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Does Bitcoin Improve Investment Portfolio Efficiency?

Author

Listed:
  • Paweł Sakowski

    (Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw)

  • Daria Turovtseva

Abstract

The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return maximization and expected shortfall (CVaR) minimization – we test the investment opportunities after adding Bitcoin to the portfolio of 10 traditional assets (among them equity, fixed income, money, commodities and money market indices). Using daily observations from 1.05.2013 till 24.05.2019, we examine the behavior of the portfolios without and with Bitcoin and check if the return-risk ratio improves for the latter. Discussing the results, we conduct the sensitivity analysis by changing the lookback window (LB) and rebalancing frequency (RB) parameters. Empirical analysis suggests that Bitcoin-inclusive portfolios provide an investor with wider diversification opportunities. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio.

Suggested Citation

  • Paweł Sakowski & Daria Turovtseva, 2020. "Does Bitcoin Improve Investment Portfolio Efficiency?," Working Papers 2020-42, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2020-42
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Portfolio optimization; portfolio theory; cryptocurrency; Bitcoin; Markowitz model; asset allocation; portfolio diversification; investment opportunities;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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