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In February, the Federal Reserve Bank of New York’s trading desk announced it will publish a new overnight bank funding rate early next year. The new rate will be based on both federal funds and Eurodollar transactions reported in a new data collection—the FR 2420 Report of Selected Money Market Rates. In a previous post, we explained how FR 2420 fed funds transaction data will replace brokered data as the base for the fed funds effective rate. This post provides insights on the Eurodollar market in advance of the publication of the overnight bank funding rate.
Marco Del Negro, Marc Giannoni, Matthew Cocci, Sara Shahanaghi, and Micah Smith
Second post in the series
In a recent series of blog posts, the former Chairman of the Federal Reserve System, Ben Bernanke, has asked the question: “Why are interest rates so low?” (See part 1, part 2, and part 3.) He refers, of course, to the fact that the U.S. government is able to borrow at an annualized rate of around 2 percent for ten years, or around 3 percent for thirty years. If you expect that inflation is going to be on average 2 percent over the next ten or thirty years, this implies that the U.S. government can borrow at real rates of interest between 0 and 1 percent at the ten- and thirty-year maturities. This phenomenon is by no means limited to the United States. Governments in Japan and Germany are able to borrow for ten years at nominal rates below 1 percent, and the ten-year yield on Swiss government debt is slightly negative. Why is that?
In our earlier post, we described how the tri-party repo arrangement was a clever way to reduce the costs and risks that individual firms faced when settling bilateral repos. In this post, we explain how the efficiencies created by this new arrangement facilitated the growth of the repo market by expanding the class of securities to be used as collateral. This expansion had benefits as well as costs. On the positive side, it led to lower interest costs for a wide variety of borrowers in the real economy. But on the negative side, tri-party repos backed by riskier assets increase the risk of fire sales, which can have negative spillovers on the broader financial system.
The conventional wisdom about financial innovation is that it is typically undertaken as a way to increase profits. However, financial innovation can also occur as a response to the need to reduce risk. Tri-party repo is an example of such innovation. While tri-party repo ultimately evolved in ways that created and amplified systemic risk (as we describe in the second post in this series), its origin was as a solution to inefficiencies and risks associated with the repo settlement arrangements prevailing at the time.
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.
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