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.
The New York Fed engages with individuals, households and businesses in the Second District and maintains an active dialogue in the region. The Bank gathers and shares regional economic intelligence to inform our community and policy makers, and promotes sound financial and economic decisions through community development and education programs.
Patrick Adams, Tobias Adrian, Nina Boyarchenko, Domenico Giannone, Nellie Liang, and Eric Qian
The economic fallout from the COVID-19 pandemic has been sharp. Real U.S. GDP growth in the first quarter of 2020 (advance estimate) was -4.8 percent at an annual rate, the worst since the global financial crisis in 2008. Most forecasters predict much weaker growth in the second quarter, ranging widely from an annual rate of -15 percent to -50 percent as the economy pauses to allow for social distancing. Although growth is expected to begin its rebound in the third quarter absent a second wave of the pandemic, the speed of the recovery is highly uncertain. In this post, we estimate the risks around the modal forecast of GDP growth as a function of financial conditions. Tighter financial conditions led to a widening in the left tail of the distribution of 2020 growth before weekly economic indicators showed any deterioration. The Federal Reserve and the U.S. Department of the Treasury took aggressive actions to reduce financial stresses and support credit flows—moves aimed at stemming long-lasting impacts from steep economic losses. While GDP growth will depend primarily on the speed with which many activities can be resumed safely, the improved financial conditions in April have reduced the likelihood that financial conditions and real growth will jointly deteriorate in the next few quarters.
This post is part of an ongoing series on the credit and liquidity facilities established by the Federal Reserve to support households and businesses during the COVID-19 outbreak.
On March 17, 2020, the Federal Reserve announced that it would re-establish the Primary Dealer Credit Facility (PDCF) to allow primary dealers to support smooth market functioning and facilitate the availability of credit to businesses and households. The PDCF started offering overnight and term funding with maturities of up to ninety days on March 20. It will be in place for at least six months and may be extended as conditions warrant. In this post, we provide an overview of the PDCF and its usage to date.
Uyanga Byambaa, Beverly Hirtle, Anna Kovner, and Matthew Plosser
Supervision and regulation are critical tools for the promotion of stability and soundness in the financial sector. In a prior post, we discussed findings from our recent research paper which examines the impact of supervision on bank performance (see earlier post How Does Supervision Affect Banks?). As described in that post, we exploit new supervisory data and develop a novel strategy to estimate the impact of supervision on bank risk taking, earnings, and growth. We find that bank holding companies (BHCs or “banks”) that receive more supervisory attention have less risky loan portfolios, but do not have lower growth or profitability. In this post, we examine the benefits of supervision over time, and especially during banking industry downturns.
In recent years there has been a lot of interest in the effect of income inequality (heterogeneity) on the economy, from both academics and policymakers. Researchers have developed Heterogeneous Agent New Keynesian (HANK) models that incorporate heterogeneity and uninsurable idiosyncratic risk into the New Keynesian models that have become a cornerstone of monetary policy analysis. This research has argued that heterogeneity and idiosyncratic risk change many features of New Keynesian models – the
transmission of conventional monetary policy, the forward guidance puzzle, fiscal multipliers, the efficacy of targeted transfers and automatic stabilizers, among others. However, the source of the difference between HANK and representative agent New Keynesian (RANK) models remains unclear. This is because HANK models are typically not analytically tractable, leaving it unclear what exactly is driving the results. To shed light on the macroeconomic consequences of heterogeneity, we develop a stylized HANK model that contains key features present in more complicated HANK models.
After the global financial crisis, regulatory changes were implemented to support financial stability, with some changes directly addressing capital and liquidity in bank holding companies (BHCs) and others targeting BHC size and complexity. Although the overall size of the largest U.S. BHCs has not decreased since the crisis, the organizational complexity of these same organizations has declined, with less notable changes being observed in their range of businesses and geographic scope (Goldberg and Meehl, forthcoming). In this post, we explore how different types of BHC risks—risks that can influence the probability that a BHC is stressed, as well as the chance of systemic implications—have changed over time. The results are mixed: Levels of most BHC risks tend to be higher than in the years immediately preceding the crisis, but are markedly lower than the levels seen during and immediately following the crisis.
Kristian Blickle, Fernando Duarte, Thomas Eisenbach, and Anna Kovner
A key part of understanding the stability of the U.S. financial system is to monitor leverage and funding risks in the financial sector and the way in which these vulnerabilities interact to amplify negative shocks. In this post, we provide an update of four analytical models, introduced in aLiberty Street Economics post last year, that aim to capture different aspects of banking system vulnerability. Since their introduction, vulnerabilities as indicated by these models have increased moderately, continuing the slow but steady upward trend that started around 2016. Despite the recent increase, the overall level of vulnerabilities according to this analysis remains subdued and is still significantly smaller than before the financial crisis of 2008-09.
Fernando M. Duarte, Collin Jones, and Francisco Ruela
Second of two posts
When a financial firm suffers sufficiently high losses, it might default on its counterparties, who may in turn become unable to pay their own creditors, and so on. This “domino” or “cascade” effect can quickly propagate through the financial system, creating undesirable spillovers and unnecessary defaults. In this post, we use the framework that we discussed in “Assessing Contagion Risk in a Financial Network,” the first part of this two-part series, to answer the question: How vulnerable is the U.S. financial system to default spillovers?
Fernando M. Duarte, Collin Jones, and Francisco Ruela
First of two posts
In compiling a list of key takeaways of the 2008 financial crisis, surely the dangers of counterparty risk would be near the top. During the crisis, speculation on which financial institution would be next to default on its obligations to creditors, and which one would come after that, dominated news cycles. Since then, there has been an explosion in research trying to understand and quantify the default spillovers that can arise through counterparty risk. This is the first of two posts delving into the analysis of financial network contagion through this spillover channel. Here we introduce a framework that is useful for thinking about default cascades, originally developed by Eisenberg and Noe.
Gara Afonso, Filippo Curti, Ping McLemore, and Atanas Mihov
Cyber risk poses a major threat to financial stability, yet financial institutions still lack consensus on the definition of and terminology around cyber risk and have no common framework for confronting these hazards. This impedes efforts to measure and manage such risk, diminishing institutions’ individual and collective readiness to handle system-level cyber threats. In this blog post, we describe the proceedings of a recent workshop where leading risk managers, academics, and policy makers gathered to discuss proposals for countering cyber risk. This workshop is part of a joint two-phase initiative run by the Federal Reserve Banks of Richmond and New York and the Fed’s Board of Governors to harmonize cyber risk identification, classification, and measurement practices.
Many market participants believe that large financial institutions enjoy an implicit guarantee that the government will step in to rescue them from potential failure. These “Too Big to Fail” (TBTF) issues became particularly salient during the 2008 crisis. From the government’s perspective, rescuing these financial institutions can be important to avoid harm to the financial system. The bailouts also artificially lower the risk borne by investors and the financing costs of big banks. The Dodd-Frank Act attempts to remove the incentive for governments to bail out banks in the first place by mandating that each large bank file a “living will” that details its strategy for a rapid and orderly resolution in the event of material distress or failure without disrupting the broader economy. In our recent New York Fed staff report, we look at whether living wills are effective at reducing the cost of implicit TBTF bailout subsidies.
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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