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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01c534fr789
Title: Cryptocurrency Portfolios: Investment Optimization and Risk Analysis
Authors: Girshik, Daniel
Advisors: Tangpi, Ludovic
Department: Operations Research and Financial Engineering
Class Year: 2019
Abstract: This thesis explores the growing sector of cryptocurrencies in an attempt to analyze investing strategies. As this space has grown in popularity, banks, hedge funds, and individual Americans have seized on the opportunity to invest in this new form of exchange, though many without a complete understanding of the best ways to include these coins in their portfolios. Cryptocurrencies vary in many aspects. This thesis starts by exploring various coins to see whether or not tokens with similar characteristics have returns that are more correlated, and finds that this is true in some cases. Next, it looks at the cryptocurrency sector against other types of investments that have historically been popular, such as equities, treasuries, fiat currencies, and commodities, to evaluate if cryptocurrencies can be a good investment as part of a broader portfolio. Markowitz optimization and Sharpe ratios are utilized to show that cryptocurrencies can play a positive role in investment portfolios. To evaluate risks and upside potential, forward-looking scenarios are generated, using Monte Carlo simulations. Regime-based simulations are also run, and fixed-mix and buy-and-hold strategies are analyzed. With cryptocurrencies added, there is both a higher upside and higher downside than with a standard portfolio. This result is magnified with regime-switching implemented, suggesting that regimes are better at capturing potential risks of investing in the space. Further research will be needed as the cryptocurrency sector continues to expand and evolve.
URI: http://arks.princeton.edu/ark:/88435/dsp01c534fr789
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Operations Research and Financial Engineering, 2000-2019

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