This dissertation examines the interaction between macroeconomic aggregates and financial markets in two different essays. The expansion of derivatives markets has prompted interest in estimating options-implied measures to analyze market participants’ beliefs about future movements in the prices of these derivatives’ underlying assets and the probability these participants assign to unlikely events (see Datta et al., 2014). In this spirit, analyzing oil market is important for two main reasons. First, among all commodities, crude oil futures and derivatives are the most traded and liquid asset in the whole commodity market. Second, the informational content of oil derivatives can be indicative of shifts in global economic expectations which may be of interests to producers, investors and policy makers. Because the risk neutral density (RND, hereafter) consists of information from various option series that have a wide range of strike prices and maturities, we can conjecture more detailed effects of news announcements on market sentiment by investigating the changes in the RND. Chapter 1 links the crude oil market to macroeconomic risk by studying the RND around the U.S. macroeconomic news announcements. I use a non-parametric method to recover the RND and conduct regression analysis using daily data. The analysis provides several noteworthy results. First, I find that the RND is systematically affected by certain macroeconomic news announcements. Second, after controlling for the content of the news, my results indicate that good news tend to make the distribution less negatively skewed, whereas bad news have an opposite effect. However, I do not find any systematic pattern between the content (bad/good) of the news and the implied volatility or kurtosis. Hence, my results show that better/worse-than-expected news in macroeconomic announcements may both increase and decrease implied volatility and kurtosis of the option implied distribution. Finally my estimates obtained from nonlinear regressions display that the magnitude of the surprise may play into this effect; for example worse-than-expected news in Housing Starts announcement decrease the implied volatility and increase the implied kurtosis only when the size of surprise is not too large. How should a central bank conduct monetary policy in the presence of financial shocks? In Chapter 2, I use different nonlinear policy rules and address this question. Most empirical work on monetary policy relies on simple linear policy rules, however it is not clear whether such a rule can be an adequate representation of a process as complex as that of monetary policy. I first estimate Markov Switching Taylor rules with constant transition probabilities to allow for state-contingent policy making during 1987.3-2008.4. As a proxy for financial stress, I use the Adjusted National Financial Conditions Index constructed by the Chicago Fed. Then, I allow transition probabilities driving the monetary policy stance to vary over time and be a function of economic and financial indicators. The paper provides clear-cut evidence that, during the Greenspan-Bernanke tenure, the U.S. monetary policy can be characterized falling into two distinct regimes; a conventional regime where the Fed puts a greater emphasis on targeting inflation while stabilizing the economic outlook and a distressed regime where the Fed responds aggressively to output gaps and is less concerned with inflation. The distressed regime is closely correlated with times of financial imbalances. The empirical results show that nonlinear models outperform the simple linear specification in terms of model fit and the ability to track the actual interest rate. Also, the economic and financial indicators are found to be informative in dating the evolution of the state of the monetary policy stance. The results have implications for nonlinear rules to be a useful guideline for forecasting and policy analysis.

My dissertation is composed of three chapters that are unified by their exploration of asset prices and macroeconomic news announcements. With respect to asset prices, my main focus is on the price discovery process: how do asset prices reveal information relevant for asset fundamentals? Through my research, I provide new answers to this question. My work gets at core issues in asset pricing: whether financial markets are informationally efficient; why some assets earn unconditionally high premia; and how the sensitivity of prices to information varies over time and across assets. Specifically, chapter one shows evidence that sophisticated traders with an informational advantage inefficiently impound their edge into the aggregate U.S. stock market and U.S. Treasury bonds. In chapter two, I explore a model in which investors are averse to ambiguity (Knightian uncertainty) to explain why the equity premium is concentrated around specific events. Finally, chapter three investigates how the Federal Reserve's zero lower bound affects the response of asset prices, in particular interest rates, to information. Each of the three chapters explores the price discovery process using the unique setting of U.S. macroeconomic news announcements, which are made by government agencies and private-sector organizations and cover macroeconomic data on inflation, output, and unemployment. Analyzing financial markets in this setting deepens our understanding of how asset prices reflect information about macroeconomic fundamentals. At the same time, the results have macroeconomic implications; for example, the assumptions of monetary policy models in theory and the effectiveness of unconventional monetary policy in practice.

This dissertation consists of three chapters about how access to financial markets and composition of the labor market determine aggregate macroeconomic outcomes. The first chapter examines the macroeconomic consequences of credit uncertainty using a structural vector autoregression model with stochastic volatility (SVAR-SV). Credit supply conditions in the U.S. is captured by the banks' reports on how credit standards for approving loans have change over time (Bank Lending Standards). The empirical analysis shows that the volatility of macroeconomic and financial variables rises in response to an increase in the credit uncertainty shock. The economic activity falls and credit growth and related interest rates decrease persistently. Moreover, credit volatility shocks explain around 10% of the FEV of endogenous variables. A dissagregated analysis shows that the effect of these shocks are mainly explained by their effects on the corporate business sector. The second chapter studies the role of time-varying credit limits through the lens of a life cycle incomplete markets model calibrated for the U.S. Changes in credit card limits are explained by observable household characteristics and the estimated unobservable variation is quite large. The quantitative exercise shows that even though young households are more indebted in an economy with stochastic borrowing limits, aggregate consumption is not greatly affected by transitory or persistent shocks of this type. However, in the presence of these shocks, households lose the ability to self-insure against other uninsurable idiosyncratic shocks, e.g., labor income shocks. A disaggregated analysis shows that the loss of self-insurance capacity is mainly explained by the effects that stochastic borrowing limits have on the wealth distribution, the precautionary savings channel households have to face unexpected risks. The third chapter studies the role of informal markets to explain economic and demographic variables during a pandemic. The quantitative exercise shows that lockdown policies are less effective in economies with large informal markets, infection and death rates will not decrease as much as formal economies. Moreover, the size of the recession would be exacerbated because informal activities are not counted in the calculation of the GDP. To generate similar results to an economy with only formal markets, the economy with informal markets must implement more severe containment policies.