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We analyze the impact of interest rate policy on financial stability in an environment where banks can experience runs on their short-term liabilities, forcing them to sell assets at fire-sale prices.

A consumer discloses information to a multi-product seller, which learns about the consumer’s preferences, sets prices, and makes product recommendations. While the consumer benefits from accurate product recommendations, the seller may use the information to price discriminate.

The secular decline in real interest rates has created a challenge for monetary policy, now confronting the zero lower bound more often. An increase in the supply of safe assets reduces downward pressure on the natural interest rate. This allows monetary policy to reach price stability and full employment, but not without cost—permanently lower investment.

This paper studies how unsecured consumer credit impacts the extent to which unemployment insurance (UI) policies smooth aggregate consumption fluctuations over the business cycle. Using a general equilibrium real business cycle model, I find that unsecured credit amplifies the extent to which UI smooths cyclical consumption fluctuations.

We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant.

Changes in survey mode (e.g., online, offline) may influence the values of survey responses, and may be particularly problematic when comparing repeated cross-sectional surveys.

The current monetary policy framework of the Fed intends to be more ’inclusive’ by running the economy hot for longer during expansions.

We show that issuing a deposit-like central bank digital currency (CBDC) with a proper interest rate would encourage banks to pay higher interest to keep their customers. Banks would then attract more deposits and offer more loans. Hence, a CBDC would not necessarily crowd out private banking.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

We develop a model in which a financial intermediary’s investment in risky assets—risk taking—is excessive due to limited liability and deposit insurance and characterize the policy tools that implement efficient risk taking.