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Correction: In the last line of the third paragraph, we mischaracterized a reference to the chart. The difference between the blue and gold bars represents the maturity differential, not the credit quality differential. We regret the error.
Since their inception in 2002, credit default swap (CDS) indexes have gained tremendous popularity and become leading barometers of the credit market. Today, investors who want to hedge credit risk or to speculate can choose from a broad menu of indexes that offer protection against the default of a firm, a European sovereign, or a U.S. municipality, among others. The major CDS indexes in the U.S. are the CDX.NA.IG and the CDX.NA.HY, composed of North American investment-grade (IG) and high-yield (HY) issuers, respectively. In this post, we focus on the CDX.NA.IG index. We discuss the interplay between the index and its constituents, specifically the “roll” process of the index, when irrelevant constituents are replaced by new ones. Analyzing the relation between the CDX.NA.IG index and its constituents in the context of the roll process allows us to gain a better understanding of how the exit of dealers from the single-name CDS market might affect pricing dynamics in the CDS market as a whole.
Luis Armona, Wilbert van der Klaauw, and Basit Zafar
The Federal Reserve Bank of New York today released results from its February 2015 Survey of Consumer Expectations Credit Access Survey, which provides information on consumers' experiences with and expectations about credit demand and credit access. The survey shows little change in application rates for credit over the last twelve months, but a decline in rejection rates, in particular for credit card limit increases. The expectations component of the survey shows an increase in the average likelihood of consumers applying for credit over the next twelve months for all five credit products; the increase is most pronounced for mortgage refinances and higher credit card limits.
Marco Del Negro, Raiden B. Hasegawa, and Frank Schorfheide
Second in a two-part series
As an economist, you make policy recommendations at any point in time that depend on what model of the economy you have in mind and on your assessment of the state of the economy. One can see these points play out in the current discussion about the timing of interest rate liftoff and the speed of the subsequent renormalization. If you think nominal rigidities are not all that important, you are likely to conclude that accommodative policies won’t do much for growth but will generate inflation. Similarly, if you are convinced that the economy is already firing on all cylinders, you may see little need for prolonged accommodation. The problem is, you are not quite sure about the state of the economy or what the right model is. If you are a Bayesian, you may want to try to put probabilities on different models/states of the world and take it from there. The first post in this series, “Combining Models for Forecasting and Policy Analysis,” introduced a procedure called dynamic pools that shows how to do just that. In this post, we apply that procedure to a policy exercise. We can’t publicly discuss current policies, so we will instead apply our method to consider alternative monetary policies at the onset of the Great Recession.
Marco Del Negro, Raiden B. Hasegawa, and Frank Schorfheide
First in a two-part series
Model uncertainty is pervasive. Economists, bloggers, policymakers all have different views of how the world works and what economic policies would make it better. These views are, like it or not, models. Some people spell them out in their entirety, equations and all. Others refuse to use the word altogether, possibly out of fear of being falsified. No model is “right,” of course, but some models are worse than others, and we can have an idea of which is which by comparing their predictions with what actually happened. If you are open-minded, you may actually want to combine models in making forecasts or policy analysis. This post discusses one way to do this, based on a recent paper of ours (Del Negro, Hasegawa, and Schorfheide 2014).
Every March, the Bureau of Labor Statistics releases benchmark revisions of state and local payroll employment for the preceding two years. While employment data are released monthly for all 50 states and many metropolitan areas, the monthly figures are estimated based on a sample of firms. The annual revisions are based on an almost complete count of workers (now available up through mid-2014) from the records of the unemployment insurance system and re-estimated data for the remainder of the year. In this post, we briefly summarize the mixed but mostly stronger performance in the region in 2014 indicated by these employment revisions. We highlight the most pronounced changes across our District—highlighted by New York City’s even stronger-looking boom—using the percentage change in total employment from the fourth quarter of 2013 to the fourth quarter of 2014 as the metric.
Ahead of the Federal Reserve’s release on Wednesday of 2015 bank stress tests results, we’ve seen a spike in traffic to a piece in our archive that offers a primer on the annual Comprehensive Capital Analysis and Review (CCAR) process and background on its role as a tool in the Fed’s bank supervisory arsenal.
Over the last twenty-five years, there has been a lot of interest in herd behavior in financial markets—that is, a trader’s decision to disregard her private information to follow the behavior of the crowd. A large theoretical literature has identified abstract mechanisms through which herding can arise, even in a world where people are fully rational. Until now, however, the empirical work on herding has been completely disconnected from this theoretical analysis; it simply looked for statistical evidence of trade clustering and, when that evidence was present, interpreted the clustering as herd behavior. However, since decision clustering may be the result of something other than herding—such as the common reaction to public announcements—the existing empirical literature cannot distinguish “spurious” herding from “true” herd behavior.
This morning, the Federal Reserve Bank of New York released a set of interactive visuals that present school spending and its various components—such as instructional spending, instructional support, leadership support, and building services spending—across all thirty-two Community School Districts (CSD) in New York City and map their progression over time. The interactive features allow the user to easily view the data and observe spending trends. Our purpose is to make data on education finance more accessible to a broader audience.
Recent news of banks scaling back on the issuance of car loans to borrowers with a weak credit history, coupled with recent media investigations into auto lending fraud, have drawn renewed attention to a surge in subprime auto lending. That boom is one we’ve tracked on our blog as part of an effort to shed light on ongoing change in the consumer lending market.
World trade fell 20 percent relative to world GDP in 2008 and 2009. Since then, there has been much debate about the role of trade finance in the Great Trade Collapse. Distress in the financial sector can have a strong impact on international trade because exporters require additional working capital and rely on specific financial products, in particular letters of credit, to cope with risks when selling abroad. In this post, which is based on a recent Staff Report, we shed new light on the link between finance and trade, showing that changes in banks’ supply of letters of credit have economically significant effects on firms’ export behavior. Our research suggests that trade finance helps explain the drop in exports in 2008–2009, especially to smaller and poorer markets.
Liberty Street Economics features insight and analysis from New York Fed economists working at the intersection of research and policy. Launched in 2011, the blog takes its name from the Bank’s headquarters at 33 Liberty Street in Manhattan’s Financial District.
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