Marcelo Jardim Sena

In frictionless financial markets, a carbon tax on energy users provides the same incentives as a replicating asset price schedule that depends on emissions. In particular, the replicating rate of return on a firm increases linearly in scope 1 emissions relative to enterprise value. We use this result to interpret pollution premia measured by recent empirical studies and conclude that markets currently provide only modest incentives. Replicating a serious carbon tax requires high returns in the right tail of the emission intensity distribution. With heterogeneous investors, such returns are not sustainable unless essentially everyone perceives large nonpecuniary costs from holding dirty capital. Substantial emission reductions can be achieved, however, when even a small share of investors perceive nonpecuniary benefits from owning clean electricity capital.

Using high-frequency identification, we measure the impact of monetary policy shocks on dividend claims across different horizons. A 1% tightening of short-term interest rates decreases expected growth rates by up to 3.3% in the 1-year horizon and increases risk-premia by up to 1% in the 9-year horizon. Our analysis shows that dividend risk-premia, particularly beyond the ten-year maturity, account for most of the effect on equity returns. Our findings can discipline models of monetary policy and risk-premia.

We construct a novel data set on the fiscal position of cities in the United States. We document a secular decline in their financial health. 61% of cities have a negative book equity position, suggesting risks of insolvency. Poor financial health is associated with higher pension and other post-employment benefits liabilities. Since book values are backward looking, we estimate the market valuation of cities’ equity through an asset pricing model that prices untraded future revenue and expenditure claims. Market values of equity are also negative for a sizeable fraction of cities. We quantify high bailout market values for insolvent governments.

What is the impact of inflation on the supply side of the banking sector? This paper draws lessons on the relationship between banking and inflation by exploring the Brazilian hyperinflationary period during the 1990s and its sharp disinflation following the Real Plan in 1994. We formalize how banks can extract rents by issuing deposits and how inflation impacts these rent dynamics leading to the entry/bankruptcy (merger) of banks. The model has three key features: (i) interest-bearing private money (deposits) that compete with public money (currency) in households’ liquid asset portfolio choice; (ii) heterogeneous productivity banks in supplying loans; (iii) banks’ market power in the deposits market. When inflation and nominal rates rise banks can extract more rents from depositors, allowing for the survival of low-productivity banks dependent on inflation profits. We derive conditions under which the existence of too-low productivity banks is inefficient and a regulator would prefer to keep them outside the banking market. Consistent with the data, when inflation drops banks benefit in the short-run due to re-evaluation of the assets, but the long-run effects of lower inflation rents lead to the exit and a more concentrated banking system. Using disaggregated bank balance sheet data, we construct a model-based index of banks’ long-run reliance on inflation and show that it predicts exit of banks following disinflation.

We show that data-driven instrument selection based on the Lasso estimator can perform well comparative to the usual ad hoc instrument set for single equation estimation of a forward-looking Phillips Curve, when the overall identification condition is strong or in cases when the instruments are not very weak. We conclude that in face of model uncertainty and/or some potentially weak instruments within a large number of candidates, data-driven selection may provide a disciplined and more reliable estimation strategy.

We develop a continuous-time macro-finance model with price stickiness and household disagreement on economic fundamentals. These generate an endogenous distribution of inflation expectations. We calibrate the model to match the time-varying dispersion of inflation expectations observed in the data. We use the model to study how optimal policy differs in the presence of endogenous inflation disagreement.

Many economic problems in macro-finance involve default decisions by heterogeneous agents. This paper proposes a deep learning based method to solve high dimensional rational expectations default models. The method can accurately solve for complex default boundaries in high-dimensional state spaces. We provide an application to a model with heterogeneous banks with rich balance sheet details and different customer base.