Search Results
Discussion Paper
Changing Risk-Return Profiles
Are stock returns predictable? This question is a perennially popular subject of debate. In this post, we highlight some results from our recent working paper, where we investigate the matter. Rather than focusing on a single object like the forecasted mean or median, we look at the entire distribution of stock returns and find that the realized volatility of stock returns, especially financial sector stock returns, has strong predictive content for the future distribution of stock returns. This is a robust feature of the data since all of our results are obtained with real-time analyses ...
Report
Money, credit, monetary policy, and the business cycle in the euro area: what has changed since the crisis?
This paper studies the relationship between the business cycle and financial intermediation in the euro area. We establish stylized facts and study their stability during the global financial crisis and the European sovereign debt crisis. Long-term interest rates have been exceptionally high and long-term loans and deposits exceptionally low since the Lehman collapse. Instead, short-term interest rates and short-term loans and deposits did not show abnormal dynamics in the course of the financial and sovereign debt crisis.
Discussion Paper
A New Perspective on Low Interest Rates
Interest rates in the United States have remained at historically low levels for many years. This series of posts explores the forces behind the persistence of low rates. We briefly discuss some of the explanations advanced in the academic literature, and propose an alternative hypothesis that centers on the premium associated with safe and liquid assets. Our argument, outlined in a paper we presented at the Brookings Conference on Economic Activity last March, suggests that the increase in this premium since the late 1990s has been a key driver of the decline in the real return on U.S. ...
Report
Multimodality in Macro-Financial Dynamics
We estimate the evolution of the conditional joint distribution of economic and financial conditions. While the joint distribution is approximately Gaussian during normal periods, sharp tightenings of financial conditions lead to the emergence of additional modes. The U.S. economy has historically resolved quickly to the “good” mode, but we conjecture that poor policy choices could lead to prolonged periods of multimodality. We argue that multimodality arises naturally in a macro-financial intermediary model with occasionally binding intermediary constraints.
Discussion Paper
A Time-Series Perspective on Safety, Liquidity, and Low Interest Rates
The previous post in this series discussed several possible explanations for the trend decline in U.S. real interest rates since the late 1990s. We noted that while interest rates have generally come down over the past two decades, this decline has been more pronounced for Treasury securities. The conclusion that we draw from this evidence is that the convenience associated with the safety and liquidity embedded in Treasuries is an important driver of the secular (long-term) decline in Treasury yields. In this post and the next, we provide an overview of the two complementary empirical ...
Discussion Paper
Economic Predictions with Big Data: The Illusion of Sparsity
The availability of large data sets, combined with advances in the fields of statistics, machine learning, and econometrics, have generated interest in forecasting models that include many possible predictive variables. Are economic data sufficiently informative to warrant selecting a handful of the most useful predictors from this larger pool of variables? This post documents that they usually are not, based on applications in macroeconomics, microeconomics, and finance.
Working Paper
Bank Capital and Real GDP Growth
We find evidence that bank capital matters for the distribution of future GDP growth but not its central tendency. Growth in the aggregate bank capital ratio compresses the tails of expected GDP growth, a relationship that is particularly robust in reducing the probability of the worst GDP outcomes. These results suggest a role for regulation to mitigate financial crises, with an additional 100 basis points of bank capital reducing the probability of negative GDP growth by 10 percent at the one-year horizon, even controlling for credit growth and financial conditions, and without a ...
Working Paper
Back to the Present: Learning about the Euro Area through a Now-casting Model
We build a model for simultaneously now-casting economic conditions in the euro area and its three largest member countries--Germany, France, and Italy. The model formalizes how market participants and policymakers monitor the euro area by incorporating all market moving indicators in real time. We find that area wide and country-specific data provide informative signals to now-cast the economic conditions in the euro area and member countries. The model provides accurate predictions of economic conditions in real time over a period that covers the past three recessions.
Report
Economic predictions with big data: the illusion of sparsity
We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Report
Changing Risk-Return Profiles
We show that realized volatility in market returns and financial sector stock returns have strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time. Current realized volatility has the most information content on the uncertainty of future returns, whereas it has only limited content about the location of the future return distribution. When volatility is low, the predicted distribution of returns is less dispersed and probabilistic forecasts are sharper.