Michael J. Fleming and Frank M. Keane

U.S. Treasury security settlement fails—whereby market participants are unable to make delivery of securities to complete transactions—spiked in March 2016 to their highest level since the financial crisis. As noted in this post, fails delay the settlement of transactions and can therefore lead to illiquidity, create operational risk, and increase counterparty credit risk. Fails in the Treasury market attract particular attention because of the market’s key role for global investors as a pricing benchmark, hedging instrument, and reserve asset. So what drove the March spike? In this post, we show that much of it reflected sequential fails of benchmark ten-year notes and thirty-year bonds, but that fails in seasoned issues—which have been trending upward for several years—were also elevated.

Jonathan McCarthy, Richard Peach, and Robert Rich

Today, the Federal Reserve Bank of New York (FRBNY) is hosting the spring meeting of its Economic Advisory Panel (EAP). As has become the custom at this meeting, the FRBNY staff is presenting its forecast for U.S. growth, inflation, and the unemployment rate. Following the presentation, members of the EAP, which consists of leading economists in academia and the private sector, are asked to critique the staff forecast. Such feedback helps the staff evaluate the assumptions and reasoning underlying its forecast as well as the forecast’s key risks. The feedback is also an important part of the forecasting process because it informs the staff’s discussions with New York Fed President William Dudley about economic conditions. In that same spirit, we are sharing a short summary of the staff forecast in this post; for more detail, see the FRBNY Staff Outlook Presentation from the EAP meeting on our website.

Paul Goldsmith-Pinkham, Beverly Hirtle, and David O. Lucca



Since the financial crisis, bank regulatory and supervisory policies have changed dramatically both in the United States (Dodd-Frank Wall Street Reform and Consumer Protection Act) and abroad (Third Basel Accord). While these shifts have occasioned much debate, the discussion surrounding supervision remains limited because most supervisory activity— both the amount of supervisory attention and the demands for corrective action by supervisors—is confidential. Drawing on our recent staff report “Parsing the Content of Bank Supervision,” this post provides a peek behind the scenes of bank supervision, presenting a statistical linguistic analysis based on confidential communications from Fed supervisors to the banks they supervise. Our analysis tackles several fundamental questions: What are the precise supervisory issues being raised? What drives the issues supervisors bring up? How does bank supervision relate to the other two pillars of the Basel Accord: capital regulations and market discipline?

Beverly Hirtle, Anna Kovner, and Matthew Plosser



Supervisors monitor banks to assess the banks’ compliance with rules and regulations but also to ensure that they engage in safe and sound practices (see our earlier post What Do Banking Supervisors Do?). Much of the work that bank supervisors do is behind the scenes and therefore difficult for outsiders to measure. In particular, it is difficult to know what impact, if any, supervisors have on the behavior of banks. In this post, we describe a new Staff Report in which we attempt to measure the impact that supervision has on bank performance. Does more attention by supervisors lead to lower risk at banks and, if so, at what cost to profitability or growth?

Thomas M. Eisenbach, David O. Lucca, and Robert M. Townsend



While bank regulation and supervision are the two main components of banking policy, the difference between them is often overlooked and the details of supervision can appear shrouded in secrecy. In this post, which is based on a recent staff report, we provide a framework for thinking about supervision and its relation to regulation. We then use data on supervisory efforts of Federal Reserve bank examiners to describe how supervisory efforts vary by bank size and risk, and to measure key trade-offs in allocating resources.

Grant Aarons, Daniele Caratelli, Matthew Cocci, Domenico Giannone, Argia Sbordone, and Andrea Tambalotti



What is the weather today? You don’t need to be a meteorologist to answer this question. Just take a look outside the window. Macroeconomists do not have this luxury. The first official estimate of GDP this quarter will not be published until the end of July. In fact, we don’t even know what GDP was last quarter yet! But while we wait for these crucial data, we float in a sea of information on all aspects of the economy: employment, production, sales, inventories, you name it. . . . Processing this information to figure out if it is rainy or sunny out there in the economy is the bread and butter of economists on trading desks, at central banks, and in the media. Thankfully, recent advances in computational and statistical methods have led to the development of automated real-time solutions to this challenging big data problem, with an approach commonly referred to as nowcasting. This post describes how we apply these techniques here at the New York Fed to produce the FRBNY Nowcast, and what we can learn from it. It also serves as an introduction to our Nowcasting Report, which we will update weekly on our website starting this Friday, April 15.

Beverly Hirtle and David O. Lucca



Last month the New York Fed held a conference on supervising large, complex financial institutions. The event featured presentations of empirical and theoretical research by economists here, commentary by academic researchers, and panel discussions with policymakers and senior supervisors. The conference was motivated by the recognition that supervision is distinct from regulation, but that the difference between them is often not well understood. The discussion focused on defining objectives for supervising the large, complex financial companies that figure so prominently in our financial system and ways of measuring how effectively supervision achieves these goals. This post summarizes the key themes from the conference and introduces the more in-depth posts that will follow in this blog series.

Robert Rich



We launched the U.S. Economy in a Snapshot in June 2015 to provide interested readers with a monthly update of current economic and financial developments. Combining charts and summary points, the packet covers a range of topics that include labor and financial markets, the behavior of consumers and firms, survey responses, and the global economy.

Bonni Brodsky, Marco Del Negro, Joseph Fiorica, Eric LeSueur, Ari Morse, and Anthony P. Rodrigues




In our previous post, we showed that the gap between the market-implied path for the federal funds rate and the survey-implied mean expectations for the federal funds rate from the Survey of Primary Dealers (SPD) and the Survey of Market Participants (SMP) narrowed from the December survey to the January survey. In particular, we provided explanations for this narrowing as well as for the subsequent widening from January to March. This post continues the discussion by presenting a novel approach called “tilting” that yields insights by measuring how much the survey probability distributions have to be altered to match the market-implied path of the federal funds rate. We interpret any discrepancy between the original and tilted distributions as arising from either risk premia or dispersion in beliefs.

Bonni Brodsky, Marco Del Negro, Joseph Fiorica, Eric LeSueur, Ari Morse, and Anthony P. Rodrigues



Over the past year, market pricing on interest rate derivatives linked to the federal funds rate has suggested a significantly lower expected path of the policy rate than responses to the New York Fed’s Survey of Primary Dealers (SPD) and Survey of Market Participants (SMP). However, this gap narrowed considerably from December 2015 to January 2016, before widening slightly at longer horizons in March. This post argues that the narrowing between December and January was mostly the result of survey respondents placing greater weight on lower rate outcomes, while the subsequent widening between January and March likely reflects an increased demand for insurance against states of the world where the policy rate remains at very low levels.