UC Irvine

This dissertation is composed of a theoretical chapter and an empirical chapter on asset pricing and cryptocurrency asset pricing.

The first chapter is a theoretical study and is motivated by the recent multiplication of privately produced cryptocurrencies and the questions raised about the dynamics of their prices. Through a New Monetary approach, I investigate a prominent question, which is, ``What causes enormous price volatility in cryptocurrency?" Specifically, I study this question using a random matching search model and relax the assumption of rational expectation by introducing an adaptive learning algorithm. My model builds on Choi and Rocheteau (2021), who study the price dynamics of monies that are privately-produced through time-consuming mining technologies under rational expectation. They extended the Lagos-Wright model by adding a time-consuming mining technology and an occupation choice, to show that there exists a unique equilibrium where a positive money value reaches to steady state. Although their model has desirable results, the extreme price volatility in empirical data can not be explained in their rational expectation version of the model. I use bounded rational expectation to explore monetary theory price dynamics. my paper contributes to this under-explored study using an adaptive learning approach. The primary contribution of this study is that I use a constant-learning-gain to demonstrate how the learning gain affects monetary equilibria, their dynamics and their stabilities. The main results are that with a relative high learning gain in the adaptive learning algorithm, a period of doubling bifurcation can occur, which can lead to chaos or explosive paths. These endogenous dynamic results shed some light on the intensity of cryptocurrency’s price volatility. In addition, when buyers have higher bargain power, the price of cryptocurrency converges to a positive value. Ceteris paribus, however, when producers have higher bargain power, the price converges to zero equilibrium. The feedback effect, which plays a significant role in cryptocurrency’s price volatility, provides the intuition behind this model.

The second chapter examines the cross-sector comovements that occurred in the U.S. stock market during the COVID-19 pandemic. This study constructs a dynamic factor model to illuminate the sources and implications of these comovements. Estimation of the model using a Markov Chain Monte Carlo method reveals that the latent sentiment is the driving force behind financial market behaviors. In addition, the latent factor had a weak daily oscillation pattern with a -0.09 autoregressive coefficient in an AR(1) process. This pattern explains the stock market’s extreme comovements and high volatility. Moreover, this study estimates the impact of the monetary policy interest rate on each stock market sector. The results indicate that when the Fed Effective Funds Rate was reduced by one percentage point, utilities and non-durable goods stock returns substantially jumped by 11.35\% and 7.328\%, respectively. In addition, this study explores the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Of the positive monetary news shocks, the strongest shocks were from the interest rate policy surprises, while unconventional monetary policy news had a more sluggish impact on stock returns. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment. Using the Wall Street Journal headlines as proxies of the market sentiment, the study finds a positive correlation, 0.31, at the 95\% statistically significant level, between the comovements and market news sentiment. Finally, in estimating the associations between the cross-sector asset returns and the government’s social distancing policy, this study finds that the stay-at-home orders and restrictions on transit have positive associations with asset returns. Conversely, increases in retail and recreation activities have negative associations with asset returns in general. Owing to the government’s policies and restrictions enacted to protect public health by slowing the spread of COVID-19, some economic activities have been curtailed in the short term. However, in the long term, these government restrictions help the public’s welfare and the economy. Future studies to explore the different impacts between government restrictions and voluntary social distancing could provide fruitful results.