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Jel Classification:C32 

Working Paper
Monitoring Banking System Fragility with Big Data

The need to monitor aggregate financial stability was made clear during the global financial crisis of 2008-2009, and, of course, the need to monitor individual financial firms from a microprudential standpoint remains. In this paper, we propose a procedure based on mixed-frequency models and network analysis to help address both of these policy concerns. We decompose firm-specific stock returns into two components: one that is explained by observed covariates (or fitted values), the other unexplained (or residuals). We construct networks based on the co-movement of these components. Analysis ...
Working Paper Series , Paper 2018-1

Working Paper
Contagious Switching

We analyze the propagation of recessions across countries. We construct a model that allows for multiple qualitative state variables in a vector autoregression (VAR) setting. The VAR structure allows us to include country-level variables to determine whether policy also propagates across countries. We consider two different versions of the model. One version assumes the discrete state of the economy (expansion or recession) is observed. The other assumes that the state of the economy is unobserved and must be inferred from movements in economic growth. We apply the model to Canada, Mexico, ...
Working Papers , Paper 2019-014

Report
Macroeconomic nowcasting and forecasting with big data

Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions?the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before ?big data? became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate the best practices of forecasters on trading desks, at central banks, and in other ...
Staff Reports , Paper 830

Report
Safety, liquidity, and the natural rate of interest

Why are interest rates so low in the Unites States? We find that they are low primarily because the premium for safety and liquidity has increased since the late 1990s, and to a lesser extent because economic growth has slowed. We reach this conclusion using two complementary perspectives: a flexible time-series model of trends in Treasury and corporate yields, inflation, and long-term survey expectations, and a medium-scale dynamic stochastic general equilibrium (DSGE) model. We discuss the implications of this finding for the natural rate of interest.
Staff Reports , Paper 812

Working Paper
Stock Market Cross-Sectional Skewness and Business Cycle Fluctuations

Using U.S. data from 1926 to 2015, I show that financial skewness?a measure comparing cross-sectional upside and downside risks of the distribution of stock market returns of financial firms?is a powerful predictor of business cycle fluctuations. I then show that shocks to financial skewness are important drivers of business cycles, identifying these shocks using both vector autoregressions and a dynamic stochastic general equilibrium model. Financial skewness appears to reflect the exposure of financial firms to the economic performance of their borrowers.
International Finance Discussion Papers , Paper 1223

Working Paper
Forecasting US Inflation Using Bayesian Nonparametric Models

The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial ...
Working Papers , Paper 22-05

Newsletter
How Interconnected Are Cryptocurrencies and What Does This Mean for Risk Management

In the past couple of years, the market for digital currencies, commonly known as cryptocurrencies because transactions are verified using cryptography, has expanded significantly in terms of transaction volumes, market capitalization, and the number of digital currencies in existence. On January 1, 2018, the market capitalizations (market caps1) of Bitcoin and Ethereum were $226 billion and $75 billion, respectively. By May 10, 2021, Bitcoin’s market cap had reached almost $1 trillion and Ethereum’s $478 billion.In this article, I measure the market’s interconnections in term of prices ...
Chicago Fed Letter , Issue 466 , Pages 5

Working Paper
Foreign exchange predictability during the financial crisis: implications for carry trade profitability

In this paper, we study the effectiveness of carry trade strategies during and after the financial crisis using a flexible approach to modeling currency returns. We decompose the currency returns into multiplicative sign and absolute return components, which exhibit much greater predictability than raw returns. We allow the two components to respond to currency-specific risk factors and use the joint conditional distribution of these components to obtain forecasts of future carry trade returns. Our results suggest that the decomposition model produces higher forecast and directional accuracy ...
FRB Atlanta Working Paper , Paper 2015-6

Working Paper
Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility

We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for ...
Working Papers , Paper 19-29

Working Paper
Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data

To examine whether including economic data on other countries could improve the forecast of U.S. GDP growth, we construct a large data set of 77 countries representing over 90 percent of global GDP. Our benchmark model is a dynamic factor model using U.S. data only, which we extend to include data from other countries. We show that using cross-country data produces more accurate forecasts during the global financial crisis period. Based on the latest vintage data on August 6, 2020, the benchmark model forecasts U.S. real GDP growth in 2020:Q3 to be −6.9 percent (year-over-year rate) or 14.9 ...
Research Working Paper , Paper RWP 20-09

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