Federal Reserve System

About: Federal Reserve System is a other organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Monetary policy & Inflation. The organization has 2373 authors who have published 10301 publications receiving 511979 citations.

Evaluating asset-market effects of unconventional monetary policy: a multi-country review

Abstract: This paper examines the effects of unconventional monetary policy by the Federal Reserve, Bank of England, European Central Bank and Bank of Japan on bond yields, stock prices and exchange rates. We use common methodologies for the four central banks, with daily and intradaily asset price data. We emphasize the use of intradaily data to identify the causal effect of monetary policy surprises. We find that these policies are effective in easing financial conditions when policy rates are stuck at the zero lower bound, apparently largely by reducing term premia. — John H. Rogers, Chiara Scotti and Jonathan H. Wright

Market Expectations in the Cross‐Section of Present Values

Abstract: Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross-section of book-tomarket ratios, we find an out-of-sample return forecasting R 2 of 13% at the annual frequency (0.9% monthly). We document similar out-of-sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios’ exposures to economic shocks are key to identifying predictability and are consistent with duration-based theories of the value premium. THE MOST COMMON APPROACH to measuring aggregate return and cash flow expectations is predictive regression. As suggested by the present value relationship between prices, discount rates, and future cash flows, research shows that the aggregate price-dividend ratio is among the most informative predictive variables. Typical in-sample estimates find that about 10% of annual return variation can be accounted for by forecasts based on the aggregate book-tomarket ratio, but find little or no out-of-sample predictive power. 1 In this paper we show that reliance on aggregate quantities drastically understates the degree of value ratios’ predictive content for both returns and cash flow growth, and hence understates the volatility of investor expectations. Our estimates suggest that as much as 13% of the out-of-sample variation in annual market returns (as much as 12% for dividend growth), and somewhat more of the insample variation, can be explained by the cross-section of past disaggregated value ratios. To harness disaggregated information we represent the cross-section of assetspecific book-to-market ratios as a dynamic latent factor model. We relate disaggregated value ratios to aggregate expected market returns and cash flow growth. Our model is based on the idea that the same dynamic state variables driving aggregate expectations also govern the dynamics of the entire panel ∗ Kelly is with Booth School of Business, University of Chicago, and Pruitt is with the Board of Governors of the Federal Reserve System. The view expressed here are those of the authors and do not necessarily reflect the views of the Federal Reserve System or its staff. 1 See Cochrane (2005) and Koijen and Van Nieuwerburgh (2011) for surveys of return and cash flow predictability evidence using the aggregate price-dividend ratio. Similar results obtain from forecasts based on the aggregate book-to-market ratio.

Judging Instrument Relevance in Instrumental Variables Estimation

Abstract: Recent research has emphasized the poor finite-sample performance of the instrumental variables (IV) estimator when the instruments are weakly correlated with the regressors. We show how the canonical correlations between regressors and instruments can provide a measure of instrument relevance in the general multiple-instrument-multiple-regressor case. However, our simulation results indicate that any such relevance measure probably has little practical merit, as its use may actually exacerbate the poor finite-sample properties of the IV estimator.