. 12(2), pages 21582440221, April.
    8. Siu Hin Tang & Mathieu Rosenbaum & Chao Zhou, 2023. "Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter," Papers 2311.04727, arXiv.org, revised Feb 2024.
    9. Daniele Bianchi & Mykola Babiak, 2021. "A Factor Model for Cryptocurrency Returns," CERGE-EI Working Papers wp710, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    10. Daniele Bianchi & Massimo Guidolin & Manuela Pedio, 2020. "Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets," BAFFI CAREFIN Working Papers 20143, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

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    More about this item

    Keywords

    Cryptocurrency markets; Alternative investments; Fund management; Bootstrap methods;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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