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 in government-provided student loans. However, these responses are too small to justify fixed repayment contracts: restructuring student loans from fixed repayment to a constrained-optimal income-contingent loan increases borrower welfare by the equivalent of a 1.3% increase in lifetime consumption at no additional fiscal cost.

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.

Traditional dynamic programming requires a mathematical model of the transition function for the state vector. Leveraging reinforcement learning techniques, we develop a framework to solve dynamic optimization problems that does not require modeling the data-generating process (DGP) of exogenous states. Instead, the method samples realizations of these states directly from the data, allowing the modeler to be "agnostic" about the DGP. We apply our method to a canonical life cycle consumption-saving problem, solving the model without specifying the DGP for income. Using income data from the CPS, we find that the welfare loss from using a standard parametric income process relative to placing no restrictions on the DGP is small. We conclude by verifying that our method achieves a global optimum when given a known DGP and discussing directions for future work.

We study the role of risk preferences and frictions in portfolio choice using variation in 401(k) default 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 observed allocations. We use this quasi-experiment to estimate a life cycle model and find a relative risk aversion of 2, EIS of 0.4, and $200 portfolio adjustment cost. Our results suggest that low levels of stock market participation in retirement accounts are due to participation frictions rather than non-standard preferences such as loss-aversion.

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 error-revision (Coibion--Gorodnichenko) regression coefficient at longer horizons, independently of over/underreaction. A parsimonious model with bounded rationality and a noisy cognitive default matches the term structures of noise and bias jointly.

This paper studies how durable decisions, such as purchasing a home or car, affect how households acquire information about macroeconomic variables and form their expectations. Using a newly-designed survey of U.S. consumers, we show that households concentrate the timing and frequency of their information acquisition about macroeconomic variables around the time period in which they make durables purchases. These patterns in information acquisition generate selective inattention, in which households that make durables adjustments hold beliefs that are around 35% more accurate than those of non-adjusters. To assess the macroeconomic implications of selective inattention, we build an incomplete markets model of non-durable and durable consumption where households acquire information dynamically about interest rates. After calibrating the parameters governing information acquisition using survey data, we study how selective inattention affects the response of the economy to changes in interest rates. Like a model with exogenous information, selective inattention dampens the response of non-durable consumption to interest rates. However, unlike a model with exogenous information, a model with selective inattention generates responses of durable consumption that are almost as large as the full information case. In sum, our results suggest that the beliefs of decision-makers, in addition to average beliefs, matter for the propagation of macroeconomic shocks.

We empirically analyze a large sample of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns.

In several countries, such as the US, multiple tax and other incentives favor the accumulation of wealth in illiquid vehicles such as deferred contribution retirement accounts and real estate. Moreover, it is common for the cost basis of inherited assets to “stepped-up” when these assets are passed on after death. This creates a strong incentive for asset holders to delay asset sales until death to reduce their tax liability. In this project, we study how these incentives affect wealth accumulation over the life cycle, the transfer of resources across generations, and, ultimately, inequality within and across generations. Empirically, we examine wealth accumulation in several illiquid assets using Census data, including housing, retirement savings, and small businesses. We provide new evidence about the magnitude of intergenerational wealth transfers using administrative and survey data of children around the time of parental death. To identify the effects of these incentives, we use differences in the timing of wealth accumulation, family structure, 401k matching rules, and changes in tax policies related to capital gains and estate taxation. Finally, we use this evidence to discipline a life cycle model with multiple assets. Using this model, we quantify the effect of step-up in basis on wealth inequality and consider implications for the distribution of income and wealth of several alternative policies.

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.