A central use of reserves held at Federal Reserve Banks (FRBs) is for the settlement of interbank obligations. These obligations are substantial—the average daily total reserves used on two main settlement systems, Fedwire Funds and Fedwire Securities, exceeds $6.5 trillion. The total amount of reserves needed to efficiently settle these obligations is an active area of debate, especially as the Federal Reserve’s current quantitative tightening (QT) policy seeks to drain reserves from the financial system. To better understand the use of reserves, in this post we examine the intraday flows of reserves over Fedwire Funds and Fedwire Securities and show that the mechanics of each settlement system result in starkly different intraday demands on reserves and differing sensitivities of those intraday demands to the total amount of reserves in the financial system.
Look for our next post on November 12, 2024.
Tracking Reserve Ampleness in Real Time Using Reserve Demand Elasticity
As central banks shrink their balance sheets to restore price stability and phase out expansionary programs, gauging the ampleness of reserves has become a central topic to policymakers and academics alike. The reason is that the ampleness of reserves informs when to slow and then stop quantitative tightening (QT). The Federal Reserve, for example, implements monetary policy in a regime of ample reserves, whereby the quantity of reserves in the banking system needs to be large enough such that everyday changes in reserves do not cause large variations in short-term rates. The goal is therefore to implement QT while ensuring that reserves remain sufficiently ample. In this post, we review how to gauge the ampleness of reserves using the new Reserve Demand Elasticity (RDE) measure, which will be published monthly on the public website of the Federal Reserve Bank of New York as a standalone product.
International Stock Markets’ Reactions to EU Climate Policy Shocks
While policies to combat climate change are designed to address a global problem, they are generally implemented at the national level. Nevertheless, the impact of domestic climate policies may spill over internationally given countries’ economic and financial interdependence. For example, a carbon tax charged to domestic firms for their use of fossil fuels may lead the firms to charge higher prices to their domestic and foreign customers; given the importance of global value chains in modern economies, the impact of that carbon tax may propagate across multiple layers of cross-border production linkages. In this post, we quantify the spillover effects of climate policies on forward-looking asset prices globally by estimating the impact of carbon price shocks in the European Union’s Emissions Trading System (EU ETS) on stock prices across a broad set of country-industry pairs. In other words, we measure how asset markets evaluate the impact of changes to the carbon price on growth and profitability prospects of the firms.
A New Indicator of Labor Market Tightness for Predicting Wage Inflation
A key question in economic policy is how labor market tightness affects wage inflation and ultimately prices. In this post, we highlight the importance of two measures of tightness in determining wage growth: the quits rate, and vacancies per searcher (V/S)—where searchers include both employed and non-employed job seekers. Amongst a broad set of indicators, we find that these two measures are independently the most strongly correlated with wage inflation. We construct a new index, called the Heise-Pearce-Weber (HPW) Tightness Index, which is a composite of quits and vacancies per searcher, and show that it performs best of all in explaining U.S. wage growth, including over the COVID pandemic and recovery.
What Do Climate Risk Indices Measure?
As interest in understanding the economic impacts of climate change grows, the climate economics and finance literature has developed a number of indices to quantify climate risks. Various approaches have been employed, utilizing firm-level emissions data, financial market data (from equity and derivatives markets), or textual data. Focusing on the latter approach, we conduct descriptive analyses of six text-based climate risk indices from published or well-cited papers. In this blog post, we highlight the differences and commonalities across these indices.
Exposure to Generative AI and Expectations About Inequality
With the rise of generative AI (genAI) tools such as ChatGPT, many worry about the tools’ potential displacement effects in the labor market and the implications for income inequality. In supplemental questions to the February 2024 Survey of Consumer Expectations (SCE), we asked a representative sample of U.S. residents about their experience with genAI tools. We find that relatively few people have used genAI, but that those who have used it have a bleaker outlook on its impacts on jobs and future inequality.
Are Nonbank Financial Institutions Systemic?
Recent events have heightened awareness of systemic risk stemming from nonbank financial sectors. For example, during the COVID-19 pandemic, liquidity demand from nonbank financial entities caused a “dash for cash” in financial markets that required government support. In this post, we provide a quantitative assessment of systemic risk in the nonbank sectors. Even though these sectors have heterogeneous business models, ranging from insurance to trading and asset management, we find that their systemic risk has common variation, and this commonality has increased over time. Moreover, nonbank sectors tend to become more systemic when banking sector systemic risk increases.
The Central Banking Beauty Contest
Expectations can play a significant role in driving economic outcomes, with central banks factoring market sentiment into policy decisions and market participants forming their own assumptions about monetary policy. But how well do central banks understand the expectations of market participants—and vice versa? Our model, developed in a recent paper, features a dynamic game between (i) a monetary authority that cannot commit to an inflation target and (ii) a set of market participants that understand the incentives created by that credibility problem. In this post, we describe the game, a type of Keynesian beauty contest: its main novelty is that each side attempts, with varying degrees of accuracy, to forecast the other’s beliefs, resulting in new findings regarding the levels and trajectories of inflation.
Flood Risk Outside Flood Zones — A Look at Mortgage Lending in Risky Areas
In support of the National Flood Insurance Program (NFIP), the Federal Emergency Management Agency (FEMA) creates flood maps that indicate areas with high flood risk, where mortgage applicants must buy flood insurance. The effects of flood insurance mandates were discussed in detail in a prior blog series. In 2021 alone, more than $200 billion worth of mortgages were originated in areas covered by a flood map. However, these maps are discrete, whereas the underlying flood risk may be continuous, and they are sometimes outdated. As a result, official flood maps may not fully capture the true flood risk an area faces. In this post, we make use of unique property-level mortgage data and find that in 2021, mortgages worth over $600 billion were originated in areas with high flood risk but no flood map. We examine what types of lenders are aware of this “unmapped” flood risk and how they adjust their lending practices. We find that—on average—lenders are more reluctant to lend in these unmapped yet risky regions. Those that do, such as nonbanks, are more aggressive at securitizing and selling off risky loans.
End‑of‑Month Liquidity in the Treasury Market
Trading activity in benchmark U.S. Treasury securities now concentrates on the last trading day of the month. Moreover, this stepped-up activity is associated with lower transaction costs, as shown by a smaller price impact of trades. We conjecture that increased turn-of-month portfolio rebalancing by passive investment funds that manage relative to fixed-income indices helps explain these patterns.