Inflation Expectations and Stock Returns July, 2022
(with Manav Chaudhary)

Do stocks protect against rising inflation expectations? We directly measure investors’ expectations using traded inflation-indexed contracts and show that, post-2000, stocks offer positive returns in response to higher expected inflation: unconditionally, a 10 basis point increase in 10-year breakeven inflation is associated with a 1.1% increase in the value-weighted stock index. Using high-frequency identification around scheduled CPI releases, we show this relationship is likely causal. We provide evidence that the price increase is driven by lowering future expected excess returns rather than changing risk-free rates or cashflows: (1) in the cross-section, return responses are almost completely explained by CAPM beta but not by cashflow or leverage related variables, (2) VAR decompositions of returns as well as mediation regressions that directly control for alternate channels attribute nearly all the changes to expected excess returns. Finally, we show inflation expectations predict future output, suggesting that investors may use information about high future inflation as a signal for economic growth, thereby lowering risk premia.

The Past is Present: Optimal Monetary Policy at the Effective Lower Bound September, 2020
(with Fernando Duarte)

We use a New Keynesian model with an effective lower bound (ELB) and a general stochastic process for the natural rate to study optimal monetary policy. The central bank has perfect commitment and an interest rate smoothing term in its loss function. Despite the ELB binding occasionally and endogenously, we can derive a closed-form solution for the optimal interest rate: it is the maximum of zero and a weighted average of all past realizations of the output gap. This implies that the optimal interest rate (i) takes a simple form, (ii) is path dependent at all times, (iii) should be pre-emptively lowered when close to the ELB — or kept at zero if at the ELB — if and only if the weighted average of past output gaps is negative, and (iv) behaves very differently from the Taylor rule. We illustrate these insights by solving for key variables in the New Keynesian model using a neural network.