We develop a stylized model of economic growth with bubbles in which changes in investor sentiment lead to the appearance and collapse of macroeconomic bubbles or pyramid schemes. These bubbles mitigate the effects of financial frictions. During bubbly episodes, unproductive investors demand bubbles while productive investors supply them. These transfers of resources improve economic efficiency thereby expanding consumption, the capital stock and output. When bubbly episodes end, there is a fall in consumption, the capital stock and output. We argue that the stochastic equilibria of the model provide a natural way of introducing bubble shocks into business cycle models.

We present a model of sovereign debt in which, contrary to conventional wisdom, government defaults are costly because they destroy the balance sheets of domestic banks. In our model, better financial institutions allow banks to be more leveraged, thereby making them more vulnerable to sovereign defaults. Our predictions: government defaults should lead to declines in private credit, and these declines should be larger in countries where financial institutions are more developed and banks hold more government bonds. In these same countries, government defaults should be less likely. Using a large panel of countries, we find evidence consistent with these predictions.

We develop a new theory of information production during credit booms. Entrepreneurs need credit to undertake investment projects, some of which enable them to divert resources. Lenders can protect themselves from such diversion in two ways: collateralization, and costly screening that generates durable information about projects. In equilibrium, the collateralization-screening mix depends on the value of aggregate collateral. High collateral values make it possible to reallocate resources towards productive projects, but they also crowd out screening. This has important dynamic implications. During credit booms driven by high collateral values (e.g. real estate booms), economic activity expands but the economy’s stock of information on existing projects gets depleted. As a result, collateral-driven booms end in deep crises and slow recoveries: when booms end, investment is constrained both by the lack of collateral and by the lack of information on existing projects, which takes time to rebuild. We provide empirical support for the mechanism using US firm-level data.

What is the role of monetary policy in a bubbly world? To address this question, we study an economy in which financial frictions limit the supply of assets. The ensuing scarcity generates a demand for “unbacked” assets, i.e., assets that are backed only by the expectation of their future value. We consider two types of unbacked assets: bubbles, which are created by the private sector, and money, which is created by the central bank. Bubbles and money share many features, but they also differ in two crucial respects. First, while the rents from the creation of bubbles accrue to entrepreneurs and foster investment, the rents from money creation accrue to the central bank. Second, while bubbles are driven by market psychology, and can rise and fall according to the whims of the market, money is under the control of the central bank. We characterize the optimal monetary policy and show that, through its ability to supply assets, monetary policy plays a key role in the bubbly world. The model sheds light on the recent expansion of central bank liabilities in response to the bursting of bubbles.

We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters—the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators of the moments of asset returns (shrinkage moments), as well as shrinkage portfolios obtained by shrinking the portfolio weights directly. We make several contributions in this field. First, we propose two novel calibration criteria for the vector of means and the inverse covariance matrix. Second, for the covariance matrix we propose a novel calibration criterion that takes the condition number optimally into account. Third, for shrinkage portfolios we study two novel calibration criteria. Fourth, we propose a simple multivariate smoothed bootstrap approach to construct the optimal shrinkage intensity. Finally, we carry out an extensive out-of-sample analysis with simulated and empirical datasets, and we characterize the performance of the different shrinkage estimators for portfolio selection.

Conventional wisdom says that, in the absence of default penalties, sovereign risk destroys all foreign asset trade. We show that this conventional wisdom rests on one implicit assumption: that assets cannot be retraded in secondary markets. Without this assumption, foreign asset trade is possible even in the absence of default penalties. This result suggests a broader perspective regarding the origins of sovereign risk and its remedies. Sovereign risk affects foreign asset trade only if default penalties are insufficient and secondary markets work imperfectly. To reduce its effects, one can either increase default penalties or improve the working of secondary markets.

How does a housing boom affect credit to non-housing firms? Using bank, firm, and loan-level microdata, we show that the Spanish housing boom reduced non-housing credit growth during its first years, but stimulated it later on. These patterns can be rationalized by financial constraints for banks. Constrained banks initially accommodated higher housing credit demand by reducing non-housing credit. Eventually, however, the housing boom increased bank net worth and expanded credit supply. A quantitative model, disciplined by our cross-sectional estimates, indicates that the crowding-out effect was substantial but temporary, and had been fully absorbed by the end of the boom.

Over the last two decades US aggregate wealth has fluctuated substantially. Against the backdrop of the Great Recession, the effects of these boom-and-bust cycles have come to dominate academic and policy discussions. How can we explain these fluctuations in wealth? Why are these fluctuations associated with changes in consumption, investment and output? In this note, we argue that answers to these questions entail the addition of two ingredients to existent macroeconomic models: rational bubbles and financial frictions. We explain why each of these building blocks is crucial to understand recent events and how they can be seamlessly integrated in standard models.

We propose a formal statistical test to compare asset-pricing models in the presence of price impact. In contrast to the case without trading costs, we show that in the presence of price-impact costs different models may be best at spanning the investment opportunities of different investors depending on their absolute risk aversion. Empirically, we find that the five-factor model of Hou et al. (2021), the six-factor model of Fama and French (2018) with cash-based operating profitability, and a high-dimensional model are best at spanning the investment opportunities of investors with high, medium, and low absolute risk aversion, respectively.

We study the impact of parameter uncertainty on the expected utility of a multiperiod investor subject to quadratic transaction costs. We characterize the utility loss associated with ignoring parameter uncertainty, and show that it is equal to the product between the single-period utility loss and another term that captures the effects of the multiperiod mean-variance utility and transaction cost losses. To mitigate the impact of parameter uncertainty, we propose two multiperiod shrinkage portfolios and demonstrate with simulated and empirical data sets that they substantially outperform portfolios that ignore parameter uncertainty, transaction costs, or both.