Working Papers

Insurance versus Moral Hazard in Income-Contingent Student Loan Repayment
[+] Summary [+] Slides

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.

What Drives Investors' Portfolio Choices? Separating Risk Preferences from Frictions
with Taha Choukhmane
Revise and Resubmit at the Journal of Finance
[+] Summary

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.

Losing is Optional: Retail Option Trading and Expected Announcement Volatility
with Eric C. So and Kevin C. Smith
[+] Appendix

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.

Model-Agnostic Dynamic Programming
with Marc de la Barrera
[+] Python Package

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.


Noise in Expectations: Evidence from Analyst Forecasts
with David Thesmar
Review of Financial Studies, 2024
[+] Appendix [+] Code [+] Summary

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.

Are Volatility Expectations in Different Countries Interdependent?
Undergraduate Economic Review, 2017

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.

Is Google Search Behavior Related to Volatility?
Undergraduate Economic Review, 2016

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.

Work in Progress

Selective Inattention
with Pierfrancesco Mei

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.