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Rising levels of uncertainty and distrust of governments and mass printing of fiat currencies in conjunction with pandemic-related events have led to a rotation into different assets such as cryptos. Without solid fundamentals, cryptocurrencies have spiked in price levels in the last few years; while popularity rises it remains heavily misunderstood. This study looks into the factors that specifically influence the cryptocurrency Ethereum's price volatility. According to previous literature and existing theories, it gathers seven explanatory variables that should impact the volatility and applies a GARCH (1,1) model. The study finds that the variables Google trends, hash rate, S&P 500, address count, and trade volume impacted the volatility. With only the hash rate and S&P 500 lowering the volatility. Both the ARCH and GARCH terms were significant, with the latter having the bigger coefficient, implying that the past volatility should be accounted for when forecasting future volatility. The findings within this study align with previous literature and other studies on different cryptos. It concludes that while Ethereum is still volatile and is in its growing phase it is headed in a positive direction in terms of stabilization. Further research or a repeated identical/similar study should be conducted again once the market has matured further.

This dissertation consists of two chapters. The first chapter examines the role of growth options on stock return continuation. Growth options are both difficult to value and risky. Daniel, Hirshleifer and Subrahmanyam (1998) argue that higher momentum profits earned by high market-to-book firms stem from investors' higher overconfidence due to the difficulty of valuing growth options. Johnson (2002) and Sagi and Seasholes (2007) offer an alternative rational explanation wherein growth options cause a wider spread in risk and expected returns between winners and losers. This paper suggests that firm-specific uncertainty helps disentangle these two different explanations. Specifically, the rational explanation is at work among firms with low firm specific uncertainty. However, the evidence is in favor of the behavioral explanation for firms with high firm specific uncertainty. This is consistent with the notion that investors are more prone to behavioral biases in the presence of firm-specific uncertainty and the resulting mispricings are less likely to be arbitraged away. The second chapter examines how investors capitalize differences of opinion when disagreements are common knowledge. We conduct an event study of the market's reaction to analysts' dispersed earnings forecast revisions. We find that investors take differences of opinion into account and do not exhibit an optimism bias. Our findings indicate that the overpricing of stocks with high forecast dispersion is not due to investors' tendency to overweight optimistic expectations, but rather due to investor credulity regarding analysts' incentives. Our findings support the notion that assets may become mispriced when rational investors face structural uncertainties as proposed by Brav and Heaton (2002). / Ph. D.