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In the wake of the global financial crisis, the Federal Reserve dramatically increased the size of its balance sheet—from about $900 billion at the end of 2007 to about $4.5 trillion today. At its September 2017 meeting, the Federal Open Market Committee (FOMC) announced that—effective October 2017—it would initiate the balance sheet normalization program described in the June 2017 addendum to the FOMC’s Policy Normalization Principles and Plans.
Andreas Fuster, Brian Greene, and Brett Rose In its September 20, 2017, statement, the Federal Open Market Committee (FOMC) said that, beginning in October 2017, it would initiate the balance sheet normalization program described in the June 2017 addendum to the Committee’s Policy Normalization Principles and Plans. Specifically, to reduce the Federal Reserve’s securities holdings, […]
In the spirit of this season of year-end lists of accomplishments, Liberty Street Economics offers a roundup of our most viewed posts. Our readers continued to gravitate toward timely, topical posts; our most popular explained how the Fed manages its enlarged balance sheet—a major focus of the FOMC, Congress, markets, and economists. Prompted by reader questions in response to their first post, the authors also penned a follow-up post. Another hit this year described an innovative indicator of economic growth—night light intensity measured via satellite—and used it to fact-check official Chinese growth estimates.
Following the 2016 presidential election, as noted on this blog and many other outlets, Americans’ political and economic outlook changed dramatically depending on partisan affiliation. Immediately after the election, Republicans became substantially more optimistic relative to Democrats. In this blog post, we revisit the issue of polarization over the past twelve months using data from the New York Fed’s Survey of Consumer Expectations (SCE)—also the focus of a detailed technical overview in the latest edition of the Bank’s journal, the Economic Policy Review. The overview walks readers through the design and implementation of the survey, as well as the computation of the various statistics released by the SCE team every month.
It is essential for policymakers and financial market participants to understand market expectations for the path of future policy rates because these expectations can have important implications for financial markets and the broader economy. In this post—which is meant to complement prior Liberty Street Economics posts, including Crump et al. (2014a, 2014b ) and Brodsky et al. (2016a, 2016b)—we offer some insights into estimating and interpreting market expectations for increases in the federal funds target range at upcoming meetings of the Federal Open Market Committee (FOMC).
Immediately following the presidential election of 2016, both consumer and investor sentiments were buoyant and financial markets boomed. That these sentiments affect financial asset prices is not so surprising, given past stock market evidence and episodes such as the dot-com bubble. Perhaps more surprising, the risk of corporate default—which is driven mainly by firms’ financial health but also by bond liquidity—also fell following the election, as indicated by lower yield spreads. In this post, I show that, although expectations of better corporate and macroeconomic conditions were the primary drivers of lower credit risk, improved investor sentiment also contributed.
This post presents our quarterly update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since August 2017.
Over the last decade, the concept of “safe assets” has received increasing attention, from regulators and private market participants, as well as researchers. This attention has led to the uncovering of some important details and nuances of what makes an asset “safe” and why it matters. In this blog post, we provide a review of the different aspects of safe assets, discuss possible reasons why they may be beneficial for investors, and give concrete examples of what these assets are in practice.
This post is the second in a two-part series on student loan default behavior. In the first post , we studied how educational characteristics (school type and selectivity, graduation, and major) and family background relate to the incidence of student loan default. In this post, we investigate whether default behavior has varied across cohorts of borrowers as the labor market evolved over time. Specifically, does the ability of student loan holders to repay their loans vary with the state of the labor market? Does the type of education these students received make any difference to this relationship?
This post seeks to understand how educational characteristics (school type and selectivity, graduation status, major) and family background relate to the incidence of student loan default. Student indebtedness has grown substantially, increasing by 170 percent between 2006 and 2016. In addition, the fraction of students who default on those loans has grown considerably. Of students who left college in 2010 and 2011, 28 percent defaulted on their student loans within five years, compared with 19 percent of those who left school in 2005 and 2006. Since defaulting on student loans can have serious consequences for credit scores and, by extension, the ability to purchase a home and take out other loans, it’s critical to understand how college and family characteristics correspond to default rates.
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