Research

We present a microfounded New Keynesian model that features financial vulnerabilities. Financial intermediaries' occasionally binding value at risk constraints give rise to variation in the pricing of risk that generates time varying risk in the conditional mean and volatility of the output gap. The conditional mean and volatility of the output gap are negatively related: during times of easy financial conditions, growth tends to be high, and risk tends to be low. Monetary policy affects output directly via the IS curve, and indirectly via the pricing of risk that relates to the tightness of the value at risk constraints. The optimal monetary policy rule always depends on financial vulnerabilities in addition to the output gap, inflation, and the natural rate. We show that a classic Taylor rule exacerbates deviations of the output gap from its target value of zero relative to an optimal interest rate rule that includes vulnerability. Simulations show that optimal policy significantly increases welfare relative to a classic Taylor rule. The model provides a microfoundation for optimal monetary policy frameworks that take into account financial stability.

We extend the New Keynesian (NK) model to include endogenous risk, microfounded using either diagnostic expectations or intermediation frictions captured by a value-at-risk constraint. Lower interest rates not only shift consumption intertemporally but also conditional output risk via their impact on risk-taking, giving rise to a vulnerability channel of monetary policy. The model fits the conditional output gap distribution and can account for medium-term increases in downside risks when financial conditions are loose. The policy prescriptions are very different from those in the standard NK model: monetary policy that focuses purely on inflation and output-gap stabilization can lead to instability. Macroprudential measures can mitigate the intertemporal risk-return tradeoff created by the vulnerability channel.

We construct an empirical measure of expected network spillovers that arise through default cascades for the US financial system for the period 2002-2016. Compared to existing studies, we include a much larger cross-section of US financial firms that comprises all bank holding companies, all broker-dealers and all insurance companies, and consider their entire empirical balance sheet exposures instead of relying on simulations or on exposures arising just through one specific market (like the Fed Funds market) or one specific financial instrument (like credit default swaps). We find negligible expected spillovers from 2002 to 2007 and from 2013 to 2016. However, between 2008 and 2012, we find that default spillovers can amplify expected losses by up to 25%, a significantly higher estimate than previously found in the literature.

I study how central banks should communicate monetary policy in liquidity trap scenarios in which the zero lower bound on nominal interest rates is binding. Using a standard New Keynesian model, I argue that the key to preventing self-fulfilling deflationary spirals and anchoring expectations is to promise to keep nominal interest rates pegged at zero for a length of time that depends on the state of the economy. I derive necessary and sufficient conditions for this type of state contingent forward guidance to implement the welfare maximizing equilibrium as a globally determinate (i.e., unique) equilibrium. Even though the zero lower bound prevents the Taylor principle from holding, determinacy can be obtained if the central bank sufficiently extends the duration of the zero interest rate peg in response to deflationary or contractionary changes in expectations or outcomes. Fiscal policy is passive, so it plays no role for determinacy. The interest rate rules I consider are easy to communicate, require little institutional change and do not entail any unnecessary social welfare losses.

We show a novel relation between the institutional investors’ intrinsic trading frequency — a commonly used proxy for the investors’ investment horizon — and the cross-section of stock returns. We show that the 20% of stocks with the lowest trading frequency earn mean returns that are 6 percentage points per year higher than the 20% of stocks that have the highest trading frequency. The magnitude and predictability of these returns persist or even increase when risk-adjusted by common indicators of systematic risks such as the Fama-French, liquidity or momentum factors. Our results show that the characteristics of stock holders affect expected returns of the very securities they hold, supporting the view that heterogeneity among investors is an important dimension of asset prices.

We show that firms in models with menu costs, when calibrated to have the empirically observed frequency and size of individual-goods price adjustments, have stock returns that are always positively correlated with inflation. The cross-sectional dispersion in this correlation is almost negligible, even though firms have very diverse micro-level pricing behavior. Because in this class of models positive nominal shocks are good states of nature and the correlation between stock returns and inflation is positive, agents are willing to pay a premium to hold assets whose returns covary negatively with inflation. In contrast, we empirically find that the dispersion in the correlation between stock returns and inflation is about 100 times larger than in the model, and that correlations are negative about half the time. Furthermore, and also at odds with sticky-price models, investors require a premium to hedge against states of high inflation.

We study the relation between returns on the aggregate stock market and aggregate real investment. While it is well known that the aggregate investment rate is negatively correlated with subsequent excess stock market returns, we find that it is also positively correlated with future stock market volatility. Thus, conditionally on past aggregate investment, the mean-variance tradeoff in aggregate stock returns is negative. We interpret these patterns within a general equilibrium production economy. In our model, investment is determined endogenously in response to two types of shocks: shocks to productivity and preference shocks affecting discount rates. Preference shocks affect expected stock returns, the aggregate investment rate, and stock return volatility in equilibrium, helping the model reproduce the empirical relations between these variables. Thus, our results emphasize that the time-varying price of aggregate risk plays an important role in shaping the aggregate investment dynamics.

We propose a long-run risk model with real effects of inflation that matches a broader set of empirical moments than has been previously possible, while simultaneously keeping risk aversion and the elasticity of intertemporal substitution low. The moments we match capture the joint dynamics of stock returns, bond returns, bond yields, and macroeconomic fundamentals. We also match moments that have remained elusive in the literature — including those from predictability regressions of stock returns, consumption, and dividends on the price-dividend ratio — as well as some that have been only matched piecemeal by a collection of different versions of the long-run risk model. The key element that we introduce in the model is that inflation non-neutralities are time-varying in a manner consistent with the data, with inflationary shocks predicting higher or lower real consumption growth depending on the current state of the economy.

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.

Many models posit that agents have a limited capacity to process information. The standard specification assumes that information processing capacity is constant, precluding agents from investing in capacity to relax their informational constraints. We compare the long-run behavior of three different specifications of learning processes — that of a Bayesian agent, an agent with fixed capacity to process information and an agent with a fixed cost of observing additional information — and find that the steady-states for the economy-wide levels of uncertainty, signal precision and information transmission rate are different across specifications. Further, the agents with an endogenously chosen capacity to process information learn “less” in the long-run, so that the distance between the modes of the posterior and prior distributions is smaller than for the other two specifications. We conclude that endogenous investment in information processing capacity can lead to more rational inattention than when information processing capacity is fixed.