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This post is the seventh in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
Paraphrasing a famous Supreme Court opinion: “I know bank complexity when I see it.” This expression probably speaks to the truth that, if we look at a given banking organization, we ought to be able to state whether it is more or less “complex.” And yet, such an approach hardly offers any guidance if one wants to understand the intricacies of global banks and to monitor and regulate them. What should be the appropriate metrics? It seems to us that there is not a consensus just yet on what complexity might mean in the context of banking. The global dimension of a bank adds many layers, so focusing on global banks is bound to yield a more comprehensive take on the issue than examining purely domestic banking entities. Therefore, in this piece, we view complexity through the lens of the operations of global banks.
Nicola Cetorelli, James McAndrews, and James Traina
This post is the sixth in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
In yesterday’s post, our colleagues discussed the historic changes in financial sector size. Here, we tackle a related question on dynamics—how has bank complexity evolved through time? Recently, academics and policymakers have proposed a variety of strong actions to curb bank complexity, stemming from the view that complex banks are undesirable. While the large banks of today are certainly complex, we lack a thorough understanding of how they got that way. In this post and in our related contribution to the Economic Policy Review (EPR) volume, we focus on organizational complexity, measured by the number and types of entities organized together under common ownership and control. Using a new data set of financial-sector acquisitions, we study the structural evolution of banks and its implications for policy. We argue that banks grew into increasingly complex conglomerates in adaptation to a changing financial sector.
This post is the fifth in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
Building upon previous posts in this series that discussed individual banks, we examine the historical growth of the entire financial sector, relative to the rest of the economy. This sector’s historically large share of the economy today (see chart below) and its role in disrupting the functioning of the real economy during the recent financial crisis have led to questions about the social value of costly financial services. While new regulations such as the Dodd-Frank Act impose restrictions on financial activities and increase their costs, especially those of large firms, our paper suggests that there may be limits to what regulation can achieve. In particular, we show that financial growth has occurred in the more opaque and harder to regulate sectors: private firms, shadow banks, and small nonbank financial firms. Moreover, we find that the stock market values these opaque areas of finance more, suggesting that they may expand even faster in the future.
This post is the fourth in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
In the previous post, João Santos showed that the largest banks benefit from a bigger discount in the bond market relative to the largest nonbank financial and nonfinancial issuers. Today’s post approaches a complementary Too-Big-to-Fail (TBTF) question—do banks take on more risk if they’re likely to receive government support? Historically, commentators have expressed concerns that TBTF status encourages banks to engage in risky behavior. However, empirical evidence to substantiate these concerns thus far has been sparse. Using new ratings from Fitch, we tackle this question by examining how changes in the perceived likelihood of government support affect bank lending policies.
This post is the third in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
Yesterday’s post presented evidence on a possible upside of very large banks, namely, lower costs. In today’s post, we focus on a possible downside, that is, whether investors in the primary bond market “discount” risk when they invest in bonds of the too-big-to-fail banks.
This post is the second in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
Despite recent financial reforms, there is still widespread concern that large banking firms remain “too big to fail.” As a solution, some reformers advocate capping the size of the largest banking firms. One consideration, however, is that while early literature found limited evidence for economies of scale, recent academic research has found evidence of scale economies in banking, even for the largest banking firms, implying that such caps could impose real costs on the economy. In our contribution to the volume on large and complex banks, we extend this line of research by studying the relationship between bank holding company (BHC) size and components of noninterest expense, in order to shed light on the sources of the scale economies identified in previous literature.
This post is the first in a series of thirteen Liberty Street Economics posts on Large and Complex Banks. For more on this topic, see this special issue of the Economic Policy Review.
The chorus of criticism levied against mega-banks has, in some cases, outrun the research needed to back the criticism. To help the research catch up with the rhetoric, financial economists here at the New York Fed have engaged in a systematic study of the economics of large and complex banks and their resolution in the event of failure. The result of those efforts is a collection of eleven papers, each of which was subject to review (internal and external). The papers are now online in our Economic Policy Review. Today, we begin a two-week series of posts that present the key findings of each paper. Here, I’ll give a taste of each and some of the essential points delivered by them.
Allan M. Malz, Ernst Schaumburg, Roman Shimonov, and Andreas Strzodka
The rise in the ten-year Treasury rate last summer was perhaps the most dramatic since the 2003 bond market sell-off. This post explains how major changes in the composition of agency mortgage-backed securities (MBS) ownership caused by the large-scale asset purchase programs (LSAPs) may have prevented a major convexity event triggered by MBS duration extension hedging. In fact, MBS hedging activity remained muted by historic standards and likely contributed only modestly to the rise in interest rates.
The relative merits of algorithmic and high-frequency trading are most often discussed in the context of equity markets. In this post, we look at the foreign exchange (FX) spot market. The growth of algorithmic and high-frequency trading in this market has introduced new entrants as well as new complexities and challenges that have important implications for the liquidity landscape and the risk management framework in FX markets. This post focuses narrowly on an important measure of FX market efficiency, absence of arbitrage opportunities, to discuss the improvements in this particular measure of efficiency that have coincided with significant growth in algorithmic and high-frequency trading.
This morning, the New York Fed released a new set of charts measuring various dimensions of the labor market. These charts are mostly generated from data available through the Current Population Survey (CPS), the Current Employment Statistics (CES) program, and the Job Openings and Labor Turnover Survey (JOLTS). This new monthly release will provide timely updates to help economists and the public understand national labor market conditions. The charts are split into eight distinct categories: unemployment, employment, hours, labor demand, job availability, job loss rate, wages, and mismatch.
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