The Federal Reserve Bank of New York works to promote sound and well-functioning financial systems and markets through its provision of industry and payment services, advancement of infrastructure reform in key markets and training and educational support to international institutions.
Regional & Community Outreach connects the Bank to Main Street via structured dialogues and two-way conversations on small business, mortgages, and household credit.
Economic Education improves public knowledge about the Federal Reserve System, monetary policy implementation, and promoting financial stability through the Museum and programs for K-16 students and educators, and the community.
Mounting evidence says that “low-risk” investing delivers superior returns, comparable to strategies based on value, size, and momentum. Such tactics include the “risk parity” (RP) asset allocation approach, which received considerable attention during the 2013 taper tantrum when many RP funds reportedly deleveraged. This strategy requires long or overweight positions in low-risk asset classes, such as government bonds, and offsetting short or underweight positions in risky asset classes, including shares. The low-risk umbrella also covers “betting against beta” (BAB) within, rather than across, asset classes. For example, investing in shorter- as opposed to longer-duration bonds beats the bond market, or owning low-beta at the expense of high-beta shares outpaces the S&P 500. Whether RP or BAB, what matters is return per unit of risk, the bang for the buck. Put more formally, RP and BAB profitability rests on an inverse relation between Sharpe ratios (SRs) and beta, the covariance of asset returns with the market portfolio. Such findings contradict the intuition that higher returns compensate for risk. Instead, investors profit handsomely by levering up relatively safe assets and shorting comparatively risky securities. However, as my New York Fed staff report argues, alternative reasoning and samples, as well as the types and number of “risks,” raise questions about not only BAB with government bonds (BABgov) but perhaps also RP. The investment implications are obvious, but the arguments and underlying data patterns also hint at key policy issues.
On April 1, 2014, the Federal Reserve began collecting transaction-level data on federal funds, Eurodollars, and certificates of deposits from a large set of domestic banks and agencies of foreign banks operating in the United States. Previously, the Fed had only received fed funds and Eurodollar data from major brokers, and not directly from the banks borrowing in these markets. These new data, collected on form FR 2420, have helped the Fed better understand activity in the fed funds and Eurodollar markets. In this post, we focus on the new data on fed funds, in light of the Federal Reserve Bank of New York’s Trading Desk announcement that it plans to use these data to calculate and publish the fed funds effective rate. We plan to publish other posts on the fed funds and Eurodollar markets over the next several months.
Tesla Motors’ shares saw a brief bounce from a far-out and fictional product (a smart watch) announced as part of an April fool's prank. While markets evidently made quick sense of the joke, that’s not always the case.
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.
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.
Forming long-term partnerships with customers and suppliers often creates a competitive advantage for firms because it permits resource sharing, eases financial constraints, and encourages investment in relationship-specific capital. While these relationships can be beneficial, they also increase firms’ exposure to their counterparties’ risk. In a recent Staff Report, Anna Costello of MIT and I study two important and unanswered questions about supply relationships. First, what specific characteristics of the trade relationship make a firm more vulnerable to adverse spillovers from their supply chain partners? Second, if managers understand these vulnerabilities, can they design contracts or diversify their partners in order to mitigate exposures to negative events along their supply chain?
Last year, IntercontinentalExchange (ICE) launched a credit default swap index futures contract. In the first two weeks there were spurts of interest in it, but soon it became evident that the new product was unable to generate sufficient demand. Given their short life span in the credit default swaps (CDS) market, the question becomes why were these futures contracts launched in the first place? And, assuming that they were created in response to a real need of market participants, will we see a revival of swap futures in the future?
Global asset market developments during the summer of 2013 have been attributed to changes in the outlook for U.S. monetary policy, starting with former Chairman Bernanke’s May 22 comments concerning future curtailing of the Federal Reserve’s asset purchase programs. A previous post found that the signal of a possible change in U.S. monetary policy coincided with an increase in global risk aversion which put downward pressure on global asset prices. This post revisits this episode by measuring the impact of changes in Fed’s expected policy rate path and in the economic outlook on the U.S. dollar and emerging market equity prices. The analysis suggests that changes in the U.S. and foreign outlooks had a meaningful role in explaining global asset price movements during the so-called taper tantrum.
Richard Crump, Emanuel Moench, William O'Boyle, Matthew Raskin, Carlo Rosa, and Lisa Stowe
Second in a two-part series
Market prices provide timely information on policy expectations. But as we emphasized in our previous post, they can deviate from investors’ expectations of the most likely path because they embed risk premiums and represent probability-weighted averages over different possible paths. In contrast, surveys explicitly ask respondents for their views on the likely path of economic variables. In this post, we highlight two surveys conducted by the Federal Reserve Bank of New York that provide information about expectations that can complement market-based measures.
Liberty Street Economics invites you to comment on a post.
We encourage you to submit comments, queries and suggestions on our blog entries. We will post them below the entry, subject to the following guidelines:
Please be brief: Comments are limited to 1500 characters.
Please be quick: Comments submitted more than 1 week after the blog entry appears will not be posted.
Please try to submit before COB on Friday: Comments submitted after that will not be posted until Monday morning.
Please be on-topic and patient: Comments are moderated and will not appear until they have been reviewed to ensure that they are substantive and clearly related to the topic of the post. The moderator will not post comments that are abusive, harassing, or threatening; obscene or vulgar; or commercial in nature; as well as comments that constitute a personal attack. We reserve the right not to post a comment; no notice will be given regarding whether a submission will or will not be posted.