Essays in cryptocurrency

Abstract

This dissertation contributes to the growing body of research on cryptocurrencies by addressing their economic, behavioral, and financial dimensions through a series of empirical essays. The studies collectively examine the determinants of cryptocurrency pricing, the role of investor sentiment and political uncertainty, and the implications of advanced portfolio optimization techniques—including machine learning approaches—for cryptocurrency investment management. The findings offer new insights into how digital assets behave as alternative investments, how they respond to external shocks, and how quantitative methods can be used to enhance portfolio performance in this highly volatile and evolving market. The first essay, Do FEARS Drive Bitcoin?, explores the relationship between investor sentiment and Bitcoin returns using a novel sentiment index derived from financial and media-based fear measures (FEARS). Employing econometric time-series models, the study finds that heightened investor fear significantly predicts short-term increases in Bitcoin trading volumes and volatility, consistent with Bitcoin’s perception as both a speculative and hedging instrument. However, the analysis also reveals asymmetric effects: while fear-driven demand raises short-term prices, sustained pessimism weakens long-term valuation. Robustness tests confirm the persistence of sentiment effects across multiple proxies and subperiods, demonstrating that behavioral factors remain central to cryptocurrency price formation. The second essay, Risk-Based Portfolio Optimization for Cryptocurrencies, examines how traditional risk-based allocation frameworks—such as minimum variance, equal risk contribution, and risk parity—perform in a cryptocurrency context characterized by extreme returns and tail dependencies. Using a dataset of major digital assets, the analysis compares the performance of various optimization strategies under different market regimes. The findings reveal that while risk parity strategies deliver superior diversification benefits, they remain vulnerable to extreme downside risk. Incorporating tail-risk measures and conditional performance adjustments substantially improves risk-adjusted returns, emphasizing the need for adaptive and non-normal risk frameworks in digital asset management. The third essay, Bitcoin and Global Political Uncertainty – Evidence from the U.S. Election Cycle, investigates Bitcoin’s role as a hedge or safe haven during periods of heightened political uncertainty. Using event-study and regression approaches, the results show that Bitcoin exhibits strong hedging characteristics during politically volatile periods, particularly around U.S. election cycles. However, its behavior varies asymmetrically with the type of uncertainty—economic versus institutional—highlighting that Bitcoin’s hedging function is conditional rather than universal. The fourth essay, Cryptocurrencies and the Low Volatility Anomaly, tests whether the well-documented low-volatility anomaly in equity markets extends to the cryptocurrency universe. Using portfolio sorting and cross-sectional regression analyses, the study finds that low-volatility cryptocurrencies outperform their high-volatility counterparts on a risk-adjusted basis, even after accounting for liquidity and size effects. This evidence challenges the perception of cryptocurrencies as uniformly speculative assets and suggests that market inefficiencies and behavioral biases may sustain persistent return anomalies in digital asset markets. The final essay, Beyond Risk Parity – A Machine Learning-Based Hierarchical Risk Parity Approach on Cryptocurrencies, proposes a novel portfolio optimization framework that integrates machine learning techniques with hierarchical clustering methods. By capturing complex non-linear relationships between assets, the hierarchical risk parity (HRP) approach outperforms traditional covariance-based methods in terms of diversification, turnover reduction, and out-of-sample stability. Empirical tests confirm that the machine learning-enhanced HRP model achieves higher Sharpe ratios and lower drawdowns across multiple rebalancing frequencies, demonstrating its robustness for high-dimensional and noisy cryptocurrency data. Collectively, the essays provide a comprehensive and multi-faceted understanding of the cryptocurrency market from both behavioral and quantitative perspectives. They highlight the dual nature of digital assets—as speculative vehicles sensitive to sentiment and uncertainty, and as emerging investment instruments that can be systematically managed through advanced quantitative techniques. The dissertation advances academic discussions on asset pricing, risk management, and market efficiency in the context of decentralized finance, while offering practical insights for institutional investors navigating the challenges and opportunities of the rapidly evolving digital asset ecosystem.