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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 ...
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. ...
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Exploiting the monthly data flow in structural forecasting
This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of ...
Discussion Paper
Global Trends in Interest Rates
Long-term government bond yields are at their lowest levels of the past 150 years in advanced economies. In this blog post, we argue that this low-interest-rate environment reflects secular global forces that have lowered real interest rates by about two percentage points over the past forty years. The magnitude of this decline has been nearly the same in all advanced economies, since their real interest rates have converged over this period. The key factors behind this development are an increase in demand for safety and liquidity among investors and a slowdown in global economic growth.
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 ...
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Global trends in interest rates
The trend in the world real interest rate for safe and liquid assets fluctuated close to 2 percent for more than a century, but has dropped significantly over the past three decades. This decline has been common among advanced economies, as trends in real interest rates across countries have converged over this period. It was driven by an increase in the convenience yield for safety and liquidity and by lower global economic growth.
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800,000 Years of Climate Risk
We use a long history of global temperature and atmospheric carbon dioxide (CO2) concentration to estimate the conditional joint evolution of temperature and CO2 at a millennial frequency. We document three basic facts. First, the temperature–CO2 dynamics are non-linear, so that large deviations in either temperature or CO2 concentrations take a long time to correct–on the scale of multiple millennia. Second, the joint dynamics of temperature and CO2 concentrations exhibit multimodality around historical turning points in temperature and concentration cycles, so that prior to the start of ...
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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.
Discussion Paper
Reading the Tea Leaves of the U.S. Business Cycle—Part Two
In our previous post, we presented evidence suggesting that labor market indicators provide the most reliable information for dating the U.S. business cycle. In this post, we further develop the case. In fact, the unemployment rate has provided an almost perfect record of distinguishing the beginning of recessions in the post-war U.S. economy. We also show that using more granular labor market data, such as by region or industry, also provides valuable information about the state of the business cycle.
Discussion Paper
Opening the Toolbox: The Nowcasting Code on GitHub
In April 2016, we unveiled--and began publishing weekly--the New York Fed Staff Nowcast, an estimate of GDP growth using an automated platform for tracking economic conditions in real time. Today we go a step further by publishing the MATLAB code for the nowcasting model, available here on GitHub, a public repository hosting service. We hope that sharing our code will make it easier for people interested in monitoring the macroeconomy to understand the details underlying the nowcast and to replicate our results.