Student loans with income-contingent repayment insure borrowers against income risk but can reduce their incentives to earn more. Using a change in Australia's income-contingent repayment schedule, I show that borrowers reduce their labor supply to lower their repayments. These responses are larger among borrowers with more hourly flexibility, a lower probability of repayment, and tighter liquidity constraints. I use these responses to estimate a dynamic model of labor supply with frictions that generate imperfect adjustment. My estimates imply that the labor supply responses to income-contingent repayment decrease the optimal amount of insurance but are too small to justify fixed repayment contracts. Moving from a fixed repayment contract to a constrained-optimal income-contingent loan increases welfare by the equivalent of a 1.3% increase in lifetime consumption at no additional fiscal cost.

We study the role of risk preferences and frictions in portfolio choice using variation in 401(k) default investment options. Patterns of active choice in response to different default funds imply that, absent participation frictions, 94% of investors prefer holding stocks, with an equity share of retirement wealth declining with age—patterns markedly different from their observed allocations. We use this quasi-experiment to estimate a lifecycle model and find relative risk aversion of 2, EIS of 0.4, and a $200 portfolio adjustment cost. Our results suggest low stock market participation is due to participation frictions rather than non-standard preferences such as loss-aversion.

We document the growth of retail options trading and provide evidence that retail investors are drawn to options by anticipated spikes in volatility. Retail investors purchase options in a concentrated fashion before earnings announcements, particularly those with greater expected abnormal volatility. Comparing across asset markets, we also find retail investors disproportionately trade options over stocks as anticipated announcement volatility increases. In doing so, retail investors display a trio of wealth-depleting behaviors: they overpay for options relative to realized volatility, incur enormous bid-ask spreads, and sluggishly respond to announcements. These translate to retail losses of 5-to-9% on average, and 10-to-14% for high expected volatility announcements.

Analyst forecasts outperform econometric forecasts in the short run but underperform in the long run. We decompose these differences in forecasting accuracy into analysts’ information advantage, forecast bias, and forecast noise. We find that noise and bias strongly increase with forecast horizon, while analysts’ information advantage decays rapidly. A noise increase with horizon generates a mechanical reversal in the sign of the Coibion and Gorodnichenko (2015) regression coefficient at longer horizons, independently of over-/underreaction. A parsimonious model with bounded rationality and a noisy cognitive default à la Patton and Timmermann (2010) matches the term structures of noise and bias jointly.

Over the past couple of decades, the number of volatility indices has increased rapidly. Although the dynamics of realized volatility spillover have been studied extensively, very few studies exist that examine the spillover between these implied volatility indices. By using DAG-based structural vector autoregression, this paper provides evidence that implied volatility spillover differs from realized volatility spillover. This paper finds that Asia, more specifically Hong Kong, plays a central role in implied volatility spillover during and after the 2008 financial crisis.

Intuitively, one would expect that internet search volume would contain valuable information about investor sentiment for a company. With the development of new data sources, such as Google Trends, this relationship can be more easily and objectively examined. This paper seeks to examine the relationship between a company’s stock price volatility and its Google search volume. A small cross-section of twenty companies is considered, and the goal of this paper is to demonstrate the power of Google Trends data in hope of initiating further research. Using a conventional GARCH framework for financial market volatility, an economically and statistically significant contemporaneous relationship between Google search volume and equity volatility is found.

We introduce the concept of selective inattention: agents in the economy selectively update their expectations about aggregate variables only when they make individual decisions for which these variables are relevant. Using a comprehensive set of household surveys, we show that households form expectations of macroeconomic variables that are more accurate, less dispersed, and closer to those of professional forecasters around periods in which they make important decisions, such as taking out a mortgage. These effects are larger for more consequential decisions and increase with proxies for financial sophistication. In ongoing work, we develop a consumption-savings model with durable and nondurable consumption, where agents can pay an observation cost to observe the return on a risky asset. In the model, agents exhibit selective inattention endogenously: they are more likely to pay the observation cost when adjusting durable consumption. This selective inattention has spillover effects on nondurable consumption and implies that the model can exhibit two features that have been difficult to reconcile jointly: a high level of macro-inattention, which refers to the sluggishness with which average expectations respond to shocks, and large responses of macro aggregates to shocks, in particular volatile durable goods spending.

Traditional dynamic programming requires a mathematical model of the state transition function. Using reinforcement learning techniques, we develop a framework that allows more general transition functions. The modeler does not need to know the transition function as long as it can simulate realizations of it or observe realizations from data. We apply it to the income fluctuations problem and show that our solution technique is able to learn the underlying data-generating process, achieving the same value as traditional methods. We then quantify the welfare loss of assuming the income process is an AR1 instead of using real income realizations.