Many violations of the efficient market hypothesis, such as bubbles, crashes, and "fat tails" in the distribution of returns, are difficult to address using a representative agent framework because in such a setting the departures from equilibrium occur only through some external perturbation. An alternative approach, sometimes referred to as the "complex systems" view, emphasizes the importance of interactions between agents. Even if each individual agent's optimization problem is known, outcomes of their interactions are probabilistic, implying that markets can evolve "spontaneously" towards an unstable state. Particularly, in a situation where traders may have private information related to the payoff of a financial assets their individual actions may trigger a cascade of similar actions by other traders. While the mechanism of a chain reaction through information revelation can potentially explain a number of stylized facts in finance, such behavior remains notoriously difficult to identify empirically. This is partly because many theoretical underpinnings of herding, such as informational asymmetry, are unobservable and partly because the complex agent-based models of herding do not yield closed-form solutions to be used for direct econometric tests. In addition, such models have been criticized for their lack of economic microfoundations. The following chapters represent a step towards filling both of these gaps. First, I identify evidence of herding behavior by institutional investment managers during the collapse of the recent real estate bubble using an established empirical approach. Then, agent based "stochastic herding" model is introduced and tested with an alternative technique of "detection by distribution". Subsequently this framework is extended to better understand the mechanisms driving extreme volatility in the dollar-yen foreign exchange market to show that traders' tendency to herd around information about the possibility of high yield currency crashes can result in self-fulfilling prophecy without a major exogenous shock. The parameter measuring the "thickness" of the tail of the probability distribution of jumps in foreign exchange rates is proportional to the herding intensity by currency speculators. I employ Bayesian econometrics to test the theoretically predicted relationships between this "tail risk" parameter and a number of economic variables related to carry trade activity. The final chapter focuses explicitly on the types of macroeconomic information that traders use to price such extreme events in foreign exchange markets. Since "stochastic herding" provides a plausible data generating mechanism for "rare event," the empirical units of observation utilized in this work have been carefully selected to match this description. Thus, in looking at domestic stock market we focus on institutional investment managers that liquidate their entire positions, not the incremental adjustments, while the examination of foreign exchange markets abstracts from Gaussian volatility and focuses on rare realized volatility jumps and deep out-of-money options used to price such events.