Thomas Mertens

Standard asset pricing models reconcile high equity premia with smooth risk-free rates by inducing an inverse functional relationship between the mean and the variance of the stochastic discount factor. This highly successful resolution to closed-economy asset pricing puzzles is fundamentally problematic when applied to open economies: It requires that differences in currency returns arise almost exclusively from predictable appreciations, not from interest rate differentials. In the data, by contrast, exchange rates are largely unpredictable, and currency returns arise from persistent interest rate differentials. We show currency risk premia arising in canonical long-run risk and habit preferences cannot match this fact. We argue this tension between canonical asset pricing and international macroeconomic models is a key reason researchers have struggled to reconcile the observed behavior of exchange rates, interest rates, and capital flows across countries. The lack of such a unifying model is a major impediment to understanding the effect of risk premia on international markets.

This paper solves a standard New Keynesian model in terms of risk-neutral expectations and estimates it using a cross-section of longer-dated financial assets at a single point in time. Inflation risk premia appear in the theory and cause inflation to deviate from its target on average. We re-estimate the model based on each day’s closing prices to capture high-frequency changes in the expected path of the economy. Our estimates show that financial markets reacted to the post-COVID surge in inflation with higher short-run inflation expectations, an increase in the inflation risk premium, and an increase in the long-run neutral real rate, 𝑟∗, while long-term inflation expectations remained well anchored. Our model produces long term inflation forecasts that outperform several standard alternative measures.

This paper provides an analytically tractable theoretical framework to study the optimal supply of central bank reserves when the demand for reserves is uncertain and nonlinear. We fully characterize the optimal supply of central bank reserves and associated market equilibrium. We find that the optimal supply of reserves under uncertainty is greater than that absent uncertainty. With a sufficient degree of uncertainty, it is optimal to supply a level of reserves that is abundant (on the flat portion of the demand curve). The model captures the empirical observation that the variability of interest rate spreads depends on reserves supply.

The correlation between uncertainty shocks, as measured by changes in the VIX, and changes in breakeven inflation rates declined and turned negative after the Great Recession. This estimated time varying correlation is shown to be consistent with the predictions of a standard New Keynesian model with a lower bound on interest rates and a trend decline in the natural rate of interest. In one equilibrium of the model, higher uncertainty raises the probability of large shocks that leave the central bank constrained by the lower bound and unable to offset negative shocks. Resulting inflation shortfalls lower average inflation rates.

This paper analyzes the effects of the lower bound for interest rates on the distributions of inflation and interest rates. In a New Keynesian model with a lower bound, two equilibria emerge: policy is mostly unconstrained in the “target equilibrium,” whereas policy is mostly constrained in the “liquidity trap equilibrium.” Using options data on interest rates and inflation, we find forecast densities consistent with the target equilibrium and find no evidence in favor of the liquidity trap equilibrium. The lower bound has a sizable effect on the distribution of interest rates, but its impact on inflation is relatively modest.

This paper applies a New Keynesian model to analyze monetary policy in the presence of a low natural rate of interest and a lower bound on interest rates. Under standard inflation-targeting, inflation expectations will be anchored at a level below the inflation target. Two themes emerge from our analysis: first, the central bank can mitigate this problem of a downward bias in inflation expectations by following an average-inflation targeting framework. Second, price-level targeting that raises inflation expectations when inflation is low can both anchor expectations at target and further reduce the effects of the lower bound on the economy.

This paper shows that perturbation methods can be applied to a DSGE model with incomplete markets and a finite but arbitrarily large number of heterogeneous agents. We develop a simple but general solution technique that handles many state and choice variables for each agent and thus has an extremely high-dimensional state space. The method is based on perturbations around a point at which the solution is known. The novel idea is to exploit the symmetry of the problem to overcome the curse of dimensionality. We use the analysis to demonstrate the impact of heterogeneity on macroeconomic quantities and the pricing of risk. Furthermore, we set our technique apart from standard methods used in the literature.

We show that the stock market may fail to aggregate information even if it appears to be efficient, and that the resulting decrease in the information content of prices may drastically reduce welfare. We solve a macroeconomic model in which information about fundamentals is dispersed and households make small, correlated errors when forming expectations about future productivity. As information aggregates in the market, these errors amplify and crowd out the information content of stock prices. When prices reflect less information, the conditional variance of stock returns rises, causing an increase in uncertainty and costly distortions in consumption, capital accumulation, and labor supply.

We investigate the link between stochastic properties of exchange rates and differences in capital-output ratios across industrialized countries. To this end, we endogenize capital accumulation within a standard model of exchange rate determination with nontraded goods. The model predicts that currencies of countries that are more “systemic” for the world economy (countries that face particularly volatile shocks or account for a large share of world GDP) appreciate when the price of traded goods in world markets is high. These currencies are better hedges against consumption risk faced by international investors because they appreciate in “bad” states of the world. As a consequence, more systemic countries face a lower cost of capital and accumulate more capital per worker. We estimate our model using data from seven industrialized countries with freely floating exchange rate regimes between 1984 and 2010 and show that cross-country variation in the stochastic properties of exchange rates accounts for 72% of the cross-country variation in capital-output ratios. In this sense, the stochastic properties of exchange rates map to fundamentals in the way predicted by the model.

We introduce the information microstructure of a canonical noisy rational expectations model (Hellwig, 1980) into the framework of a conventional real business cycle model. Each household receives a private signal about future productivity. In equilibrium, the stock price serves to aggregate and transmit this information. We find that dispersed information about future productivity affects the quantitative properties of our real business cycle model in three dimensions. First, households’ ability to learn about the future affects their consumption-savings decision. The equity premium falls and the risk-free interest rate rises when the stock price perfectly reveals innovations to future productivity. Second, when noise trader demand shocks limit the stock market’s capacity to aggregate information, households hold heterogeneous expectations in equilibrium. However, for a reasonable size of noise trader demand shocks the model cannot generate the kind of disagreement observed in the data. Third, even moderate heterogeneity in the equilibrium expectations held by households has a sizable effect on the level of all economic aggregates and on the correlations and standard deviations produced by the model. For example, the correlation between consumption and investment growth is 0.29 when households have no information about the future, but 0.41 when information is dispersed.

Insurance schemes rely on legal consequences to deter fraud and tax evasion. This observation guides us to introduce random state verification in a dynamic economy with private information. With some probability, an agent’s skill becomes known to the planner who prescribes punishments to misreporting agents. Deferring consumption can ease the provision of incentives creating a motive for subsidizing savings. In an infinite horizon economy, the constrained-efficient allocation converges to high consumption, full insurance, and no labor distortions for any positive probability of state verification.

We present a model in which investors decide whether or to what degree they want to allow their behavior to be influenced by “market sentiment.” Investors who choose to insulate their decisions from market sentiment earn higher expected returns, but incur a small mental cost. We show that if information is moderately dispersed across investors, even a very small mental cost may result in a significant amount of sentiment in equilibrium: Individuals who choose to be swayed by sentiment increase uncertainty about the future and make it less costly for others to be swayed by sentiment as well.